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py-3d-face-reconstruction
Commit beedf9a5 authored by Richard Torenvliet's avatar Richard Torenvliet
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Moved tests and made them work again, added sphinx documentation and... 

Moved tests and made them work again, added sphinx documentation and added/updated documentation, #4, #5
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View file @ beedf9a5
......@@ -17,21 +17,16 @@ data/pca_shape_model.npy:
python src/main.py \
--save_pca_shape \
--files `./scripts/imm_train_set.sh` \
--model_shape_file data/pca_shape_model
--model_shape_file data/pca_shape_model \
--shape_type imm
data/pca_texture_model.npy:
python src/main.py \
--save_pca_texture \
--files `./scripts/imm_train_set.sh` \
--model_texture_file data/pca_texture_model \
--model_shape_file data/pca_shape_model.npy
show_pca:
python src/main.py \
--show_pca \
--model_texture_file data/pca_texture_model.npy \
--model_shape_file data/pca_shape_model.npy
--model_shape_file data/pca_shape_model.npy \
--shape_type imm
test_model:
python src/main.py \
......@@ -47,6 +42,7 @@ show_reconstruction:
--files data/imm_face_db/*.asf \
--model_texture_file data/pca_texture_model.npy \
--model_shape_file data/pca_shape_model.npy \
--shape_type imm \
--n_components 6
profile_reconstruction:
......@@ -55,6 +51,7 @@ profile_reconstruction:
--files data/imm_face_db/*.asf \
--model_texture_file data/pca_texture_model.npy \
--model_shape_file data/pca_shape_model.npy \
--shape_type imm \
--n_components 6
graph_reconstruction:
......@@ -63,6 +60,7 @@ graph_reconstruction:
--files data/imm_face_db/*.asf \
--model_texture_file data/pca_texture_model.npy \
--model_shape_file data/pca_shape_model.npy \
--shape_type imm \
--n_components 6
show_kivy:
......@@ -74,10 +72,7 @@ show_kivy:
--n_components 6
test:
python -m py.test -f src/*_test.py
test_modules:
python -m py.test -f src/*/*_test.py
python -m py.test -f src/test/*_test.py
server:
(cd src/; python -m tornado.autoreload server.py)
......
docs/.ipynb_checkpoints/Least squares-checkpoint.ipynb 0 → 100644
View file @ beedf9a5
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"input": [
"import numpy as np\n",
"import matplotlib.pyplot as plt"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = np.array([8, 2, 11, 6, 5, 4, 12, 9, 6, 1])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xzm = x - x.mean()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"y = np.array([3, 10, 3, 6, 8, 12, 1, 4, 9, 14])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"yzm = y - y.mean()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xzm"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"array([ 1.6, -4.4, 4.6, -0.4, -1.4, -2.4, 5.6, 2.6, -0.4, -5.4])"
]
}
],
"prompt_number": 8
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{
"cell_type": "code",
"collapsed": false,
"input": [
"yzm"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
"array([-4., 3., -4., -1., 1., 5., -6., -3., 2., 7.])"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"covXY = np.dot(xzm, yzm.T)\n",
"print covXY"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"-131.0\n"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"covXX = np.dot(xzm, xzm.T)\n",
"print covXX"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"118.4\n"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"m = covXY / covXX\n",
"print m"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"-1.10641891892\n"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"b = y.mean() - m * x.mean()\n",
"print b"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"14.0810810811\n"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.clf()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(x, y)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 25,
"text": [
"[<matplotlib.lines.Line2D at 0x10fb1de50>]"
]
}
],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
\ No newline at end of file
docs/.ipynb_checkpoints/Reduced U-checkpoint.ipynb 0 → 100644
View file @ beedf9a5
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"worksheets": [
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],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"U = np.array([[1,2,3]])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"s = np.array([4,5,6])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Vt = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"S = np.diag(s)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Us = np.dot(U, S)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print Us"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[ 4 10 18]]\n"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.dot(Us, Vt)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"array([[170, 202, 234]])"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
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\ No newline at end of file
docs/notebooks/Least squares.ipynb 0 → 100644
View file @ beedf9a5
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"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = np.array([8, 2, 11, 6, 5, 4, 12, 9, 6, 1])"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xzm = x - x.mean()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"y = np.array([3, 10, 3, 6, 8, 12, 1, 4, 9, 14])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"yzm = y - y.mean()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xzm"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"array([ 1.6, -4.4, 4.6, -0.4, -1.4, -2.4, 5.6, 2.6, -0.4, -5.4])"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"yzm"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
"array([-4., 3., -4., -1., 1., 5., -6., -3., 2., 7.])"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"covXY = np.dot(xzm, yzm.T)\n",
"print covXY"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"-131.0\n"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"covXX = np.dot(xzm, xzm.T)\n",
"print covXX"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"118.4\n"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"m = covXY / covXX\n",
"print m"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"-1.10641891892\n"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"b = y.mean() - m * x.mean()\n",
"print b"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"14.0810810811\n"
]
}
],
"prompt_number": 82
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.clf()\n",
"plt.grid()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 97
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.scatter(x, y)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 104,
"text": [
"<matplotlib.collections.PathCollection at 0x1106a3710>"
]
}
],
"prompt_number": 104
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"range_x = np.arange(0, 14, 0.5)\n",
"print range_x"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[ 0. 0.5 1. 1.5 2. 2.5 3. 3.5 4. 4.5 5. 5.5\n",
" 6. 6.5 7. 7.5 8. 8.5 9. 9.5 10. 10.5 11. 11.5\n",
" 12. 12.5 13. 13.5]\n"
]
}
],
"prompt_number": 105
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"y_lstq = [m * i + b for i in x]\n",
"print y_lstq"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[5.2297297297297298, 11.868243243243242, 1.9104729729729737, 7.4425675675675675, 8.548986486486486, 9.6554054054054053, 0.80405405405405439, 4.1233108108108105, 7.4425675675675675, 12.974662162162161]\n"
]
}
],
"prompt_number": 106
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(x, y_lstq)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 107,
"text": [
"[<matplotlib.lines.Line2D at 0x1106ae150>]"
]
}
],
"prompt_number": 107
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.scatter(x.mean(), y.mean(), marker='s', color='b')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 108,
"text": [
"<matplotlib.collections.PathCollection at 0x110659390>"
]
}
],
"prompt_number": 108
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.scatter(xzm, yzm, color='r')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 110,
"text": [
"<matplotlib.collections.PathCollection at 0x110b91650>"
]
}
],
"prompt_number": 110
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
\ No newline at end of file
docs/notebooks/Reduced U.ipynb 0 → 100644
View file @ beedf9a5
{
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"input": [
"U = np.array([1,2,3])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 20
},
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"cell_type": "code",
"collapsed": false,
"input": [
"print U"
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"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[1 2 3]\n"
]
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"prompt_number": 21
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{
"cell_type": "code",
"collapsed": false,
"input": [
"s = np.array([4,5,6])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Vt = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"S = np.diag(s)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Us = np.dot(U, S)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print Us"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[ 4 10 18]\n"
]
}
],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.dot(Us, Vt)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 27,
"text": [
"array([170, 202, 234])"
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"prompt_number": 27
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{
"cell_type": "code",
"collapsed": false,
"input": [],
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\ No newline at end of file
src/aam.py
View file @ beedf9a5
"""
.. module:: active_appearance_model
:platform: Unix, Windows
:synopsis: Contains the aam data format abstraction layer
"""
import logging
import numpy as np
from matplotlib.tri import Triangulation
......@@ -26,8 +33,8 @@ class AAMPoints():
normalized_flattened_points_list(ndarray): flattened list of points.
This means that if the points consist of x,y coordinates, then all this
list will be: [x1, y1, x2, y2, ... xi, yi]
points_list(ndarray): this list is the same points but then not
flattened, [[x1, y1], [x2, y2], ... [xi, yi]]. You either create
points_list(ndarray): this list is the same points but then not
flattened, [[x1, y1], [x2, y2], ... [xi, yi]]. You either create
this object with this argument or the normalized_flattened_points_list
actual_shape(tuple): this is important if you want to reconstruct
the original list, see get_scaled_points() for usage.
......@@ -55,11 +62,13 @@ class AAMPoints():
"""
Scale the normalized flattened points list to a scale given by 'shape'.
The x and y values should be scaled to the width and height of the image.
Args:
shape(tuple): (height, width)
rescal(boolean): flag if we should rescale or not because if we
already scaled, we are not going to do it again by
default.
rescale(boolean): flag if we should rescale or not because if we
already scaled, we are not going to do it again by
default.
Returns:
ndarray scaled to 'shape' width and height.
"""
......@@ -106,14 +115,16 @@ class AAMPoints():
#return cv2.boundingRect()
def get_mean(vector):
""" construct a mean from a matrix of x,y values
"""
Construct a mean from a matrix of x,y values
Args:
points(numpy array) that follows the following structure:
Returns:
mean_values (numpy array)
Examples:
Example:
Input observations:
0. [[x_0_0, y_0_0], ... , [x_0_m, y_0_m]],
1. [[x_1_0, y_1_0], ... , [x_1_m, y_1_m]],
......@@ -140,7 +151,13 @@ def get_mean(vector):
def get_triangles(x_vector, y_vector):
""" perform triangulation between two 2d vectors"""
"""
Perform triangulation between two 2d vectors
Args:
x_vector(ndarray): list of x locations
y_vector(ndarray): list of y locations
"""
return Triangulation(x_vector, y_vector).triangles
......@@ -151,9 +168,11 @@ def build_shape_feature_vectors(files, get_points, flattened=False):
Args:
files (list): list files
get_points(function): function that gets the points/landmarks given
a list of files.
return:
list: list of feature vectors
Returns:
list. List of feature vectors
"""
points = get_points(files)
......@@ -166,10 +185,14 @@ def build_shape_feature_vectors(files, get_points, flattened=False):
def sample_from_triangles(src, points2d_src, points2d_dst, triangles, dst):
"""
Get pixels from within the triangles [[p1, p2, p3]_0, .. [p1, p2, p3]_n].
Args:
src(ndarray, dtype=uint8): input image
points2d_src(ndarray, dtype=np.int32): shape array [[x, y], ... [x, y]]
points2d_dst(ndarray, dtype=np.int32): shape array [[x, y], ... [x, y]]
triangles(ndarray, ndim=3, dtype=np.int32): shape array [[p1, p2, p3]_0, .. [p1, p2, p3]_n].
"""
......@@ -193,7 +216,7 @@ def build_texture_feature_vectors(files, get_image_with_points, mean_points, tri
Args:
files (list): list files
get_image_with_points (function): That can return the image together
with the location.
with the location.
mean_points(AAMPoints): AAMPoints object
Returns:
......@@ -233,7 +256,7 @@ def build_texture_feature_vectors(files, get_image_with_points, mean_points, tri
def get_pixel_values(image, points):
""" docstring """
""" deprecated """
h, w, c = image.shape
points[:, 0] = points[:, 0] * w
......
src/datasets/imm.py
View file @ beedf9a5
"""
.. module:: datasets
:platform: Unix, Windows
:synopsis: Contains imm dataset abstraction layer
"""
from matplotlib.tri import Triangulation
import cv2
......@@ -30,12 +38,46 @@ class IMMPoints(aam.AAMPoints):
)
def get_points(self):
"""
Get the flattened list of points
Returns:
ndarray. flattened array of points, see AAMPoints for more
information.
"""
return self.normalized_flattened_points_list
def __get_image(self):
"""
Get the image corresponding to the self.image_file
Returns:
ndarray image
"""
assert hasattr(self, 'image_file'), 'image_file name should be set, \
import file must be invoked first'
self.image = cv2.imread(self.image_file)
def get_image(self):
return cv2.imread(self.image_file)
"""
Get the image corresponding to the filename
If filename == image_1.asf, then we read image_1.jpg from disk
and return this to the user.
Returns:
ndarray image
"""
return self.image
def import_file(self, filename):
"""
Import an .asf filename. Load the points into a list of points and
store the relative path to image file.
Returns:
ndarray(float). Numpy array of landmark locations as stated in the
.asf files.
"""
points_list = []
with open(filename, 'r') as f:
......@@ -43,6 +85,7 @@ class IMMPoints(aam.AAMPoints):
data = lines[16:74]
dir_name = os.path.dirname(filename)
self.image_file = "{}/{}".format(dir_name, lines[-1].strip())
self.__get_image()
for d in data:
points_list.append(d.split()[2:4])
......@@ -85,6 +128,16 @@ class IMMPoints(aam.AAMPoints):
def get_imm_points(files):
"""
This function does something.
Args:
files (array): Array of .asf full or relative path to .asf files.
Returns:
ndarray. Array of landmarks.
"""
points = []
for f in files:
......@@ -95,6 +148,15 @@ def get_imm_points(files):
def get_imm_image_with_landmarks(filename):
"""
Get Points with image and landmarks/points
Args:
filename(fullpath): .asf file
Returns:
image, points
"""
imm = IMMPoints(filename=filename)
return imm.get_image(), imm.get_points()
......
src/docs/Makefile 0 → 100644
View file @ beedf9a5
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View file @ beedf9a5
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<li class="toctree-l1"><a class="reference internal" href="../datasets.html">Datasets</a></li>
<li class="toctree-l1"><a class="reference internal" href="../aam.html">AAM Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../pca.html">PCA Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../reconstruction/reconstruction.html">Reconstruction Module</a></li>
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<h1>Source code for aam</h1><div class="highlight"><pre>
<span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">.. module:: active_appearance_model</span>
<span class="sd"> :platform: Unix, Windows</span>
<span class="sd"> :synopsis: Contains the aam data format abstraction layer</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">import</span> <span class="nn">logging</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">matplotlib.tri</span> <span class="k">import</span> <span class="n">Triangulation</span>
<span class="kn">import</span> <span class="nn">cv2</span>
<span class="c1"># local imports</span>
<span class="kn">import</span> <span class="nn">pca</span>
<span class="kn">import</span> <span class="nn">reconstruction.texture</span> <span class="k">as</span> <span class="nn">tx</span>
<span class="n">logging</span><span class="o">.</span><span class="n">basicConfig</span><span class="p">(</span>
<span class="n">level</span><span class="o">=</span><span class="n">logging</span><span class="o">.</span><span class="n">INFO</span><span class="p">,</span>
<span class="nb">format</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%(asctime)s</span><span class="s1"> </span><span class="si">%(levelname)s</span><span class="s1"> </span><span class="si">%(name)s</span><span class="s1">: </span><span class="si">%(message)s</span><span class="s1">&#39;</span>
<span class="p">)</span>
<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="n">__name__</span><span class="p">)</span>
<div class="viewcode-block" id="AAMPoints"><a class="viewcode-back" href="../aam.html#aam.AAMPoints">[docs]</a><span class="k">class</span> <span class="nc">AAMPoints</span><span class="p">():</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Object to store AAM points / landmarks. Tries to keep the scaling of</span>
<span class="sd"> these points transparent.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">normalized_flattened_points_list</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">points_list</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">actual_shape</span><span class="o">=</span><span class="p">()):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Args:</span>
<span class="sd"> normalized_flattened_points_list(ndarray): flattened list of points.</span>
<span class="sd"> This means that if the points consist of x,y coordinates, then all this</span>
<span class="sd"> list will be: [x1, y1, x2, y2, ... xi, yi]</span>
<span class="sd"> points_list(ndarray): this list is the same points but then not</span>
<span class="sd"> flattened, [[x1, y1], [x2, y2], ... [xi, yi]]. You either create</span>
<span class="sd"> this object with this argument or the normalized_flattened_points_list</span>
<span class="sd"> actual_shape(tuple): this is important if you want to reconstruct</span>
<span class="sd"> the original list, see get_scaled_points() for usage.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">normalized_flattened_points_list</span> <span class="o">=</span> <span class="n">normalized_flattened_points_list</span>
<span class="bp">self</span><span class="o">.</span><span class="n">points_list</span> <span class="o">=</span> <span class="n">points_list</span>
<span class="bp">self</span><span class="o">.</span><span class="n">actual_shape</span> <span class="o">=</span> <span class="n">actual_shape</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bounding_box</span> <span class="o">=</span> <span class="kc">None</span>
<div class="viewcode-block" id="AAMPoints.get_bounding_box"><a class="viewcode-back" href="../aam.html#aam.AAMPoints.get_bounding_box">[docs]</a> <span class="k">def</span> <span class="nf">get_bounding_box</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Get the bounding box around the points.</span>
<span class="sd"> Returns:</span>
<span class="sd"> OpenCV rectangle:</span>
<span class="sd"> x, y, w, h</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">bounding_box</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">calculate_bounding_box</span><span class="p">()</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">bounding_box</span></div>
<div class="viewcode-block" id="AAMPoints.get_scaled_points"><a class="viewcode-back" href="../aam.html#aam.AAMPoints.get_scaled_points">[docs]</a> <span class="k">def</span> <span class="nf">get_scaled_points</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">width_height_dimensions</span><span class="p">,</span> <span class="n">rescale</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Scale the normalized flattened points list to a scale given by &#39;shape&#39;.</span>
<span class="sd"> The x and y values should be scaled to the width and height of the image.</span>
<span class="sd"> Args:</span>
<span class="sd"> shape(tuple): (height, width)</span>
<span class="sd"> rescal(boolean): flag if we should rescale or not because if we</span>
<span class="sd"> already scaled, we are not going to do it again by</span>
<span class="sd"> default.</span>
<span class="sd"> Returns:</span>
<span class="sd"> ndarray scaled to &#39;shape&#39; width and height.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">points_list</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">rescale</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">points_list</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">normalized_flattened_points_list</span>
<span class="c1"># shape into [[x, y(, z)], [x, y, (,z)]]</span>
<span class="c1"># we use the &#39;actual_shape&#39; which is known from the creation of</span>
<span class="c1"># this object.</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">actual_shape</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">points_list</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">points_list</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">actual_shape</span><span class="p">)</span>
<span class="n">h</span> <span class="o">=</span> <span class="n">width_height_dimensions</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">w</span> <span class="o">=</span> <span class="n">width_height_dimensions</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">points_list</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">points_list</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">w</span>
<span class="bp">self</span><span class="o">.</span><span class="n">points_list</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">points_list</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">h</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">points_list</span></div>
<div class="viewcode-block" id="AAMPoints.calculate_bounding_box"><a class="viewcode-back" href="../aam.html#aam.AAMPoints.calculate_bounding_box">[docs]</a> <span class="k">def</span> <span class="nf">calculate_bounding_box</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Calculate bounding box in the **scaled** points list</span>
<span class="sd"> The empasis on on scaled because the convexHull does not support</span>
<span class="sd"> small values, the normalized_flattened_points_list does not work.</span>
<span class="sd"> Use get_scaled_points first, with a shape that is needed. The shape</span>
<span class="sd"> should be the dimensions of the out image, example (480, 640), ie., (height,</span>
<span class="sd"> width)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">points_list</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> \
<span class="s1">&#39;the list points already need to be scaled order to correctly work,</span><span class="se">\</span>
<span class="s1"> this requires that get_scaled_points is executed first.&#39;</span>
<span class="n">hull</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">convexHull</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">points_list</span><span class="p">,</span> <span class="n">returnPoints</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">return</span> <span class="n">cv2</span><span class="o">.</span><span class="n">boundingRect</span><span class="p">(</span><span class="n">hull</span><span class="p">)</span></div>
<span class="c1"># TODO: impove by not using opencv but just min-max of the lists</span>
<span class="k">def</span> <span class="nf">get_bounding_box_2</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">pass</span></div>
<span class="c1">#hull = cv2.convexHull(self.points_list, returnPoints=True)</span>
<span class="c1">#x, y, w_slice, h_slice = cv2.boundingRect(hull)</span>
<span class="c1">#return cv2.boundingRect()</span>
<div class="viewcode-block" id="get_mean"><a class="viewcode-back" href="../aam.html#aam.get_mean">[docs]</a><span class="k">def</span> <span class="nf">get_mean</span><span class="p">(</span><span class="n">vector</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Construct a mean from a matrix of x,y values</span>
<span class="sd"> Args:</span>
<span class="sd"> points(numpy array) that follows the following structure:</span>
<span class="sd"> Returns:</span>
<span class="sd"> mean_values (numpy array)</span>
<span class="sd"> Example:</span>
<span class="sd"> Input observations:</span>
<span class="sd"> 0. [[x_0_0, y_0_0], ... , [x_0_m, y_0_m]],</span>
<span class="sd"> 1. [[x_1_0, y_1_0], ... , [x_1_m, y_1_m]],</span>
<span class="sd"> 2. [[x_2_0, y_2_0], ... , [x_2_m, y_2_m]],</span>
<span class="sd"> 3. [[x_3_0, y_3_0], ... , [x_3_m, y_3_m]]</span>
<span class="sd"> .... .... .....</span>
<span class="sd"> n. [[x_4_0, y_4_0], ... , [x_n_m, y_n_m]]</span>
<span class="sd"> This vector containts the mean values of the corresponding column, like so:</span>
<span class="sd"> 0. [[x_0_0, y_0_0], ... , [x_0_k, y_0_k]],</span>
<span class="sd"> 1. [[x_1_0, y_1_0], ... , [x_1_k, y_1_k]],</span>
<span class="sd"> 2. [[x_2_0, y_2_0], ... , [x_2_k, y_2_k]],</span>
<span class="sd"> 3. [[x_3_0, y_3_0], ... , [x_3_k, y_3_k]]</span>
<span class="sd"> .... .... .....</span>
<span class="sd"> n. [[x_4_0, y_4_0], ... , [x_n_k, y_n_k]]</span>
<span class="sd"> mean. [[x_mean_0, y_mean_0], ... [x_mean_n, y_mean_n]]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">vector</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span></div>
<div class="viewcode-block" id="get_triangles"><a class="viewcode-back" href="../aam.html#aam.get_triangles">[docs]</a><span class="k">def</span> <span class="nf">get_triangles</span><span class="p">(</span><span class="n">x_vector</span><span class="p">,</span> <span class="n">y_vector</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Perform triangulation between two 2d vectors</span>
<span class="sd"> Args:</span>
<span class="sd"> x_vector(ndarray): list of x locations</span>
<span class="sd"> y_vector(ndarray): list of y locations</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">Triangulation</span><span class="p">(</span><span class="n">x_vector</span><span class="p">,</span> <span class="n">y_vector</span><span class="p">)</span><span class="o">.</span><span class="n">triangles</span></div>
<div class="viewcode-block" id="build_shape_feature_vectors"><a class="viewcode-back" href="../aam.html#aam.build_shape_feature_vectors">[docs]</a><span class="k">def</span> <span class="nf">build_shape_feature_vectors</span><span class="p">(</span><span class="n">files</span><span class="p">,</span> <span class="n">get_points</span><span class="p">,</span> <span class="n">flattened</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Gets the aam points from the files and appends them seperately to one</span>
<span class="sd"> array.</span>
<span class="sd"> Args:</span>
<span class="sd"> files (list): list files</span>
<span class="sd"> get_points(function): function that gets the points/landmarks given</span>
<span class="sd"> a list of files.</span>
<span class="sd"> Returns:</span>
<span class="sd"> list. List of feature vectors</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">points</span> <span class="o">=</span> <span class="n">get_points</span><span class="p">(</span><span class="n">files</span><span class="p">)</span>
<span class="k">if</span> <span class="n">flattened</span><span class="p">:</span>
<span class="n">points</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">flatten_feature_vectors</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="k">return</span> <span class="n">points</span></div>
<div class="viewcode-block" id="sample_from_triangles"><a class="viewcode-back" href="../aam.html#aam.sample_from_triangles">[docs]</a><span class="k">def</span> <span class="nf">sample_from_triangles</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">points2d_src</span><span class="p">,</span> <span class="n">points2d_dst</span><span class="p">,</span> <span class="n">triangles</span><span class="p">,</span> <span class="n">dst</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Get pixels from within the triangles [[p1, p2, p3]_0, .. [p1, p2, p3]_n].</span>
<span class="sd"> Args:</span>
<span class="sd"> src(ndarray, dtype=uint8): input image</span>
<span class="sd"> points2d_src(ndarray, dtype=np.int32): shape array [[x, y], ... [x, y]]</span>
<span class="sd"> points2d_dst(ndarray, dtype=np.int32): shape array [[x, y], ... [x, y]]</span>
<span class="sd"> triangles(ndarray, ndim=3, dtype=np.int32): shape array [[p1, p2, p3]_0, .. [p1, p2, p3]_n].</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">for</span> <span class="n">tri</span> <span class="ow">in</span> <span class="n">triangles</span><span class="p">:</span>
<span class="n">src_p1</span><span class="p">,</span> <span class="n">src_p2</span><span class="p">,</span> <span class="n">src_p3</span> <span class="o">=</span> <span class="n">points2d_src</span><span class="p">[</span><span class="n">tri</span><span class="p">]</span>
<span class="n">dst_p1</span><span class="p">,</span> <span class="n">dst_p2</span><span class="p">,</span> <span class="n">dst_p3</span> <span class="o">=</span> <span class="n">points2d_dst</span><span class="p">[</span><span class="n">tri</span><span class="p">]</span>
<span class="n">tx</span><span class="o">.</span><span class="n">fill_triangle_src_dst</span><span class="p">(</span>
<span class="n">src</span><span class="p">,</span> <span class="n">dst</span><span class="p">,</span>
<span class="n">src_p1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">src_p1</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
<span class="n">src_p2</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">src_p2</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
<span class="n">src_p3</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">src_p3</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
<span class="n">dst_p1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dst_p1</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
<span class="n">dst_p2</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dst_p2</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
<span class="n">dst_p3</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dst_p3</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="p">)</span></div>
<div class="viewcode-block" id="build_texture_feature_vectors"><a class="viewcode-back" href="../aam.html#aam.build_texture_feature_vectors">[docs]</a><span class="k">def</span> <span class="nf">build_texture_feature_vectors</span><span class="p">(</span><span class="n">files</span><span class="p">,</span> <span class="n">get_image_with_points</span><span class="p">,</span> <span class="n">mean_points</span><span class="p">,</span> <span class="n">triangles</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Args:</span>
<span class="sd"> files (list): list files</span>
<span class="sd"> get_image_with_points (function): That can return the image together</span>
<span class="sd"> with the location.</span>
<span class="sd"> mean_points(AAMPoints): AAMPoints object</span>
<span class="sd"> Returns:</span>
<span class="sd"> list: list of feature vectors</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">mean_texture</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">image</span><span class="p">,</span> <span class="n">points</span> <span class="o">=</span> <span class="n">get_image_with_points</span><span class="p">(</span><span class="n">files</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">mean_points</span><span class="o">.</span><span class="n">get_scaled_points</span><span class="p">(</span><span class="n">image</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="c1"># improve this, see issue #1</span>
<span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">w_slice</span><span class="p">,</span> <span class="n">h_slice</span> <span class="o">=</span> <span class="n">mean_points</span><span class="o">.</span><span class="n">get_bounding_box</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">f</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">files</span><span class="p">):</span>
<span class="n">image</span><span class="p">,</span> <span class="n">points</span> <span class="o">=</span> <span class="n">get_image_with_points</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
<span class="n">Points</span> <span class="o">=</span> <span class="n">AAMPoints</span><span class="p">(</span>
<span class="n">normalized_flattened_points_list</span><span class="o">=</span><span class="n">points</span><span class="p">,</span>
<span class="n">actual_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">58</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="p">)</span>
<span class="c1"># empty colored image</span>
<span class="n">dst</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="n">image</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">image</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">3</span><span class="p">),</span> <span class="n">fill_value</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span>
<span class="n">sample_from_triangles</span><span class="p">(</span>
<span class="n">image</span><span class="p">,</span>
<span class="n">Points</span><span class="o">.</span><span class="n">get_scaled_points</span><span class="p">(</span><span class="n">image</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span>
<span class="n">mean_points</span><span class="o">.</span><span class="n">get_scaled_points</span><span class="p">(</span><span class="n">image</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span>
<span class="n">triangles</span><span class="p">,</span>
<span class="n">dst</span>
<span class="p">)</span>
<span class="n">dst_flattened</span> <span class="o">=</span> <span class="n">dst</span><span class="p">[</span><span class="n">y</span><span class="p">:</span> <span class="n">y</span> <span class="o">+</span> <span class="n">h_slice</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="o">+</span> <span class="n">w_slice</span><span class="p">]</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
<span class="n">mean_texture</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dst_flattened</span><span class="p">)</span>
<span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">&#39;processed file: </span><span class="si">{}</span><span class="s1"> </span><span class="si">{}</span><span class="s1">/</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">files</span><span class="p">)))</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">mean_texture</span><span class="p">)</span></div>
<div class="viewcode-block" id="get_pixel_values"><a class="viewcode-back" href="../aam.html#aam.get_pixel_values">[docs]</a><span class="k">def</span> <span class="nf">get_pixel_values</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">points</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot; deprecated &quot;&quot;&quot;</span>
<span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">c</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">shape</span>
<span class="n">points</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">points</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">w</span>
<span class="n">points</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">points</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">h</span>
<span class="n">image</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">blur</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="n">hull</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">convexHull</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">returnPoints</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">rect</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">boundingRect</span><span class="p">(</span><span class="n">hull</span><span class="p">)</span>
<span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">h</span> <span class="o">=</span> <span class="n">rect</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="mi">150</span><span class="p">):</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="mi">150</span><span class="p">):</span>
<span class="n">y_loc_g</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">i</span> <span class="o">*</span> <span class="n">h</span> <span class="o">+</span> <span class="n">y</span><span class="p">)</span>
<span class="n">x_loc_g</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">j</span> <span class="o">*</span> <span class="n">w</span> <span class="o">+</span> <span class="n">x</span><span class="p">)</span>
<span class="k">if</span> <span class="n">cv2</span><span class="o">.</span><span class="n">pointPolygonTest</span><span class="p">(</span><span class="n">hull</span><span class="p">,</span> <span class="p">(</span><span class="n">x_loc_g</span><span class="p">,</span> <span class="n">y_loc_g</span><span class="p">),</span> <span class="n">measureDist</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">image</span><span class="p">[</span><span class="n">y_loc_g</span><span class="p">][</span><span class="n">x_loc_g</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">image</span><span class="p">[</span><span class="n">y_loc_g</span><span class="p">][</span><span class="n">x_loc_g</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">image</span><span class="p">[</span><span class="n">y_loc_g</span><span class="p">][</span><span class="n">x_loc_g</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="c1"># return np.asarray(pixels, dtype=np.uint8), hull</span>
<span class="k">return</span> <span class="n">image</span><span class="p">,</span> <span class="n">hull</span></div>
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<li class="toctree-l1"><a class="reference internal" href="../../datasets.html">Datasets</a></li>
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<h1>Source code for datasets.imm</h1><div class="highlight"><pre>
<span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">.. module:: datasets</span>
<span class="sd"> :platform: Unix, Windows</span>
<span class="sd"> :synopsis: Contains imm dataset abstraction layer</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">from</span> <span class="nn">matplotlib.tri</span> <span class="k">import</span> <span class="n">Triangulation</span>
<span class="kn">import</span> <span class="nn">cv2</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">argparse</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">aam</span>
<div class="viewcode-block" id="IMMPoints"><a class="viewcode-back" href="../../datasets.html#datasets.imm.IMMPoints">[docs]</a><span class="k">class</span> <span class="nc">IMMPoints</span><span class="p">(</span><span class="n">aam</span><span class="o">.</span><span class="n">AAMPoints</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Accepts IMM datapoint file which can be shown or used&quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">points_list</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Args:</span>
<span class="sd"> filename: optional .asf file with the imm format</span>
<span class="sd"> points: optional list of x,y points</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">assert</span> <span class="n">filename</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">points_list</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> <span class="s1">&#39;filename or </span><span class="se">\</span>
<span class="s1"> a ndarray of points list should be given&#39;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">filename</span> <span class="o">=</span> <span class="n">filename</span>
<span class="k">if</span> <span class="n">filename</span><span class="p">:</span>
<span class="n">points_list</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">import_file</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span>
<span class="n">aam</span><span class="o">.</span><span class="n">AAMPoints</span><span class="o">.</span><span class="n">__init__</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span> <span class="n">normalized_flattened_points_list</span><span class="o">=</span><span class="n">points_list</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span>
<span class="n">actual_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">58</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="p">)</span>
<div class="viewcode-block" id="IMMPoints.get_points"><a class="viewcode-back" href="../../datasets.html#datasets.imm.IMMPoints.get_points">[docs]</a> <span class="k">def</span> <span class="nf">get_points</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Get the flattened list of points</span>
<span class="sd"> Returns:</span>
<span class="sd"> ndarray. flattened array of points, see AAMPoints for more</span>
<span class="sd"> information.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">normalized_flattened_points_list</span></div>
<div class="viewcode-block" id="IMMPoints.get_image"><a class="viewcode-back" href="../../datasets.html#datasets.imm.IMMPoints.get_image">[docs]</a> <span class="k">def</span> <span class="nf">get_image</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Get the image corresponding to the filename</span>
<span class="sd"> If filename == image_1.asf, then we read image_1.jpg from disk</span>
<span class="sd"> and return this to the user.</span>
<span class="sd"> Returns:</span>
<span class="sd"> ndarray image</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">cv2</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">image_file</span><span class="p">)</span></div>
<div class="viewcode-block" id="IMMPoints.import_file"><a class="viewcode-back" href="../../datasets.html#datasets.imm.IMMPoints.import_file">[docs]</a> <span class="k">def</span> <span class="nf">import_file</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">filename</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Import an .asf filename. Load the points into a list of points and</span>
<span class="sd"> store the relative path to image file.</span>
<span class="sd"> Returns:</span>
<span class="sd"> ndarray(float). Numpy array of landmark locations as stated in the</span>
<span class="sd"> .asf files.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">points_list</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">lines</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">readlines</span><span class="p">()</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">lines</span><span class="p">[</span><span class="mi">16</span><span class="p">:</span><span class="mi">74</span><span class="p">]</span>
<span class="n">dir_name</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">image_file</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">{}</span><span class="s2">/</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dir_name</span><span class="p">,</span> <span class="n">lines</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">strip</span><span class="p">())</span>
<span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">data</span><span class="p">:</span>
<span class="n">points_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">d</span><span class="o">.</span><span class="n">split</span><span class="p">()[</span><span class="mi">2</span><span class="p">:</span><span class="mi">4</span><span class="p">])</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">points_list</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;f&#39;</span><span class="p">)</span></div>
<span class="k">def</span> <span class="nf">draw_triangles</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">image</span><span class="p">,</span> <span class="n">points</span><span class="p">,</span> <span class="n">multiply</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="k">if</span> <span class="n">multiply</span><span class="p">:</span>
<span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">c</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">shape</span>
<span class="n">points</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">points</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">w</span>
<span class="n">points</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">points</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">h</span>
<span class="n">point_indices</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">58</span><span class="p">))</span>
<span class="n">triangles</span> <span class="o">=</span> <span class="n">Triangulation</span><span class="p">(</span><span class="n">points</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">points</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">])</span>
<span class="k">for</span> <span class="n">t</span><span class="p">,</span> <span class="n">tri</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">triangles</span><span class="o">.</span><span class="n">triangles</span><span class="p">):</span>
<span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">,</span> <span class="n">p3</span> <span class="o">=</span> <span class="n">points</span><span class="p">[</span><span class="n">tri</span><span class="p">]</span>
<span class="n">cv2</span><span class="o">.</span><span class="n">line</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">p1</span><span class="p">),</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">p2</span><span class="p">),</span> <span class="p">(</span><span class="mi">255</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">100</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">cv2</span><span class="o">.</span><span class="n">line</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">p2</span><span class="p">),</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">p3</span><span class="p">),</span> <span class="p">(</span><span class="mi">255</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">100</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">cv2</span><span class="o">.</span><span class="n">line</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">p3</span><span class="p">),</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">p1</span><span class="p">),</span> <span class="p">(</span><span class="mi">255</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">100</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">p</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">points</span><span class="p">):</span>
<span class="n">point_index</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">point_indices</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
<span class="n">cv2</span><span class="o">.</span><span class="n">putText</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">point_index</span><span class="p">),</span> <span class="nb">tuple</span><span class="p">((</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">])),</span>
<span class="n">cv2</span><span class="o">.</span><span class="n">FONT_HERSHEY_SIMPLEX</span><span class="p">,</span> <span class="o">.</span><span class="mi">5</span><span class="p">,</span> <span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">))</span>
<span class="n">cv2</span><span class="o">.</span><span class="n">circle</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">p</span><span class="p">),</span> <span class="mi">3</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">,</span> <span class="mi">100</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">show_on_image</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">image</span><span class="p">,</span> <span class="n">window_name</span><span class="o">=</span><span class="s1">&#39;image&#39;</span><span class="p">,</span> <span class="n">multiply</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">draw_triangles</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">points_list</span><span class="p">,</span> <span class="n">multiply</span><span class="o">=</span><span class="n">multiply</span><span class="p">)</span>
<div class="viewcode-block" id="IMMPoints.show"><a class="viewcode-back" href="../../datasets.html#datasets.imm.IMMPoints.show">[docs]</a> <span class="k">def</span> <span class="nf">show</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">window_name</span><span class="o">=</span><span class="s1">&#39;image&#39;</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;show the image and datapoints on the image&quot;&quot;&quot;</span>
<span class="k">assert</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">points_list</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span>
<span class="k">assert</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">filename</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">image</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_image</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">draw_triangles</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">points_list</span><span class="p">)</span></div></div>
<div class="viewcode-block" id="get_imm_points"><a class="viewcode-back" href="../../datasets.html#datasets.imm.get_imm_points">[docs]</a><span class="k">def</span> <span class="nf">get_imm_points</span><span class="p">(</span><span class="n">files</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> This function does something.</span>
<span class="sd"> Args:</span>
<span class="sd"> files (array): Array of .asf full or relative path to .asf files.</span>
<span class="sd"> Returns:</span>
<span class="sd"> ndarray. Array of landmarks.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">points</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">files</span><span class="p">:</span>
<span class="n">imm</span> <span class="o">=</span> <span class="n">IMMPoints</span><span class="p">(</span><span class="n">filename</span><span class="o">=</span><span class="n">f</span><span class="p">)</span>
<span class="n">points</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">imm</span><span class="o">.</span><span class="n">get_points</span><span class="p">())</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">points</span><span class="p">)</span></div>
<div class="viewcode-block" id="get_imm_image_with_landmarks"><a class="viewcode-back" href="../../datasets.html#datasets.imm.get_imm_image_with_landmarks">[docs]</a><span class="k">def</span> <span class="nf">get_imm_image_with_landmarks</span><span class="p">(</span><span class="n">filename</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Get Points with image and landmarks/points</span>
<span class="sd"> Args:</span>
<span class="sd"> filename(fullpath): .asf file</span>
<span class="sd"> Returns:</span>
<span class="sd"> image, points</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">imm</span> <span class="o">=</span> <span class="n">IMMPoints</span><span class="p">(</span><span class="n">filename</span><span class="o">=</span><span class="n">filename</span><span class="p">)</span>
<span class="k">return</span> <span class="n">imm</span><span class="o">.</span><span class="n">get_image</span><span class="p">(),</span> <span class="n">imm</span><span class="o">.</span><span class="n">get_points</span><span class="p">()</span></div>
<span class="k">def</span> <span class="nf">add_parser_options</span><span class="p">():</span>
<span class="n">parser</span> <span class="o">=</span> <span class="n">argparse</span><span class="o">.</span><span class="n">ArgumentParser</span><span class="p">(</span><span class="n">description</span><span class="o">=</span><span class="s1">&#39;IMMPoints tool&#39;</span><span class="p">)</span>
<span class="c1"># asf files</span>
<span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
<span class="s1">&#39;asf&#39;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">str</span><span class="p">,</span> <span class="n">nargs</span><span class="o">=</span><span class="s1">&#39;+&#39;</span><span class="p">,</span> <span class="n">help</span><span class="o">=</span><span class="s1">&#39;asf files to process&#39;</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">parser</span>
<span class="k">if</span> <span class="n">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
<span class="n">parser</span> <span class="o">=</span> <span class="n">add_parser_options</span><span class="p">()</span>
<span class="n">args</span> <span class="o">=</span> <span class="n">parser</span><span class="o">.</span><span class="n">parse_args</span><span class="p">()</span>
<span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">args</span><span class="o">.</span><span class="n">asf</span><span class="p">:</span>
<span class="n">imm</span> <span class="o">=</span> <span class="n">IMMPoints</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
<span class="n">imm</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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<h1>All modules for which code is available</h1>
<ul><li><a href="aam.html">aam</a></li>
<li><a href="datasets/imm.html">datasets.imm</a></li>
<li><a href="main.html">main</a></li>
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<h1>Source code for main</h1><div class="highlight"><pre>
<span></span><span class="ch">#!/usr/local/bin/python</span>
<span class="c1"># python std</span>
<span class="kn">import</span> <span class="nn">argparse</span>
<span class="kn">import</span> <span class="nn">logging</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">import</span> <span class="nn">importlib</span>
<span class="c1"># installed packages</span>
<span class="kn">import</span> <span class="nn">cv2</span>
<span class="c1"># local imports</span>
<span class="kn">import</span> <span class="nn">pca</span>
<span class="kn">import</span> <span class="nn">aam</span>
<span class="c1"># import imm</span>
<span class="kn">from</span> <span class="nn">reconstruction</span> <span class="k">import</span> <span class="n">reconstruction</span>
<span class="n">logging</span><span class="o">.</span><span class="n">basicConfig</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="n">logging</span><span class="o">.</span><span class="n">INFO</span><span class="p">,</span>
<span class="nb">format</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%(asctime)s</span><span class="s1"> </span><span class="si">%(levelname)s</span><span class="s1"> </span><span class="si">%(name)s</span><span class="s1">: </span><span class="si">%(message)s</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="n">__name__</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">add_parser_options</span><span class="p">():</span>
<span class="n">parser</span> <span class="o">=</span> <span class="n">argparse</span><span class="o">.</span><span class="n">ArgumentParser</span><span class="p">(</span><span class="n">description</span><span class="o">=</span><span class="s1">&#39;IMMPoints tool&#39;</span><span class="p">)</span>
<span class="n">pca_group</span> <span class="o">=</span> <span class="n">parser</span><span class="o">.</span><span class="n">add_argument_group</span><span class="p">(</span><span class="s1">&#39;show_pca&#39;</span><span class="p">)</span>
<span class="n">pca_group</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
<span class="s1">&#39;--reconstruct&#39;</span><span class="p">,</span> <span class="n">action</span><span class="o">=</span><span class="s1">&#39;store_true&#39;</span><span class="p">,</span>
<span class="n">help</span><span class="o">=</span><span class="s1">&#39;Reconstruct one face with a given pca model&#39;</span>
<span class="p">)</span>
<span class="n">pca_group</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
<span class="s1">&#39;--show_kivy&#39;</span><span class="p">,</span> <span class="n">action</span><span class="o">=</span><span class="s1">&#39;store_true&#39;</span><span class="p">,</span>
<span class="n">help</span><span class="o">=</span><span class="s1">&#39;Reconstruct using kivy as a GUI&#39;</span>
<span class="p">)</span>
<span class="n">pca_group</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
<span class="s1">&#39;--generate_call_graph&#39;</span><span class="p">,</span> <span class="n">action</span><span class="o">=</span><span class="s1">&#39;store_true&#39;</span><span class="p">,</span>
<span class="n">help</span><span class="o">=</span><span class="s1">&#39;Generate call graph from the reconstruction&#39;</span>
<span class="p">)</span>
<span class="n">pca_group</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
<span class="s1">&#39;--save_pca_shape&#39;</span><span class="p">,</span> <span class="n">action</span><span class="o">=</span><span class="s1">&#39;store_true&#39;</span><span class="p">,</span>
<span class="n">help</span><span class="o">=</span><span class="s1">&#39;save the pca shape model&#39;</span>
<span class="p">)</span>
<span class="n">pca_group</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
<span class="s1">&#39;--save_pca_texture&#39;</span><span class="p">,</span> <span class="n">action</span><span class="o">=</span><span class="s1">&#39;store_true&#39;</span><span class="p">,</span>
<span class="n">help</span><span class="o">=</span><span class="s1">&#39;save the pca texture model&#39;</span>
<span class="p">)</span>
<span class="n">pca_group</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
<span class="s1">&#39;--show_pca&#39;</span><span class="p">,</span> <span class="n">action</span><span class="o">=</span><span class="s1">&#39;store_true&#39;</span><span class="p">,</span>
<span class="n">help</span><span class="o">=</span><span class="s1">&#39;Show and manipulate the saved PCA model&#39;</span>
<span class="p">)</span>
<span class="n">pca_group</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
<span class="s1">&#39;--files&#39;</span><span class="p">,</span> <span class="n">nargs</span><span class="o">=</span><span class="s1">&#39;+&#39;</span><span class="p">,</span> <span class="n">help</span><span class="o">=</span><span class="s1">&#39;files to process&#39;</span>
<span class="p">)</span>
<span class="n">pca_group</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
<span class="s1">&#39;--n_components&#39;</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span>
<span class="n">help</span><span class="o">=</span><span class="s1">&#39;number of principle components to keep and are able to manipulate&#39;</span>
<span class="p">)</span>
<span class="n">pca_group</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
<span class="s1">&#39;--model_shape_file&#39;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">str</span><span class="p">,</span>
<span class="n">help</span><span class="o">=</span><span class="s1">&#39;pca model file that contains or is going to contain the pca shape model&#39;</span>
<span class="p">)</span>
<span class="n">pca_group</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
<span class="s1">&#39;--shape_type&#39;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">str</span><span class="p">,</span> <span class="n">choices</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;imm&#39;</span><span class="p">],</span>
<span class="n">help</span><span class="o">=</span><span class="s1">&#39;type of shape, annotated dataset&#39;</span>
<span class="p">)</span>
<span class="n">pca_group</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
<span class="s1">&#39;--model_texture_file&#39;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">str</span><span class="p">,</span>
<span class="n">help</span><span class="o">=</span><span class="s1">&#39;pca model file that contains or is going to contain the pca texture model&#39;</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">parser</span>
<div class="viewcode-block" id="import_dataset_module"><a class="viewcode-back" href="../index.html#main.import_dataset_module">[docs]</a><span class="k">def</span> <span class="nf">import_dataset_module</span><span class="p">(</span><span class="n">shape_type</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Includes the right implementation for the right dataset implementation for</span>
<span class="sd"> the given shape type, see --help for the available options.</span>
<span class="sd"> Args:</span>
<span class="sd"> shape_type(string): Name of the python file inside the</span>
<span class="sd"> `src/datasets` folder.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">importlib</span><span class="o">.</span><span class="n">import_module</span><span class="p">(</span><span class="s1">&#39;datasets.</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">shape_type</span><span class="p">))</span></div>
<div class="viewcode-block" id="save_pca_model_texture"><a class="viewcode-back" href="../index.html#main.save_pca_model_texture">[docs]</a><span class="k">def</span> <span class="nf">save_pca_model_texture</span><span class="p">(</span><span class="n">args</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> save the U, s, Vt and mean of all the asf datafiles given by the asf</span>
<span class="sd"> files.</span>
<span class="sd"> It is saved in the following way:</span>
<span class="sd"> np.load(filename, np.assary([Vt, mean_values])</span>
<span class="sd"> And accessed by:</span>
<span class="sd"> Vtm = np.load(args.model_file_texture)</span>
<span class="sd"> Vt = Vtm[0]</span>
<span class="sd"> mean_values = Vtm[1][0]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">assert</span> <span class="n">args</span><span class="o">.</span><span class="n">files</span><span class="p">,</span> <span class="s1">&#39;--files should be given&#39;</span>
<span class="k">assert</span> <span class="n">args</span><span class="o">.</span><span class="n">model_shape_file</span><span class="p">,</span> <span class="s1">&#39;--model_texture_file needs to be provided to save the pca model&#39;</span>
<span class="k">assert</span> <span class="n">args</span><span class="o">.</span><span class="n">shape_type</span><span class="p">,</span> <span class="s1">&#39;--shape_type the type of dataset, see datasets module&#39;</span>
<span class="n">dataset_module</span> <span class="o">=</span> <span class="n">import_dataset_module</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">shape_type</span><span class="p">)</span>
<span class="n">shape_model</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">PcaModel</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">model_shape_file</span><span class="p">)</span>
<span class="n">mean_points</span> <span class="o">=</span> <span class="n">dataset_module</span><span class="o">.</span><span class="n">IMMPoints</span><span class="p">(</span><span class="n">points_list</span><span class="o">=</span><span class="n">shape_model</span><span class="o">.</span><span class="n">mean_values</span><span class="p">)</span>
<span class="n">textures</span> <span class="o">=</span> <span class="n">aam</span><span class="o">.</span><span class="n">build_texture_feature_vectors</span><span class="p">(</span>
<span class="n">args</span><span class="o">.</span><span class="n">files</span><span class="p">,</span>
<span class="n">dataset_module</span><span class="o">.</span><span class="n">get_imm_image_with_landmarks</span><span class="p">,</span> <span class="c1"># function</span>
<span class="n">mean_points</span><span class="p">,</span>
<span class="n">shape_model</span><span class="o">.</span><span class="n">triangles</span>
<span class="p">)</span>
<span class="n">mean_texture</span> <span class="o">=</span> <span class="n">aam</span><span class="o">.</span><span class="n">get_mean</span><span class="p">(</span><span class="n">textures</span><span class="p">)</span>
<span class="n">_</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">Vt</span><span class="p">,</span> <span class="n">n_components</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">pca</span><span class="p">(</span><span class="n">textures</span><span class="p">,</span> <span class="n">mean_texture</span><span class="p">)</span>
<span class="n">pca</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">Vt</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">n_components</span><span class="p">,</span> <span class="n">mean_texture</span><span class="p">,</span> <span class="n">shape_model</span><span class="o">.</span><span class="n">triangles</span><span class="p">,</span> <span class="n">args</span><span class="o">.</span><span class="n">model_texture_file</span><span class="p">)</span>
<span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">&#39;texture pca model saved in </span><span class="si">%s</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">args</span><span class="o">.</span><span class="n">model_texture_file</span><span class="p">)</span></div>
<div class="viewcode-block" id="save_pca_model_shape"><a class="viewcode-back" href="../index.html#main.save_pca_model_shape">[docs]</a><span class="k">def</span> <span class="nf">save_pca_model_shape</span><span class="p">(</span><span class="n">args</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> save the U, s, Vt and mean of all the asf datafiles given by the asf</span>
<span class="sd"> files.</span>
<span class="sd"> It is saved in the following way:</span>
<span class="sd"> np.load(filename, np.assary([Vt, mean_values])</span>
<span class="sd"> And accessed by:</span>
<span class="sd"> Vtm = np.load(args.model_shape_file)</span>
<span class="sd"> Vt = Vtm[0]</span>
<span class="sd"> mean_values = Vtm[1][0]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">assert</span> <span class="n">args</span><span class="o">.</span><span class="n">files</span><span class="p">,</span> <span class="s1">&#39;--files should be given&#39;</span>
<span class="k">assert</span> <span class="n">args</span><span class="o">.</span><span class="n">model_shape_file</span><span class="p">,</span> <span class="s1">&#39;--model_shape_file needs to be provided to save the pca model&#39;</span>
<span class="k">assert</span> <span class="n">args</span><span class="o">.</span><span class="n">shape_type</span><span class="p">,</span> <span class="s1">&#39;--shape_type the type of dataset, see datasets module&#39;</span>
<span class="n">dataset_module</span> <span class="o">=</span> <span class="n">import_dataset_module</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">shape_type</span><span class="p">)</span>
<span class="n">points</span> <span class="o">=</span> <span class="n">aam</span><span class="o">.</span><span class="n">build_shape_feature_vectors</span><span class="p">(</span>
<span class="n">args</span><span class="o">.</span><span class="n">files</span><span class="p">,</span> <span class="n">dataset_module</span><span class="o">.</span><span class="n">get_imm_points</span><span class="p">,</span> <span class="n">flattened</span><span class="o">=</span><span class="kc">True</span>
<span class="p">)</span>
<span class="n">mean_values</span> <span class="o">=</span> <span class="n">aam</span><span class="o">.</span><span class="n">get_mean</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
<span class="n">_</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">Vt</span><span class="p">,</span> <span class="n">n_components</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">pca</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">mean_values</span><span class="p">)</span>
<span class="n">mean_xy</span> <span class="o">=</span> <span class="n">mean_values</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
<span class="n">triangles</span> <span class="o">=</span> <span class="n">aam</span><span class="o">.</span><span class="n">get_triangles</span><span class="p">(</span><span class="n">mean_xy</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">mean_xy</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">])</span>
<span class="n">pca</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">Vt</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">n_components</span><span class="p">,</span> <span class="n">mean_values</span><span class="p">,</span> <span class="n">triangles</span><span class="p">,</span> <span class="n">args</span><span class="o">.</span><span class="n">model_shape_file</span><span class="p">)</span>
<span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">&#39;shape pca model saved in </span><span class="si">%s</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">args</span><span class="o">.</span><span class="n">model_shape_file</span> <span class="o">+</span> <span class="s1">&#39;_shape&#39;</span><span class="p">)</span></div>
<span class="k">def</span> <span class="nf">reconstruct_with_model</span><span class="p">(</span><span class="n">args</span><span class="p">):</span>
<span class="k">assert</span> <span class="n">args</span><span class="o">.</span><span class="n">files</span><span class="p">,</span> <span class="s1">&#39;--files should be given to allow the image to be shown&#39;</span>
<span class="k">assert</span> <span class="n">args</span><span class="o">.</span><span class="n">model_shape_file</span><span class="p">,</span> <span class="s1">&#39;--model_shape_file needs to be provided to get the pca model&#39;</span>
<span class="k">assert</span> <span class="n">args</span><span class="o">.</span><span class="n">shape_type</span><span class="p">,</span> <span class="s1">&#39;--shape_type the type of dataset, see datasets module&#39;</span>
<span class="n">dataset_module</span> <span class="o">=</span> <span class="n">import_dataset_module</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">shape_type</span><span class="p">)</span>
<span class="c1"># clear sys args. arguments are conflicting with parseargs</span>
<span class="c1"># kivy will parse args upon import and will crash if it finds our</span>
<span class="c1"># &#39;unsupported by kivy&#39; arguments.</span>
<span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> <span class="o">=</span> <span class="p">[]</span>
<span class="kn">from</span> <span class="nn">view.reconstruct</span> <span class="k">import</span> <span class="n">ReconstructApp</span>
<span class="n">Vt_shape</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">n_shape_components</span><span class="p">,</span> <span class="n">mean_value_points</span><span class="p">,</span> <span class="n">triangles</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">model_shape_file</span><span class="p">)</span>
<span class="n">Vt_texture</span><span class="p">,</span> <span class="n">s_texture</span><span class="p">,</span> <span class="n">n_texture_components</span><span class="p">,</span> <span class="n">mean_values_texture</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">model_texture_file</span><span class="p">)</span>
<span class="n">app</span> <span class="o">=</span> <span class="n">ReconstructApp</span><span class="p">()</span>
<span class="n">app</span><span class="o">.</span><span class="n">set_values</span><span class="p">(</span>
<span class="n">args</span><span class="o">=</span><span class="n">args</span><span class="p">,</span>
<span class="n">eigenv_shape</span><span class="o">=</span><span class="n">Vt_shape</span><span class="p">,</span>
<span class="n">eigenv_texture</span><span class="o">=</span><span class="n">Vt_texture</span><span class="p">,</span>
<span class="n">mean_value_points</span><span class="o">=</span><span class="n">mean_value_points</span><span class="p">,</span>
<span class="n">n_shape_components</span><span class="o">=</span><span class="n">n_shape_components</span><span class="p">,</span>
<span class="n">mean_values_texture</span><span class="o">=</span><span class="n">mean_values_texture</span><span class="p">,</span>
<span class="n">n_texture_components</span><span class="o">=</span><span class="n">n_texture_components</span><span class="p">,</span>
<span class="n">triangles</span><span class="o">=</span><span class="n">triangles</span>
<span class="p">)</span>
<span class="n">app</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">show_pca_model</span><span class="p">(</span><span class="n">args</span><span class="p">):</span>
<span class="k">assert</span> <span class="n">args</span><span class="o">.</span><span class="n">model_shape_file</span><span class="p">,</span> <span class="s1">&#39;--model_texture_file needs to be provided to save the pca model&#39;</span>
<span class="k">assert</span> <span class="n">args</span><span class="o">.</span><span class="n">model_texture_file</span><span class="p">,</span> <span class="s1">&#39;--model_texture_file needs to be provided to save the pca model&#39;</span>
<span class="k">assert</span> <span class="n">args</span><span class="o">.</span><span class="n">shape_type</span><span class="p">,</span> <span class="s1">&#39;--shape_type the type of dataset, see datasets module&#39;</span>
<span class="n">dataset_module</span> <span class="o">=</span> <span class="n">import_dataset_module</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">shape_type</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">reconstruction.triangles</span> <span class="k">import</span> <span class="n">draw_shape</span><span class="p">,</span> <span class="n">get_texture</span>
<span class="n">Vt_shape</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">n_shape_components</span><span class="p">,</span> <span class="n">mean_value_points</span><span class="p">,</span> <span class="n">triangles</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">model_shape_file</span><span class="p">)</span>
<span class="n">Vt_texture</span><span class="p">,</span> <span class="n">s_texture</span><span class="p">,</span> <span class="n">n_texture_components</span><span class="p">,</span> <span class="n">mean_values_texture</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">model_texture_file</span><span class="p">)</span>
<span class="n">imm_points</span> <span class="o">=</span> <span class="n">dataset_module</span><span class="o">.</span><span class="n">IMMPoints</span><span class="p">(</span>
<span class="n">filename</span><span class="o">=</span><span class="s1">&#39;data/imm_face_db/40-1m.asf&#39;</span>
<span class="p">)</span>
<span class="n">input_image</span> <span class="o">=</span> <span class="n">imm_points</span><span class="o">.</span><span class="n">get_image</span><span class="p">()</span>
<span class="n">input_points</span> <span class="o">=</span> <span class="n">imm_points</span><span class="o">.</span><span class="n">get_points</span><span class="p">()</span>
<span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">c</span> <span class="o">=</span> <span class="n">input_image</span><span class="o">.</span><span class="n">shape</span>
<span class="n">input_points</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">input_points</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">w</span>
<span class="n">input_points</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">input_points</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">h</span>
<span class="n">mean_value_points</span> <span class="o">=</span> <span class="n">mean_value_points</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">58</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
<span class="n">mean_value_points</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">mean_value_points</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">w</span>
<span class="n">mean_value_points</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">mean_value_points</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">h</span>
<span class="k">while</span> <span class="kc">True</span><span class="p">:</span>
<span class="n">dst</span> <span class="o">=</span> <span class="n">get_texture</span><span class="p">(</span><span class="n">mean_value_points</span><span class="p">,</span> <span class="n">mean_values_texture</span><span class="p">)</span>
<span class="n">cv2</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="s1">&#39;input_image&#39;</span><span class="p">,</span> <span class="n">input_image</span><span class="p">)</span>
<span class="n">cv2</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="s1">&#39;image&#39;</span><span class="p">,</span> <span class="n">dst</span><span class="p">)</span>
<span class="n">k</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">waitKey</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">&amp;</span> <span class="mh">0xFF</span>
<span class="k">if</span> <span class="n">k</span> <span class="o">==</span> <span class="mi">27</span><span class="p">:</span>
<span class="k">break</span>
<span class="n">cv2</span><span class="o">.</span><span class="n">destroyAllWindows</span><span class="p">()</span>
<div class="viewcode-block" id="generate_call_graph"><a class="viewcode-back" href="../index.html#main.generate_call_graph">[docs]</a><span class="k">def</span> <span class="nf">generate_call_graph</span><span class="p">(</span><span class="n">args</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Performance debug function, will be (re)moved later. &quot;&quot;&quot;</span>
<span class="k">assert</span> <span class="n">args</span><span class="o">.</span><span class="n">model_shape_file</span><span class="p">,</span> <span class="s1">&#39;--model_texture_file needs to be provided to save the pca model&#39;</span>
<span class="k">assert</span> <span class="n">args</span><span class="o">.</span><span class="n">model_texture_file</span><span class="p">,</span> <span class="s1">&#39;--model_texture_file needs to be provided to save the pca model&#39;</span>
<span class="k">assert</span> <span class="n">args</span><span class="o">.</span><span class="n">shape_type</span><span class="p">,</span> <span class="s1">&#39;--shape_type the type of dataset, see datasets module&#39;</span>
<span class="n">dataset_module</span> <span class="o">=</span> <span class="n">import_dataset_module</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">shape_type</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">pycallgraph</span> <span class="k">import</span> <span class="n">PyCallGraph</span>
<span class="kn">from</span> <span class="nn">pycallgraph.output</span> <span class="k">import</span> <span class="n">GraphvizOutput</span>
<span class="n">graphviz</span> <span class="o">=</span> <span class="n">GraphvizOutput</span><span class="p">(</span><span class="n">output_file</span><span class="o">=</span><span class="s1">&#39;filter_none.png&#39;</span><span class="p">)</span>
<span class="k">with</span> <span class="n">PyCallGraph</span><span class="p">(</span><span class="n">output</span><span class="o">=</span><span class="n">graphviz</span><span class="p">):</span>
<span class="n">shape_model</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">PcaModel</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">model_shape_file</span><span class="p">)</span>
<span class="n">texture_model</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">PcaModel</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">model_texture_file</span><span class="p">)</span>
<span class="n">input_points</span> <span class="o">=</span> <span class="n">dataset_module</span><span class="o">.</span><span class="n">IMMPoints</span><span class="p">(</span><span class="n">filename</span><span class="o">=</span><span class="s1">&#39;data/imm_face_db/40-3m.asf&#39;</span><span class="p">)</span>
<span class="n">input_image</span> <span class="o">=</span> <span class="n">input_points</span><span class="o">.</span><span class="n">get_image</span><span class="p">()</span>
<span class="n">mean_points</span> <span class="o">=</span> <span class="n">dataset_module</span><span class="o">.</span><span class="n">IMMPoints</span><span class="p">(</span><span class="n">points_list</span><span class="o">=</span><span class="n">shape_model</span><span class="o">.</span><span class="n">mean_values</span><span class="p">)</span>
<span class="n">mean_points</span><span class="o">.</span><span class="n">get_scaled_points</span><span class="p">(</span><span class="n">input_image</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">reconstruction</span><span class="o">.</span><span class="n">reconstruct_texture</span><span class="p">(</span>
<span class="n">input_image</span><span class="p">,</span> <span class="c1"># src image</span>
<span class="n">input_image</span><span class="p">,</span> <span class="c1"># dst image</span>
<span class="n">texture_model</span><span class="p">,</span>
<span class="n">input_points</span><span class="p">,</span> <span class="c1"># shape points input</span>
<span class="n">mean_points</span><span class="p">,</span> <span class="c1"># shape points mean</span>
<span class="p">)</span></div>
<span class="k">def</span> <span class="nf">show_reconstruction</span><span class="p">(</span><span class="n">args</span><span class="p">):</span>
<span class="k">assert</span> <span class="n">args</span><span class="o">.</span><span class="n">model_shape_file</span><span class="p">,</span> <span class="s1">&#39;--model_texture_file needs to be provided to save the pca model&#39;</span>
<span class="k">assert</span> <span class="n">args</span><span class="o">.</span><span class="n">model_texture_file</span><span class="p">,</span> <span class="s1">&#39;--model_texture_file needs to be provided to save the pca model&#39;</span>
<span class="k">assert</span> <span class="n">args</span><span class="o">.</span><span class="n">shape_type</span><span class="p">,</span> <span class="s1">&#39;--shape_type the type of dataset, see datasets module&#39;</span>
<span class="n">dataset_module</span> <span class="o">=</span> <span class="n">import_dataset_module</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">shape_type</span><span class="p">)</span>
<span class="n">shape_model</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">PcaModel</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">model_shape_file</span><span class="p">)</span>
<span class="n">texture_model</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">PcaModel</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">model_texture_file</span><span class="p">)</span>
<span class="n">input_points</span> <span class="o">=</span> <span class="n">dataset_module</span><span class="o">.</span><span class="n">IMMPoints</span><span class="p">(</span>
<span class="n">filename</span><span class="o">=</span><span class="s1">&#39;data/imm_face_db/40-3m.asf&#39;</span>
<span class="p">)</span>
<span class="n">input_image</span> <span class="o">=</span> <span class="n">input_points</span><span class="o">.</span><span class="n">get_image</span><span class="p">()</span>
<span class="n">mean_points</span> <span class="o">=</span> <span class="n">dataset_module</span><span class="o">.</span><span class="n">IMMPoints</span><span class="p">(</span><span class="n">points_list</span><span class="o">=</span><span class="n">shape_model</span><span class="o">.</span><span class="n">mean_values</span><span class="p">)</span>
<span class="n">mean_points</span><span class="o">.</span><span class="n">get_scaled_points</span><span class="p">(</span><span class="n">input_image</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="k">while</span> <span class="kc">True</span><span class="p">:</span>
<span class="n">reconstruction</span><span class="o">.</span><span class="n">reconstruct_texture</span><span class="p">(</span>
<span class="n">input_image</span><span class="p">,</span> <span class="c1"># src image</span>
<span class="n">input_image</span><span class="p">,</span> <span class="c1"># dst image</span>
<span class="n">texture_model</span><span class="p">,</span>
<span class="n">input_points</span><span class="p">,</span> <span class="c1"># shape points input</span>
<span class="n">mean_points</span><span class="p">,</span> <span class="c1"># shape points mean</span>
<span class="p">)</span>
<span class="n">dst</span> <span class="o">=</span> <span class="n">reconstruction</span><span class="o">.</span><span class="n">get_texture</span><span class="p">(</span>
<span class="n">mean_points</span><span class="p">,</span> <span class="n">texture_model</span><span class="o">.</span><span class="n">mean_values</span>
<span class="p">)</span>
<span class="n">cv2</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="s1">&#39;original&#39;</span><span class="p">,</span> <span class="n">input_points</span><span class="o">.</span><span class="n">get_image</span><span class="p">())</span>
<span class="n">cv2</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="s1">&#39;reconstructed&#39;</span><span class="p">,</span> <span class="n">input_image</span><span class="p">)</span>
<span class="n">cv2</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="s1">&#39;main face&#39;</span><span class="p">,</span> <span class="n">dst</span><span class="p">)</span>
<span class="n">k</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">waitKey</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">&amp;</span> <span class="mh">0xFF</span>
<span class="k">if</span> <span class="n">k</span> <span class="o">==</span> <span class="mi">27</span><span class="p">:</span>
<span class="k">break</span>
<span class="n">cv2</span><span class="o">.</span><span class="n">destroyAllWindows</span><span class="p">()</span>
<div class="viewcode-block" id="main"><a class="viewcode-back" href="../index.html#main.main">[docs]</a><span class="k">def</span> <span class="nf">main</span><span class="p">():</span>
<span class="sd">&quot;&quot;&quot;main&quot;&quot;&quot;</span>
<span class="n">parser</span> <span class="o">=</span> <span class="n">add_parser_options</span><span class="p">()</span>
<span class="n">args</span> <span class="o">=</span> <span class="n">parser</span><span class="o">.</span><span class="n">parse_args</span><span class="p">()</span>
<span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">show_pca</span><span class="p">:</span>
<span class="n">show_pca_model</span><span class="p">(</span><span class="n">args</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">args</span><span class="o">.</span><span class="n">save_pca_shape</span><span class="p">:</span>
<span class="n">save_pca_model_shape</span><span class="p">(</span><span class="n">args</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">args</span><span class="o">.</span><span class="n">save_pca_texture</span><span class="p">:</span>
<span class="n">save_pca_model_texture</span><span class="p">(</span><span class="n">args</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">args</span><span class="o">.</span><span class="n">reconstruct</span><span class="p">:</span>
<span class="c1">#reconstruct_with_model(args)</span>
<span class="n">show_reconstruction</span><span class="p">(</span><span class="n">args</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">args</span><span class="o">.</span><span class="n">show_kivy</span><span class="p">:</span>
<span class="n">reconstruct_with_model</span><span class="p">(</span><span class="n">args</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">args</span><span class="o">.</span><span class="n">generate_call_graph</span><span class="p">:</span>
<span class="n">generate_call_graph</span><span class="p">(</span><span class="n">args</span><span class="p">)</span></div>
<span class="k">if</span> <span class="n">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
<span class="n">main</span><span class="p">()</span>
</pre></div>
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<li class="toctree-l1"><a class="reference internal" href="../datasets.html">Datasets</a></li>
<li class="toctree-l1"><a class="reference internal" href="../aam.html">AAM Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../pca.html">PCA Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../reconstruction/reconstruction.html">Reconstruction Module</a></li>
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<h1>Source code for pca</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<div class="viewcode-block" id="PcaModel"><a class="viewcode-back" href="../pca.html#pca.PcaModel">[docs]</a><span class="k">class</span> <span class="nc">PcaModel</span><span class="p">:</span>
<span class="sd">&quot;&quot;&quot;Abstraction for a pca model&quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model_file</span><span class="p">):</span>
<span class="n">Vtm</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">model_file</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">Vt</span> <span class="o">=</span> <span class="n">Vtm</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">s</span> <span class="o">=</span> <span class="n">Vtm</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">n_components</span> <span class="o">=</span> <span class="n">Vtm</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">mean_values</span> <span class="o">=</span> <span class="n">Vtm</span><span class="p">[</span><span class="mi">3</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">triangles</span> <span class="o">=</span> <span class="n">Vtm</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span></div>
<div class="viewcode-block" id="pca"><a class="viewcode-back" href="../pca.html#pca.pca">[docs]</a><span class="k">def</span> <span class="nf">pca</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">mean_values</span><span class="p">,</span> <span class="n">variance_percentage</span><span class="o">=</span><span class="mi">90</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Perform Singlar Value Decomposition</span>
<span class="sd"> Returns:</span>
<span class="sd"> U (ndarray): U matrix</span>
<span class="sd"> s (ndarray): 1d singular values (diagonal in array form)</span>
<span class="sd"> Vt (ndarray): Vt matrix</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="c1"># subtract mean</span>
<span class="n">zero_mean</span> <span class="o">=</span> <span class="n">data</span> <span class="o">-</span> <span class="n">mean_values</span>
<span class="n">U</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">Vt</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">svd</span><span class="p">(</span><span class="n">zero_mean</span><span class="p">,</span> <span class="n">full_matrices</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="c1"># calculate n_components which captures 90 percent of the variance</span>
<span class="n">total</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">subtotal</span> <span class="o">=</span> <span class="mf">0.0</span>
<span class="n">i</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">while</span> <span class="p">(</span><span class="n">subtotal</span> <span class="o">*</span> <span class="mf">100.0</span><span class="p">)</span> <span class="o">/</span> <span class="n">total</span> <span class="o">&lt;=</span> <span class="n">variance_percentage</span><span class="p">:</span>
<span class="n">subtotal</span> <span class="o">+=</span> <span class="n">s</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">n_components</span> <span class="o">=</span> <span class="n">i</span>
<span class="k">return</span> <span class="n">U</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">Vt</span><span class="p">,</span> <span class="n">n_components</span></div>
<div class="viewcode-block" id="reconstruct"><a class="viewcode-back" href="../pca.html#pca.reconstruct">[docs]</a><span class="k">def</span> <span class="nf">reconstruct</span><span class="p">(</span><span class="n">feature_vector</span><span class="p">,</span> <span class="n">Vt</span><span class="p">,</span> <span class="n">mean_values</span><span class="p">,</span> <span class="n">n_components</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Reconstruct with U, s, Vt</span>
<span class="sd"> Args:</span>
<span class="sd"> U (numpy ndarray): One feature vector from the reduced SVD.</span>
<span class="sd"> U should have shape (n_features,), (i.e., one dimensional)</span>
<span class="sd"> s (numpy ndarray): The singular values as a one dimensional array</span>
<span class="sd"> Vt (numpy ndarray): Two dimensional array with dimensions</span>
<span class="sd"> (n_features, n_features)</span>
<span class="sd"> mean_values (numpy ndarray): mean values of the features of the model,</span>
<span class="sd"> this should have dimensions (n_features, )</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="n">n_components</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">n_components</span> <span class="o">=</span> <span class="n">Vt</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">zm</span> <span class="o">=</span> <span class="n">feature_vector</span> <span class="o">-</span> <span class="n">mean_values</span>
<span class="n">yk</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">Vt</span><span class="p">[:</span><span class="n">n_components</span><span class="p">],</span> <span class="n">zm</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">Vt</span><span class="p">[:</span><span class="n">n_components</span><span class="p">]</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">yk</span><span class="p">)</span> <span class="o">+</span> <span class="n">mean_values</span></div>
<div class="viewcode-block" id="save"><a class="viewcode-back" href="../pca.html#pca.save">[docs]</a><span class="k">def</span> <span class="nf">save</span><span class="p">(</span><span class="n">Vt</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">n_components</span><span class="p">,</span> <span class="n">mean_values</span><span class="p">,</span> <span class="n">triangles</span><span class="p">,</span> <span class="n">filename</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Store the U, s, Vt and mean of all the asf datafiles given by the asf</span>
<span class="sd"> files.</span>
<span class="sd"> It is stored in the following way:</span>
<span class="sd"> np.load(filename, np.assary([Vt, [mean_values]])</span>
<span class="sd"> And accessed by:</span>
<span class="sd"> Vtm = np.load(args.model_file)</span>
<span class="sd"> Vt = Vtm[0]</span>
<span class="sd"> mean_values = Vtm[1][0]</span>
<span class="sd"> triangles = Vtm[2]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">saving</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="n">Vt</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">n_components</span><span class="p">,</span> <span class="p">[</span><span class="n">mean_values</span><span class="p">],</span> <span class="n">triangles</span><span class="p">])</span>
<span class="n">np</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">saving</span><span class="p">)</span></div>
<div class="viewcode-block" id="load"><a class="viewcode-back" href="../pca.html#pca.load">[docs]</a><span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="n">filename</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> The model stored by pca.store (see ``pca.store`` method above) is loaded as:</span>
<span class="sd"> UsVtm = np.load(args.model_file)</span>
<span class="sd"> Vt = Vtm[0]</span>
<span class="sd"> mean_values = Vtm[1][0]</span>
<span class="sd"> Returns:</span>
<span class="sd"> (tuple): Vt, mean_values</span>
<span class="sd"> Vt (numpy ndarray): Two dimensional array with dimensions</span>
<span class="sd"> (n_features, n_features)</span>
<span class="sd"> mean_values (numpy ndarray): mean values of the features of the model,</span>
<span class="sd"> this should have dimensions (n_featurs, )</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="c1"># load the stored model file</span>
<span class="n">Vtm</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span>
<span class="n">Vt</span> <span class="o">=</span> <span class="n">Vtm</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">s</span> <span class="o">=</span> <span class="n">Vtm</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">n_components</span> <span class="o">=</span> <span class="n">Vtm</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
<span class="n">mean_values</span> <span class="o">=</span> <span class="n">Vtm</span><span class="p">[</span><span class="mi">3</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
<span class="n">triangles</span> <span class="o">=</span> <span class="n">Vtm</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span>
<span class="k">return</span> <span class="n">Vt</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">n_components</span><span class="p">,</span> <span class="n">mean_values</span><span class="p">,</span> <span class="n">triangles</span></div>
<span class="c1">#def load_model(filename):</span>
<span class="c1"># # load the stored model file</span>
<span class="c1"># return PcaModel(filename)</span>
<div class="viewcode-block" id="flatten_feature_vectors"><a class="viewcode-back" href="../pca.html#pca.flatten_feature_vectors">[docs]</a><span class="k">def</span> <span class="nf">flatten_feature_vectors</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Flattens the feature vectors inside a ndarray</span>
<span class="sd"> Example:</span>
<span class="sd"> input:</span>
<span class="sd"> [</span>
<span class="sd"> [[1, 2], [3, 4], [5, 6]],</span>
<span class="sd"> ...</span>
<span class="sd"> [[1, 2], [3, 4], [5, 6]]</span>
<span class="sd"> ]</span>
<span class="sd"> output:</span>
<span class="sd"> [</span>
<span class="sd"> [1, 2, 3, 4, 5, 6],</span>
<span class="sd"> ...</span>
<span class="sd"> [1, 2, 3, 4, 5, 6]</span>
<span class="sd"> ]</span>
<span class="sd"> Args:</span>
<span class="sd"> data (numpy array): array of feature vectors</span>
<span class="sd"> dim (int): dimension to flatten the data</span>
<span class="sd"> return:</span>
<span class="sd"> array: (numpy array): array flattened feature vectors</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">flattened</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">n</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">dim</span><span class="p">]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
<span class="n">flattened</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">flattened</span><span class="p">)</span></div>
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<li class="toctree-l1"><a class="reference internal" href="../../datasets.html">Datasets</a></li>
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<h1>Source code for reconstruction.reconstruction</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">cv2</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pca</span>
<span class="kn">import</span> <span class="nn">aam</span>
<span class="kn">from</span> <span class="nn">.texture</span> <span class="k">import</span> <span class="n">fill_triangle_src_dst</span>
<div class="viewcode-block" id="cartesian2barycentric"><a class="viewcode-back" href="../../reconstruction/reconstruction.html#reconstruction.reconstruction.cartesian2barycentric">[docs]</a><span class="k">def</span> <span class="nf">cartesian2barycentric</span><span class="p">(</span><span class="n">r1</span><span class="p">,</span> <span class="n">r2</span><span class="p">,</span> <span class="n">r3</span><span class="p">,</span> <span class="n">r</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Given a triangle spanned by three cartesion points</span>
<span class="sd"> r1, r2, r2, and point r, return the barycentric weights l1, l2, l3.</span>
<span class="sd"> Returns:</span>
<span class="sd"> ndarray (of dim 3) weights of the barycentric coordinates</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">r</span>
<span class="n">x1</span><span class="p">,</span> <span class="n">y1</span> <span class="o">=</span> <span class="n">r1</span>
<span class="n">x2</span><span class="p">,</span> <span class="n">y2</span> <span class="o">=</span> <span class="n">r2</span>
<span class="n">x3</span><span class="p">,</span> <span class="n">y3</span> <span class="o">=</span> <span class="n">r3</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">,</span> <span class="n">x3</span><span class="p">],</span> <span class="p">[</span><span class="n">y1</span><span class="p">,</span> <span class="n">y2</span><span class="p">,</span> <span class="n">y3</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]])</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span></div>
<div class="viewcode-block" id="barycentric2cartesian"><a class="viewcode-back" href="../../reconstruction/reconstruction.html#reconstruction.reconstruction.barycentric2cartesian">[docs]</a><span class="k">def</span> <span class="nf">barycentric2cartesian</span><span class="p">(</span><span class="n">r1</span><span class="p">,</span> <span class="n">r2</span><span class="p">,</span> <span class="n">r3</span><span class="p">,</span> <span class="n">L</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Given the barycentric weights in L, and cartesian r1, r2, r3 coordinates of</span>
<span class="sd"> points that span the triangle, return the cartesian coordinate of the</span>
<span class="sd"> points that is located at the weights of L.</span>
<span class="sd"> Returns:</span>
<span class="sd"> ndarray [x,y] cartesian points.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">x1</span><span class="p">,</span> <span class="n">y1</span> <span class="o">=</span> <span class="n">r1</span>
<span class="n">x2</span><span class="p">,</span> <span class="n">y2</span> <span class="o">=</span> <span class="n">r2</span>
<span class="n">x3</span><span class="p">,</span> <span class="n">y3</span> <span class="o">=</span> <span class="n">r3</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">,</span> <span class="n">x3</span><span class="p">],</span> <span class="p">[</span><span class="n">y1</span><span class="p">,</span> <span class="n">y2</span><span class="p">,</span> <span class="n">y3</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]])</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">L</span><span class="p">)</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">uint32</span><span class="p">)</span></div>
<span class="k">def</span> <span class="nf">draw_shape</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">points</span><span class="p">,</span> <span class="n">triangles</span><span class="p">,</span> <span class="n">multiply</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="k">if</span> <span class="n">multiply</span><span class="p">:</span>
<span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">c</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">shape</span>
<span class="n">points</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">points</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">w</span>
<span class="n">points</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">points</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">h</span>
<span class="n">dim</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">points</span><span class="o">.</span><span class="n">shape</span>
<span class="n">point_indices</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">dim</span><span class="p">))</span>
<span class="k">for</span> <span class="n">t</span><span class="p">,</span> <span class="n">tri</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">triangles</span><span class="p">):</span>
<span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">,</span> <span class="n">p3</span> <span class="o">=</span> <span class="n">points</span><span class="p">[</span><span class="n">tri</span><span class="p">]</span>
<span class="n">cv2</span><span class="o">.</span><span class="n">line</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">p1</span><span class="p">),</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">p2</span><span class="p">),</span> <span class="p">(</span><span class="mi">255</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">100</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">cv2</span><span class="o">.</span><span class="n">line</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">p2</span><span class="p">),</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">p3</span><span class="p">),</span> <span class="p">(</span><span class="mi">255</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">100</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">cv2</span><span class="o">.</span><span class="n">line</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">p3</span><span class="p">),</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">p1</span><span class="p">),</span> <span class="p">(</span><span class="mi">255</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">100</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">p</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">points</span><span class="p">):</span>
<span class="n">point_index</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">point_indices</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
<span class="n">cv2</span><span class="o">.</span><span class="n">putText</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">point_index</span><span class="p">),</span> <span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span>
<span class="n">cv2</span><span class="o">.</span><span class="n">FONT_HERSHEY_SIMPLEX</span><span class="p">,</span> <span class="o">.</span><span class="mi">5</span><span class="p">,</span> <span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">))</span>
<span class="n">cv2</span><span class="o">.</span><span class="n">putText</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">),</span> <span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span>
<span class="n">cv2</span><span class="o">.</span><span class="n">FONT_HERSHEY_SIMPLEX</span><span class="p">,</span> <span class="o">.</span><span class="mi">5</span><span class="p">,</span> <span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">))</span>
<span class="n">cv2</span><span class="o">.</span><span class="n">circle</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">p</span><span class="p">),</span> <span class="mi">3</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">,</span> <span class="mi">100</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">get_texture</span><span class="p">(</span><span class="n">Points</span><span class="p">,</span> <span class="n">flattened_texture</span><span class="p">):</span>
<span class="n">offset_x</span><span class="p">,</span> <span class="n">offset_y</span><span class="p">,</span> <span class="n">w_slice</span><span class="p">,</span> <span class="n">h_slice</span> <span class="o">=</span> <span class="n">Points</span><span class="o">.</span><span class="n">get_bounding_box</span><span class="p">()</span>
<span class="c1"># Make a rectangle image from the flattened texture array</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">flattened_texture</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">h_slice</span><span class="p">,</span> <span class="n">w_slice</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<div class="viewcode-block" id="reconstruct_texture"><a class="viewcode-back" href="../../reconstruction/reconstruction.html#reconstruction.reconstruction.reconstruct_texture">[docs]</a><span class="k">def</span> <span class="nf">reconstruct_texture</span><span class="p">(</span><span class="n">src_image</span><span class="p">,</span> <span class="n">dst_image</span><span class="p">,</span> <span class="n">texture_model</span><span class="p">,</span> <span class="n">src_points</span><span class="p">,</span> <span class="n">dst_points</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Recontruct texture given the src and dst image</span>
<span class="sd"> Args:</span>
<span class="sd"> src_points(aam.AAMPoints)</span>
<span class="sd"> dst_points(aam.AAMPoints)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">Vt</span> <span class="o">=</span> <span class="n">texture_model</span><span class="o">.</span><span class="n">Vt</span>
<span class="n">triangles</span> <span class="o">=</span> <span class="n">texture_model</span><span class="o">.</span><span class="n">triangles</span>
<span class="n">mean_texture</span> <span class="o">=</span> <span class="n">texture_model</span><span class="o">.</span><span class="n">mean_values</span>
<span class="c1"># n_components = texture_model.n_components</span>
<span class="c1"># S_mean format</span>
<span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">c</span> <span class="o">=</span> <span class="n">src_image</span><span class="o">.</span><span class="n">shape</span>
<span class="n">input_texture</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">fill_value</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span>
<span class="n">points2d_src</span> <span class="o">=</span> <span class="n">src_points</span><span class="o">.</span><span class="n">get_scaled_points</span><span class="p">(</span><span class="n">src_image</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">points2d_dst</span> <span class="o">=</span> <span class="n">dst_points</span><span class="o">.</span><span class="n">get_scaled_points</span><span class="p">(</span><span class="n">dst_image</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">aam</span><span class="o">.</span><span class="n">sample_from_triangles</span><span class="p">(</span>
<span class="n">src_image</span><span class="p">,</span>
<span class="n">points2d_src</span><span class="p">,</span>
<span class="n">points2d_dst</span><span class="p">,</span>
<span class="n">triangles</span><span class="p">,</span>
<span class="n">input_texture</span>
<span class="p">)</span>
<span class="n">offset_x</span><span class="p">,</span> <span class="n">offset_y</span><span class="p">,</span> <span class="n">w_slice</span><span class="p">,</span> <span class="n">h_slice</span> <span class="o">=</span> <span class="n">dst_points</span><span class="o">.</span><span class="n">get_bounding_box</span><span class="p">()</span>
<span class="n">input_texture</span> <span class="o">=</span> <span class="n">input_texture</span><span class="p">[</span><span class="n">offset_y</span><span class="p">:</span> <span class="n">offset_y</span> <span class="o">+</span> <span class="n">h_slice</span><span class="p">,</span>
<span class="n">offset_x</span><span class="p">:</span> <span class="n">offset_x</span> <span class="o">+</span> <span class="n">w_slice</span><span class="p">]</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
<span class="c1"># Still in S_mean format</span>
<span class="n">r_texture</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">reconstruct</span><span class="p">(</span><span class="n">input_texture</span><span class="p">,</span> <span class="n">Vt</span><span class="p">,</span> <span class="n">mean_texture</span><span class="p">)</span>
<span class="c1"># Make an image from the float data</span>
<span class="n">r_texture</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">r_texture</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">h_slice</span><span class="p">,</span> <span class="n">w_slice</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="c1"># subtract the offset</span>
<span class="n">points2d_dst</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">-=</span> <span class="n">offset_x</span>
<span class="n">points2d_dst</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">-=</span> <span class="n">offset_y</span>
<span class="k">for</span> <span class="n">tri</span> <span class="ow">in</span> <span class="n">triangles</span><span class="p">:</span>
<span class="n">src_p1</span><span class="p">,</span> <span class="n">src_p2</span><span class="p">,</span> <span class="n">src_p3</span> <span class="o">=</span> <span class="n">points2d_src</span><span class="p">[</span><span class="n">tri</span><span class="p">]</span>
<span class="n">dst_p1</span><span class="p">,</span> <span class="n">dst_p2</span><span class="p">,</span> <span class="n">dst_p3</span> <span class="o">=</span> <span class="n">points2d_dst</span><span class="p">[</span><span class="n">tri</span><span class="p">]</span>
<span class="n">fill_triangle_src_dst</span><span class="p">(</span>
<span class="n">r_texture</span><span class="p">,</span> <span class="n">dst_image</span><span class="p">,</span>
<span class="n">dst_p1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dst_p1</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
<span class="n">dst_p2</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dst_p2</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
<span class="n">dst_p3</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dst_p3</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
<span class="n">src_p1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">src_p1</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
<span class="n">src_p2</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">src_p2</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
<span class="n">src_p3</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">src_p3</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="p">)</span></div>
</pre></div>
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src/docs/build/html/_sources/aam.txt 0 → 100644
View file @ beedf9a5
AAM Module
==========
.. automodule:: aam
:members:
src/docs/build/html/_sources/datasets.txt 0 → 100644
View file @ beedf9a5
Datasets
========
.. automodule:: datasets.imm
:members:
src/docs/build/html/_sources/index.txt 0 → 100644
View file @ beedf9a5
.. 3D Face Reconstruction documentation master file, created by
sphinx-quickstart on Mon Aug 1 16:41:23 2016.
You can adapt this file completely to your liking, but it should at least
contain the root `toctree` directive.
Welcome to 3D Face Reconstruction's documentation!
==================================================
.. toctree::
:maxdepth: 2
:caption: Table of Contents
:name: mastertoc
datasets
aam
pca
reconstruction/reconstruction
reconstruction/texture
!!!Work in progress!!!
======================
PCA reconstruction
==================
Principle Component Analysis is one of the most used methods in the field of statistics, it is used for dimension reduction of data and is capable of removing outliers which ultimately improves learning algorithms. In this case we use PCA for both shape and texture reconstruction. Given an image of person's face we would be able to reconstruct it using a PCA Model. The motivation for using PCA is that we can fill in missing data and remove outliers given one image of person. If for some reason the image is very cluttered, we would still be able to 'predict' how this person would look like, given all the faces we have used to train the PCA Model.
For the PCA reconstruction method has a couple of prerequisites are required. First off, the PCA Model itself. For those who are familiar with PCA know that we need to have a flattened feature vector. Both the dimensions and the content of this feature vector may be arbitrary, but have to be exactly the same from subject to subject, (i.e., there can be no difference in the number of annotated landmarks or order, landmark 1 in subject A, is landmark 1 in subject B). In this case we use it for the shape and texture. The shape feature vector contains the following data:
```
[[x_1, y_1], [x_2, y_2], ..., [x_n, y_n]] -> (flattened) [x_1, y_1, x_2, y_2, x_n, y_n]
```
The x,y values are the location of landmarks in an image. Such a cluster of annotated locations in an image construct a shape we call Active Appearance Model(AAM)[1]. For a serie of annotated pictures with landmark location we can build mean AAM. For this particular implementation we started with supporting the Imm Dataset[^imm_dataset], for the simple reason that it is open for usage without any license agreement before hand (make sure we are correct about this). This is what we call the mean face, which is very important for the construction of the PCA Model, any PCA Model for that matter.
The texture PCA data is somewhat more difficult and depends on a given shape. In our case this given shape is the mean AAM that we have built previously. We need to add extra information to this AAM mean shape, namely a unique set of triangles that can be constructed from the set of landmarks. For this we use the Delaunay algorithm which does exactly this. The triangles help us find corresponding pixels in shape A and B. This solves the problem of pixel correspondences and is important for constructing a mean texture for the reasons explained previously about how a feature vector should look like. Pixel 1 in triangle 1 in subject A needs to correspond to exactly the same pixel (relatively) to pixel 1 in triangle 1 in subject B. This of course is sensitive to noise, but the pixels in the nose region must correspond from subject to subject, this prevents that we reconstruct an eye with a nose for instance (Note: remove this last sentence in a serious text).
References
==========
[1]: Cootes, T. F., Edwards, G. J., & Taylor, C. J. (1998, June). Active appearance models. In European conference on computer vision (pp. 484-498). Springer Berlin Heidelberg.
Links
=====
[^imm_dataset]: http://www.imm.dtu.dk/~aam/datasets/datasets.html "Imm dataset"
Indices and tables
==================
* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`
src/docs/build/html/_sources/pca.txt 0 → 100644
View file @ beedf9a5
PCA Module
==========
.. automodule:: pca
:members:
src/docs/build/html/_sources/reconstruction/reconstruction.txt 0 → 100644
View file @ beedf9a5
Reconstruction Module
=====================
As explained in [PCA Reconstruction](home) we need a flattened feature vector to able to build a PCA Model. This holds for both shape and texture model. Currently we implement the independent AAM model where we keep the feature vector separate. Note that we could also choose to combine the shape and appearance in a single flattened feature vector (TODO: elaborate our choice more about this, if possible).
We use the imm dataset[^imm_dataset] for this. We first need to build the mean shape of the all the images. The dataset has a .asf file and an equally named .jpg file. The .asf file contains the locations of the landmars (normalized by the width and height of the image). In `src/imm_points.py` we find the ImmPoints class that implements all functions needed to read this file.
[^imm_dataset]: http://www.imm.dtu.dk/~aam/datasets/datasets.html "Imm dataset"
.. automodule:: reconstruction.reconstruction
:members:
src/docs/build/html/_sources/reconstruction/texture.txt 0 → 100644
View file @ beedf9a5
Texture Module
==============
.. automodule:: reconstruction.texture
:members:
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margin-left: 20px;
list-style: square;
}
div.sphinxsidebar ul ul {
margin-top: 0;
margin-bottom: 0;
}
div.sphinxsidebar form {
margin-top: 10px;
}
div.sphinxsidebar input {
border: 1px solid #98dbcc;
font-family: sans-serif;
font-size: 1em;
}
div.sphinxsidebar #searchbox input[type="text"] {
width: 170px;
}
img {
border: 0;
max-width: 100%;
}
/* -- search page ----------------------------------------------------------- */
ul.search {
margin: 10px 0 0 20px;
padding: 0;
}
ul.search li {
padding: 5px 0 5px 20px;
background-image: url(file.png);
background-repeat: no-repeat;
background-position: 0 7px;
}
ul.search li a {
font-weight: bold;
}
ul.search li div.context {
color: #888;
margin: 2px 0 0 30px;
text-align: left;
}
ul.keywordmatches li.goodmatch a {
font-weight: bold;
}
/* -- index page ------------------------------------------------------------ */
table.contentstable {
width: 90%;
}
table.contentstable p.biglink {
line-height: 150%;
}
a.biglink {
font-size: 1.3em;
}
span.linkdescr {
font-style: italic;
padding-top: 5px;
font-size: 90%;
}
/* -- general index --------------------------------------------------------- */
table.indextable {
width: 100%;
}
table.indextable td {
text-align: left;
vertical-align: top;
}
table.indextable dl, table.indextable dd {
margin-top: 0;
margin-bottom: 0;
}
table.indextable tr.pcap {
height: 10px;
}
table.indextable tr.cap {
margin-top: 10px;
background-color: #f2f2f2;
}
img.toggler {
margin-right: 3px;
margin-top: 3px;
cursor: pointer;
}
div.modindex-jumpbox {
border-top: 1px solid #ddd;
border-bottom: 1px solid #ddd;
margin: 1em 0 1em 0;
padding: 0.4em;
}
div.genindex-jumpbox {
border-top: 1px solid #ddd;
border-bottom: 1px solid #ddd;
margin: 1em 0 1em 0;
padding: 0.4em;
}
/* -- general body styles --------------------------------------------------- */
div.body p, div.body dd, div.body li, div.body blockquote {
-moz-hyphens: auto;
-ms-hyphens: auto;
-webkit-hyphens: auto;
hyphens: auto;
}
a.headerlink {
visibility: hidden;
}
h1:hover > a.headerlink,
h2:hover > a.headerlink,
h3:hover > a.headerlink,
h4:hover > a.headerlink,
h5:hover > a.headerlink,
h6:hover > a.headerlink,
dt:hover > a.headerlink,
caption:hover > a.headerlink,
p.caption:hover > a.headerlink,
div.code-block-caption:hover > a.headerlink {
visibility: visible;
}
div.body p.caption {
text-align: inherit;
}
div.body td {
text-align: left;
}
.field-list ul {
padding-left: 1em;
}
.first {
margin-top: 0 !important;
}
p.rubric {
margin-top: 30px;
font-weight: bold;
}
img.align-left, .figure.align-left, object.align-left {
clear: left;
float: left;
margin-right: 1em;
}
img.align-right, .figure.align-right, object.align-right {
clear: right;
float: right;
margin-left: 1em;
}
img.align-center, .figure.align-center, object.align-center {
display: block;
margin-left: auto;
margin-right: auto;
}
.align-left {
text-align: left;
}
.align-center {
text-align: center;
}
.align-right {
text-align: right;
}
/* -- sidebars -------------------------------------------------------------- */
div.sidebar {
margin: 0 0 0.5em 1em;
border: 1px solid #ddb;
padding: 7px 7px 0 7px;
background-color: #ffe;
width: 40%;
float: right;
}
p.sidebar-title {
font-weight: bold;
}
/* -- topics ---------------------------------------------------------------- */
div.topic {
border: 1px solid #ccc;
padding: 7px 7px 0 7px;
margin: 10px 0 10px 0;
}
p.topic-title {
font-size: 1.1em;
font-weight: bold;
margin-top: 10px;
}
/* -- admonitions ----------------------------------------------------------- */
div.admonition {
margin-top: 10px;
margin-bottom: 10px;
padding: 7px;
}
div.admonition dt {
font-weight: bold;
}
div.admonition dl {
margin-bottom: 0;
}
p.admonition-title {
margin: 0px 10px 5px 0px;
font-weight: bold;
}
div.body p.centered {
text-align: center;
margin-top: 25px;
}
/* -- tables ---------------------------------------------------------------- */
table.docutils {
border: 0;
border-collapse: collapse;
}
table caption span.caption-number {
font-style: italic;
}
table caption span.caption-text {
}
table.docutils td, table.docutils th {
padding: 1px 8px 1px 5px;
border-top: 0;
border-left: 0;
border-right: 0;
border-bottom: 1px solid #aaa;
}
table.field-list td, table.field-list th {
border: 0 !important;
}
table.footnote td, table.footnote th {
border: 0 !important;
}
th {
text-align: left;
padding-right: 5px;
}
table.citation {
border-left: solid 1px gray;
margin-left: 1px;
}
table.citation td {
border-bottom: none;
}
/* -- figures --------------------------------------------------------------- */
div.figure {
margin: 0.5em;
padding: 0.5em;
}
div.figure p.caption {
padding: 0.3em;
}
div.figure p.caption span.caption-number {
font-style: italic;
}
div.figure p.caption span.caption-text {
}
/* -- other body styles ----------------------------------------------------- */
ol.arabic {
list-style: decimal;
}
ol.loweralpha {
list-style: lower-alpha;
}
ol.upperalpha {
list-style: upper-alpha;
}
ol.lowerroman {
list-style: lower-roman;
}
ol.upperroman {
list-style: upper-roman;
}
dl {
margin-bottom: 15px;
}
dd p {
margin-top: 0px;
}
dd ul, dd table {
margin-bottom: 10px;
}
dd {
margin-top: 3px;
margin-bottom: 10px;
margin-left: 30px;
}
dt:target, .highlighted {
background-color: #fbe54e;
}
dl.glossary dt {
font-weight: bold;
font-size: 1.1em;
}
.field-list ul {
margin: 0;
padding-left: 1em;
}
.field-list p {
margin: 0;
}
.optional {
font-size: 1.3em;
}
.sig-paren {
font-size: larger;
}
.versionmodified {
font-style: italic;
}
.system-message {
background-color: #fda;
padding: 5px;
border: 3px solid red;
}
.footnote:target {
background-color: #ffa;
}
.line-block {
display: block;
margin-top: 1em;
margin-bottom: 1em;
}
.line-block .line-block {
margin-top: 0;
margin-bottom: 0;
margin-left: 1.5em;
}
.guilabel, .menuselection {
font-family: sans-serif;
}
.accelerator {
text-decoration: underline;
}
.classifier {
font-style: oblique;
}
abbr, acronym {
border-bottom: dotted 1px;
cursor: help;
}
/* -- code displays --------------------------------------------------------- */
pre {
overflow: auto;
overflow-y: hidden; /* fixes display issues on Chrome browsers */
}
td.linenos pre {
padding: 5px 0px;
border: 0;
background-color: transparent;
color: #aaa;
}
table.highlighttable {
margin-left: 0.5em;
}
table.highlighttable td {
padding: 0 0.5em 0 0.5em;
}
div.code-block-caption {
padding: 2px 5px;
font-size: small;
}
div.code-block-caption code {
background-color: transparent;
}
div.code-block-caption + div > div.highlight > pre {
margin-top: 0;
}
div.code-block-caption span.caption-number {
padding: 0.1em 0.3em;
font-style: italic;
}
div.code-block-caption span.caption-text {
}
div.literal-block-wrapper {
padding: 1em 1em 0;
}
div.literal-block-wrapper div.highlight {
margin: 0;
}
code.descname {
background-color: transparent;
font-weight: bold;
font-size: 1.2em;
}
code.descclassname {
background-color: transparent;
}
code.xref, a code {
background-color: transparent;
font-weight: bold;
}
h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
background-color: transparent;
}
.viewcode-link {
float: right;
}
.viewcode-back {
float: right;
font-family: sans-serif;
}
div.viewcode-block:target {
margin: -1px -10px;
padding: 0 10px;
}
/* -- math display ---------------------------------------------------------- */
img.math {
vertical-align: middle;
}
div.body div.math p {
text-align: center;
}
span.eqno {
float: right;
}
/* -- printout stylesheet --------------------------------------------------- */
@media print {
div.document,
div.documentwrapper,
div.bodywrapper {
margin: 0 !important;
width: 100%;
}
div.sphinxsidebar,
div.related,
div.footer,
#top-link {
display: none;
}
}
\ No newline at end of file
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.fa:before{-webkit-font-smoothing:antialiased}.clearfix{*zoom:1}.clearfix:before,.clearfix:after{display:table;content:""}.clearfix:after{clear:both}@font-face{font-family:FontAwesome;font-weight:normal;font-style:normal;src:url("../font/fontawesome_webfont.eot");src:url("../font/fontawesome_webfont.eot?#iefix") format("embedded-opentype"),url("../font/fontawesome_webfont.woff") format("woff"),url("../font/fontawesome_webfont.ttf") format("truetype"),url("../font/fontawesome_webfont.svg#FontAwesome") format("svg")}.fa:before{display:inline-block;font-family:FontAwesome;font-style:normal;font-weight:normal;line-height:1;text-decoration:inherit}a .fa{display:inline-block;text-decoration:inherit}li .fa{display:inline-block}li .fa-large:before,li .fa-large:before{width:1.875em}ul.fas{list-style-type:none;margin-left:2em;text-indent:-0.8em}ul.fas li .fa{width:0.8em}ul.fas li .fa-large:before,ul.fas li .fa-large:before{vertical-align:baseline}.fa-book:before{content:""}.icon-book:before{content:""}.fa-caret-down:before{content:""}.icon-caret-down:before{content:""}.fa-caret-up:before{content:""}.icon-caret-up:before{content:""}.fa-caret-left:before{content:""}.icon-caret-left:before{content:""}.fa-caret-right:before{content:""}.icon-caret-right:before{content:""}.rst-versions{position:fixed;bottom:0;left:0;width:300px;color:#fcfcfc;background:#1f1d1d;border-top:solid 10px #343131;font-family:"Lato","proxima-nova","Helvetica Neue",Arial,sans-serif;z-index:400}.rst-versions a{color:#2980B9;text-decoration:none}.rst-versions .rst-badge-small{display:none}.rst-versions .rst-current-version{padding:12px;background-color:#272525;display:block;text-align:right;font-size:90%;cursor:pointer;color:#27AE60;*zoom:1}.rst-versions .rst-current-version:before,.rst-versions .rst-current-version:after{display:table;content:""}.rst-versions .rst-current-version:after{clear:both}.rst-versions .rst-current-version .fa{color:#fcfcfc}.rst-versions .rst-current-version .fa-book{float:left}.rst-versions .rst-current-version .icon-book{float:left}.rst-versions .rst-current-version.rst-out-of-date{background-color:#E74C3C;color:#fff}.rst-versions .rst-current-version.rst-active-old-version{background-color:#F1C40F;color:#000}.rst-versions.shift-up .rst-other-versions{display:block}.rst-versions .rst-other-versions{font-size:90%;padding:12px;color:gray;display:none}.rst-versions .rst-other-versions hr{display:block;height:1px;border:0;margin:20px 0;padding:0;border-top:solid 1px #413d3d}.rst-versions .rst-other-versions dd{display:inline-block;margin:0}.rst-versions .rst-other-versions dd a{display:inline-block;padding:6px;color:#fcfcfc}.rst-versions.rst-badge{width:auto;bottom:20px;right:20px;left:auto;border:none;max-width:300px}.rst-versions.rst-badge .icon-book{float:none}.rst-versions.rst-badge .fa-book{float:none}.rst-versions.rst-badge.shift-up .rst-current-version{text-align:right}.rst-versions.rst-badge.shift-up .rst-current-version .fa-book{float:left}.rst-versions.rst-badge.shift-up .rst-current-version .icon-book{float:left}.rst-versions.rst-badge .rst-current-version{width:auto;height:30px;line-height:30px;padding:0 6px;display:block;text-align:center}@media screen and (max-width: 768px){.rst-versions{width:85%;display:none}.rst-versions.shift{display:block}img{width:100%;height:auto}}
/*# sourceMappingURL=badge_only.css.map */
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/*
* doctools.js
* ~~~~~~~~~~~
*
* Sphinx JavaScript utilities for all documentation.
*
* :copyright: Copyright 2007-2016 by the Sphinx team, see AUTHORS.
* :license: BSD, see LICENSE for details.
*
*/
/**
* select a different prefix for underscore
*/
$u = _.noConflict();
/**
* make the code below compatible with browsers without
* an installed firebug like debugger
if (!window.console || !console.firebug) {
var names = ["log", "debug", "info", "warn", "error", "assert", "dir",
"dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace",
"profile", "profileEnd"];
window.console = {};
for (var i = 0; i < names.length; ++i)
window.console[names[i]] = function() {};
}
*/
/**
* small helper function to urldecode strings
*/
jQuery.urldecode = function(x) {
return decodeURIComponent(x).replace(/\+/g, ' ');
};
/**
* small helper function to urlencode strings
*/
jQuery.urlencode = encodeURIComponent;
/**
* This function returns the parsed url parameters of the
* current request. Multiple values per key are supported,
* it will always return arrays of strings for the value parts.
*/
jQuery.getQueryParameters = function(s) {
if (typeof s == 'undefined')
s = document.location.search;
var parts = s.substr(s.indexOf('?') + 1).split('&');
var result = {};
for (var i = 0; i < parts.length; i++) {
var tmp = parts[i].split('=', 2);
var key = jQuery.urldecode(tmp[0]);
var value = jQuery.urldecode(tmp[1]);
if (key in result)
result[key].push(value);
else
result[key] = [value];
}
return result;
};
/**
* highlight a given string on a jquery object by wrapping it in
* span elements with the given class name.
*/
jQuery.fn.highlightText = function(text, className) {
function highlight(node) {
if (node.nodeType == 3) {
var val = node.nodeValue;
var pos = val.toLowerCase().indexOf(text);
if (pos >= 0 && !jQuery(node.parentNode).hasClass(className)) {
var span = document.createElement("span");
span.className = className;
span.appendChild(document.createTextNode(val.substr(pos, text.length)));
node.parentNode.insertBefore(span, node.parentNode.insertBefore(
document.createTextNode(val.substr(pos + text.length)),
node.nextSibling));
node.nodeValue = val.substr(0, pos);
}
}
else if (!jQuery(node).is("button, select, textarea")) {
jQuery.each(node.childNodes, function() {
highlight(this);
});
}
}
return this.each(function() {
highlight(this);
});
};
/*
* backward compatibility for jQuery.browser
* This will be supported until firefox bug is fixed.
*/
if (!jQuery.browser) {
jQuery.uaMatch = function(ua) {
ua = ua.toLowerCase();
var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
/(webkit)[ \/]([\w.]+)/.exec(ua) ||
/(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
/(msie) ([\w.]+)/.exec(ua) ||
ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
[];
return {
browser: match[ 1 ] || "",
version: match[ 2 ] || "0"
};
};
jQuery.browser = {};
jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
}
/**
* Small JavaScript module for the documentation.
*/
var Documentation = {
init : function() {
this.fixFirefoxAnchorBug();
this.highlightSearchWords();
this.initIndexTable();
},
/**
* i18n support
*/
TRANSLATIONS : {},
PLURAL_EXPR : function(n) { return n == 1 ? 0 : 1; },
LOCALE : 'unknown',
// gettext and ngettext don't access this so that the functions
// can safely bound to a different name (_ = Documentation.gettext)
gettext : function(string) {
var translated = Documentation.TRANSLATIONS[string];
if (typeof translated == 'undefined')
return string;
return (typeof translated == 'string') ? translated : translated[0];
},
ngettext : function(singular, plural, n) {
var translated = Documentation.TRANSLATIONS[singular];
if (typeof translated == 'undefined')
return (n == 1) ? singular : plural;
return translated[Documentation.PLURALEXPR(n)];
},
addTranslations : function(catalog) {
for (var key in catalog.messages)
this.TRANSLATIONS[key] = catalog.messages[key];
this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')');
this.LOCALE = catalog.locale;
},
/**
* add context elements like header anchor links
*/
addContextElements : function() {
$('div[id] > :header:first').each(function() {
$('<a class="headerlink">\u00B6</a>').
attr('href', '#' + this.id).
attr('title', _('Permalink to this headline')).
appendTo(this);
});
$('dt[id]').each(function() {
$('<a class="headerlink">\u00B6</a>').
attr('href', '#' + this.id).
attr('title', _('Permalink to this definition')).
appendTo(this);
});
},
/**
* workaround a firefox stupidity
* see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075
*/
fixFirefoxAnchorBug : function() {
if (document.location.hash)
window.setTimeout(function() {
document.location.href += '';
}, 10);
},
/**
* highlight the search words provided in the url in the text
*/
highlightSearchWords : function() {
var params = $.getQueryParameters();
var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : [];
if (terms.length) {
var body = $('div.body');
if (!body.length) {
body = $('body');
}
window.setTimeout(function() {
$.each(terms, function() {
body.highlightText(this.toLowerCase(), 'highlighted');
});
}, 10);
$('<p class="highlight-link"><a href="javascript:Documentation.' +
'hideSearchWords()">' + _('Hide Search Matches') + '</a></p>')
.appendTo($('#searchbox'));
}
},
/**
* init the domain index toggle buttons
*/
initIndexTable : function() {
var togglers = $('img.toggler').click(function() {
var src = $(this).attr('src');
var idnum = $(this).attr('id').substr(7);
$('tr.cg-' + idnum).toggle();
if (src.substr(-9) == 'minus.png')
$(this).attr('src', src.substr(0, src.length-9) + 'plus.png');
else
$(this).attr('src', src.substr(0, src.length-8) + 'minus.png');
}).css('display', '');
if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) {
togglers.click();
}
},
/**
* helper function to hide the search marks again
*/
hideSearchWords : function() {
$('#searchbox .highlight-link').fadeOut(300);
$('span.highlighted').removeClass('highlighted');
},
/**
* make the url absolute
*/
makeURL : function(relativeURL) {
return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL;
},
/**
* get the current relative url
*/
getCurrentURL : function() {
var path = document.location.pathname;
var parts = path.split(/\//);
$.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() {
if (this == '..')
parts.pop();
});
var url = parts.join('/');
return path.substring(url.lastIndexOf('/') + 1, path.length - 1);
},
initOnKeyListeners: function() {
$(document).keyup(function(event) {
var activeElementType = document.activeElement.tagName;
// don't navigate when in search box or textarea
if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT') {
switch (event.keyCode) {
case 37: // left
var prevHref = $('link[rel="prev"]').prop('href');
if (prevHref) {
window.location.href = prevHref;
return false;
}
case 39: // right
var nextHref = $('link[rel="next"]').prop('href');
if (nextHref) {
window.location.href = nextHref;
return false;
}
}
}
});
}
};
// quick alias for translations
_ = Documentation.gettext;
$(document).ready(function() {
Documentation.init();
});
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if (!self.linkScroll) {
self.winScroll = true;
}
});
setInterval(function () { if (self.winScroll) self.onScroll(); }, 25);
// Set resize monitor
self.win.on('resize', function () {
self.winResize = true;
});
setInterval(function () { if (self.winResize) self.onResize(); }, 25);
self.onResize();
});
};
nav.init = function ($) {
var doc = $(document),
self = this;
this.navBar = $('div.wy-side-scroll:first');
this.win = $(window);
// Set up javascript UX bits
$(document)
// Shift nav in mobile when clicking the menu.
.on('click', "[data-toggle='wy-nav-top']", function() {
$("[data-toggle='wy-nav-shift']").toggleClass("shift");
$("[data-toggle='rst-versions']").toggleClass("shift");
})
// Nav menu link click operations
.on('click', ".wy-menu-vertical .current ul li a", function() {
var target = $(this);
// Close menu when you click a link.
$("[data-toggle='wy-nav-shift']").removeClass("shift");
$("[data-toggle='rst-versions']").toggleClass("shift");
// Handle dynamic display of l3 and l4 nav lists
self.toggleCurrent(target);
self.hashChange();
})
.on('click', "[data-toggle='rst-current-version']", function() {
$("[data-toggle='rst-versions']").toggleClass("shift-up");
})
// Make tables responsive
$("table.docutils:not(.field-list)")
.wrap("<div class='wy-table-responsive'></div>");
// Add expand links to all parents of nested ul
$('.wy-menu-vertical ul').not('.simple').siblings('a').each(function () {
var link = $(this);
expand = $('<span class="toctree-expand"></span>');
expand.on('click', function (ev) {
self.toggleCurrent(link);
ev.stopPropagation();
return false;
});
link.prepend(expand);
});
};
nav.reset = function () {
// Get anchor from URL and open up nested nav
var anchor = encodeURI(window.location.hash);
if (anchor) {
try {
var link = $('.wy-menu-vertical')
.find('[href="' + anchor + '"]');
$('.wy-menu-vertical li.toctree-l1 li.current')
.removeClass('current');
link.closest('li.toctree-l2').addClass('current');
link.closest('li.toctree-l3').addClass('current');
link.closest('li.toctree-l4').addClass('current');
}
catch (err) {
console.log("Error expanding nav for anchor", err);
}
}
};
nav.onScroll = function () {
this.winScroll = false;
var newWinPosition = this.win.scrollTop(),
winBottom = newWinPosition + this.winHeight,
navPosition = this.navBar.scrollTop(),
newNavPosition = navPosition + (newWinPosition - this.winPosition);
if (newWinPosition < 0 || winBottom > this.docHeight) {
return;
}
this.navBar.scrollTop(newNavPosition);
this.winPosition = newWinPosition;
};
nav.onResize = function () {
this.winResize = false;
this.winHeight = this.win.height();
this.docHeight = $(document).height();
};
nav.hashChange = function () {
this.linkScroll = true;
this.win.one('hashchange', function () {
this.linkScroll = false;
});
};
nav.toggleCurrent = function (elem) {
var parent_li = elem.closest('li');
parent_li.siblings('li.current').removeClass('current');
parent_li.siblings().find('li.current').removeClass('current');
parent_li.find('> ul li.current').removeClass('current');
parent_li.toggleClass('current');
}
return nav;
};
module.exports.ThemeNav = ThemeNav();
if (typeof(window) != 'undefined') {
window.SphinxRtdTheme = { StickyNav: module.exports.ThemeNav };
}
},{"jquery":"jquery"}]},{},["sphinx-rtd-theme"]);
src/docs/build/html/_static/pygments.css 0 → 100644
View file @ beedf9a5
.highlight .hll { background-color: #ffffcc }
.highlight { background: #eeffcc; }
.highlight .c { color: #408090; font-style: italic } /* Comment */
.highlight .err { border: 1px solid #FF0000 } /* Error */
.highlight .k { color: #007020; font-weight: bold } /* Keyword */
.highlight .o { color: #666666 } /* Operator */
.highlight .ch { color: #408090; font-style: italic } /* Comment.Hashbang */
.highlight .cm { color: #408090; font-style: italic } /* Comment.Multiline */
.highlight .cp { color: #007020 } /* Comment.Preproc */
.highlight .cpf { color: #408090; font-style: italic } /* Comment.PreprocFile */
.highlight .c1 { color: #408090; font-style: italic } /* Comment.Single */
.highlight .cs { color: #408090; background-color: #fff0f0 } /* Comment.Special */
.highlight .gd { color: #A00000 } /* Generic.Deleted */
.highlight .ge { font-style: italic } /* Generic.Emph */
.highlight .gr { color: #FF0000 } /* Generic.Error */
.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
.highlight .gi { color: #00A000 } /* Generic.Inserted */
.highlight .go { color: #333333 } /* Generic.Output */
.highlight .gp { color: #c65d09; font-weight: bold } /* Generic.Prompt */
.highlight .gs { font-weight: bold } /* Generic.Strong */
.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
.highlight .gt { color: #0044DD } /* Generic.Traceback */
.highlight .kc { color: #007020; font-weight: bold } /* Keyword.Constant */
.highlight .kd { color: #007020; font-weight: bold } /* Keyword.Declaration */
.highlight .kn { color: #007020; font-weight: bold } /* Keyword.Namespace */
.highlight .kp { color: #007020 } /* Keyword.Pseudo */
.highlight .kr { color: #007020; font-weight: bold } /* Keyword.Reserved */
.highlight .kt { color: #902000 } /* Keyword.Type */
.highlight .m { color: #208050 } /* Literal.Number */
.highlight .s { color: #4070a0 } /* Literal.String */
.highlight .na { color: #4070a0 } /* Name.Attribute */
.highlight .nb { color: #007020 } /* Name.Builtin */
.highlight .nc { color: #0e84b5; font-weight: bold } /* Name.Class */
.highlight .no { color: #60add5 } /* Name.Constant */
.highlight .nd { color: #555555; font-weight: bold } /* Name.Decorator */
.highlight .ni { color: #d55537; font-weight: bold } /* Name.Entity */
.highlight .ne { color: #007020 } /* Name.Exception */
.highlight .nf { color: #06287e } /* Name.Function */
.highlight .nl { color: #002070; font-weight: bold } /* Name.Label */
.highlight .nn { color: #0e84b5; font-weight: bold } /* Name.Namespace */
.highlight .nt { color: #062873; font-weight: bold } /* Name.Tag */
.highlight .nv { color: #bb60d5 } /* Name.Variable */
.highlight .ow { color: #007020; font-weight: bold } /* Operator.Word */
.highlight .w { color: #bbbbbb } /* Text.Whitespace */
.highlight .mb { color: #208050 } /* Literal.Number.Bin */
.highlight .mf { color: #208050 } /* Literal.Number.Float */
.highlight .mh { color: #208050 } /* Literal.Number.Hex */
.highlight .mi { color: #208050 } /* Literal.Number.Integer */
.highlight .mo { color: #208050 } /* Literal.Number.Oct */
.highlight .sb { color: #4070a0 } /* Literal.String.Backtick */
.highlight .sc { color: #4070a0 } /* Literal.String.Char */
.highlight .sd { color: #4070a0; font-style: italic } /* Literal.String.Doc */
.highlight .s2 { color: #4070a0 } /* Literal.String.Double */
.highlight .se { color: #4070a0; font-weight: bold } /* Literal.String.Escape */
.highlight .sh { color: #4070a0 } /* Literal.String.Heredoc */
.highlight .si { color: #70a0d0; font-style: italic } /* Literal.String.Interpol */
.highlight .sx { color: #c65d09 } /* Literal.String.Other */
.highlight .sr { color: #235388 } /* Literal.String.Regex */
.highlight .s1 { color: #4070a0 } /* Literal.String.Single */
.highlight .ss { color: #517918 } /* Literal.String.Symbol */
.highlight .bp { color: #007020 } /* Name.Builtin.Pseudo */
.highlight .vc { color: #bb60d5 } /* Name.Variable.Class */
.highlight .vg { color: #bb60d5 } /* Name.Variable.Global */
.highlight .vi { color: #bb60d5 } /* Name.Variable.Instance */
.highlight .il { color: #208050 } /* Literal.Number.Integer.Long */
\ No newline at end of file
src/docs/build/html/_static/searchtools.js 0 → 100644
View file @ beedf9a5
/*
* searchtools.js_t
* ~~~~~~~~~~~~~~~~
*
* Sphinx JavaScript utilities for the full-text search.
*
* :copyright: Copyright 2007-2016 by the Sphinx team, see AUTHORS.
* :license: BSD, see LICENSE for details.
*
*/
/* Non-minified version JS is _stemmer.js if file is provided */
/**
* Porter Stemmer
*/
var Stemmer = function() {
var step2list = {
ational: 'ate',
tional: 'tion',
enci: 'ence',
anci: 'ance',
izer: 'ize',
bli: 'ble',
alli: 'al',
entli: 'ent',
eli: 'e',
ousli: 'ous',
ization: 'ize',
ation: 'ate',
ator: 'ate',
alism: 'al',
iveness: 'ive',
fulness: 'ful',
ousness: 'ous',
aliti: 'al',
iviti: 'ive',
biliti: 'ble',
logi: 'log'
};
var step3list = {
icate: 'ic',
ative: '',
alize: 'al',
iciti: 'ic',
ical: 'ic',
ful: '',
ness: ''
};
var c = "[^aeiou]"; // consonant
var v = "[aeiouy]"; // vowel
var C = c + "[^aeiouy]*"; // consonant sequence
var V = v + "[aeiou]*"; // vowel sequence
var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0
var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1
var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1
var s_v = "^(" + C + ")?" + v; // vowel in stem
this.stemWord = function (w) {
var stem;
var suffix;
var firstch;
var origword = w;
if (w.length < 3)
return w;
var re;
var re2;
var re3;
var re4;
firstch = w.substr(0,1);
if (firstch == "y")
w = firstch.toUpperCase() + w.substr(1);
// Step 1a
re = /^(.+?)(ss|i)es$/;
re2 = /^(.+?)([^s])s$/;
if (re.test(w))
w = w.replace(re,"$1$2");
else if (re2.test(w))
w = w.replace(re2,"$1$2");
// Step 1b
re = /^(.+?)eed$/;
re2 = /^(.+?)(ed|ing)$/;
if (re.test(w)) {
var fp = re.exec(w);
re = new RegExp(mgr0);
if (re.test(fp[1])) {
re = /.$/;
w = w.replace(re,"");
}
}
else if (re2.test(w)) {
var fp = re2.exec(w);
stem = fp[1];
re2 = new RegExp(s_v);
if (re2.test(stem)) {
w = stem;
re2 = /(at|bl|iz)$/;
re3 = new RegExp("([^aeiouylsz])\\1$");
re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
if (re2.test(w))
w = w + "e";
else if (re3.test(w)) {
re = /.$/;
w = w.replace(re,"");
}
else if (re4.test(w))
w = w + "e";
}
}
// Step 1c
re = /^(.+?)y$/;
if (re.test(w)) {
var fp = re.exec(w);
stem = fp[1];
re = new RegExp(s_v);
if (re.test(stem))
w = stem + "i";
}
// Step 2
re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
if (re.test(w)) {
var fp = re.exec(w);
stem = fp[1];
suffix = fp[2];
re = new RegExp(mgr0);
if (re.test(stem))
w = stem + step2list[suffix];
}
// Step 3
re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
if (re.test(w)) {
var fp = re.exec(w);
stem = fp[1];
suffix = fp[2];
re = new RegExp(mgr0);
if (re.test(stem))
w = stem + step3list[suffix];
}
// Step 4
re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
re2 = /^(.+?)(s|t)(ion)$/;
if (re.test(w)) {
var fp = re.exec(w);
stem = fp[1];
re = new RegExp(mgr1);
if (re.test(stem))
w = stem;
}
else if (re2.test(w)) {
var fp = re2.exec(w);
stem = fp[1] + fp[2];
re2 = new RegExp(mgr1);
if (re2.test(stem))
w = stem;
}
// Step 5
re = /^(.+?)e$/;
if (re.test(w)) {
var fp = re.exec(w);
stem = fp[1];
re = new RegExp(mgr1);
re2 = new RegExp(meq1);
re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
w = stem;
}
re = /ll$/;
re2 = new RegExp(mgr1);
if (re.test(w) && re2.test(w)) {
re = /.$/;
w = w.replace(re,"");
}
// and turn initial Y back to y
if (firstch == "y")
w = firstch.toLowerCase() + w.substr(1);
return w;
}
}
/**
* Simple result scoring code.
*/
var Scorer = {
// Implement the following function to further tweak the score for each result
// The function takes a result array [filename, title, anchor, descr, score]
// and returns the new score.
/*
score: function(result) {
return result[4];
},
*/
// query matches the full name of an object
objNameMatch: 11,
// or matches in the last dotted part of the object name
objPartialMatch: 6,
// Additive scores depending on the priority of the object
objPrio: {0: 15, // used to be importantResults
1: 5, // used to be objectResults
2: -5}, // used to be unimportantResults
// Used when the priority is not in the mapping.
objPrioDefault: 0,
// query found in title
title: 15,
// query found in terms
term: 5
};
/**
* Search Module
*/
var Search = {
_index : null,
_queued_query : null,
_pulse_status : -1,
init : function() {
var params = $.getQueryParameters();
if (params.q) {
var query = params.q[0];
$('input[name="q"]')[0].value = query;
this.performSearch(query);
}
},
loadIndex : function(url) {
$.ajax({type: "GET", url: url, data: null,
dataType: "script", cache: true,
complete: function(jqxhr, textstatus) {
if (textstatus != "success") {
document.getElementById("searchindexloader").src = url;
}
}});
},
setIndex : function(index) {
var q;
this._index = index;
if ((q = this._queued_query) !== null) {
this._queued_query = null;
Search.query(q);
}
},
hasIndex : function() {
return this._index !== null;
},
deferQuery : function(query) {
this._queued_query = query;
},
stopPulse : function() {
this._pulse_status = 0;
},
startPulse : function() {
if (this._pulse_status >= 0)
return;
function pulse() {
var i;
Search._pulse_status = (Search._pulse_status + 1) % 4;
var dotString = '';
for (i = 0; i < Search._pulse_status; i++)
dotString += '.';
Search.dots.text(dotString);
if (Search._pulse_status > -1)
window.setTimeout(pulse, 500);
}
pulse();
},
/**
* perform a search for something (or wait until index is loaded)
*/
performSearch : function(query) {
// create the required interface elements
this.out = $('#search-results');
this.title = $('<h2>' + _('Searching') + '</h2>').appendTo(this.out);
this.dots = $('<span></span>').appendTo(this.title);
this.status = $('<p style="display: none"></p>').appendTo(this.out);
this.output = $('<ul class="search"/>').appendTo(this.out);
$('#search-progress').text(_('Preparing search...'));
this.startPulse();
// index already loaded, the browser was quick!
if (this.hasIndex())
this.query(query);
else
this.deferQuery(query);
},
/**
* execute search (requires search index to be loaded)
*/
query : function(query) {
var i;
var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"];
// stem the searchterms and add them to the correct list
var stemmer = new Stemmer();
var searchterms = [];
var excluded = [];
var hlterms = [];
var tmp = query.split(/\s+/);
var objectterms = [];
for (i = 0; i < tmp.length; i++) {
if (tmp[i] !== "") {
objectterms.push(tmp[i].toLowerCase());
}
if ($u.indexOf(stopwords, tmp[i].toLowerCase()) != -1 || tmp[i].match(/^\d+$/) ||
tmp[i] === "") {
// skip this "word"
continue;
}
// stem the word
var word = stemmer.stemWord(tmp[i].toLowerCase());
var toAppend;
// select the correct list
if (word[0] == '-') {
toAppend = excluded;
word = word.substr(1);
}
else {
toAppend = searchterms;
hlterms.push(tmp[i].toLowerCase());
}
// only add if not already in the list
if (!$u.contains(toAppend, word))
toAppend.push(word);
}
var highlightstring = '?highlight=' + $.urlencode(hlterms.join(" "));
// console.debug('SEARCH: searching for:');
// console.info('required: ', searchterms);
// console.info('excluded: ', excluded);
// prepare search
var terms = this._index.terms;
var titleterms = this._index.titleterms;
// array of [filename, title, anchor, descr, score]
var results = [];
$('#search-progress').empty();
// lookup as object
for (i = 0; i < objectterms.length; i++) {
var others = [].concat(objectterms.slice(0, i),
objectterms.slice(i+1, objectterms.length));
results = results.concat(this.performObjectSearch(objectterms[i], others));
}
// lookup as search terms in fulltext
results = results.concat(this.performTermsSearch(searchterms, excluded, terms, titleterms));
// let the scorer override scores with a custom scoring function
if (Scorer.score) {
for (i = 0; i < results.length; i++)
results[i][4] = Scorer.score(results[i]);
}
// now sort the results by score (in opposite order of appearance, since the
// display function below uses pop() to retrieve items) and then
// alphabetically
results.sort(function(a, b) {
var left = a[4];
var right = b[4];
if (left > right) {
return 1;
} else if (left < right) {
return -1;
} else {
// same score: sort alphabetically
left = a[1].toLowerCase();
right = b[1].toLowerCase();
return (left > right) ? -1 : ((left < right) ? 1 : 0);
}
});
// for debugging
//Search.lastresults = results.slice(); // a copy
//console.info('search results:', Search.lastresults);
// print the results
var resultCount = results.length;
function displayNextItem() {
// results left, load the summary and display it
if (results.length) {
var item = results.pop();
var listItem = $('<li style="display:none"></li>');
if (DOCUMENTATION_OPTIONS.FILE_SUFFIX === '') {
// dirhtml builder
var dirname = item[0] + '/';
if (dirname.match(/\/index\/$/)) {
dirname = dirname.substring(0, dirname.length-6);
} else if (dirname == 'index/') {
dirname = '';
}
listItem.append($('<a/>').attr('href',
DOCUMENTATION_OPTIONS.URL_ROOT + dirname +
highlightstring + item[2]).html(item[1]));
} else {
// normal html builders
listItem.append($('<a/>').attr('href',
item[0] + DOCUMENTATION_OPTIONS.FILE_SUFFIX +
highlightstring + item[2]).html(item[1]));
}
if (item[3]) {
listItem.append($('<span> (' + item[3] + ')</span>'));
Search.output.append(listItem);
listItem.slideDown(5, function() {
displayNextItem();
});
} else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) {
$.ajax({url: DOCUMENTATION_OPTIONS.URL_ROOT + '_sources/' + item[0] + '.txt',
dataType: "text",
complete: function(jqxhr, textstatus) {
var data = jqxhr.responseText;
if (data !== '' && data !== undefined) {
listItem.append(Search.makeSearchSummary(data, searchterms, hlterms));
}
Search.output.append(listItem);
listItem.slideDown(5, function() {
displayNextItem();
});
}});
} else {
// no source available, just display title
Search.output.append(listItem);
listItem.slideDown(5, function() {
displayNextItem();
});
}
}
// search finished, update title and status message
else {
Search.stopPulse();
Search.title.text(_('Search Results'));
if (!resultCount)
Search.status.text(_('Your search did not match any documents. Please make sure that all words are spelled correctly and that you\'ve selected enough categories.'));
else
Search.status.text(_('Search finished, found %s page(s) matching the search query.').replace('%s', resultCount));
Search.status.fadeIn(500);
}
}
displayNextItem();
},
/**
* search for object names
*/
performObjectSearch : function(object, otherterms) {
var filenames = this._index.filenames;
var objects = this._index.objects;
var objnames = this._index.objnames;
var titles = this._index.titles;
var i;
var results = [];
for (var prefix in objects) {
for (var name in objects[prefix]) {
var fullname = (prefix ? prefix + '.' : '') + name;
if (fullname.toLowerCase().indexOf(object) > -1) {
var score = 0;
var parts = fullname.split('.');
// check for different match types: exact matches of full name or
// "last name" (i.e. last dotted part)
if (fullname == object || parts[parts.length - 1] == object) {
score += Scorer.objNameMatch;
// matches in last name
} else if (parts[parts.length - 1].indexOf(object) > -1) {
score += Scorer.objPartialMatch;
}
var match = objects[prefix][name];
var objname = objnames[match[1]][2];
var title = titles[match[0]];
// If more than one term searched for, we require other words to be
// found in the name/title/description
if (otherterms.length > 0) {
var haystack = (prefix + ' ' + name + ' ' +
objname + ' ' + title).toLowerCase();
var allfound = true;
for (i = 0; i < otherterms.length; i++) {
if (haystack.indexOf(otherterms[i]) == -1) {
allfound = false;
break;
}
}
if (!allfound) {
continue;
}
}
var descr = objname + _(', in ') + title;
var anchor = match[3];
if (anchor === '')
anchor = fullname;
else if (anchor == '-')
anchor = objnames[match[1]][1] + '-' + fullname;
// add custom score for some objects according to scorer
if (Scorer.objPrio.hasOwnProperty(match[2])) {
score += Scorer.objPrio[match[2]];
} else {
score += Scorer.objPrioDefault;
}
results.push([filenames[match[0]], fullname, '#'+anchor, descr, score]);
}
}
}
return results;
},
/**
* search for full-text terms in the index
*/
performTermsSearch : function(searchterms, excluded, terms, titleterms) {
var filenames = this._index.filenames;
var titles = this._index.titles;
var i, j, file;
var fileMap = {};
var scoreMap = {};
var results = [];
// perform the search on the required terms
for (i = 0; i < searchterms.length; i++) {
var word = searchterms[i];
var files = [];
var _o = [
{files: terms[word], score: Scorer.term},
{files: titleterms[word], score: Scorer.title}
];
// no match but word was a required one
if ($u.every(_o, function(o){return o.files === undefined;})) {
break;
}
// found search word in contents
$u.each(_o, function(o) {
var _files = o.files;
if (_files === undefined)
return
if (_files.length === undefined)
_files = [_files];
files = files.concat(_files);
// set score for the word in each file to Scorer.term
for (j = 0; j < _files.length; j++) {
file = _files[j];
if (!(file in scoreMap))
scoreMap[file] = {}
scoreMap[file][word] = o.score;
}
});
// create the mapping
for (j = 0; j < files.length; j++) {
file = files[j];
if (file in fileMap)
fileMap[file].push(word);
else
fileMap[file] = [word];
}
}
// now check if the files don't contain excluded terms
for (file in fileMap) {
var valid = true;
// check if all requirements are matched
if (fileMap[file].length != searchterms.length)
continue;
// ensure that none of the excluded terms is in the search result
for (i = 0; i < excluded.length; i++) {
if (terms[excluded[i]] == file ||
titleterms[excluded[i]] == file ||
$u.contains(terms[excluded[i]] || [], file) ||
$u.contains(titleterms[excluded[i]] || [], file)) {
valid = false;
break;
}
}
// if we have still a valid result we can add it to the result list
if (valid) {
// select one (max) score for the file.
// for better ranking, we should calculate ranking by using words statistics like basic tf-idf...
var score = $u.max($u.map(fileMap[file], function(w){return scoreMap[file][w]}));
results.push([filenames[file], titles[file], '', null, score]);
}
}
return results;
},
/**
* helper function to return a node containing the
* search summary for a given text. keywords is a list
* of stemmed words, hlwords is the list of normal, unstemmed
* words. the first one is used to find the occurrence, the
* latter for highlighting it.
*/
makeSearchSummary : function(text, keywords, hlwords) {
var textLower = text.toLowerCase();
var start = 0;
$.each(keywords, function() {
var i = textLower.indexOf(this.toLowerCase());
if (i > -1)
start = i;
});
start = Math.max(start - 120, 0);
var excerpt = ((start > 0) ? '...' : '') +
$.trim(text.substr(start, 240)) +
((start + 240 - text.length) ? '...' : '');
var rv = $('<div class="context"></div>').text(excerpt);
$.each(hlwords, function() {
rv = rv.highlightText(this, 'highlighted');
});
return rv;
}
};
$(document).ready(function() {
Search.init();
});
\ No newline at end of file
src/docs/build/html/_static/underscore-1.3.1.js 0 → 100644
View file @ beedf9a5
// Underscore.js 1.3.1
// (c) 2009-2012 Jeremy Ashkenas, DocumentCloud Inc.
// Underscore is freely distributable under the MIT license.
// Portions of Underscore are inspired or borrowed from Prototype,
// Oliver Steele's Functional, and John Resig's Micro-Templating.
// For all details and documentation:
// http://documentcloud.github.com/underscore
(function() {
// Baseline setup
// --------------
// Establish the root object, `window` in the browser, or `global` on the server.
var root = this;
// Save the previous value of the `_` variable.
var previousUnderscore = root._;
// Establish the object that gets returned to break out of a loop iteration.
var breaker = {};
// Save bytes in the minified (but not gzipped) version:
var ArrayProto = Array.prototype, ObjProto = Object.prototype, FuncProto = Function.prototype;
// Create quick reference variables for speed access to core prototypes.
var slice = ArrayProto.slice,
unshift = ArrayProto.unshift,
toString = ObjProto.toString,
hasOwnProperty = ObjProto.hasOwnProperty;
// All **ECMAScript 5** native function implementations that we hope to use
// are declared here.
var
nativeForEach = ArrayProto.forEach,
nativeMap = ArrayProto.map,
nativeReduce = ArrayProto.reduce,
nativeReduceRight = ArrayProto.reduceRight,
nativeFilter = ArrayProto.filter,
nativeEvery = ArrayProto.every,
nativeSome = ArrayProto.some,
nativeIndexOf = ArrayProto.indexOf,
nativeLastIndexOf = ArrayProto.lastIndexOf,
nativeIsArray = Array.isArray,
nativeKeys = Object.keys,
nativeBind = FuncProto.bind;
// Create a safe reference to the Underscore object for use below.
var _ = function(obj) { return new wrapper(obj); };
// Export the Underscore object for **Node.js**, with
// backwards-compatibility for the old `require()` API. If we're in
// the browser, add `_` as a global object via a string identifier,
// for Closure Compiler "advanced" mode.
if (typeof exports !== 'undefined') {
if (typeof module !== 'undefined' && module.exports) {
exports = module.exports = _;
}
exports._ = _;
} else {
root['_'] = _;
}
// Current version.
_.VERSION = '1.3.1';
// Collection Functions
// --------------------
// The cornerstone, an `each` implementation, aka `forEach`.
// Handles objects with the built-in `forEach`, arrays, and raw objects.
// Delegates to **ECMAScript 5**'s native `forEach` if available.
var each = _.each = _.forEach = function(obj, iterator, context) {
if (obj == null) return;
if (nativeForEach && obj.forEach === nativeForEach) {
obj.forEach(iterator, context);
} else if (obj.length === +obj.length) {
for (var i = 0, l = obj.length; i < l; i++) {
if (i in obj && iterator.call(context, obj[i], i, obj) === breaker) return;
}
} else {
for (var key in obj) {
if (_.has(obj, key)) {
if (iterator.call(context, obj[key], key, obj) === breaker) return;
}
}
}
};
// Return the results of applying the iterator to each element.
// Delegates to **ECMAScript 5**'s native `map` if available.
_.map = _.collect = function(obj, iterator, context) {
var results = [];
if (obj == null) return results;
if (nativeMap && obj.map === nativeMap) return obj.map(iterator, context);
each(obj, function(value, index, list) {
results[results.length] = iterator.call(context, value, index, list);
});
if (obj.length === +obj.length) results.length = obj.length;
return results;
};
// **Reduce** builds up a single result from a list of values, aka `inject`,
// or `foldl`. Delegates to **ECMAScript 5**'s native `reduce` if available.
_.reduce = _.foldl = _.inject = function(obj, iterator, memo, context) {
var initial = arguments.length > 2;
if (obj == null) obj = [];
if (nativeReduce && obj.reduce === nativeReduce) {
if (context) iterator = _.bind(iterator, context);
return initial ? obj.reduce(iterator, memo) : obj.reduce(iterator);
}
each(obj, function(value, index, list) {
if (!initial) {
memo = value;
initial = true;
} else {
memo = iterator.call(context, memo, value, index, list);
}
});
if (!initial) throw new TypeError('Reduce of empty array with no initial value');
return memo;
};
// The right-associative version of reduce, also known as `foldr`.
// Delegates to **ECMAScript 5**'s native `reduceRight` if available.
_.reduceRight = _.foldr = function(obj, iterator, memo, context) {
var initial = arguments.length > 2;
if (obj == null) obj = [];
if (nativeReduceRight && obj.reduceRight === nativeReduceRight) {
if (context) iterator = _.bind(iterator, context);
return initial ? obj.reduceRight(iterator, memo) : obj.reduceRight(iterator);
}
var reversed = _.toArray(obj).reverse();
if (context && !initial) iterator = _.bind(iterator, context);
return initial ? _.reduce(reversed, iterator, memo, context) : _.reduce(reversed, iterator);
};
// Return the first value which passes a truth test. Aliased as `detect`.
_.find = _.detect = function(obj, iterator, context) {
var result;
any(obj, function(value, index, list) {
if (iterator.call(context, value, index, list)) {
result = value;
return true;
}
});
return result;
};
// Return all the elements that pass a truth test.
// Delegates to **ECMAScript 5**'s native `filter` if available.
// Aliased as `select`.
_.filter = _.select = function(obj, iterator, context) {
var results = [];
if (obj == null) return results;
if (nativeFilter && obj.filter === nativeFilter) return obj.filter(iterator, context);
each(obj, function(value, index, list) {
if (iterator.call(context, value, index, list)) results[results.length] = value;
});
return results;
};
// Return all the elements for which a truth test fails.
_.reject = function(obj, iterator, context) {
var results = [];
if (obj == null) return results;
each(obj, function(value, index, list) {
if (!iterator.call(context, value, index, list)) results[results.length] = value;
});
return results;
};
// Determine whether all of the elements match a truth test.
// Delegates to **ECMAScript 5**'s native `every` if available.
// Aliased as `all`.
_.every = _.all = function(obj, iterator, context) {
var result = true;
if (obj == null) return result;
if (nativeEvery && obj.every === nativeEvery) return obj.every(iterator, context);
each(obj, function(value, index, list) {
if (!(result = result && iterator.call(context, value, index, list))) return breaker;
});
return result;
};
// Determine if at least one element in the object matches a truth test.
// Delegates to **ECMAScript 5**'s native `some` if available.
// Aliased as `any`.
var any = _.some = _.any = function(obj, iterator, context) {
iterator || (iterator = _.identity);
var result = false;
if (obj == null) return result;
if (nativeSome && obj.some === nativeSome) return obj.some(iterator, context);
each(obj, function(value, index, list) {
if (result || (result = iterator.call(context, value, index, list))) return breaker;
});
return !!result;
};
// Determine if a given value is included in the array or object using `===`.
// Aliased as `contains`.
_.include = _.contains = function(obj, target) {
var found = false;
if (obj == null) return found;
if (nativeIndexOf && obj.indexOf === nativeIndexOf) return obj.indexOf(target) != -1;
found = any(obj, function(value) {
return value === target;
});
return found;
};
// Invoke a method (with arguments) on every item in a collection.
_.invoke = function(obj, method) {
var args = slice.call(arguments, 2);
return _.map(obj, function(value) {
return (_.isFunction(method) ? method || value : value[method]).apply(value, args);
});
};
// Convenience version of a common use case of `map`: fetching a property.
_.pluck = function(obj, key) {
return _.map(obj, function(value){ return value[key]; });
};
// Return the maximum element or (element-based computation).
_.max = function(obj, iterator, context) {
if (!iterator && _.isArray(obj)) return Math.max.apply(Math, obj);
if (!iterator && _.isEmpty(obj)) return -Infinity;
var result = {computed : -Infinity};
each(obj, function(value, index, list) {
var computed = iterator ? iterator.call(context, value, index, list) : value;
computed >= result.computed && (result = {value : value, computed : computed});
});
return result.value;
};
// Return the minimum element (or element-based computation).
_.min = function(obj, iterator, context) {
if (!iterator && _.isArray(obj)) return Math.min.apply(Math, obj);
if (!iterator && _.isEmpty(obj)) return Infinity;
var result = {computed : Infinity};
each(obj, function(value, index, list) {
var computed = iterator ? iterator.call(context, value, index, list) : value;
computed < result.computed && (result = {value : value, computed : computed});
});
return result.value;
};
// Shuffle an array.
_.shuffle = function(obj) {
var shuffled = [], rand;
each(obj, function(value, index, list) {
if (index == 0) {
shuffled[0] = value;
} else {
rand = Math.floor(Math.random() * (index + 1));
shuffled[index] = shuffled[rand];
shuffled[rand] = value;
}
});
return shuffled;
};
// Sort the object's values by a criterion produced by an iterator.
_.sortBy = function(obj, iterator, context) {
return _.pluck(_.map(obj, function(value, index, list) {
return {
value : value,
criteria : iterator.call(context, value, index, list)
};
}).sort(function(left, right) {
var a = left.criteria, b = right.criteria;
return a < b ? -1 : a > b ? 1 : 0;
}), 'value');
};
// Groups the object's values by a criterion. Pass either a string attribute
// to group by, or a function that returns the criterion.
_.groupBy = function(obj, val) {
var result = {};
var iterator = _.isFunction(val) ? val : function(obj) { return obj[val]; };
each(obj, function(value, index) {
var key = iterator(value, index);
(result[key] || (result[key] = [])).push(value);
});
return result;
};
// Use a comparator function to figure out at what index an object should
// be inserted so as to maintain order. Uses binary search.
_.sortedIndex = function(array, obj, iterator) {
iterator || (iterator = _.identity);
var low = 0, high = array.length;
while (low < high) {
var mid = (low + high) >> 1;
iterator(array[mid]) < iterator(obj) ? low = mid + 1 : high = mid;
}
return low;
};
// Safely convert anything iterable into a real, live array.
_.toArray = function(iterable) {
if (!iterable) return [];
if (iterable.toArray) return iterable.toArray();
if (_.isArray(iterable)) return slice.call(iterable);
if (_.isArguments(iterable)) return slice.call(iterable);
return _.values(iterable);
};
// Return the number of elements in an object.
_.size = function(obj) {
return _.toArray(obj).length;
};
// Array Functions
// ---------------
// Get the first element of an array. Passing **n** will return the first N
// values in the array. Aliased as `head`. The **guard** check allows it to work
// with `_.map`.
_.first = _.head = function(array, n, guard) {
return (n != null) && !guard ? slice.call(array, 0, n) : array[0];
};
// Returns everything but the last entry of the array. Especcialy useful on
// the arguments object. Passing **n** will return all the values in
// the array, excluding the last N. The **guard** check allows it to work with
// `_.map`.
_.initial = function(array, n, guard) {
return slice.call(array, 0, array.length - ((n == null) || guard ? 1 : n));
};
// Get the last element of an array. Passing **n** will return the last N
// values in the array. The **guard** check allows it to work with `_.map`.
_.last = function(array, n, guard) {
if ((n != null) && !guard) {
return slice.call(array, Math.max(array.length - n, 0));
} else {
return array[array.length - 1];
}
};
// Returns everything but the first entry of the array. Aliased as `tail`.
// Especially useful on the arguments object. Passing an **index** will return
// the rest of the values in the array from that index onward. The **guard**
// check allows it to work with `_.map`.
_.rest = _.tail = function(array, index, guard) {
return slice.call(array, (index == null) || guard ? 1 : index);
};
// Trim out all falsy values from an array.
_.compact = function(array) {
return _.filter(array, function(value){ return !!value; });
};
// Return a completely flattened version of an array.
_.flatten = function(array, shallow) {
return _.reduce(array, function(memo, value) {
if (_.isArray(value)) return memo.concat(shallow ? value : _.flatten(value));
memo[memo.length] = value;
return memo;
}, []);
};
// Return a version of the array that does not contain the specified value(s).
_.without = function(array) {
return _.difference(array, slice.call(arguments, 1));
};
// Produce a duplicate-free version of the array. If the array has already
// been sorted, you have the option of using a faster algorithm.
// Aliased as `unique`.
_.uniq = _.unique = function(array, isSorted, iterator) {
var initial = iterator ? _.map(array, iterator) : array;
var result = [];
_.reduce(initial, function(memo, el, i) {
if (0 == i || (isSorted === true ? _.last(memo) != el : !_.include(memo, el))) {
memo[memo.length] = el;
result[result.length] = array[i];
}
return memo;
}, []);
return result;
};
// Produce an array that contains the union: each distinct element from all of
// the passed-in arrays.
_.union = function() {
return _.uniq(_.flatten(arguments, true));
};
// Produce an array that contains every item shared between all the
// passed-in arrays. (Aliased as "intersect" for back-compat.)
_.intersection = _.intersect = function(array) {
var rest = slice.call(arguments, 1);
return _.filter(_.uniq(array), function(item) {
return _.every(rest, function(other) {
return _.indexOf(other, item) >= 0;
});
});
};
// Take the difference between one array and a number of other arrays.
// Only the elements present in just the first array will remain.
_.difference = function(array) {
var rest = _.flatten(slice.call(arguments, 1));
return _.filter(array, function(value){ return !_.include(rest, value); });
};
// Zip together multiple lists into a single array -- elements that share
// an index go together.
_.zip = function() {
var args = slice.call(arguments);
var length = _.max(_.pluck(args, 'length'));
var results = new Array(length);
for (var i = 0; i < length; i++) results[i] = _.pluck(args, "" + i);
return results;
};
// If the browser doesn't supply us with indexOf (I'm looking at you, **MSIE**),
// we need this function. Return the position of the first occurrence of an
// item in an array, or -1 if the item is not included in the array.
// Delegates to **ECMAScript 5**'s native `indexOf` if available.
// If the array is large and already in sort order, pass `true`
// for **isSorted** to use binary search.
_.indexOf = function(array, item, isSorted) {
if (array == null) return -1;
var i, l;
if (isSorted) {
i = _.sortedIndex(array, item);
return array[i] === item ? i : -1;
}
if (nativeIndexOf && array.indexOf === nativeIndexOf) return array.indexOf(item);
for (i = 0, l = array.length; i < l; i++) if (i in array && array[i] === item) return i;
return -1;
};
// Delegates to **ECMAScript 5**'s native `lastIndexOf` if available.
_.lastIndexOf = function(array, item) {
if (array == null) return -1;
if (nativeLastIndexOf && array.lastIndexOf === nativeLastIndexOf) return array.lastIndexOf(item);
var i = array.length;
while (i--) if (i in array && array[i] === item) return i;
return -1;
};
// Generate an integer Array containing an arithmetic progression. A port of
// the native Python `range()` function. See
// [the Python documentation](http://docs.python.org/library/functions.html#range).
_.range = function(start, stop, step) {
if (arguments.length <= 1) {
stop = start || 0;
start = 0;
}
step = arguments[2] || 1;
var len = Math.max(Math.ceil((stop - start) / step), 0);
var idx = 0;
var range = new Array(len);
while(idx < len) {
range[idx++] = start;
start += step;
}
return range;
};
// Function (ahem) Functions
// ------------------
// Reusable constructor function for prototype setting.
var ctor = function(){};
// Create a function bound to a given object (assigning `this`, and arguments,
// optionally). Binding with arguments is also known as `curry`.
// Delegates to **ECMAScript 5**'s native `Function.bind` if available.
// We check for `func.bind` first, to fail fast when `func` is undefined.
_.bind = function bind(func, context) {
var bound, args;
if (func.bind === nativeBind && nativeBind) return nativeBind.apply(func, slice.call(arguments, 1));
if (!_.isFunction(func)) throw new TypeError;
args = slice.call(arguments, 2);
return bound = function() {
if (!(this instanceof bound)) return func.apply(context, args.concat(slice.call(arguments)));
ctor.prototype = func.prototype;
var self = new ctor;
var result = func.apply(self, args.concat(slice.call(arguments)));
if (Object(result) === result) return result;
return self;
};
};
// Bind all of an object's methods to that object. Useful for ensuring that
// all callbacks defined on an object belong to it.
_.bindAll = function(obj) {
var funcs = slice.call(arguments, 1);
if (funcs.length == 0) funcs = _.functions(obj);
each(funcs, function(f) { obj[f] = _.bind(obj[f], obj); });
return obj;
};
// Memoize an expensive function by storing its results.
_.memoize = function(func, hasher) {
var memo = {};
hasher || (hasher = _.identity);
return function() {
var key = hasher.apply(this, arguments);
return _.has(memo, key) ? memo[key] : (memo[key] = func.apply(this, arguments));
};
};
// Delays a function for the given number of milliseconds, and then calls
// it with the arguments supplied.
_.delay = function(func, wait) {
var args = slice.call(arguments, 2);
return setTimeout(function(){ return func.apply(func, args); }, wait);
};
// Defers a function, scheduling it to run after the current call stack has
// cleared.
_.defer = function(func) {
return _.delay.apply(_, [func, 1].concat(slice.call(arguments, 1)));
};
// Returns a function, that, when invoked, will only be triggered at most once
// during a given window of time.
_.throttle = function(func, wait) {
var context, args, timeout, throttling, more;
var whenDone = _.debounce(function(){ more = throttling = false; }, wait);
return function() {
context = this; args = arguments;
var later = function() {
timeout = null;
if (more) func.apply(context, args);
whenDone();
};
if (!timeout) timeout = setTimeout(later, wait);
if (throttling) {
more = true;
} else {
func.apply(context, args);
}
whenDone();
throttling = true;
};
};
// Returns a function, that, as long as it continues to be invoked, will not
// be triggered. The function will be called after it stops being called for
// N milliseconds.
_.debounce = function(func, wait) {
var timeout;
return function() {
var context = this, args = arguments;
var later = function() {
timeout = null;
func.apply(context, args);
};
clearTimeout(timeout);
timeout = setTimeout(later, wait);
};
};
// Returns a function that will be executed at most one time, no matter how
// often you call it. Useful for lazy initialization.
_.once = function(func) {
var ran = false, memo;
return function() {
if (ran) return memo;
ran = true;
return memo = func.apply(this, arguments);
};
};
// Returns the first function passed as an argument to the second,
// allowing you to adjust arguments, run code before and after, and
// conditionally execute the original function.
_.wrap = function(func, wrapper) {
return function() {
var args = [func].concat(slice.call(arguments, 0));
return wrapper.apply(this, args);
};
};
// Returns a function that is the composition of a list of functions, each
// consuming the return value of the function that follows.
_.compose = function() {
var funcs = arguments;
return function() {
var args = arguments;
for (var i = funcs.length - 1; i >= 0; i--) {
args = [funcs[i].apply(this, args)];
}
return args[0];
};
};
// Returns a function that will only be executed after being called N times.
_.after = function(times, func) {
if (times <= 0) return func();
return function() {
if (--times < 1) { return func.apply(this, arguments); }
};
};
// Object Functions
// ----------------
// Retrieve the names of an object's properties.
// Delegates to **ECMAScript 5**'s native `Object.keys`
_.keys = nativeKeys || function(obj) {
if (obj !== Object(obj)) throw new TypeError('Invalid object');
var keys = [];
for (var key in obj) if (_.has(obj, key)) keys[keys.length] = key;
return keys;
};
// Retrieve the values of an object's properties.
_.values = function(obj) {
return _.map(obj, _.identity);
};
// Return a sorted list of the function names available on the object.
// Aliased as `methods`
_.functions = _.methods = function(obj) {
var names = [];
for (var key in obj) {
if (_.isFunction(obj[key])) names.push(key);
}
return names.sort();
};
// Extend a given object with all the properties in passed-in object(s).
_.extend = function(obj) {
each(slice.call(arguments, 1), function(source) {
for (var prop in source) {
obj[prop] = source[prop];
}
});
return obj;
};
// Fill in a given object with default properties.
_.defaults = function(obj) {
each(slice.call(arguments, 1), function(source) {
for (var prop in source) {
if (obj[prop] == null) obj[prop] = source[prop];
}
});
return obj;
};
// Create a (shallow-cloned) duplicate of an object.
_.clone = function(obj) {
if (!_.isObject(obj)) return obj;
return _.isArray(obj) ? obj.slice() : _.extend({}, obj);
};
// Invokes interceptor with the obj, and then returns obj.
// The primary purpose of this method is to "tap into" a method chain, in
// order to perform operations on intermediate results within the chain.
_.tap = function(obj, interceptor) {
interceptor(obj);
return obj;
};
// Internal recursive comparison function.
function eq(a, b, stack) {
// Identical objects are equal. `0 === -0`, but they aren't identical.
// See the Harmony `egal` proposal: http://wiki.ecmascript.org/doku.php?id=harmony:egal.
if (a === b) return a !== 0 || 1 / a == 1 / b;
// A strict comparison is necessary because `null == undefined`.
if (a == null || b == null) return a === b;
// Unwrap any wrapped objects.
if (a._chain) a = a._wrapped;
if (b._chain) b = b._wrapped;
// Invoke a custom `isEqual` method if one is provided.
if (a.isEqual && _.isFunction(a.isEqual)) return a.isEqual(b);
if (b.isEqual && _.isFunction(b.isEqual)) return b.isEqual(a);
// Compare `[[Class]]` names.
var className = toString.call(a);
if (className != toString.call(b)) return false;
switch (className) {
// Strings, numbers, dates, and booleans are compared by value.
case '[object String]':
// Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is
// equivalent to `new String("5")`.
return a == String(b);
case '[object Number]':
// `NaN`s are equivalent, but non-reflexive. An `egal` comparison is performed for
// other numeric values.
return a != +a ? b != +b : (a == 0 ? 1 / a == 1 / b : a == +b);
case '[object Date]':
case '[object Boolean]':
// Coerce dates and booleans to numeric primitive values. Dates are compared by their
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src/docs/build/html/_static/underscore.js 0 → 100644
View file @ beedf9a5
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u(b)+"),'"}).replace(d.interpolate||t,function(a,b){return"',"+u(b)+",'"}).replace(d.evaluate||t,function(a,b){return"');"+u(b).replace(/[\r\n\t]/g," ")+";__p.push('"}).replace(/\r/g,"\\r").replace(/\n/g,"\\n").replace(/\t/g,"\\t")+"');}return __p.join('');",e=new Function("obj","_",d);return c?e(c,b):function(a){return e.call(this,a,b)}};b.chain=function(a){return b(a).chain()};var m=function(a){this._wrapped=a};b.prototype=m.prototype;var v=function(a,c){return c?b(a).chain():a},K=function(a,c){m.prototype[a]=
function(){var a=i.call(arguments);H.call(a,this._wrapped);return v(c.apply(b,a),this._chain)}};b.mixin(b);j("pop,push,reverse,shift,sort,splice,unshift".split(","),function(a){var b=k[a];m.prototype[a]=function(){var d=this._wrapped;b.apply(d,arguments);var e=d.length;(a=="shift"||a=="splice")&&e===0&&delete d[0];return v(d,this._chain)}});j(["concat","join","slice"],function(a){var b=k[a];m.prototype[a]=function(){return v(b.apply(this._wrapped,arguments),this._chain)}});m.prototype.chain=function(){this._chain=
true;return this};m.prototype.value=function(){return this._wrapped}}).call(this);
src/docs/build/html/_static/websupport.js 0 → 100644
View file @ beedf9a5
/*
* websupport.js
* ~~~~~~~~~~~~~
*
* sphinx.websupport utilities for all documentation.
*
* :copyright: Copyright 2007-2016 by the Sphinx team, see AUTHORS.
* :license: BSD, see LICENSE for details.
*
*/
(function($) {
$.fn.autogrow = function() {
return this.each(function() {
var textarea = this;
$.fn.autogrow.resize(textarea);
$(textarea)
.focus(function() {
textarea.interval = setInterval(function() {
$.fn.autogrow.resize(textarea);
}, 500);
})
.blur(function() {
clearInterval(textarea.interval);
});
});
};
$.fn.autogrow.resize = function(textarea) {
var lineHeight = parseInt($(textarea).css('line-height'), 10);
var lines = textarea.value.split('\n');
var columns = textarea.cols;
var lineCount = 0;
$.each(lines, function() {
lineCount += Math.ceil(this.length / columns) || 1;
});
var height = lineHeight * (lineCount + 1);
$(textarea).css('height', height);
};
})(jQuery);
(function($) {
var comp, by;
function init() {
initEvents();
initComparator();
}
function initEvents() {
$(document).on("click", 'a.comment-close', function(event) {
event.preventDefault();
hide($(this).attr('id').substring(2));
});
$(document).on("click", 'a.vote', function(event) {
event.preventDefault();
handleVote($(this));
});
$(document).on("click", 'a.reply', function(event) {
event.preventDefault();
openReply($(this).attr('id').substring(2));
});
$(document).on("click", 'a.close-reply', function(event) {
event.preventDefault();
closeReply($(this).attr('id').substring(2));
});
$(document).on("click", 'a.sort-option', function(event) {
event.preventDefault();
handleReSort($(this));
});
$(document).on("click", 'a.show-proposal', function(event) {
event.preventDefault();
showProposal($(this).attr('id').substring(2));
});
$(document).on("click", 'a.hide-proposal', function(event) {
event.preventDefault();
hideProposal($(this).attr('id').substring(2));
});
$(document).on("click", 'a.show-propose-change', function(event) {
event.preventDefault();
showProposeChange($(this).attr('id').substring(2));
});
$(document).on("click", 'a.hide-propose-change', function(event) {
event.preventDefault();
hideProposeChange($(this).attr('id').substring(2));
});
$(document).on("click", 'a.accept-comment', function(event) {
event.preventDefault();
acceptComment($(this).attr('id').substring(2));
});
$(document).on("click", 'a.delete-comment', function(event) {
event.preventDefault();
deleteComment($(this).attr('id').substring(2));
});
$(document).on("click", 'a.comment-markup', function(event) {
event.preventDefault();
toggleCommentMarkupBox($(this).attr('id').substring(2));
});
}
/**
* Set comp, which is a comparator function used for sorting and
* inserting comments into the list.
*/
function setComparator() {
// If the first three letters are "asc", sort in ascending order
// and remove the prefix.
if (by.substring(0,3) == 'asc') {
var i = by.substring(3);
comp = function(a, b) { return a[i] - b[i]; };
} else {
// Otherwise sort in descending order.
comp = function(a, b) { return b[by] - a[by]; };
}
// Reset link styles and format the selected sort option.
$('a.sel').attr('href', '#').removeClass('sel');
$('a.by' + by).removeAttr('href').addClass('sel');
}
/**
* Create a comp function. If the user has preferences stored in
* the sortBy cookie, use those, otherwise use the default.
*/
function initComparator() {
by = 'rating'; // Default to sort by rating.
// If the sortBy cookie is set, use that instead.
if (document.cookie.length > 0) {
var start = document.cookie.indexOf('sortBy=');
if (start != -1) {
start = start + 7;
var end = document.cookie.indexOf(";", start);
if (end == -1) {
end = document.cookie.length;
by = unescape(document.cookie.substring(start, end));
}
}
}
setComparator();
}
/**
* Show a comment div.
*/
function show(id) {
$('#ao' + id).hide();
$('#ah' + id).show();
var context = $.extend({id: id}, opts);
var popup = $(renderTemplate(popupTemplate, context)).hide();
popup.find('textarea[name="proposal"]').hide();
popup.find('a.by' + by).addClass('sel');
var form = popup.find('#cf' + id);
form.submit(function(event) {
event.preventDefault();
addComment(form);
});
$('#s' + id).after(popup);
popup.slideDown('fast', function() {
getComments(id);
});
}
/**
* Hide a comment div.
*/
function hide(id) {
$('#ah' + id).hide();
$('#ao' + id).show();
var div = $('#sc' + id);
div.slideUp('fast', function() {
div.remove();
});
}
/**
* Perform an ajax request to get comments for a node
* and insert the comments into the comments tree.
*/
function getComments(id) {
$.ajax({
type: 'GET',
url: opts.getCommentsURL,
data: {node: id},
success: function(data, textStatus, request) {
var ul = $('#cl' + id);
var speed = 100;
$('#cf' + id)
.find('textarea[name="proposal"]')
.data('source', data.source);
if (data.comments.length === 0) {
ul.html('<li>No comments yet.</li>');
ul.data('empty', true);
} else {
// If there are comments, sort them and put them in the list.
var comments = sortComments(data.comments);
speed = data.comments.length * 100;
appendComments(comments, ul);
ul.data('empty', false);
}
$('#cn' + id).slideUp(speed + 200);
ul.slideDown(speed);
},
error: function(request, textStatus, error) {
showError('Oops, there was a problem retrieving the comments.');
},
dataType: 'json'
});
}
/**
* Add a comment via ajax and insert the comment into the comment tree.
*/
function addComment(form) {
var node_id = form.find('input[name="node"]').val();
var parent_id = form.find('input[name="parent"]').val();
var text = form.find('textarea[name="comment"]').val();
var proposal = form.find('textarea[name="proposal"]').val();
if (text == '') {
showError('Please enter a comment.');
return;
}
// Disable the form that is being submitted.
form.find('textarea,input').attr('disabled', 'disabled');
// Send the comment to the server.
$.ajax({
type: "POST",
url: opts.addCommentURL,
dataType: 'json',
data: {
node: node_id,
parent: parent_id,
text: text,
proposal: proposal
},
success: function(data, textStatus, error) {
// Reset the form.
if (node_id) {
hideProposeChange(node_id);
}
form.find('textarea')
.val('')
.add(form.find('input'))
.removeAttr('disabled');
var ul = $('#cl' + (node_id || parent_id));
if (ul.data('empty')) {
$(ul).empty();
ul.data('empty', false);
}
insertComment(data.comment);
var ao = $('#ao' + node_id);
ao.find('img').attr({'src': opts.commentBrightImage});
if (node_id) {
// if this was a "root" comment, remove the commenting box
// (the user can get it back by reopening the comment popup)
$('#ca' + node_id).slideUp();
}
},
error: function(request, textStatus, error) {
form.find('textarea,input').removeAttr('disabled');
showError('Oops, there was a problem adding the comment.');
}
});
}
/**
* Recursively append comments to the main comment list and children
* lists, creating the comment tree.
*/
function appendComments(comments, ul) {
$.each(comments, function() {
var div = createCommentDiv(this);
ul.append($(document.createElement('li')).html(div));
appendComments(this.children, div.find('ul.comment-children'));
// To avoid stagnating data, don't store the comments children in data.
this.children = null;
div.data('comment', this);
});
}
/**
* After adding a new comment, it must be inserted in the correct
* location in the comment tree.
*/
function insertComment(comment) {
var div = createCommentDiv(comment);
// To avoid stagnating data, don't store the comments children in data.
comment.children = null;
div.data('comment', comment);
var ul = $('#cl' + (comment.node || comment.parent));
var siblings = getChildren(ul);
var li = $(document.createElement('li'));
li.hide();
// Determine where in the parents children list to insert this comment.
for(i=0; i < siblings.length; i++) {
if (comp(comment, siblings[i]) <= 0) {
$('#cd' + siblings[i].id)
.parent()
.before(li.html(div));
li.slideDown('fast');
return;
}
}
// If we get here, this comment rates lower than all the others,
// or it is the only comment in the list.
ul.append(li.html(div));
li.slideDown('fast');
}
function acceptComment(id) {
$.ajax({
type: 'POST',
url: opts.acceptCommentURL,
data: {id: id},
success: function(data, textStatus, request) {
$('#cm' + id).fadeOut('fast');
$('#cd' + id).removeClass('moderate');
},
error: function(request, textStatus, error) {
showError('Oops, there was a problem accepting the comment.');
}
});
}
function deleteComment(id) {
$.ajax({
type: 'POST',
url: opts.deleteCommentURL,
data: {id: id},
success: function(data, textStatus, request) {
var div = $('#cd' + id);
if (data == 'delete') {
// Moderator mode: remove the comment and all children immediately
div.slideUp('fast', function() {
div.remove();
});
return;
}
// User mode: only mark the comment as deleted
div
.find('span.user-id:first')
.text('[deleted]').end()
.find('div.comment-text:first')
.text('[deleted]').end()
.find('#cm' + id + ', #dc' + id + ', #ac' + id + ', #rc' + id +
', #sp' + id + ', #hp' + id + ', #cr' + id + ', #rl' + id)
.remove();
var comment = div.data('comment');
comment.username = '[deleted]';
comment.text = '[deleted]';
div.data('comment', comment);
},
error: function(request, textStatus, error) {
showError('Oops, there was a problem deleting the comment.');
}
});
}
function showProposal(id) {
$('#sp' + id).hide();
$('#hp' + id).show();
$('#pr' + id).slideDown('fast');
}
function hideProposal(id) {
$('#hp' + id).hide();
$('#sp' + id).show();
$('#pr' + id).slideUp('fast');
}
function showProposeChange(id) {
$('#pc' + id).hide();
$('#hc' + id).show();
var textarea = $('#pt' + id);
textarea.val(textarea.data('source'));
$.fn.autogrow.resize(textarea[0]);
textarea.slideDown('fast');
}
function hideProposeChange(id) {
$('#hc' + id).hide();
$('#pc' + id).show();
var textarea = $('#pt' + id);
textarea.val('').removeAttr('disabled');
textarea.slideUp('fast');
}
function toggleCommentMarkupBox(id) {
$('#mb' + id).toggle();
}
/** Handle when the user clicks on a sort by link. */
function handleReSort(link) {
var classes = link.attr('class').split(/\s+/);
for (var i=0; i<classes.length; i++) {
if (classes[i] != 'sort-option') {
by = classes[i].substring(2);
}
}
setComparator();
// Save/update the sortBy cookie.
var expiration = new Date();
expiration.setDate(expiration.getDate() + 365);
document.cookie= 'sortBy=' + escape(by) +
';expires=' + expiration.toUTCString();
$('ul.comment-ul').each(function(index, ul) {
var comments = getChildren($(ul), true);
comments = sortComments(comments);
appendComments(comments, $(ul).empty());
});
}
/**
* Function to process a vote when a user clicks an arrow.
*/
function handleVote(link) {
if (!opts.voting) {
showError("You'll need to login to vote.");
return;
}
var id = link.attr('id');
if (!id) {
// Didn't click on one of the voting arrows.
return;
}
// If it is an unvote, the new vote value is 0,
// Otherwise it's 1 for an upvote, or -1 for a downvote.
var value = 0;
if (id.charAt(1) != 'u') {
value = id.charAt(0) == 'u' ? 1 : -1;
}
// The data to be sent to the server.
var d = {
comment_id: id.substring(2),
value: value
};
// Swap the vote and unvote links.
link.hide();
$('#' + id.charAt(0) + (id.charAt(1) == 'u' ? 'v' : 'u') + d.comment_id)
.show();
// The div the comment is displayed in.
var div = $('div#cd' + d.comment_id);
var data = div.data('comment');
// If this is not an unvote, and the other vote arrow has
// already been pressed, unpress it.
if ((d.value !== 0) && (data.vote === d.value * -1)) {
$('#' + (d.value == 1 ? 'd' : 'u') + 'u' + d.comment_id).hide();
$('#' + (d.value == 1 ? 'd' : 'u') + 'v' + d.comment_id).show();
}
// Update the comments rating in the local data.
data.rating += (data.vote === 0) ? d.value : (d.value - data.vote);
data.vote = d.value;
div.data('comment', data);
// Change the rating text.
div.find('.rating:first')
.text(data.rating + ' point' + (data.rating == 1 ? '' : 's'));
// Send the vote information to the server.
$.ajax({
type: "POST",
url: opts.processVoteURL,
data: d,
error: function(request, textStatus, error) {
showError('Oops, there was a problem casting that vote.');
}
});
}
/**
* Open a reply form used to reply to an existing comment.
*/
function openReply(id) {
// Swap out the reply link for the hide link
$('#rl' + id).hide();
$('#cr' + id).show();
// Add the reply li to the children ul.
var div = $(renderTemplate(replyTemplate, {id: id})).hide();
$('#cl' + id)
.prepend(div)
// Setup the submit handler for the reply form.
.find('#rf' + id)
.submit(function(event) {
event.preventDefault();
addComment($('#rf' + id));
closeReply(id);
})
.find('input[type=button]')
.click(function() {
closeReply(id);
});
div.slideDown('fast', function() {
$('#rf' + id).find('textarea').focus();
});
}
/**
* Close the reply form opened with openReply.
*/
function closeReply(id) {
// Remove the reply div from the DOM.
$('#rd' + id).slideUp('fast', function() {
$(this).remove();
});
// Swap out the hide link for the reply link
$('#cr' + id).hide();
$('#rl' + id).show();
}
/**
* Recursively sort a tree of comments using the comp comparator.
*/
function sortComments(comments) {
comments.sort(comp);
$.each(comments, function() {
this.children = sortComments(this.children);
});
return comments;
}
/**
* Get the children comments from a ul. If recursive is true,
* recursively include childrens' children.
*/
function getChildren(ul, recursive) {
var children = [];
ul.children().children("[id^='cd']")
.each(function() {
var comment = $(this).data('comment');
if (recursive)
comment.children = getChildren($(this).find('#cl' + comment.id), true);
children.push(comment);
});
return children;
}
/** Create a div to display a comment in. */
function createCommentDiv(comment) {
if (!comment.displayed && !opts.moderator) {
return $('<div class="moderate">Thank you! Your comment will show up '
+ 'once it is has been approved by a moderator.</div>');
}
// Prettify the comment rating.
comment.pretty_rating = comment.rating + ' point' +
(comment.rating == 1 ? '' : 's');
// Make a class (for displaying not yet moderated comments differently)
comment.css_class = comment.displayed ? '' : ' moderate';
// Create a div for this comment.
var context = $.extend({}, opts, comment);
var div = $(renderTemplate(commentTemplate, context));
// If the user has voted on this comment, highlight the correct arrow.
if (comment.vote) {
var direction = (comment.vote == 1) ? 'u' : 'd';
div.find('#' + direction + 'v' + comment.id).hide();
div.find('#' + direction + 'u' + comment.id).show();
}
if (opts.moderator || comment.text != '[deleted]') {
div.find('a.reply').show();
if (comment.proposal_diff)
div.find('#sp' + comment.id).show();
if (opts.moderator && !comment.displayed)
div.find('#cm' + comment.id).show();
if (opts.moderator || (opts.username == comment.username))
div.find('#dc' + comment.id).show();
}
return div;
}
/**
* A simple template renderer. Placeholders such as <%id%> are replaced
* by context['id'] with items being escaped. Placeholders such as <#id#>
* are not escaped.
*/
function renderTemplate(template, context) {
var esc = $(document.createElement('div'));
function handle(ph, escape) {
var cur = context;
$.each(ph.split('.'), function() {
cur = cur[this];
});
return escape ? esc.text(cur || "").html() : cur;
}
return template.replace(/<([%#])([\w\.]*)\1>/g, function() {
return handle(arguments[2], arguments[1] == '%' ? true : false);
});
}
/** Flash an error message briefly. */
function showError(message) {
$(document.createElement('div')).attr({'class': 'popup-error'})
.append($(document.createElement('div'))
.attr({'class': 'error-message'}).text(message))
.appendTo('body')
.fadeIn("slow")
.delay(2000)
.fadeOut("slow");
}
/** Add a link the user uses to open the comments popup. */
$.fn.comment = function() {
return this.each(function() {
var id = $(this).attr('id').substring(1);
var count = COMMENT_METADATA[id];
var title = count + ' comment' + (count == 1 ? '' : 's');
var image = count > 0 ? opts.commentBrightImage : opts.commentImage;
var addcls = count == 0 ? ' nocomment' : '';
$(this)
.append(
$(document.createElement('a')).attr({
href: '#',
'class': 'sphinx-comment-open' + addcls,
id: 'ao' + id
})
.append($(document.createElement('img')).attr({
src: image,
alt: 'comment',
title: title
}))
.click(function(event) {
event.preventDefault();
show($(this).attr('id').substring(2));
})
)
.append(
$(document.createElement('a')).attr({
href: '#',
'class': 'sphinx-comment-close hidden',
id: 'ah' + id
})
.append($(document.createElement('img')).attr({
src: opts.closeCommentImage,
alt: 'close',
title: 'close'
}))
.click(function(event) {
event.preventDefault();
hide($(this).attr('id').substring(2));
})
);
});
};
var opts = {
processVoteURL: '/_process_vote',
addCommentURL: '/_add_comment',
getCommentsURL: '/_get_comments',
acceptCommentURL: '/_accept_comment',
deleteCommentURL: '/_delete_comment',
commentImage: '/static/_static/comment.png',
closeCommentImage: '/static/_static/comment-close.png',
loadingImage: '/static/_static/ajax-loader.gif',
commentBrightImage: '/static/_static/comment-bright.png',
upArrow: '/static/_static/up.png',
downArrow: '/static/_static/down.png',
upArrowPressed: '/static/_static/up-pressed.png',
downArrowPressed: '/static/_static/down-pressed.png',
voting: false,
moderator: false
};
if (typeof COMMENT_OPTIONS != "undefined") {
opts = jQuery.extend(opts, COMMENT_OPTIONS);
}
var popupTemplate = '\
<div class="sphinx-comments" id="sc<%id%>">\
<p class="sort-options">\
Sort by:\
<a href="#" class="sort-option byrating">best rated</a>\
<a href="#" class="sort-option byascage">newest</a>\
<a href="#" class="sort-option byage">oldest</a>\
</p>\
<div class="comment-header">Comments</div>\
<div class="comment-loading" id="cn<%id%>">\
loading comments... <img src="<%loadingImage%>" alt="" /></div>\
<ul id="cl<%id%>" class="comment-ul"></ul>\
<div id="ca<%id%>">\
<p class="add-a-comment">Add a comment\
(<a href="#" class="comment-markup" id="ab<%id%>">markup</a>):</p>\
<div class="comment-markup-box" id="mb<%id%>">\
reStructured text markup: <i>*emph*</i>, <b>**strong**</b>, \
<code>``code``</code>, \
code blocks: <code>::</code> and an indented block after blank line</div>\
<form method="post" id="cf<%id%>" class="comment-form" action="">\
<textarea name="comment" cols="80"></textarea>\
<p class="propose-button">\
<a href="#" id="pc<%id%>" class="show-propose-change">\
Propose a change &#9657;\
</a>\
<a href="#" id="hc<%id%>" class="hide-propose-change">\
Propose a change &#9663;\
</a>\
</p>\
<textarea name="proposal" id="pt<%id%>" cols="80"\
spellcheck="false"></textarea>\
<input type="submit" value="Add comment" />\
<input type="hidden" name="node" value="<%id%>" />\
<input type="hidden" name="parent" value="" />\
</form>\
</div>\
</div>';
var commentTemplate = '\
<div id="cd<%id%>" class="sphinx-comment<%css_class%>">\
<div class="vote">\
<div class="arrow">\
<a href="#" id="uv<%id%>" class="vote" title="vote up">\
<img src="<%upArrow%>" />\
</a>\
<a href="#" id="uu<%id%>" class="un vote" title="vote up">\
<img src="<%upArrowPressed%>" />\
</a>\
</div>\
<div class="arrow">\
<a href="#" id="dv<%id%>" class="vote" title="vote down">\
<img src="<%downArrow%>" id="da<%id%>" />\
</a>\
<a href="#" id="du<%id%>" class="un vote" title="vote down">\
<img src="<%downArrowPressed%>" />\
</a>\
</div>\
</div>\
<div class="comment-content">\
<p class="tagline comment">\
<span class="user-id"><%username%></span>\
<span class="rating"><%pretty_rating%></span>\
<span class="delta"><%time.delta%></span>\
</p>\
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<div class="section" id="module-aam">
<span id="aam-module"></span><h1>AAM Module<a class="headerlink" href="#module-aam" title="Permalink to this headline"></a></h1>
<span class="target" id="module-active_appearance_model"></span><dl class="class">
<dt id="aam.AAMPoints">
<em class="property">class </em><code class="descclassname">aam.</code><code class="descname">AAMPoints</code><span class="sig-paren">(</span><em>normalized_flattened_points_list=None</em>, <em>points_list=None</em>, <em>actual_shape=()</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/aam.html#AAMPoints"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#aam.AAMPoints" title="Permalink to this definition"></a></dt>
<dd><p>Object to store AAM points / landmarks. Tries to keep the scaling of
these points transparent.</p>
<dl class="method">
<dt id="aam.AAMPoints.calculate_bounding_box">
<code class="descname">calculate_bounding_box</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/aam.html#AAMPoints.calculate_bounding_box"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#aam.AAMPoints.calculate_bounding_box" title="Permalink to this definition"></a></dt>
<dd><p>Calculate bounding box in the <strong>scaled</strong> points list
The empasis on on scaled because the convexHull does not support
small values, the normalized_flattened_points_list does not work.</p>
<p>Use get_scaled_points first, with a shape that is needed. The shape
should be the dimensions of the out image, example (480, 640), ie., (height,
width)</p>
</dd></dl>
<dl class="method">
<dt id="aam.AAMPoints.get_bounding_box">
<code class="descname">get_bounding_box</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/aam.html#AAMPoints.get_bounding_box"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#aam.AAMPoints.get_bounding_box" title="Permalink to this definition"></a></dt>
<dd><p>Get the bounding box around the points.</p>
<dl class="docutils">
<dt>Returns:</dt>
<dd><dl class="first last docutils">
<dt>OpenCV rectangle:</dt>
<dd>x, y, w, h</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="aam.AAMPoints.get_scaled_points">
<code class="descname">get_scaled_points</code><span class="sig-paren">(</span><em>width_height_dimensions</em>, <em>rescale=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/aam.html#AAMPoints.get_scaled_points"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#aam.AAMPoints.get_scaled_points" title="Permalink to this definition"></a></dt>
<dd><p>Scale the normalized flattened points list to a scale given by &#8216;shape&#8217;.
The x and y values should be scaled to the width and height of the image.</p>
<dl class="docutils">
<dt>Args:</dt>
<dd>shape(tuple): (height, width)
rescal(boolean): flag if we should rescale or not because if we
already scaled, we are not going to do it again by
default.</dd>
<dt>Returns:</dt>
<dd>ndarray scaled to &#8216;shape&#8217; width and height.</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="function">
<dt id="aam.build_shape_feature_vectors">
<code class="descclassname">aam.</code><code class="descname">build_shape_feature_vectors</code><span class="sig-paren">(</span><em>files</em>, <em>get_points</em>, <em>flattened=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/aam.html#build_shape_feature_vectors"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#aam.build_shape_feature_vectors" title="Permalink to this definition"></a></dt>
<dd><p>Gets the aam points from the files and appends them seperately to one
array.</p>
<dl class="docutils">
<dt>Args:</dt>
<dd>files (list): list files
get_points(function): function that gets the points/landmarks given
a list of files.</dd>
<dt>Returns:</dt>
<dd>list. List of feature vectors</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="aam.build_texture_feature_vectors">
<code class="descclassname">aam.</code><code class="descname">build_texture_feature_vectors</code><span class="sig-paren">(</span><em>files</em>, <em>get_image_with_points</em>, <em>mean_points</em>, <em>triangles</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/aam.html#build_texture_feature_vectors"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#aam.build_texture_feature_vectors" title="Permalink to this definition"></a></dt>
<dd><dl class="docutils">
<dt>Args:</dt>
<dd>files (list): list files
get_image_with_points (function): That can return the image together
with the location.
mean_points(AAMPoints): AAMPoints object</dd>
<dt>Returns:</dt>
<dd>list: list of feature vectors</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="aam.get_mean">
<code class="descclassname">aam.</code><code class="descname">get_mean</code><span class="sig-paren">(</span><em>vector</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/aam.html#get_mean"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#aam.get_mean" title="Permalink to this definition"></a></dt>
<dd><p>Construct a mean from a matrix of x,y values</p>
<dl class="docutils">
<dt>Args:</dt>
<dd>points(numpy array) that follows the following structure:</dd>
<dt>Returns:</dt>
<dd>mean_values (numpy array)</dd>
<dt>Example:</dt>
<dd><dl class="first last docutils">
<dt>Input observations:</dt>
<dd><ol class="first arabic" start="0">
<li><p class="first">[[x_0_0, y_0_0], ... , [x_0_m, y_0_m]],</p>
</li>
<li><p class="first">[[x_1_0, y_1_0], ... , [x_1_m, y_1_m]],</p>
</li>
<li><p class="first">[[x_2_0, y_2_0], ... , [x_2_m, y_2_m]],</p>
</li>
<li><p class="first">[[x_3_0, y_3_0], ... , [x_3_m, y_3_m]]</p>
<blockquote>
<div><p>.... .... .....</p>
</div></blockquote>
</li>
</ol>
<ol class="last loweralpha simple" start="14">
<li>[[x_4_0, y_4_0], ... , [x_n_m, y_n_m]]</li>
</ol>
</dd>
<dt>This vector containts the mean values of the corresponding column, like so:</dt>
<dd><ol class="first arabic" start="0">
<li><p class="first">[[x_0_0, y_0_0], ... , [x_0_k, y_0_k]],</p>
</li>
<li><p class="first">[[x_1_0, y_1_0], ... , [x_1_k, y_1_k]],</p>
</li>
<li><p class="first">[[x_2_0, y_2_0], ... , [x_2_k, y_2_k]],</p>
</li>
<li><p class="first">[[x_3_0, y_3_0], ... , [x_3_k, y_3_k]]</p>
<blockquote>
<div><p>.... .... .....</p>
</div></blockquote>
</li>
</ol>
<ol class="loweralpha simple" start="14">
<li>[[x_4_0, y_4_0], ... , [x_n_k, y_n_k]]</li>
</ol>
<p class="last">mean. [[x_mean_0, y_mean_0], ... [x_mean_n, y_mean_n]]</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="aam.get_pixel_values">
<code class="descclassname">aam.</code><code class="descname">get_pixel_values</code><span class="sig-paren">(</span><em>image</em>, <em>points</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/aam.html#get_pixel_values"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#aam.get_pixel_values" title="Permalink to this definition"></a></dt>
<dd><p>deprecated</p>
</dd></dl>
<dl class="function">
<dt id="aam.get_triangles">
<code class="descclassname">aam.</code><code class="descname">get_triangles</code><span class="sig-paren">(</span><em>x_vector</em>, <em>y_vector</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/aam.html#get_triangles"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#aam.get_triangles" title="Permalink to this definition"></a></dt>
<dd><p>Perform triangulation between two 2d vectors</p>
<dl class="docutils">
<dt>Args:</dt>
<dd>x_vector(ndarray): list of x locations
y_vector(ndarray): list of y locations</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="aam.sample_from_triangles">
<code class="descclassname">aam.</code><code class="descname">sample_from_triangles</code><span class="sig-paren">(</span><em>src</em>, <em>points2d_src</em>, <em>points2d_dst</em>, <em>triangles</em>, <em>dst</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/aam.html#sample_from_triangles"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#aam.sample_from_triangles" title="Permalink to this definition"></a></dt>
<dd><p>Get pixels from within the triangles [[p1, p2, p3]_0, .. [p1, p2, p3]_n].</p>
<dl class="docutils">
<dt>Args:</dt>
<dd><p class="first">src(ndarray, dtype=uint8): input image</p>
<p>points2d_src(ndarray, dtype=np.int32): shape array [[x, y], ... [x, y]]</p>
<p>points2d_dst(ndarray, dtype=np.int32): shape array [[x, y], ... [x, y]]</p>
<p class="last">triangles(ndarray, ndim=3, dtype=np.int32): shape array [[p1, p2, p3]_0, .. [p1, p2, p3]_n].</p>
</dd>
</dl>
</dd></dl>
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<div class="section" id="module-datasets.imm">
<span id="datasets"></span><h1>Datasets<a class="headerlink" href="#module-datasets.imm" title="Permalink to this headline"></a></h1>
<span class="target" id="module-datasets"></span><dl class="class">
<dt id="datasets.imm.IMMPoints">
<em class="property">class </em><code class="descclassname">datasets.imm.</code><code class="descname">IMMPoints</code><span class="sig-paren">(</span><em>filename=None</em>, <em>points_list=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/datasets/imm.html#IMMPoints"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#datasets.imm.IMMPoints" title="Permalink to this definition"></a></dt>
<dd><p>Accepts IMM datapoint file which can be shown or used</p>
<dl class="method">
<dt id="datasets.imm.IMMPoints.get_image">
<code class="descname">get_image</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/datasets/imm.html#IMMPoints.get_image"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#datasets.imm.IMMPoints.get_image" title="Permalink to this definition"></a></dt>
<dd><p>Get the image corresponding to the filename
If filename == image_1.asf, then we read image_1.jpg from disk
and return this to the user.</p>
<dl class="docutils">
<dt>Returns:</dt>
<dd>ndarray image</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="datasets.imm.IMMPoints.get_points">
<code class="descname">get_points</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/datasets/imm.html#IMMPoints.get_points"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#datasets.imm.IMMPoints.get_points" title="Permalink to this definition"></a></dt>
<dd><p>Get the flattened list of points</p>
<dl class="docutils">
<dt>Returns:</dt>
<dd>ndarray. flattened array of points, see AAMPoints for more
information.</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="datasets.imm.IMMPoints.import_file">
<code class="descname">import_file</code><span class="sig-paren">(</span><em>filename</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/datasets/imm.html#IMMPoints.import_file"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#datasets.imm.IMMPoints.import_file" title="Permalink to this definition"></a></dt>
<dd><p>Import an .asf filename. Load the points into a list of points and
store the relative path to image file.</p>
<dl class="docutils">
<dt>Returns:</dt>
<dd>ndarray(float). Numpy array of landmark locations as stated in the
.asf files.</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="datasets.imm.IMMPoints.show">
<code class="descname">show</code><span class="sig-paren">(</span><em>window_name='image'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/datasets/imm.html#IMMPoints.show"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#datasets.imm.IMMPoints.show" title="Permalink to this definition"></a></dt>
<dd><p>show the image and datapoints on the image</p>
</dd></dl>
</dd></dl>
<dl class="function">
<dt id="datasets.imm.get_imm_image_with_landmarks">
<code class="descclassname">datasets.imm.</code><code class="descname">get_imm_image_with_landmarks</code><span class="sig-paren">(</span><em>filename</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/datasets/imm.html#get_imm_image_with_landmarks"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#datasets.imm.get_imm_image_with_landmarks" title="Permalink to this definition"></a></dt>
<dd><p>Get Points with image and landmarks/points</p>
<dl class="docutils">
<dt>Args:</dt>
<dd>filename(fullpath): .asf file</dd>
<dt>Returns:</dt>
<dd>image, points</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="datasets.imm.get_imm_points">
<code class="descclassname">datasets.imm.</code><code class="descname">get_imm_points</code><span class="sig-paren">(</span><em>files</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/datasets/imm.html#get_imm_points"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#datasets.imm.get_imm_points" title="Permalink to this definition"></a></dt>
<dd><p>This function does something.</p>
<dl class="docutils">
<dt>Args:</dt>
<dd>files (array): Array of .asf full or relative path to .asf files.</dd>
<dt>Returns:</dt>
<dd>ndarray. Array of landmarks.</dd>
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<h2 id="A">A</h2>
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<dt><a href="aam.html#module-aam">aam (module)</a>
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<dt><a href="aam.html#aam.AAMPoints">AAMPoints (class in aam)</a>
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<dt><a href="aam.html#module-active_appearance_model">active_appearance_model (module)</a>
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<h2 id="B">B</h2>
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<td style="width: 33%" valign="top"><dl>
<dt><a href="reconstruction/reconstruction.html#reconstruction.reconstruction.barycentric2cartesian">barycentric2cartesian() (in module reconstruction.reconstruction)</a>
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<dt><a href="aam.html#aam.build_shape_feature_vectors">build_shape_feature_vectors() (in module aam)</a>
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<td style="width: 33%" valign="top"><dl>
<dt><a href="aam.html#aam.build_texture_feature_vectors">build_texture_feature_vectors() (in module aam)</a>
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<h2 id="C">C</h2>
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<dt><a href="aam.html#aam.AAMPoints.calculate_bounding_box">calculate_bounding_box() (aam.AAMPoints method)</a>
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<dt><a href="reconstruction/reconstruction.html#reconstruction.reconstruction.cartesian2barycentric">cartesian2barycentric() (in module reconstruction.reconstruction)</a>
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<dt><a href="reconstruction/texture.html#reconstruction.texture.cartesian2barycentric_slow_test">cartesian2barycentric_slow_test() (in module reconstruction.texture)</a>
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<dt><a href="reconstruction/texture.html#reconstruction.texture.cartesian2barycentric_test">cartesian2barycentric_test() (in module reconstruction.texture)</a>
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<dt><a href="datasets.html#module-datasets">datasets (module)</a>
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<dt><a href="datasets.html#module-datasets.imm">datasets.imm (module)</a>
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<h2 id="F">F</h2>
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<dt><a href="reconstruction/texture.html#reconstruction.texture.fill_triangle">fill_triangle() (in module reconstruction.texture)</a>
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<dt><a href="reconstruction/texture.html#reconstruction.texture.fill_triangle_src_dst">fill_triangle_src_dst() (in module reconstruction.texture)</a>
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<dt><a href="pca.html#pca.flatten_feature_vectors">flatten_feature_vectors() (in module pca)</a>
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<h2 id="G">G</h2>
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<dt><a href="aam.html#aam.AAMPoints.get_bounding_box">get_bounding_box() (aam.AAMPoints method)</a>
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<dt><a href="datasets.html#datasets.imm.IMMPoints.get_image">get_image() (datasets.imm.IMMPoints method)</a>
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<dt><a href="datasets.html#datasets.imm.get_imm_image_with_landmarks">get_imm_image_with_landmarks() (in module datasets.imm)</a>
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<dt><a href="datasets.html#datasets.imm.get_imm_points">get_imm_points() (in module datasets.imm)</a>
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<dt><a href="aam.html#aam.get_mean">get_mean() (in module aam)</a>
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<td style="width: 33%" valign="top"><dl>
<dt><a href="aam.html#aam.get_pixel_values">get_pixel_values() (in module aam)</a>
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<dt><a href="datasets.html#datasets.imm.IMMPoints.get_points">get_points() (datasets.imm.IMMPoints method)</a>
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<dt><a href="aam.html#aam.AAMPoints.get_scaled_points">get_scaled_points() (aam.AAMPoints method)</a>
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<dt><a href="aam.html#aam.get_triangles">get_triangles() (in module aam)</a>
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<h2 id="I">I</h2>
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<dt><a href="datasets.html#datasets.imm.IMMPoints">IMMPoints (class in datasets.imm)</a>
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<dt><a href="datasets.html#datasets.imm.IMMPoints.import_file">import_file() (datasets.imm.IMMPoints method)</a>
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<h2 id="L">L</h2>
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<dt><a href="pca.html#pca.load">load() (in module pca)</a>
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<h2 id="P">P</h2>
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<dt><a href="pca.html#module-pca">pca (module)</a>
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<dt><a href="pca.html#pca.pca">pca() (in module pca)</a>
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<dt><a href="pca.html#pca.PcaModel">PcaModel (class in pca)</a>
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<h2 id="R">R</h2>
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<dt><a href="pca.html#pca.reconstruct">reconstruct() (in module pca)</a>
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<dt><a href="reconstruction/reconstruction.html#reconstruction.reconstruction.reconstruct_texture">reconstruct_texture() (in module reconstruction.reconstruction)</a>
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<dt><a href="reconstruction/reconstruction.html#module-reconstruction.reconstruction">reconstruction.reconstruction (module)</a>
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<dt><a href="reconstruction/texture.html#module-reconstruction.texture">reconstruction.texture (module)</a>
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<h2 id="S">S</h2>
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<dt><a href="aam.html#aam.sample_from_triangles">sample_from_triangles() (in module aam)</a>
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<dt><a href="pca.html#pca.save">save() (in module pca)</a>
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<dt><a href="datasets.html#datasets.imm.IMMPoints.show">show() (datasets.imm.IMMPoints method)</a>
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<h1>Welcome to 3D Face Reconstruction&#8217;s documentation!<a class="headerlink" href="#welcome-to-3d-face-reconstruction-s-documentation" title="Permalink to this headline"></a></h1>
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<div class="section" id="work-in-progress">
<h1>!!!Work in progress!!!<a class="headerlink" href="#work-in-progress" title="Permalink to this headline"></a></h1>
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<div class="section" id="pca-reconstruction">
<h1>PCA reconstruction<a class="headerlink" href="#pca-reconstruction" title="Permalink to this headline"></a></h1>
<p>Principle Component Analysis is one of the most used methods in the field of statistics, it is used for dimension reduction of data and is capable of removing outliers which ultimately improves learning algorithms. In this case we use PCA for both shape and texture reconstruction. Given an image of person&#8217;s face we would be able to reconstruct it using a PCA Model. The motivation for using PCA is that we can fill in missing data and remove outliers given one image of person. If for some reason the image is very cluttered, we would still be able to &#8216;predict&#8217; how this person would look like, given all the faces we have used to train the PCA Model.</p>
<p>For the PCA reconstruction method has a couple of prerequisites are required. First off, the PCA Model itself. For those who are familiar with PCA know that we need to have a flattened feature vector. Both the dimensions and the content of this feature vector may be arbitrary, but have to be exactly the same from subject to subject, (i.e., there can be no difference in the number of annotated landmarks or order, landmark 1 in subject A, is landmark 1 in subject B). In this case we use it for the shape and texture. The shape feature vector contains the following data:</p>
<p><code class="docutils literal"><span class="pre">`</span>
<span class="pre">[[x_1,</span> <span class="pre">y_1],</span> <span class="pre">[x_2,</span> <span class="pre">y_2],</span> <span class="pre">...,</span> <span class="pre">[x_n,</span> <span class="pre">y_n]]</span>&nbsp; <span class="pre">-&gt;</span> <span class="pre">(flattened)</span> <span class="pre">[x_1,</span> <span class="pre">y_1,</span> <span class="pre">x_2,</span> <span class="pre">y_2,</span> <span class="pre">x_n,</span> <span class="pre">y_n]</span>
<span class="pre">`</span></code></p>
<p>The x,y values are the location of landmarks in an image. Such a cluster of annotated locations in an image construct a shape we call Active Appearance Model(AAM)[1]. For a serie of annotated pictures with landmark location we can build mean AAM. For this particular implementation we started with supporting the Imm Dataset[^imm_dataset], for the simple reason that it is open for usage without any license agreement before hand (make sure we are correct about this). This is what we call the mean face, which is very important for the construction of the PCA Model, any PCA Model for that matter.</p>
<p>The texture PCA data is somewhat more difficult and depends on a given shape. In our case this given shape is the mean AAM that we have built previously. We need to add extra information to this AAM mean shape, namely a unique set of triangles that can be constructed from the set of landmarks. For this we use the Delaunay algorithm which does exactly this. The triangles help us find corresponding pixels in shape A and B. This solves the problem of pixel correspondences and is important for constructing a mean texture for the reasons explained previously about how a feature vector should look like. Pixel 1 in triangle 1 in subject A needs to correspond to exactly the same pixel (relatively) to pixel 1 in triangle 1 in subject B. This of course is sensitive to noise, but the pixels in the nose region must correspond from subject to subject, this prevents that we reconstruct an eye with a nose for instance (Note: remove this last sentence in a serious text).</p>
</div>
<div class="section" id="references">
<h1>References<a class="headerlink" href="#references" title="Permalink to this headline"></a></h1>
<p>[1]: Cootes, T. F., Edwards, G. J., &amp; Taylor, C. J. (1998, June). Active appearance models. In European conference on computer vision (pp. 484-498). Springer Berlin Heidelberg.</p>
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<div class="section" id="links">
<h1>Links<a class="headerlink" href="#links" title="Permalink to this headline"></a></h1>
<p>[^imm_dataset]: <a class="reference external" href="http://www.imm.dtu.dk/~aam/datasets/datasets.html">http://www.imm.dtu.dk/~aam/datasets/datasets.html</a> &#8220;Imm dataset&#8221;</p>
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<div class="section" id="module-pca">
<span id="pca-module"></span><h1>PCA Module<a class="headerlink" href="#module-pca" title="Permalink to this headline"></a></h1>
<dl class="class">
<dt id="pca.PcaModel">
<em class="property">class </em><code class="descclassname">pca.</code><code class="descname">PcaModel</code><span class="sig-paren">(</span><em>model_file</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pca.html#PcaModel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pca.PcaModel" title="Permalink to this definition"></a></dt>
<dd><p>Abstraction for a pca model</p>
</dd></dl>
<dl class="function">
<dt id="pca.flatten_feature_vectors">
<code class="descclassname">pca.</code><code class="descname">flatten_feature_vectors</code><span class="sig-paren">(</span><em>data</em>, <em>dim=0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pca.html#flatten_feature_vectors"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pca.flatten_feature_vectors" title="Permalink to this definition"></a></dt>
<dd><p>Flattens the feature vectors inside a ndarray</p>
<dl class="docutils">
<dt>Example:</dt>
<dd><p class="first">input:
[</p>
<blockquote>
<div>[[1, 2], [3, 4], [5, 6]],
...
[[1, 2], [3, 4], [5, 6]]</div></blockquote>
<p>]
output:
[</p>
<blockquote>
<div>[1, 2, 3, 4, 5, 6],
...
[1, 2, 3, 4, 5, 6]</div></blockquote>
<p class="last">]</p>
</dd>
<dt>Args:</dt>
<dd>data (numpy array): array of feature vectors
dim (int): dimension to flatten the data</dd>
<dt>return:</dt>
<dd>array: (numpy array): array flattened feature vectors</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="pca.load">
<code class="descclassname">pca.</code><code class="descname">load</code><span class="sig-paren">(</span><em>filename</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pca.html#load"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pca.load" title="Permalink to this definition"></a></dt>
<dd><dl class="docutils">
<dt>The model stored by pca.store (see <code class="docutils literal"><span class="pre">pca.store</span></code> method above) is loaded as:</dt>
<dd><p class="first">UsVtm = np.load(args.model_file)</p>
<p>Vt = Vtm[0]
mean_values = Vtm[1][0]</p>
<dl class="last docutils">
<dt>Returns:</dt>
<dd><p class="first">(tuple): Vt, mean_values</p>
<blockquote class="last">
<div>Vt (numpy ndarray): Two dimensional array with dimensions
(n_features, n_features)
mean_values (numpy ndarray): mean values of the features of the model,
this should have dimensions (n_featurs, )</div></blockquote>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="pca.pca">
<code class="descclassname">pca.</code><code class="descname">pca</code><span class="sig-paren">(</span><em>data</em>, <em>mean_values</em>, <em>variance_percentage=90</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pca.html#pca"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pca.pca" title="Permalink to this definition"></a></dt>
<dd><p>Perform Singlar Value Decomposition</p>
<dl class="docutils">
<dt>Returns:</dt>
<dd>U (ndarray): U matrix
s (ndarray): 1d singular values (diagonal in array form)
Vt (ndarray): Vt matrix</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="pca.reconstruct">
<code class="descclassname">pca.</code><code class="descname">reconstruct</code><span class="sig-paren">(</span><em>feature_vector</em>, <em>Vt</em>, <em>mean_values</em>, <em>n_components=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pca.html#reconstruct"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pca.reconstruct" title="Permalink to this definition"></a></dt>
<dd><p>Reconstruct with U, s, Vt</p>
<dl class="docutils">
<dt>Args:</dt>
<dd><dl class="first docutils">
<dt>U (numpy ndarray): One feature vector from the reduced SVD.</dt>
<dd>U should have shape (n_features,), (i.e., one dimensional)</dd>
</dl>
<p class="last">s (numpy ndarray): The singular values as a one dimensional array
Vt (numpy ndarray): Two dimensional array with dimensions
(n_features, n_features)
mean_values (numpy ndarray): mean values of the features of the model,
this should have dimensions (n_features, )</p>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="pca.save">
<code class="descclassname">pca.</code><code class="descname">save</code><span class="sig-paren">(</span><em>Vt</em>, <em>s</em>, <em>n_components</em>, <em>mean_values</em>, <em>triangles</em>, <em>filename</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pca.html#save"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pca.save" title="Permalink to this definition"></a></dt>
<dd><p>Store the U, s, Vt and mean of all the asf datafiles given by the asf
files.</p>
<dl class="docutils">
<dt>It is stored in the following way:</dt>
<dd>np.load(filename, np.assary([Vt, [mean_values]])</dd>
<dt>And accessed by:</dt>
<dd><p class="first">Vtm = np.load(args.model_file)</p>
<p class="last">Vt = Vtm[0]
mean_values = Vtm[1][0]
triangles = Vtm[2]</p>
</dd>
</dl>
</dd></dl>
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<h1>Python Module Index</h1>
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<a href="#cap-a"><strong>a</strong></a> |
<a href="#cap-d"><strong>d</strong></a> |
<a href="#cap-p"><strong>p</strong></a> |
<a href="#cap-r"><strong>r</strong></a>
</div>
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<tr class="pcap"><td></td><td>&nbsp;</td><td></td></tr>
<tr class="cap" id="cap-a"><td></td><td>
<strong>a</strong></td><td></td></tr>
<tr>
<td></td>
<td>
<a href="aam.html#module-aam"><code class="xref">aam</code></a></td><td>
<em></em></td></tr>
<tr>
<td></td>
<td>
<a href="aam.html#module-active_appearance_model"><code class="xref">active_appearance_model</code></a> <em>(Unix, Windows)</em></td><td>
<em>Contains the aam data format abstraction layer</em></td></tr>
<tr class="pcap"><td></td><td>&nbsp;</td><td></td></tr>
<tr class="cap" id="cap-d"><td></td><td>
<strong>d</strong></td><td></td></tr>
<tr>
<td><img src="_static/minus.png" class="toggler"
id="toggle-1" style="display: none" alt="-" /></td>
<td>
<a href="datasets.html#module-datasets"><code class="xref">datasets</code></a> <em>(Unix, Windows)</em></td><td>
<em>Contains imm dataset abstraction layer</em></td></tr>
<tr class="cg-1">
<td></td>
<td>&nbsp;&nbsp;&nbsp;
<a href="datasets.html#module-datasets.imm"><code class="xref">datasets.imm</code></a></td><td>
<em></em></td></tr>
<tr class="pcap"><td></td><td>&nbsp;</td><td></td></tr>
<tr class="cap" id="cap-p"><td></td><td>
<strong>p</strong></td><td></td></tr>
<tr>
<td></td>
<td>
<a href="pca.html#module-pca"><code class="xref">pca</code></a></td><td>
<em></em></td></tr>
<tr class="pcap"><td></td><td>&nbsp;</td><td></td></tr>
<tr class="cap" id="cap-r"><td></td><td>
<strong>r</strong></td><td></td></tr>
<tr>
<td><img src="_static/minus.png" class="toggler"
id="toggle-2" style="display: none" alt="-" /></td>
<td>
<code class="xref">reconstruction</code></td><td>
<em></em></td></tr>
<tr class="cg-2">
<td></td>
<td>&nbsp;&nbsp;&nbsp;
<a href="reconstruction/reconstruction.html#module-reconstruction.reconstruction"><code class="xref">reconstruction.reconstruction</code></a></td><td>
<em></em></td></tr>
<tr class="cg-2">
<td></td>
<td>&nbsp;&nbsp;&nbsp;
<a href="reconstruction/texture.html#module-reconstruction.texture"><code class="xref">reconstruction.texture</code></a></td><td>
<em></em></td></tr>
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<div class="section" id="reconstruction-module">
<h1>Reconstruction Module<a class="headerlink" href="#reconstruction-module" title="Permalink to this headline"></a></h1>
<p>As explained in [PCA Reconstruction](home) we need a flattened feature vector to able to build a PCA Model. This holds for both shape and texture model. Currently we implement the independent AAM model where we keep the feature vector separate. Note that we could also choose to combine the shape and appearance in a single flattened feature vector (TODO: elaborate our choice more about this, if possible).</p>
<p>We use the imm dataset[^imm_dataset] for this. We first need to build the mean shape of the all the images. The dataset has a .asf file and an equally named .jpg file. The .asf file contains the locations of the landmars (normalized by the width and height of the image). In <cite>src/imm_points.py</cite> we find the ImmPoints class that implements all functions needed to read this file.</p>
<p>[^imm_dataset]: <a class="reference external" href="http://www.imm.dtu.dk/~aam/datasets/datasets.html">http://www.imm.dtu.dk/~aam/datasets/datasets.html</a> &#8220;Imm dataset&#8221;</p>
<span class="target" id="module-reconstruction.reconstruction"></span><dl class="function">
<dt id="reconstruction.reconstruction.barycentric2cartesian">
<code class="descclassname">reconstruction.reconstruction.</code><code class="descname">barycentric2cartesian</code><span class="sig-paren">(</span><em>r1</em>, <em>r2</em>, <em>r3</em>, <em>L</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/reconstruction/reconstruction.html#barycentric2cartesian"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#reconstruction.reconstruction.barycentric2cartesian" title="Permalink to this definition"></a></dt>
<dd><p>Given the barycentric weights in L, and cartesian r1, r2, r3 coordinates of
points that span the triangle, return the cartesian coordinate of the
points that is located at the weights of L.</p>
<dl class="docutils">
<dt>Returns:</dt>
<dd>ndarray [x,y] cartesian points.</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="reconstruction.reconstruction.cartesian2barycentric">
<code class="descclassname">reconstruction.reconstruction.</code><code class="descname">cartesian2barycentric</code><span class="sig-paren">(</span><em>r1</em>, <em>r2</em>, <em>r3</em>, <em>r</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/reconstruction/reconstruction.html#cartesian2barycentric"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#reconstruction.reconstruction.cartesian2barycentric" title="Permalink to this definition"></a></dt>
<dd><p>Given a triangle spanned by three cartesion points
r1, r2, r2, and point r, return the barycentric weights l1, l2, l3.</p>
<dl class="docutils">
<dt>Returns:</dt>
<dd>ndarray (of dim 3) weights of the barycentric coordinates</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="reconstruction.reconstruction.reconstruct_texture">
<code class="descclassname">reconstruction.reconstruction.</code><code class="descname">reconstruct_texture</code><span class="sig-paren">(</span><em>src_image</em>, <em>dst_image</em>, <em>texture_model</em>, <em>src_points</em>, <em>dst_points</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/reconstruction/reconstruction.html#reconstruct_texture"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#reconstruction.reconstruction.reconstruct_texture" title="Permalink to this definition"></a></dt>
<dd><p>Recontruct texture given the src and dst image</p>
<dl class="docutils">
<dt>Args:</dt>
<dd>src_points(aam.AAMPoints)
dst_points(aam.AAMPoints)</dd>
</dl>
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<div class="section" id="module-reconstruction.texture">
<span id="texture-module"></span><h1>Texture Module<a class="headerlink" href="#module-reconstruction.texture" title="Permalink to this headline"></a></h1>
<dl class="function">
<dt id="reconstruction.texture.cartesian2barycentric_slow_test">
<code class="descclassname">reconstruction.texture.</code><code class="descname">cartesian2barycentric_slow_test</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#reconstruction.texture.cartesian2barycentric_slow_test" title="Permalink to this definition"></a></dt>
<dd><p>Given a triangle spanned by three cartesion points
r1, r2, r2, and point r, return the barycentric weights l1, l2, l3.</p>
<dl class="docutils">
<dt>Returns:</dt>
<dd>ndarray (of dim 3) weights of the barycentric coordinates</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="reconstruction.texture.cartesian2barycentric_test">
<code class="descclassname">reconstruction.texture.</code><code class="descname">cartesian2barycentric_test</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#reconstruction.texture.cartesian2barycentric_test" title="Permalink to this definition"></a></dt>
<dd><dl class="docutils">
<dt>lambda_1 = (y_2 - y_3)(x - x_3) + (x_3 - x_2)(y - y_3) /</dt>
<dd>(y_2-y_3)(x_1-x_3)+(x_3-x_2)(y_1-y_3)</dd>
<dt>lambda_2 = (y_3 - y_1)(x - x_3) + (x_1 - x_3)(y - y_3) /</dt>
<dd>(y_2-y_3)(x_1-x_3)+(x_3-x_2)(y_1-y_3)</dd>
</dl>
<p>lambda_3 = 1 lambda_1 - lambda_2</p>
<dl class="docutils">
<dt>Returns:</dt>
<dd>ndarray (of dim 3) weights of the barycentric coordinates</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="reconstruction.texture.fill_triangle">
<code class="descclassname">reconstruction.texture.</code><code class="descname">fill_triangle</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#reconstruction.texture.fill_triangle" title="Permalink to this definition"></a></dt>
<dd><p>Fill a triangle by applying the Barycentric Algorithm for deciding if a
point lies inside or outside a triangle.</p>
</dd></dl>
<dl class="function">
<dt id="reconstruction.texture.fill_triangle_src_dst">
<code class="descclassname">reconstruction.texture.</code><code class="descname">fill_triangle_src_dst</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#reconstruction.texture.fill_triangle_src_dst" title="Permalink to this definition"></a></dt>
<dd><p>Fill a triangle by applying the Barycentric Algorithm for deciding if a
point lies inside or outside a triangle.</p>
</dd></dl>
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function"]},objtypes:{"0":"py:module","1":"py:class","2":"py:method","3":"py:function"},terms:{"boolean":0,"case":2,"class":[0,1,3,4],"default":0,"float":1,"function":[0,1,4],"import":[1,2],"int":3,"return":[0,1,3,4,5],"true":[],"try":[],aampoint:[0,1,4],abl:[2,4],about:[2,4],abov:3,abstraction:3,accept:1,access:3,active:2,actual_shap:0,add:2,add_parser_opt:[],again:0,agreement:2,algorithm:[2,5],all:[2,3,4],alreadi:0,also:4,analysi:2,ani:2,annot:2,appear:[2,4],appearanc:2,append:0,appli:5,arbitrari:2,arg:[0,1,3,4],around:0,arrai:[0,1,3],asf:[1,3,4],assari:3,attributeerror:[],avail:[],barycentr:[4,5],barycentric2cartesian:4,becaus:0,befor:2,berlin:2,between:0,bool:[],both:[2,4],bound:0,box:0,build:[2,4],build_shape_feature_vector:0,build_texture_feature_vector:0,built:2,calcul:0,calculate_bounding_box:0,call:2,can:[0,1,2],capabl:2,cartes:[4,5],cartesian2barycentr:4,cartesian2barycentric_slow_test:5,cartesian2barycentric_test:5,cartesian:4,choic:4,choos:4,cluster:2,clutter:2,code:[],column:0,combin:4,compon:2,comput:2,confer:2,construct:[0,2],contain:[2,4],containt:0,content:2,convexhul:0,coordin:[4,5],coot:2,correct:2,correspond:[0,1,2],could:4,coupl:2,cours:2,current:4,data:[2,3],datafil:3,datapoint:1,dataset:[],debug:[],decid:5,decomposit:3,delaunai:2,depend:2,deprec:0,diagon:3,differ:2,difficult:2,dim:[3,4,5],dimens:[0,2,3],dimension:3,disk:1,docstr:[],doe:[0,1,2],draw_triangl:[],dst:[0,4],dst_imag:4,dst_point:4,dtu:[2,4],dtype:0,edward:2,elabor:4,empasi:0,equal:4,european:2,exactli:2,exampl:[0,3],explain:[2,4],extra:2,fals:0,familiar:2,featur:[0,2,3,4],feature_vector:3,field:2,file:[0,1,3,4],filenam:[1,3],fill:[2,5],fill_triangl:5,fill_triangle_src_dst:5,find:[2,4],first:[0,2,4],flag:0,flatten:[0,1,2,3,4],flatten_feature_vector:3,folder:[],follow:[0,2,3],form:3,from:[0,1,2,3],full:1,fullpath:1,generate_call_graph:[],get:[0,1],get_bounding_box:0,get_imag:1,get_image_with_point:0,get_imm_image_with_landmark:1,get_imm_point:1,get_mean:0,get_pixel_valu:0,get_point:[0,1],get_scaled_point:0,get_triangl:0,given:[0,2,3,4,5],good:[],great:[],hand:2,have:[2,3],heidelberg:2,height:[0,4],help:2,hold:4,home:4,how:2,html:[2,4],http:[2,4],idea:[],imag:[0,1,2,4],image_1:1,imm:[1,2,4],imm_dataset:[2,4],imm_point:4,immpoint:[1,4],implement:[2,4],import_dataset_modul:[],import_fil:1,improv:2,include:[],independ:4,index:2,inform:[1,2],input:[0,3],insid:[3,5],instanc:2,int32:0,itself:2,jpg:[1,4],june:2,keep:[0,4],keyerror:[],know:2,kwarg:[],lambda_1:5,lambda_2:5,lambda_3:5,landmar:4,landmark:[0,1,2],last:2,later:[],learn:2,licens:2,like:[0,2],list:[0,1],load:[1,3],locat:[0,1,2,4],look:2,mai:2,main:[],make:2,matrix:[0,3],matter:2,mean:[0,2,3,4],mean_point:0,mean_valu:[0,3],method:[2,3],might:[],miss:2,model:[2,3,4],model_fil:3,model_file_textur:[],model_shape_fil:[],modul:[],more:[1,2,4],most:2,motiv:2,move:[],multipli:[],must:2,n_compon:3,n_featur:3,name:[2,4],ndarrai:[0,1,3,4,5],ndim:0,need:[0,2,4],nois:2,none:[0,1,3],normal:[0,4],normalized_flattened_points_list:0,nose:2,note:[2,4],number:2,numpi:[0,1,3],object:0,observ:0,off:2,one:3,open:2,opencv:0,option:[],order:2,our:[2,4],out:0,outlier:2,output:3,outsid:5,page:2,particular:2,path:1,pca:[],pcamodel:3,perform:[0,3],person:2,pictur:2,pixel:[0,2],point:[0,1,4,5],points2d_dst:0,points2d_src:0,points_list:[0,1],possibl:4,predict:2,prerequisit:2,prevent:2,previous:2,principl:2,problem:2,python:[],rais:[],read:[1,4],realli:[],reason:2,reconstruct_textur:4,recontruct:4,rectangl:0,reduc:3,reduct:2,region:2,rel:[1,2],remov:2,requir:2,rescal:0,right:[],same:2,sample_from_triangl:0,save:3,save_pca_model_shap:[],save_pca_model_textur:[],scale:0,search:2,see:[1,3],sensit:2,sentenc:2,separ:4,seper:0,seri:2,seriou:2,set:2,shape:[0,2,3,4],shape_typ:[],should:[0,2,3],show:1,show_on_imag:[],shown:1,simpl:2,singl:4,singlar:3,singular:3,small:0,solv:2,some:2,someth:1,somewhat:2,sourc:[0,1,3,4],span:[4,5],springer:2,src:[0,4],src_imag:4,src_point:4,start:2,state:1,statist:2,still:2,store:[0,1,3],str:[],string:[],structur:0,subject:2,success:[],support:[0,2],sure:2,svd:3,taylor:2,text:2,textur:[],texture_model:4,them:0,thi:[0,1,2,3,4],those:2,three:[4,5],todo:4,togeth:0,train:2,transpar:0,tri:0,triangl:[0,2,3,4,5],triangul:0,tupl:[0,3],two:[0,3],type:[],uint8:0,ultim:2,uniqu:2,usag:2,use:0,user:1,usvtm:3,valu:[0,2,3],variance_percentag:3,vector:[0,2,3,4],veri:2,vision:2,vtm:3,wai:3,weight:[4,5],what:2,where:4,which:[1,2],who:2,width:[0,4],width_height_dimens:0,window_nam:1,within:0,without:2,work:0,would:2,www:[2,4],x_0_0:0,x_0_k:0,x_0_m:0,x_1:[2,5],x_1_0:0,x_1_k:0,x_1_m:0,x_2:[2,5],x_2_0:0,x_2_k:0,x_2_m:0,x_3:5,x_3_0:0,x_3_k:0,x_3_m:0,x_4_0:0,x_mean_0:0,x_mean_n:0,x_n:2,x_n_k:0,x_n_m:0,x_vector:0,y_0_0:0,y_0_k:0,y_0_m:0,y_1:[2,5],y_1_0:0,y_1_k:0,y_1_m:0,y_2:[2,5],y_2_0:0,y_2_k:0,y_2_m:0,y_3:5,y_3_0:0,y_3_k:0,y_3_m:0,y_4_0:0,y_mean_0:0,y_mean_n:0,y_n:2,y_n_k:0,y_n_m:0,y_vector:0,you:[]},titles:["AAM Module","Datasets","Welcome to 3D Face Reconstruction&#8217;s documentation!","PCA Module","Reconstruction Module","Texture Module"],titleterms:{aam:0,dataset:1,document:2,face:2,indice:2,link:2,modul:[0,3,4,5],pca:[2,3],progress:2,reconstruct:[2,4],refer:2,tabl:2,textur:5,welcom:2,work:2}})
\ No newline at end of file
src/docs/make.bat 0 → 100644
View file @ beedf9a5
@ECHO OFF
REM Command file for Sphinx documentation
if "%SPHINXBUILD%" == "" (
set SPHINXBUILD=sphinx-build
)
set BUILDDIR=build
set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% source
set I18NSPHINXOPTS=%SPHINXOPTS% source
if NOT "%PAPER%" == "" (
set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS%
set I18NSPHINXOPTS=-D latex_paper_size=%PAPER% %I18NSPHINXOPTS%
)
if "%1" == "" goto help
if "%1" == "help" (
:help
echo.Please use `make ^<target^>` where ^<target^> is one of
echo. html to make standalone HTML files
echo. dirhtml to make HTML files named index.html in directories
echo. singlehtml to make a single large HTML file
echo. pickle to make pickle files
echo. json to make JSON files
echo. htmlhelp to make HTML files and a HTML help project
echo. qthelp to make HTML files and a qthelp project
echo. devhelp to make HTML files and a Devhelp project
echo. epub to make an epub
echo. epub3 to make an epub3
echo. latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter
echo. text to make text files
echo. man to make manual pages
echo. texinfo to make Texinfo files
echo. gettext to make PO message catalogs
echo. changes to make an overview over all changed/added/deprecated items
echo. xml to make Docutils-native XML files
echo. pseudoxml to make pseudoxml-XML files for display purposes
echo. linkcheck to check all external links for integrity
echo. doctest to run all doctests embedded in the documentation if enabled
echo. coverage to run coverage check of the documentation if enabled
echo. dummy to check syntax errors of document sources
goto end
)
if "%1" == "clean" (
for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i
del /q /s %BUILDDIR%\*
goto end
)
REM Check if sphinx-build is available and fallback to Python version if any
%SPHINXBUILD% 1>NUL 2>NUL
if errorlevel 9009 goto sphinx_python
goto sphinx_ok
:sphinx_python
set SPHINXBUILD=python -m sphinx.__init__
%SPHINXBUILD% 2> nul
if errorlevel 9009 (
echo.
echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
echo.installed, then set the SPHINXBUILD environment variable to point
echo.to the full path of the 'sphinx-build' executable. Alternatively you
echo.may add the Sphinx directory to PATH.
echo.
echo.If you don't have Sphinx installed, grab it from
echo.http://sphinx-doc.org/
exit /b 1
)
:sphinx_ok
if "%1" == "html" (
%SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html
if errorlevel 1 exit /b 1
echo.
echo.Build finished. The HTML pages are in %BUILDDIR%/html.
goto end
)
if "%1" == "dirhtml" (
%SPHINXBUILD% -b dirhtml %ALLSPHINXOPTS% %BUILDDIR%/dirhtml
if errorlevel 1 exit /b 1
echo.
echo.Build finished. The HTML pages are in %BUILDDIR%/dirhtml.
goto end
)
if "%1" == "singlehtml" (
%SPHINXBUILD% -b singlehtml %ALLSPHINXOPTS% %BUILDDIR%/singlehtml
if errorlevel 1 exit /b 1
echo.
echo.Build finished. The HTML pages are in %BUILDDIR%/singlehtml.
goto end
)
if "%1" == "pickle" (
%SPHINXBUILD% -b pickle %ALLSPHINXOPTS% %BUILDDIR%/pickle
if errorlevel 1 exit /b 1
echo.
echo.Build finished; now you can process the pickle files.
goto end
)
if "%1" == "json" (
%SPHINXBUILD% -b json %ALLSPHINXOPTS% %BUILDDIR%/json
if errorlevel 1 exit /b 1
echo.
echo.Build finished; now you can process the JSON files.
goto end
)
if "%1" == "htmlhelp" (
%SPHINXBUILD% -b htmlhelp %ALLSPHINXOPTS% %BUILDDIR%/htmlhelp
if errorlevel 1 exit /b 1
echo.
echo.Build finished; now you can run HTML Help Workshop with the ^
.hhp project file in %BUILDDIR%/htmlhelp.
goto end
)
if "%1" == "qthelp" (
%SPHINXBUILD% -b qthelp %ALLSPHINXOPTS% %BUILDDIR%/qthelp
if errorlevel 1 exit /b 1
echo.
echo.Build finished; now you can run "qcollectiongenerator" with the ^
.qhcp project file in %BUILDDIR%/qthelp, like this:
echo.^> qcollectiongenerator %BUILDDIR%\qthelp\3DFaceReconstruction.qhcp
echo.To view the help file:
echo.^> assistant -collectionFile %BUILDDIR%\qthelp\3DFaceReconstruction.ghc
goto end
)
if "%1" == "devhelp" (
%SPHINXBUILD% -b devhelp %ALLSPHINXOPTS% %BUILDDIR%/devhelp
if errorlevel 1 exit /b 1
echo.
echo.Build finished.
goto end
)
if "%1" == "epub" (
%SPHINXBUILD% -b epub %ALLSPHINXOPTS% %BUILDDIR%/epub
if errorlevel 1 exit /b 1
echo.
echo.Build finished. The epub file is in %BUILDDIR%/epub.
goto end
)
if "%1" == "epub3" (
%SPHINXBUILD% -b epub3 %ALLSPHINXOPTS% %BUILDDIR%/epub3
if errorlevel 1 exit /b 1
echo.
echo.Build finished. The epub3 file is in %BUILDDIR%/epub3.
goto end
)
if "%1" == "latex" (
%SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex
if errorlevel 1 exit /b 1
echo.
echo.Build finished; the LaTeX files are in %BUILDDIR%/latex.
goto end
)
if "%1" == "latexpdf" (
%SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex
cd %BUILDDIR%/latex
make all-pdf
cd %~dp0
echo.
echo.Build finished; the PDF files are in %BUILDDIR%/latex.
goto end
)
if "%1" == "latexpdfja" (
%SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex
cd %BUILDDIR%/latex
make all-pdf-ja
cd %~dp0
echo.
echo.Build finished; the PDF files are in %BUILDDIR%/latex.
goto end
)
if "%1" == "text" (
%SPHINXBUILD% -b text %ALLSPHINXOPTS% %BUILDDIR%/text
if errorlevel 1 exit /b 1
echo.
echo.Build finished. The text files are in %BUILDDIR%/text.
goto end
)
if "%1" == "man" (
%SPHINXBUILD% -b man %ALLSPHINXOPTS% %BUILDDIR%/man
if errorlevel 1 exit /b 1
echo.
echo.Build finished. The manual pages are in %BUILDDIR%/man.
goto end
)
if "%1" == "texinfo" (
%SPHINXBUILD% -b texinfo %ALLSPHINXOPTS% %BUILDDIR%/texinfo
if errorlevel 1 exit /b 1
echo.
echo.Build finished. The Texinfo files are in %BUILDDIR%/texinfo.
goto end
)
if "%1" == "gettext" (
%SPHINXBUILD% -b gettext %I18NSPHINXOPTS% %BUILDDIR%/locale
if errorlevel 1 exit /b 1
echo.
echo.Build finished. The message catalogs are in %BUILDDIR%/locale.
goto end
)
if "%1" == "changes" (
%SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes
if errorlevel 1 exit /b 1
echo.
echo.The overview file is in %BUILDDIR%/changes.
goto end
)
if "%1" == "linkcheck" (
%SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck
if errorlevel 1 exit /b 1
echo.
echo.Link check complete; look for any errors in the above output ^
or in %BUILDDIR%/linkcheck/output.txt.
goto end
)
if "%1" == "doctest" (
%SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest
if errorlevel 1 exit /b 1
echo.
echo.Testing of doctests in the sources finished, look at the ^
results in %BUILDDIR%/doctest/output.txt.
goto end
)
if "%1" == "coverage" (
%SPHINXBUILD% -b coverage %ALLSPHINXOPTS% %BUILDDIR%/coverage
if errorlevel 1 exit /b 1
echo.
echo.Testing of coverage in the sources finished, look at the ^
results in %BUILDDIR%/coverage/python.txt.
goto end
)
if "%1" == "xml" (
%SPHINXBUILD% -b xml %ALLSPHINXOPTS% %BUILDDIR%/xml
if errorlevel 1 exit /b 1
echo.
echo.Build finished. The XML files are in %BUILDDIR%/xml.
goto end
)
if "%1" == "pseudoxml" (
%SPHINXBUILD% -b pseudoxml %ALLSPHINXOPTS% %BUILDDIR%/pseudoxml
if errorlevel 1 exit /b 1
echo.
echo.Build finished. The pseudo-XML files are in %BUILDDIR%/pseudoxml.
goto end
)
if "%1" == "dummy" (
%SPHINXBUILD% -b dummy %ALLSPHINXOPTS% %BUILDDIR%/dummy
if errorlevel 1 exit /b 1
echo.
echo.Build finished. Dummy builder generates no files.
goto end
)
:end
src/docs/source/aam.rst 0 → 100644
View file @ beedf9a5
AAM Module
==========
.. automodule:: aam
:members:
src/docs/source/conf.py 0 → 100644
View file @ beedf9a5
# -*- coding: utf-8 -*-
#
# 3D Face Reconstruction documentation build configuration file, created by
# sphinx-quickstart on Mon Aug 1 16:41:23 2016.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#
import os
import sys
sys.path.insert(0, os.path.abspath('../../'))
# -- General configuration ------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
#
# needs_sphinx = '1.0'
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
'sphinx.ext.autodoc',
'sphinx.ext.doctest',
'sphinx.ext.intersphinx',
'sphinx.ext.todo',
'sphinx.ext.coverage',
'sphinx.ext.mathjax',
'sphinx.ext.viewcode',
'sphinx.ext.githubpages',
]
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# The suffix(es) of source filenames.
# You can specify multiple suffix as a list of string:
#
# source_suffix = ['.rst', '.md']
source_suffix = '.rst'
# The encoding of source files.
#
# source_encoding = 'utf-8-sig'
# The master toctree document.
master_doc = 'index'
# General information about the project.
project = u'3D Face Reconstruction'
copyright = u'2016, Richard Torenvliet'
author = u'Richard Torenvliet'
# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# The short X.Y version.
version = u'0.1'
# The full version, including alpha/beta/rc tags.
release = u'0.1'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#
# This is also used if you do content translation via gettext catalogs.
# Usually you set "language" from the command line for these cases.
language = None
# There are two options for replacing |today|: either, you set today to some
# non-false value, then it is used:
#
# today = ''
#
# Else, today_fmt is used as the format for a strftime call.
#
# today_fmt = '%B %d, %Y'
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This patterns also effect to html_static_path and html_extra_path
exclude_patterns = []
# The reST default role (used for this markup: `text`) to use for all
# documents.
#
# default_role = None
# If true, '()' will be appended to :func: etc. cross-reference text.
#
# add_function_parentheses = True
# If true, the current module name will be prepended to all description
# unit titles (such as .. function::).
#
# add_module_names = True
# If true, sectionauthor and moduleauthor directives will be shown in the
# output. They are ignored by default.
#
# show_authors = False
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'
# A list of ignored prefixes for module index sorting.
# modindex_common_prefix = []
# If true, keep warnings as "system message" paragraphs in the built documents.
# keep_warnings = False
# If true, `todo` and `todoList` produce output, else they produce nothing.
todo_include_todos = True
# -- Options for HTML output ----------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
html_theme = 'sphinx_rtd_theme'
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
#
# html_theme_options = {}
# Add any paths that contain custom themes here, relative to this directory.
# html_theme_path = []
# The name for this set of Sphinx documents.
# "<project> v<release> documentation" by default.
#
# html_title = u'3D Face Reconstruction v0.1'
# A shorter title for the navigation bar. Default is the same as html_title.
#
# html_short_title = None
# The name of an image file (relative to this directory) to place at the top
# of the sidebar.
#
# html_logo = None
# The name of an image file (relative to this directory) to use as a favicon of
# the docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
# pixels large.
#
# html_favicon = None
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']
# Add any extra paths that contain custom files (such as robots.txt or
# .htaccess) here, relative to this directory. These files are copied
# directly to the root of the documentation.
#
# html_extra_path = []
# If not None, a 'Last updated on:' timestamp is inserted at every page
# bottom, using the given strftime format.
# The empty string is equivalent to '%b %d, %Y'.
#
# html_last_updated_fmt = None
# If true, SmartyPants will be used to convert quotes and dashes to
# typographically correct entities.
#
# html_use_smartypants = True
# Custom sidebar templates, maps document names to template names.
#
# html_sidebars = {}
# Additional templates that should be rendered to pages, maps page names to
# template names.
#
# html_additional_pages = {}
# If false, no module index is generated.
#
# html_domain_indices = True
# If false, no index is generated.
#
# html_use_index = True
# If true, the index is split into individual pages for each letter.
#
# html_split_index = False
# If true, links to the reST sources are added to the pages.
#
# html_show_sourcelink = True
# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
#
# html_show_sphinx = True
# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
#
# html_show_copyright = True
# If true, an OpenSearch description file will be output, and all pages will
# contain a <link> tag referring to it. The value of this option must be the
# base URL from which the finished HTML is served.
#
# html_use_opensearch = ''
# This is the file name suffix for HTML files (e.g. ".xhtml").
# html_file_suffix = None
# Language to be used for generating the HTML full-text search index.
# Sphinx supports the following languages:
# 'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja'
# 'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr', 'zh'
#
# html_search_language = 'en'
# A dictionary with options for the search language support, empty by default.
# 'ja' uses this config value.
# 'zh' user can custom change `jieba` dictionary path.
#
# html_search_options = {'type': 'default'}
# The name of a javascript file (relative to the configuration directory) that
# implements a search results scorer. If empty, the default will be used.
#
# html_search_scorer = 'scorer.js'
# Output file base name for HTML help builder.
htmlhelp_basename = '3DFaceReconstructiondoc'
# -- Options for LaTeX output ---------------------------------------------
latex_elements = {
# The paper size ('letterpaper' or 'a4paper').
#
# 'papersize': 'letterpaper',
# The font size ('10pt', '11pt' or '12pt').
#
# 'pointsize': '10pt',
# Additional stuff for the LaTeX preamble.
#
# 'preamble': '',
# Latex figure (float) alignment
#
# 'figure_align': 'htbp',
}
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title,
# author, documentclass [howto, manual, or own class]).
latex_documents = [
(master_doc, '3DFaceReconstruction.tex', u'3D Face Reconstruction Documentation',
u'Richard Torenvliet', 'manual'),
]
# The name of an image file (relative to this directory) to place at the top of
# the title page.
#
# latex_logo = None
# For "manual" documents, if this is true, then toplevel headings are parts,
# not chapters.
#
# latex_use_parts = False
# If true, show page references after internal links.
#
# latex_show_pagerefs = False
# If true, show URL addresses after external links.
#
# latex_show_urls = False
# Documents to append as an appendix to all manuals.
#
# latex_appendices = []
# It false, will not define \strong, \code, itleref, \crossref ... but only
# \sphinxstrong, ..., \sphinxtitleref, ... To help avoid clash with user added
# packages.
#
# latex_keep_old_macro_names = True
# If false, no module index is generated.
#
# latex_domain_indices = True
# -- Options for manual page output ---------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
(master_doc, '3dfacereconstruction', u'3D Face Reconstruction Documentation',
[author], 1)
]
# If true, show URL addresses after external links.
#
# man_show_urls = False
# -- Options for Texinfo output -------------------------------------------
# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
(master_doc, '3DFaceReconstruction', u'3D Face Reconstruction Documentation',
author, '3DFaceReconstruction', 'One line description of project.',
'Miscellaneous'),
]
# Documents to append as an appendix to all manuals.
#
# texinfo_appendices = []
# If false, no module index is generated.
#
# texinfo_domain_indices = True
# How to display URL addresses: 'footnote', 'no', or 'inline'.
#
# texinfo_show_urls = 'footnote'
# If true, do not generate a @detailmenu in the "Top" node's menu.
#
# texinfo_no_detailmenu = False
# Example configuration for intersphinx: refer to the Python standard library.
intersphinx_mapping = {'https://docs.python.org/': None}
src/docs/source/datasets.rst 0 → 100644
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Datasets
========
.. automodule:: datasets.imm
:members:
src/docs/source/index.rst 0 → 100644
View file @ beedf9a5
.. 3D Face Reconstruction documentation master file, created by
sphinx-quickstart on Mon Aug 1 16:41:23 2016.
You can adapt this file completely to your liking, but it should at least
contain the root `toctree` directive.
Welcome to 3D Face Reconstruction's documentation!
==================================================
.. toctree::
:maxdepth: 2
:caption: Table of Contents
:name: mastertoc
datasets
aam
pca
reconstruction/reconstruction
reconstruction/texture
!!!Work in progress!!!
======================
PCA reconstruction
==================
Principle Component Analysis is one of the most used methods in the field of statistics, it is used for dimension reduction of data and is capable of removing outliers which ultimately improves learning algorithms. In this case we use PCA for both shape and texture reconstruction. Given an image of person's face we would be able to reconstruct it using a PCA Model. The motivation for using PCA is that we can fill in missing data and remove outliers given one image of person. If for some reason the image is very cluttered, we would still be able to 'predict' how this person would look like, given all the faces we have used to train the PCA Model.
For the PCA reconstruction method has a couple of prerequisites are required. First off, the PCA Model itself. For those who are familiar with PCA know that we need to have a flattened feature vector. Both the dimensions and the content of this feature vector may be arbitrary, but have to be exactly the same from subject to subject, (i.e., there can be no difference in the number of annotated landmarks or order, landmark 1 in subject A, is landmark 1 in subject B). In this case we use it for the shape and texture. The shape feature vector contains the following data:
```
[[x_1, y_1], [x_2, y_2], ..., [x_n, y_n]] -> (flattened) [x_1, y_1, x_2, y_2, x_n, y_n]
```
The x,y values are the location of landmarks in an image. Such a cluster of annotated locations in an image construct a shape we call Active Appearance Model(AAM)[1]. For a serie of annotated pictures with landmark location we can build mean AAM. For this particular implementation we started with supporting the Imm Dataset[^imm_dataset], for the simple reason that it is open for usage without any license agreement before hand (make sure we are correct about this). This is what we call the mean face, which is very important for the construction of the PCA Model, any PCA Model for that matter.
The texture PCA data is somewhat more difficult and depends on a given shape. In our case this given shape is the mean AAM that we have built previously. We need to add extra information to this AAM mean shape, namely a unique set of triangles that can be constructed from the set of landmarks. For this we use the Delaunay algorithm which does exactly this. The triangles help us find corresponding pixels in shape A and B. This solves the problem of pixel correspondences and is important for constructing a mean texture for the reasons explained previously about how a feature vector should look like. Pixel 1 in triangle 1 in subject A needs to correspond to exactly the same pixel (relatively) to pixel 1 in triangle 1 in subject B. This of course is sensitive to noise, but the pixels in the nose region must correspond from subject to subject, this prevents that we reconstruct an eye with a nose for instance (Note: remove this last sentence in a serious text).
References
==========
[1]: Cootes, T. F., Edwards, G. J., & Taylor, C. J. (1998, June). Active appearance models. In European conference on computer vision (pp. 484-498). Springer Berlin Heidelberg.
Links
=====
[^imm_dataset]: http://www.imm.dtu.dk/~aam/datasets/datasets.html "Imm dataset"
Indices and tables
==================
* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`
src/docs/source/pca.rst 0 → 100644
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PCA Module
==========
.. automodule:: pca
:members:
src/docs/source/reconstruction/reconstruction.rst 0 → 100644
View file @ beedf9a5
Reconstruction Module
=====================
As explained in [PCA Reconstruction](home) we need a flattened feature vector to able to build a PCA Model. This holds for both shape and texture model. Currently we implement the independent AAM model where we keep the feature vector separate. Note that we could also choose to combine the shape and appearance in a single flattened feature vector (TODO: elaborate our choice more about this, if possible).
We use the imm dataset[^imm_dataset] for this. We first need to build the mean shape of the all the images. The dataset has a .asf file and an equally named .jpg file. The .asf file contains the locations of the landmars (normalized by the width and height of the image). In `src/imm_points.py` we find the ImmPoints class that implements all functions needed to read this file.
[^imm_dataset]: http://www.imm.dtu.dk/~aam/datasets/datasets.html "Imm dataset"
.. automodule:: reconstruction.reconstruction
:members:
src/docs/source/reconstruction/texture.rst 0 → 100644
View file @ beedf9a5
Texture Module
==============
.. automodule:: reconstruction.texture
:members:
src/main.py
View file @ beedf9a5
......@@ -3,6 +3,7 @@
import argparse
import logging
import sys
import importlib
# installed packages
import cv2
......@@ -21,7 +22,7 @@ logger = logging.getLogger(__name__)
def add_parser_options():
parser = argparse.ArgumentParser(description='IMMPoints tool')
pca_group = parser.add_argument_group('show_pca')
pca_group = parser.add_argument_group('show_reconstruction')
pca_group.add_argument(
'--reconstruct', action='store_true',
......@@ -48,11 +49,6 @@ def add_parser_options():
help='save the pca texture model'
)
pca_group.add_argument(
'--show_pca', action='store_true',
help='Show and manipulate the saved PCA model'
)
pca_group.add_argument(
'--files', nargs='+', help='files to process'
)
......@@ -80,6 +76,18 @@ def add_parser_options():
return parser
def import_dataset_module(shape_type):
"""
Includes the right implementation for the right dataset implementation for
the given shape type, see --help for the available options.
Args:
shape_type(string): Name of the python file inside the
`src/datasets` folder.
"""
return importlib.import_module('datasets.{}'.format(shape_type))
def save_pca_model_texture(args):
"""
save the U, s, Vt and mean of all the asf datafiles given by the asf
......@@ -99,17 +107,15 @@ def save_pca_model_texture(args):
"""
assert args.files, '--files should be given'
assert args.model_shape_file, '--model_texture_file needs to be provided to save the pca model'
assert args.model_texture_file, '--model_texture_file needs to be provided to save the pca model'
assert args.shape_type, '--shape_type the type of dataset, see datasets module'
dataset_module = import_dataset_module(args.shape_type)
shape_model = pca.PcaModel(args.model_shape_file)
from datasets import imm
mean_points = imm.IMMPoints(points_list=shape_model.mean_values)
mean_points = dataset_module.IMMPoints(points_list=shape_model.mean_values)
textures = aam.build_texture_feature_vectors(
args.files,
imm.get_imm_image_with_landmarks,
dataset_module.get_imm_image_with_landmarks, # function
mean_points,
shape_model.triangles
)
......@@ -141,10 +147,12 @@ def save_pca_model_shape(args):
"""
assert args.files, '--files should be given'
assert args.model_shape_file, '--model_shape_file needs to be provided to save the pca model'
from datasets import imm
assert args.shape_type, '--shape_type the type of dataset, see datasets module'
dataset_module = import_dataset_module(args.shape_type)
points = aam.build_shape_feature_vectors(
args.files, imm.get_imm_points, flattened=True
args.files, dataset_module.get_imm_points, flattened=True
)
mean_values = aam.get_mean(points)
......@@ -161,6 +169,9 @@ def save_pca_model_shape(args):
def reconstruct_with_model(args):
assert args.files, '--files should be given to allow the image to be shown'
assert args.model_shape_file, '--model_shape_file needs to be provided to get the pca model'
assert args.shape_type, '--shape_type the type of dataset, see datasets module'
dataset_module = import_dataset_module(args.shape_type)
# clear sys args. arguments are conflicting with parseargs
# kivy will parse args upon import and will crash if it finds our
......@@ -188,44 +199,13 @@ def reconstruct_with_model(args):
app.run()
def show_pca_model(args):
assert args.model_shape_file, '--model_texture_file needs to be provided to save the pca model'
assert args.model_texture_file, '--model_texture_file needs to be provided to save the pca model'
from reconstruction.triangles import draw_shape, get_texture
Vt_shape, s, n_shape_components, mean_value_points, triangles = pca.load(args.model_shape_file)
Vt_texture, s_texture, n_texture_components, mean_values_texture, _ = pca.load(args.model_texture_file)
imm_points = imm.IMMPoints(filename='data/imm_face_db/40-1m.asf')
input_image = imm_points.get_image()
input_points = imm_points.get_points()
h, w, c = input_image.shape
input_points[:, 0] = input_points[:, 0] * w
input_points[:, 1] = input_points[:, 1] * h
mean_value_points = mean_value_points.reshape((58, 2))
mean_value_points[:, 0] = mean_value_points[:, 0] * w
mean_value_points[:, 1] = mean_value_points[:, 1] * h
while True:
dst = get_texture(mean_value_points, mean_values_texture)
cv2.imshow('input_image', input_image)
cv2.imshow('image', dst)
k = cv2.waitKey(0) & 0xFF
if k == 27:
break
cv2.destroyAllWindows()
def generate_call_graph(args):
"""Performance debug function, will be (re)moved later. """
assert args.model_shape_file, '--model_texture_file needs to be provided to save the pca model'
assert args.model_texture_file, '--model_texture_file needs to be provided to save the pca model'
assert args.shape_type, '--shape_type the type of dataset, see datasets module'
dataset_module = import_dataset_module(args.shape_type)
from pycallgraph import PyCallGraph
from pycallgraph.output import GraphvizOutput
......@@ -236,10 +216,10 @@ def generate_call_graph(args):
shape_model = pca.PcaModel(args.model_shape_file)
texture_model = pca.PcaModel(args.model_texture_file)
input_points = imm.IMMPoints(filename='data/imm_face_db/40-3m.asf')
input_points = dataset_module.IMMPoints(filename='data/imm_face_db/40-3m.asf')
input_image = input_points.get_image()
mean_points = imm.IMMPoints(points_list=shape_model.mean_values)
mean_points = dataset_module.IMMPoints(points_list=shape_model.mean_values)
mean_points.get_scaled_points(input_image.shape)
reconstruction.reconstruct_texture(
......@@ -250,21 +230,23 @@ def generate_call_graph(args):
mean_points, # shape points mean
)
def show_reconstruction(args):
assert args.model_shape_file, '--model_texture_file needs to be provided to save the pca model'
assert args.model_texture_file, '--model_texture_file needs to be provided to save the pca model'
assert args.shape_type, '--shape_type the type of dataset, see datasets module'
dataset_module = import_dataset_module(args.shape_type)
# Vt_shape, s, n_shape_components, mean_value_points, triangles = pca.load(args.model_shape_file)
# Vt_texture, s_texture, n_texture_components, mean_values_texture, _ = pca.load(args.model_texture_file)
from datasets import imm
shape_model = pca.PcaModel(args.model_shape_file)
texture_model = pca.PcaModel(args.model_texture_file)
input_points = imm.IMMPoints(filename='data/imm_face_db/40-3m.asf')
input_points = dataset_module.IMMPoints(
filename='data/imm_face_db/40-3m.asf'
)
input_image = input_points.get_image()
mean_points = imm.IMMPoints(points_list=shape_model.mean_values)
mean_points = dataset_module.IMMPoints(points_list=shape_model.mean_values)
mean_points.get_scaled_points(input_image.shape)
while True:
......@@ -297,9 +279,7 @@ def main():
parser = add_parser_options()
args = parser.parse_args()
if args.show_pca:
show_pca_model(args)
elif args.save_pca_shape:
if args.save_pca_shape:
save_pca_model_shape(args)
elif args.save_pca_texture:
save_pca_model_texture(args)
......
src/pca.py
View file @ beedf9a5
......@@ -2,8 +2,10 @@ import numpy as np
class PcaModel:
"""Abstraction for a pca model"""
def __init__(self, model_file):
"""
Abstraction for a pca model file. The pca model is stored in a numpy file
using numpy.save. The following information is stored:
Vtm = np.load(model_file)
self.Vt = Vtm[0]
self.s = Vtm[1]
......@@ -12,6 +14,84 @@ class PcaModel:
self.triangles = Vtm[4]
Examples:
pca = PcaModel(path_to_numpy_model_file)
"""
def __init__(self, filename=None):
self.filename = filename
if filename:
self.load()
def save(self):
"""
Store the information inside this PCA Model instance in a numpy file.
Args:
Vt (numpy ndarray): Two dimensional array with dimensions
s (numpy ndarray): The singular values as a one dimensional array
n_components: number of components needed to cover .90 percent of the
variance
Examples:
It is stored in the following way:
np.load(filename, np.assary([Vt, [mean_values]])
And accessed by:
Vtm = np.load(args.model_file)
Vt = Vtm[0]
mean_values = Vtm[1][0]
triangles = Vtm[2]
"""
assert hasattr(self, 'Vt')
assert hasattr(self, 's')
assert hasattr(self, 'n_components')
assert hasattr(self, 'mean_values')
assert hasattr(self, 'triangles')
saving = np.asarray(
[
self.Vt,
self.s,
self.n_components,
[self.mean_values],
self.triangles
]
)
np.save(self.filename, saving)
def load(self):
"""
Loads the numpy file, see PcaModel whichs uses this function to load
the PCA Model data.
Returns:
(tuple): Vt, s, n_components, mean_values and triangles
Vt (numpy ndarray): Two dimensional array with dimensions
(n_features, n_features)
n_components: number of components needed to cover .90 percent of the
variance
mean_values (numpy ndarray): mean values of the features of the model,
this should have dimensions (n_featurs, )
triangles: a list of lists of indices that form a triangles in the
AAM list.
Examples:
We would advise not to use this function directly but to use the
PcaModel. See the :class:`PcaModel`
"""
pca_model = np.load(self.filename)
self.Vt = pca_model[0]
self.s = pca_model[1]
self.n_components = pca_model[2]
self.mean_values = pca_model[3][0]
self.triangles = pca_model[4]
def pca(data, mean_values, variance_percentage=90):
"""
Perform Singlar Value Decomposition
......@@ -51,7 +131,9 @@ def reconstruct(feature_vector, Vt, mean_values, n_components=None):
(n_features, n_features)
mean_values (numpy ndarray): mean values of the features of the model,
this should have dimensions (n_features, )
"""
if n_components is None:
n_components = Vt.shape[1]
......@@ -63,61 +145,41 @@ def reconstruct(feature_vector, Vt, mean_values, n_components=None):
def save(Vt, s, n_components, mean_values, triangles, filename):
"""
Store the U, s, Vt and mean of all the asf datafiles given by the asf
files.
Store the necessary information for a PCA Model in a numpy file.
It is stored in the following way:
np.load(filename, np.assary([Vt, [mean_values]])
Args:
Vt (numpy ndarray): Two dimensional array with dimensions
s (numpy ndarray): The singular values as a one dimensional array
n_components: number of components needed to cover .90 percent of the
variance
And accessed by:
Vtm = np.load(args.model_file)
Examples:
It is stored in the following way:
np.load(filename, np.assary([Vt, [mean_values]])
Vt = Vtm[0]
mean_values = Vtm[1][0]
triangles = Vtm[2]
And accessed by:
Vtm = np.load(args.model_file)
Vt = Vtm[0]
mean_values = Vtm[1][0]
triangles = Vtm[2]
"""
saving = np.asarray([Vt, s, n_components, [mean_values], triangles])
np.save(filename, saving)
def load(filename):
"""
The model stored by pca.store (see ``pca.store`` method above) is loaded as:
UsVtm = np.load(args.model_file)
Vt = Vtm[0]
mean_values = Vtm[1][0]
Returns:
(tuple): Vt, mean_values
Vt (numpy ndarray): Two dimensional array with dimensions
(n_features, n_features)
mean_values (numpy ndarray): mean values of the features of the model,
this should have dimensions (n_featurs, )
"""
# load the stored model file
Vtm = np.load(filename)
Vt = Vtm[0]
s = Vtm[1]
n_components = Vtm[2]
mean_values = Vtm[3][0]
triangles = Vtm[4]
return Vt, s, n_components, mean_values, triangles
#def load_model(filename):
# # load the stored model file
# return PcaModel(filename)
def flatten_feature_vectors(data, dim=0):
"""
Flattens the feature vectors inside a ndarray
Args:
data (numpy array): array of feature vectors
dim (int): dimension to flatten the data
Returns:
array:(numpy array): array flattened feature vectors
Example:
input:
[
......@@ -132,13 +194,6 @@ def flatten_feature_vectors(data, dim=0):
[1, 2, 3, 4, 5, 6]
]
Args:
data (numpy array): array of feature vectors
dim (int): dimension to flatten the data
return:
array: (numpy array): array flattened feature vectors
"""
flattened = []
......
src/reconstruction/texture.pyx
View file @ beedf9a5
......@@ -20,48 +20,29 @@ cdef inline float cross_product(int v1_x, int v1_y, int v2_x, int v2_y):
return (v1_x * v2_y) - (v1_y * v2_x)
def cartesian2barycentric_slow_test(int r1_x, r1_y, int r2_x, int r2_y, int r3_x, int r3_y, int r_x, int r_y):
"""
Given a triangle spanned by three cartesion points
r1, r2, r2, and point r, return the barycentric weights l1, l2, l3.
Returns:
ndarray (of dim 3) weights of the barycentric coordinates
"""
a = np.array([
[r1_x, r2_x, r3_x],
[r1_y, r2_y, r3_y],
[1, 1, 1]
])
b = np.array([r_x, r_y, 1])
return np.linalg.solve(a, b)
def cartesian2barycentric_test(
float x_1, float y_1, float x_2, float y_2, float x_3, float y_3, float x, float y):
float x1, float y1, float x2, float y2, float x3, float y3, float x, float y):
"""
lambda_1 = (y_2 - y_3)(x - x_3) + (x_3 - x_2)(y - y_3) /
(y_2-y_3)(x_1-x_3)+(x_3-x_2)(y_1-y_3)
lambda_2 = (y_3 - y_1)(x - x_3) + (x_1 - x_3)(y - y_3) /
(y_2-y_3)(x_1-x_3)+(x_3-x_2)(y_1-y_3)
lambda_3 = 1 lambda_1 - lambda_2
Returns:
ndarray (of dim 3) weights of the barycentric coordinates
"""
cdef float lambda_1 = ((y_2 - y_3) * (x - x_3) + (x_3 - x_2) * (y - y_3)) / \
((y_2 - y_3) * (x_1 - x_3) + (x_3 - x_2) * (y_1 - y_3))
cdef float lambda_2 = ((y_3 - y_1) * (x - x_3) + (x_1 - x_3) * (y - y_3)) / \
((y_2 - y_3) * (x_1 - x_3) + (x_3 - x_2) * (y_1 - y_3))
cdef c_array.array dst_loc = c_array.array('f', [0., 0., 0.])
cdef float lambda_3 = 1 - lambda_1 - lambda_2
cartesian2barycentric(
x1, y1, x2, y2, x3, y3, x, y, dst_loc
)
return [lambda_1, lambda_2, lambda_3]
return dst_loc
cdef inline cartesian2barycentric(
......
src/server.py
View file @ beedf9a5
......@@ -143,6 +143,7 @@ app = web.Application([
(r'/reconstruction[\/0-9]?', ImageWebSocketHandler),
(r'/api/v1/faces[\/0-9]?', FaceHandler),
(r'/data/(.*)', web.StaticFileHandler, {'path': '../data'}),
(r'/docs/(.*)', web.StaticFileHandler, {'path': 'docs/build/html'}),
])
......
src/datasets/aam_test.py → src/test/aam_test.py
View file @ beedf9a5
......@@ -4,9 +4,9 @@ import pytest
import aam
import pca
import imm
import datasets.imm as imm
from reconstruction import triangles as tri
from reconstruction import reconstruction
def test_build_mean_aan():
......@@ -48,33 +48,21 @@ def test_zero_mean_aan():
def test_build_texture_feature_vectors():
Vt_shape, s, n_shape_components, mean_value_points, triangles = pca.load('data/test_data/pca_shape_model.npy')
Vt_texture, s_texture, n_texture_components, mean_values_texture, _ = pca.load('data/test_data/pca_texture_model.npy')
shape_model = pca.PcaModel('data/test_data/pca_shape_model.npy')
texture_model = pca.PcaModel('data/test_data/pca_texture_model.npy')
InputPoints = imm.IMMPoints(filename='data/imm_face_db/40-3m.asf')
input_image = InputPoints.get_image()
input_points = imm.IMMPoints(filename='data/imm_face_db/40-3m.asf')
input_image = input_points.get_image()
MeanPoints = imm.IMMPoints(points_list=mean_value_points)
mean_points = MeanPoints.get_scaled_points(input_image.shape)
input_points = InputPoints.get_scaled_points(input_image.shape)
tri.reconstruct_texture(input_image, input_image, Vt_texture, input_points, mean_points,
mean_values_texture, triangles, n_texture_components)
dst = tri.get_texture(mean_points, mean_values_texture)
mean_points = imm.IMMPoints(points_list=shape_model.mean_values)
mean_points = mean_points.get_scaled_points(input_image.shape)
input_points = input_points.get_scaled_points(input_image.shape)
assert np.mean(input_points) > 1.0, 'should be greater than 1.0, because \
it array should be scaled to the image width and height'
assert np.mean(mean_points) > 1.0, 'should be greater than 1.0, because \
it array should be scaled to the image width and height'
#cv2.imshow('original', imm_points.get_image())
#cv2.imshow('reconstructed', input_image)
#cv2.imshow('main face', dst)
#cv2.waitKey(0) & 0xFF
#cv2.destroyAllWindows()
@pytest.mark.skipif(True, reason='not suitable for pytest')
def test_get_pixel_values():
......
src/reconstruction/texture_test.py → src/test/texture_test.py
View file @ beedf9a5
import cv2
import aam
import numpy as np
import pytest
from .texture import cartesian2barycentric_slow_test, cartesian2barycentric_test
from reconstruction.texture import cartesian2barycentric_test
def test_cartesian2barycentricy():
def test_cartesian2barycentric():
"""
Test if the output of the cartesian2barycentric coordinates are equal to
the expected result.
"""
blue_points = [[20, 20], [50, 160], [160, 20]]
expected = np.array([
0.5051020383834839, 0.3571428656578064, 0.13775509595870972
])
x_1 = blue_points[0][0]
y_1 = blue_points[0][1]
......@@ -19,13 +27,11 @@ def test_cartesian2barycentricy():
y = 70
lambdas_quick = cartesian2barycentric_test(x_1, y_1, x_2, y_2, x_3, y_3, x, y)
lambdas_quick = np.asarray(lambdas_quick)
lambdas_slow = cartesian2barycentric_slow_test(x_1, y_1, x_2, y_2, x_3, y_3, x, y)
np.testing.assert_array_equal(lambdas_quick, lambdas_slow)
np.testing.assert_array_equal(lambdas_quick, expected)
@pytest.mark.skipif(True, reason='not suitable for pytest')
def test_sample_from_triangles():
blue_points = [[20, 20], [50, 160], [160, 20],
[50, 20], [60, 200], [180, 20]]
......
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