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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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],
"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()
......
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<title>Overview: module code &mdash; 3D Face Reconstruction 0.1 documentation</title>
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<ul>
<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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<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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AAM Module
==========
.. automodule:: aam
:members:
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Datasets
========
.. automodule:: datasets.imm
:members:
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.. 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`
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PCA Module
==========
.. automodule:: pca
:members:
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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:
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Texture Module
==============
.. automodule:: reconstruction.texture
:members:
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