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py-3d-face-reconstruction
Commit 3c33b0b3 authored by Richard Torenvliet's avatar Richard Torenvliet
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Fix building and 'reconstructing' mean texture from dataset

parent e007d4c1
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Showing with 203 additions and 106 deletions
src/aam.py
View file @ 3c33b0b3
......@@ -5,7 +5,8 @@ import cv2
# local imports
import pca
from utils.generate_head_texture import fill_triangle, get_colors_triangle
from utils.generate_head_texture import fill_triangle, get_colors_triangle, \
get_row_colors_triangle
import utils.triangles as tu
logging.basicConfig(level=logging.INFO,
......@@ -66,47 +67,40 @@ def build_shape_feature_vectors(files, get_points, flattened=False):
points = get_points(files)
if flattened:
points = pca.flatten_feature_vectors(points)
points = pca.flatten_feature_vectors(points, dim=0)
return points
def sample_from_triangles(image, points2d, triangles, n_samples=80):
all_triangles = []
h, w, c = image.shape
def sample_from_triangles(src, points2d_src, points2d_dst, triangles):
# texture = np.asarray(texture, dtype=np.uint8).reshape((-1, 3))
triangles_pixels = []
pixels = 0
for tri in triangles:
p1 = points2d[tri[0]]
p2 = points2d[tri[1]]
p3 = points2d[tri[2]]
bary_centric_range = np.linspace(0, 1, num=n_samples)
pixels = np.full((n_samples * n_samples, 3), fill_value=-1, dtype=np.int)
L = np.zeros((3, 1))
for s_i, s in enumerate(bary_centric_range):
for t_i, t in enumerate(bary_centric_range):
# make sure the coordinates are inside the triangle
if s + t <= 1:
# build lambda's
L[0] = s
L[1] = t
L[2] = 1 - s - t
src_p1, src_p2, src_p3 = points2d_src[tri]
dst_p1, dst_p2, dst_p3 = points2d_dst[tri]
dst = get_row_colors_triangle(
src,
src_p1[0], src_p1[1],
src_p2[0], src_p2[1],
src_p3[0], src_p3[1],
dst_p1[0], dst_p1[1],
dst_p2[0], dst_p2[1],
dst_p3[0], dst_p3[1]
)
# cartesian x, y coordinates inside the triangle
cart_x, cart_y, _ = tu.barycentric2cartesian(p1, p2, p3, L)
pixels[s_i * n_samples + t_i, :] = image[cart_y, cart_x, :]
pixels += dst.flatten().shape[0]
# cv2.circle(b, tuple([cart_x, cart_y]), 1, color=(0, 255, 100))
triangles_pixels.extend(dst.flatten())
all_triangles.append(pixels[np.where(pixels >= 0)])
result = np.asarray(triangles_pixels, dtype=np.uint8)
return np.asarray(all_triangles, dtype=np.uint8)
return result
def build_texture_feature_vectors(files, get_image_with_landmarks, triangles,
flattened=True):
mean_texture = []
def build_texture_feature_vectors(files, get_image_with_shape, mean_shape, triangles):
"""
Args:
files (list): list files
......@@ -116,16 +110,22 @@ def build_texture_feature_vectors(files, get_image_with_landmarks, triangles,
Returns:
list: list of feature vectors
"""
mean_texture = []
for i, f in enumerate(files[:1]):
image, landmarks = get_image_with_landmarks(f)
mean_shape_scaled = mean_shape.reshape((58, 2))
mean_shape_scaled[:, 0] = mean_shape_scaled[:, 0] * 640
mean_shape_scaled[:, 1] = mean_shape_scaled[:, 1] * 480
for i, f in enumerate(files):
image, shape = get_image_with_shape(f)
h, w, c = image.shape
landmarks[:, 0] = landmarks[:, 0] * w
landmarks[:, 1] = landmarks[:, 1] * h
shape[:, 0] = shape[:, 0] * w
shape[:, 1] = shape[:, 1] * h
triangles_colors = sample_from_triangles(
image, landmarks, triangles, n_samples=80
)
image, shape, mean_shape_scaled, triangles
)
mean_texture.append(triangles_colors)
logger.info('processed file: {} {}/{}'.format(f, i, len(files)))
......@@ -135,11 +135,8 @@ def build_texture_feature_vectors(files, get_image_with_landmarks, triangles,
# if k == 27:
# break
mean_texture = np.asarray(mean_texture)
if flattened:
mean_texture = pca.flatten_feature_vectors(mean_texture)
#mean_texture = pca.flatten_feature_vectors(mean_texture)
return mean_texture
......
src/main.py
View file @ 3c33b0b3
......@@ -84,16 +84,17 @@ def save_pca_model_texture(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'
Vt, mean_values, triangles = pca.load(args.model_shape_file)
Vt, s, mean_shape, triangles = pca.load(args.model_shape_file)
textures = aam.build_texture_feature_vectors(
args.files, imm.get_imm_image_with_landmarks, triangles, flattened=True
args.files, imm.get_imm_image_with_landmarks, mean_shape, triangles
)
mean_texture = aam.get_mean(textures)
_, _, Vt = pca.pca(textures, mean_texture)
_, s, Vt = pca.pca(textures, mean_texture)
pca.save(Vt, s, mean_texture, triangles, args.model_texture_file)
pca.save(Vt, mean_texture, triangles, args.model_texture_file)
logger.info('texture pca model saved in %s', args.model_texture_file)
......@@ -122,12 +123,12 @@ def save_pca_model_shape(args):
mean_values = aam.get_mean(points)
_, _, Vt = pca.pca(points, mean_values)
_, s, Vt = pca.pca(points, mean_values)
mean_xy = mean_values.reshape((-1, 2))
triangles = aam.get_triangles(mean_xy[:, 0], mean_xy[:, 1])
pca.save(Vt, mean_values, triangles, args.model_shape_file)
pca.save(Vt, s, mean_values, triangles, args.model_shape_file)
logger.info('shape pca model saved in %s', args.model_shape_file + '_shape')
......@@ -164,14 +165,25 @@ def show_pca_model(args):
from utils.triangles import draw_shape, draw_texture
Vt_shape, mean_values_shape, triangles = pca.load(args.model_shape_file)
Vt_texture, mean_values_texture, _ = pca.load(args.model_texture_file)
Vt_shape, s, mean_values_shape, triangles = pca.load(args.model_shape_file)
Vt_texture, s_texture, mean_values_texture, _ = pca.load(args.model_texture_file)
# calculate n_components which captures 90 percent of the variance
total = s_texture.sum()
subtotal = 0.0
i = 0
while (subtotal * 100.0) / total <= 90.0:
subtotal += s_texture[i]
i += 1
n_components = i
image = np.full((480, 640, 3), fill_value=255, dtype=np.uint8)
immPoints = imm.IMMPoints(filename='data/imm_face_db/40-1m.asf')
input_image = immPoints.get_image()
input_points = immPoints.get_points()
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
......@@ -182,9 +194,9 @@ def show_pca_model(args):
mean_values_shape[:, 1] = mean_values_shape[:, 1] * h
while True:
draw_texture(input_image, image, input_points, mean_values_shape,
mean_values_texture, triangles, n_samples=80)
draw_shape(image, mean_values_shape, triangles, multiply=False)
draw_texture(input_image, image, Vt_texture, input_points, mean_values_shape,
mean_values_texture, triangles)
#draw_shape(image, mean_values_shape, triangles, multiply=False)
cv2.imshow('input_image', input_image)
cv2.imshow('image', image)
......
src/pca.py
View file @ 3c33b0b3
......@@ -36,7 +36,7 @@ def reconstruct(feature_vector, Vt, mean_values, n_components=10):
return np.dot(Vt[:n_components].T, yk) + mean_values
def save(Vt, mean_values, triangles, filename):
def save(Vt, s, mean_values, triangles, filename):
"""
Store the U, s, Vt and mean of all the asf datafiles given by the asf
files.
......@@ -52,7 +52,7 @@ def save(Vt, mean_values, triangles, filename):
triangles = Vtm[2]
"""
saving = np.asarray([Vt, [mean_values], triangles])
saving = np.asarray([Vt, s, [mean_values], triangles])
np.save(filename, saving)
......@@ -76,13 +76,14 @@ def load(filename):
Vtm = np.load(filename)
Vt = Vtm[0]
mean_values = Vtm[1][0]
triangles = Vtm[2]
s = Vtm[1]
mean_values = Vtm[2][0]
triangles = Vtm[3]
return Vt, mean_values, triangles
return Vt, s, mean_values, triangles
def flatten_feature_vectors(data):
def flatten_feature_vectors(data, dim=0):
"""
Flattens the feature vectors inside a ndarray
......@@ -102,6 +103,7 @@ def flatten_feature_vectors(data):
Args:
data (numpy array): array of feature vectors
dim (int): dimension to flatten the data
return:
array: (numpy array): array flattened feature vectors
......@@ -109,9 +111,9 @@ def flatten_feature_vectors(data):
"""
flattened = []
rows, _, _ = data.shape
n = data.shape[dim]
for i in range(rows):
for i in range(n):
flattened.append(np.ndarray.flatten(data[i]))
return np.array(flattened)
src/utils/generate_head_texture.pyx
View file @ 3c33b0b3
......@@ -18,6 +18,23 @@ 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)
cdef inline np.ndarray[double, ndim=1] cartesian2barycentric(
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)
cdef inline np.ndarray[double, ndim=2] barycentric2cartesian(
int x1, int x2, int x3, int y1, int y2, int y3,
np.ndarray[long, ndim=2] matrix,
......@@ -35,9 +52,10 @@ cdef inline np.ndarray[double, ndim=2] barycentric2cartesian(
@cython.boundscheck(False)
@cython.wraparound(False)
def fill_triangle(np.ndarray[unsigned char, ndim=3] src,
def fill_triangle(np.ndarray[unsigned char, ndim=1] src,
np.ndarray[unsigned char, ndim=3] dst,
int x1, int y1, int x2, int y2, int x3, int y3):
int x1, int y1, int x2, int y2, int x3, int y3, int offset,
int index):
"""
Fill a triangle by applying the Barycentric Algorithm for deciding if a
point lies inside or outside a triangle.
......@@ -49,26 +67,90 @@ def fill_triangle(np.ndarray[unsigned char, ndim=3] src,
cdef int y_min = min(y1, min(y2, y3))
cdef int y_max = max(y1, max(y2, y3))
cdef int vs1_x = x2 - x1
cdef int vs1_y = y2 - y1
cdef int vs2_x = x3 - x1
cdef int vs2_y = y3 - y1
cdef np.ndarray L = np.zeros([3, 1], dtype=DTYPE_float32)
cdef np.ndarray matrix = np.full([3, 3], fill_value=1, dtype=DTYPE_int)
cdef float s
cdef float t
cdef np.ndarray src_loc = np.zeros([3, 1], dtype=DTYPE_float64)
cdef np.ndarray dst_loc = np.zeros([3, 1], dtype=DTYPE_float64)
for y in xrange(y_min, y_max):
for x in xrange(x_min, x_max):
q_x = x - x1
q_y = y - y1
cdef int w = x_max - x_min
cdef int h = y_max - y_min
cdef int new_offset
cdef np.ndarray src_reshaped = src[offset:offset + (w * h * 3)].reshape((h, w, 3))
#print src_reshaped
#print '(', w, '*', h, '*', 3, ') * ', ' = ', offset
for j, y in enumerate(xrange(y_min, y_max)):
for i, x in enumerate(xrange(x_min, x_max)):
dst_loc = cartesian2barycentric(
x1, y1, x2, y2, x3, y3, x, y
)
s = dst_loc[0]
t = dst_loc[1]
# notice we have a soft margin of -0.00001, which makes sure there are no
# gaps due to rounding issues
if s >= -0.000000001 and t >= -0.000000001 and s + t <= 1.0:
dst[y, x, :] = src_reshaped[j, i, :]
new_offset = (w * h * 3)
return new_offset
@cython.boundscheck(False)
@cython.wraparound(False)
def get_row_colors_triangle(np.ndarray[unsigned char, ndim=3] src,
int src_x1, int src_y1, int src_x2, int src_y2,
int src_x3, int src_y3,
int dst_x1, int dst_y1, int dst_x2, int dst_y2,
int dst_x3, int dst_y3):
"""
Fill a triangle by applying the Barycentric Algorithm for deciding if a
point lies inside or outside a triangle.
"""
cdef int x_min = min(dst_x1, min(dst_x2, dst_x3))
cdef int x_max = max(dst_x1, max(dst_x2, dst_x3))
cdef int y_min = min(dst_y1, min(dst_y2, dst_y3))
cdef int y_max = max(dst_y1, max(dst_y2, dst_y3))
s = cross_product(q_x, q_y, vs2_x, vs2_y) / \
cross_product(vs1_x, vs1_y, vs2_x, vs2_y)
t = cross_product(vs1_x, vs1_y, q_x, q_y) / \
cross_product(vs1_x, vs1_y, vs2_x, vs2_y)
cdef np.ndarray L = np.zeros([3, 1], dtype=DTYPE_float32)
cdef np.ndarray matrix = np.full([3, 3], fill_value=1, dtype=DTYPE_int)
if s >= 0 and t >= 0 and s + t <= 1:
dst[y, x, :] = src[y, x, :]
cdef np.ndarray src_loc = np.zeros([3, 1], dtype=DTYPE_float64)
cdef np.ndarray dst_loc = np.zeros([3, 1], dtype=DTYPE_float64)
cdef np.ndarray dst = np.full(
[y_max - y_min, x_max - x_min, 3], fill_value=255, dtype=DTYPE_float64
)
for j, y in enumerate(xrange(y_min, y_max)):
for i, x in enumerate(xrange(x_min, x_max)):
dst_loc = cartesian2barycentric(
dst_x1, dst_y1, dst_x2, dst_y2, dst_x3, dst_y3, x, y
)
s = dst_loc[0]
t = dst_loc[1]
# notice we have a soft margin of -0.00001, which makes sure there are no
# gaps due to rounding issues
if s >= -0.000001 and t >= -0.000001 and s + t <= 1.0:
L[0] = s
L[1] = t
L[2] = 1 - s - t
src_loc = barycentric2cartesian(
src_x1, src_x2, src_x3,
src_y1, src_y2, src_y3,
matrix,
L
)
dst[j, i, :] = src[src_loc[1][0], src_loc[0][0], :]
return dst
@cython.boundscheck(False)
......@@ -83,6 +165,11 @@ def get_colors_triangle(np.ndarray[unsigned char, ndim=3] src,
Fill a triangle by applying the Barycentric Algorithm for deciding if a
point lies inside or outside a triangle.
"""
cdef int x_min = min(dst_x1, min(dst_x2, dst_x3))
cdef int x_max = max(dst_x1, max(dst_x2, dst_x3))
cdef int y_min = min(dst_y1, min(dst_y2, dst_y3))
cdef int y_max = max(dst_y1, max(dst_y2, dst_y3))
cdef float s
cdef float t
......@@ -92,12 +179,18 @@ def get_colors_triangle(np.ndarray[unsigned char, ndim=3] src,
cdef np.ndarray src_loc = np.zeros([3, 1], dtype=DTYPE_float64)
cdef np.ndarray dst_loc = np.zeros([3, 1], dtype=DTYPE_float64)
cdef np.ndarray bary_centric_range = np.linspace(0, 1, num=80)
for y in xrange(y_min, y_max):
for x in xrange(x_min, x_max):
dst_loc = cartesian2barycentric(
dst_x1, dst_y1, dst_x2, dst_y2, dst_x3, dst_y3, x, y
)
s = dst_loc[0]
t = dst_loc[1]
# get a float value for every pixel
for s in bary_centric_range:
for t in bary_centric_range:
if s + t <= 1:
# notice we have a soft margin of -0.00001, which makes sure there are no
# gaps due to rounding issues
if s >= -0.000001 and t >= -0.000001 and s + t <= 1.0:
L[0] = s
L[1] = t
L[2] = 1 - s - t
......@@ -109,11 +202,4 @@ def get_colors_triangle(np.ndarray[unsigned char, ndim=3] src,
L
)
dst_loc = barycentric2cartesian(
dst_x1, dst_x2, dst_x3,
dst_y1, dst_y2, dst_y3,
matrix,
L
)
dst[dst_loc[1][0], dst_loc[0][0], :] = src[src_loc[1][0], src_loc[0][0], :]
dst[y, x, :] = src[src_loc[1][0], src_loc[0][0], :]
src/utils/triangles.py
View file @ 3c33b0b3
......@@ -67,20 +67,20 @@ def draw_shape(image, points, triangles, multiply=True):
cv2.circle(image, tuple(p), 3, color=(0, 255, 100))
def draw_texture(src, dest, points2d_src, points2d_dest, texture,
triangles, multiply=True, n_samples=20):
texture = np.asarray(texture, dtype=np.uint8).reshape((-1, 3))
def draw_texture(src, dest, Vt, points2d_src, points2d_dst, texture, triangles):
# texture = np.asarray(texture, dtype=np.uint8).reshape((-1, 3))
texture = np.asarray(texture, np.uint8)
offset = 0
for t, tri in enumerate(triangles):
src_p1, src_p2, src_p3 = points2d_src[tri]
dest_p1, dest_p2, dest_p3 = points2d_dest[tri]
get_colors_triangle(
src, dest,
src_p1[0], src_p1[1],
src_p2[0], src_p2[1],
src_p3[0], src_p3[1],
dest_p1[0], dest_p1[1],
dest_p2[0], dest_p2[1],
dest_p3[0], dest_p3[1]
dst_p1, dst_p2, dst_p3 = points2d_dst[tri]
offset += fill_triangle(
texture, dest,
dst_p1[0], dst_p1[1],
dst_p2[0], dst_p2[1],
dst_p3[0], dst_p3[1],
offset,
t
)
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