-
Notifications
You must be signed in to change notification settings - Fork 0
/
photos_face_swapping.py
176 lines (123 loc) · 5.57 KB
/
photos_face_swapping.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
import cv2
import numpy as np
import dlib
import time
def extract_index_nparray(nparray):
index = None
for num in nparray[0]:
index = num
break
return index
img = cv2.imread("bradley_cooper.jpg")
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
mask = np.zeros_like(img_gray)
img2 = cv2.imread("jim_carrey.jpg")
img2_gray = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
height, width, channels = img2.shape
img2_new_face = np.zeros((height, width, channels), np.uint8)
# Face 1
faces = detector(img_gray)
for face in faces:
landmarks = predictor(img_gray, face)
landmarks_points = []
for n in range(0, 68):
x = landmarks.part(n).x
y = landmarks.part(n).y
landmarks_points.append((x, y))
points = np.array(landmarks_points, np.int32)
convexhull = cv2.convexHull(points)
# cv2.polylines(img, [convexhull], True, (255, 0, 0), 3)
cv2.fillConvexPoly(mask, convexhull, 255)
face_image_1 = cv2.bitwise_and(img, img, mask=mask)
# Delaunay triangulation
rect = cv2.boundingRect(convexhull)
subdiv = cv2.Subdiv2D(rect)
subdiv.insert(landmarks_points)
triangles = subdiv.getTriangleList()
triangles = np.array(triangles, dtype=np.int32)
indexes_triangles = []
for t in triangles:
pt1 = (t[0], t[1])
pt2 = (t[2], t[3])
pt3 = (t[4], t[5])
index_pt1 = np.where((points == pt1).all(axis=1))
index_pt1 = extract_index_nparray(index_pt1)
index_pt2 = np.where((points == pt2).all(axis=1))
index_pt2 = extract_index_nparray(index_pt2)
index_pt3 = np.where((points == pt3).all(axis=1))
index_pt3 = extract_index_nparray(index_pt3)
if index_pt1 is not None and index_pt2 is not None and index_pt3 is not None:
triangle = [index_pt1, index_pt2, index_pt3]
indexes_triangles.append(triangle)
# Face 2
faces2 = detector(img2_gray)
for face in faces2:
landmarks = predictor(img2_gray, face)
landmarks_points2 = []
for n in range(0, 68):
x = landmarks.part(n).x
y = landmarks.part(n).y
landmarks_points2.append((x, y))
points2 = np.array(landmarks_points2, np.int32)
convexhull2 = cv2.convexHull(points2)
lines_space_mask = np.zeros_like(img_gray)
lines_space_new_face = np.zeros_like(img2)
# Triangulation of both faces
for triangle_index in indexes_triangles:
# Triangulation of the first face
tr1_pt1 = landmarks_points[triangle_index[0]]
tr1_pt2 = landmarks_points[triangle_index[1]]
tr1_pt3 = landmarks_points[triangle_index[2]]
triangle1 = np.array([tr1_pt1, tr1_pt2, tr1_pt3], np.int32)
rect1 = cv2.boundingRect(triangle1)
(x, y, w, h) = rect1
cropped_triangle = img[y: y + h, x: x + w]
cropped_tr1_mask = np.zeros((h, w), np.uint8)
points = np.array([[tr1_pt1[0] - x, tr1_pt1[1] - y],
[tr1_pt2[0] - x, tr1_pt2[1] - y],
[tr1_pt3[0] - x, tr1_pt3[1] - y]], np.int32)
cv2.fillConvexPoly(cropped_tr1_mask, points, 255)
# Lines space
cv2.line(lines_space_mask, tr1_pt1, tr1_pt2, 255)
cv2.line(lines_space_mask, tr1_pt2, tr1_pt3, 255)
cv2.line(lines_space_mask, tr1_pt1, tr1_pt3, 255)
lines_space = cv2.bitwise_and(img, img, mask=lines_space_mask)
# Triangulation of second face
tr2_pt1 = landmarks_points2[triangle_index[0]]
tr2_pt2 = landmarks_points2[triangle_index[1]]
tr2_pt3 = landmarks_points2[triangle_index[2]]
triangle2 = np.array([tr2_pt1, tr2_pt2, tr2_pt3], np.int32)
rect2 = cv2.boundingRect(triangle2)
(x, y, w, h) = rect2
cropped_tr2_mask = np.zeros((h, w), np.uint8)
points2 = np.array([[tr2_pt1[0] - x, tr2_pt1[1] - y],
[tr2_pt2[0] - x, tr2_pt2[1] - y],
[tr2_pt3[0] - x, tr2_pt3[1] - y]], np.int32)
cv2.fillConvexPoly(cropped_tr2_mask, points2, 255)
# Warp triangles
points = np.float32(points)
points2 = np.float32(points2)
M = cv2.getAffineTransform(points, points2)
warped_triangle = cv2.warpAffine(cropped_triangle, M, (w, h))
warped_triangle = cv2.bitwise_and(warped_triangle, warped_triangle, mask=cropped_tr2_mask)
# Reconstructing destination face
img2_new_face_rect_area = img2_new_face[y: y + h, x: x + w]
img2_new_face_rect_area_gray = cv2.cvtColor(img2_new_face_rect_area, cv2.COLOR_BGR2GRAY)
_, mask_triangles_designed = cv2.threshold(img2_new_face_rect_area_gray, 1, 255, cv2.THRESH_BINARY_INV)
warped_triangle = cv2.bitwise_and(warped_triangle, warped_triangle, mask=mask_triangles_designed)
img2_new_face_rect_area = cv2.add(img2_new_face_rect_area, warped_triangle)
img2_new_face[y: y + h, x: x + w] = img2_new_face_rect_area
# Face swapped (putting 1st face into 2nd face)
img2_face_mask = np.zeros_like(img2_gray)
img2_head_mask = cv2.fillConvexPoly(img2_face_mask, convexhull2, 255)
img2_face_mask = cv2.bitwise_not(img2_head_mask)
img2_head_noface = cv2.bitwise_and(img2, img2, mask=img2_face_mask)
result = cv2.add(img2_head_noface, img2_new_face)
(x, y, w, h) = cv2.boundingRect(convexhull2)
center_face2 = (int((x + x + w) / 2), int((y + y + h) / 2))
seamlessclone = cv2.seamlessClone(result, img2, img2_head_mask, center_face2, cv2.NORMAL_CLONE)
cv2.imshow("seamlessclone", seamlessclone)
cv2.waitKey(0)
cv2.destroyAllWindows()