def get_triangle(landmarks, landmark_mode=68):
feature_index_dict = _get_facial_feature_landmarks(
landmark_mode=landmark_mode)
triangle_indices = list()
triangle_mesh = list()
for key in feature_index_dict.keys():
index = feature_index_dict[key]
tmp = np.array(landmarks)[index]
_, triangles_tmp, triangle_mesh_tmp = face_segmentation.get_triangle_landmarks(
tmp)
if key == 'up_lip':
bottom_contour = [0]
bottom_contour.extend(np.arange(6, 12).tolist())
selected_triangle_index = list()
for i, triangle_tmp in enumerate(triangles_tmp):
if (triangle_tmp[0] in bottom_contour) & \
(triangle_tmp[1] in bottom_contour) & \
(triangle_tmp[2] in bottom_contour):
continue
selected_triangle_index.append(i)
triangle_mesh_tmp = np.array(
triangle_mesh_tmp)[selected_triangle_index]
triangle_mesh_tmp = triangle_mesh_tmp.tolist()
if key == 'bottom_lip':
up_contour = np.arange(7, 12).tolist()
selected_triangle_index = list()
for i, triangle_tmp in enumerate(triangles_tmp):
if (triangle_tmp[0] in up_contour) & \
(triangle_tmp[1] in up_contour) & \
(triangle_tmp[2] in up_contour):
continue
selected_triangle_index.append(i)
triangle_mesh_tmp = np.array(
triangle_mesh_tmp)[selected_triangle_index]
triangle_mesh_tmp = triangle_mesh_tmp.tolist()
triangles_tmp = np.array(triangles_tmp)[selected_triangle_index]
triangles_tmp = triangles_tmp.tolist()
origin_triangles = list()
for triangle_tmp in triangles_tmp:
origin_triangles.append([
index[triangle_tmp[0]], index[triangle_tmp[1]],
index[triangle_tmp[2]]
])
triangle_indices.extend(origin_triangles)
triangle_mesh.extend(triangle_mesh_tmp)
return np.array(triangle_indices), np.array(triangle_mesh)
def test_facial_features(self):
image_path = '../assets/targets/target_12.jpg'
image = cv2.imread(image_path)
landmarks = facial_landmarks_detection_dlib.get_facial_landmarks(image)
_, triangle, triangle_mesh = face_segmentation.get_triangle_landmarks(
landmarks)
triangle, triangle_mesh = makeup_by_facial_features.get_triangle(
landmarks)
image = face_segmentation.draw_landmark(image, landmarks)
for triangle in triangle_mesh:
cv2.polylines(image, [np.asarray(triangle)], True, (0, 255, 255))
cv2.imwrite('facial_features.jpg', image)
pass
def test_face_recognition(self):
image_path = '../assets/examples/after-makeup2.jpeg'
image = cv2.imread(image_path)
landmarks = face_landmarks(image)[0]
uplip = landmarks['top_lip']
landmarks_coords, triangle_indices, triangles = face_segmentation.get_triangle_landmarks(
uplip)
tmp_triangle_indices = []
# for triangle_index in triangle_indices:
# if (triangle_index[0] in inner_mouse) & \
# (triangle_index[1] in inner_mouse) & \
# (triangle_index[2] in inner_mouse):
# continue
# tmp_triangle_indices.append(triangle_index)
# triangles = face_segmentation.get_triangle_mesh(landmarks, tmp_triangle_indices)
for triangle in triangles:
cv2.polylines(image, [np.asarray(triangle)], True, (0, 255, 255))
cv2.imwrite('test1.jpg', image)
def test_color_blending(self):
example_path = '../assets/targets/target_12.jpg'
example_image = cv2.imread(example_path)
example_landmarks = facial_landmarks_detection_dlib.get_facial_landmarks(
example_image, add_points=True)
target_path = '../assets/targets/target_9.jpg'
target = cv2.imread(target_path)
target_landmarks = facial_landmarks_detection_dlib.get_facial_landmarks(
target, add_points=True)
_, target_triangle_indices, target_triangle_meshes = \
face_segmentation.get_triangle_landmarks(target_landmarks)
example_triangle_meshes = face_segmentation.get_triangle_mesh(
example_landmarks, target_triangle_indices)
target_pts, example_pts = face_segmentation.get_pixels_warp(
target_triangle_meshes, target.shape, example_triangle_meshes)
fusion_image = makeup_transfer_by_example.color_blending(
target, target_pts, example_image, example_pts)
cv2.imwrite('test_blending_result.jpg', fusion_image)
pass
def test_get_triangle_mesh(self):
image_path = '../assets/examples/example_8.jpeg'
image = cv2.imread(image_path)
landmarks = facial_landmarks_detection_dlib.get_facial_landmarks(
image, add_points=True)
lip = np.arange(48, 68).tolist()
landmarks_coords, triangle_indices, triangles = face_segmentation.get_triangle_landmarks(
landmarks)
tmp_triangle_indices = []
for triangle_index in triangle_indices:
if (triangle_index[0] in lip) & \
(triangle_index[1] in lip) & \
(triangle_index[2] in lip):
continue
tmp_triangle_indices.append(triangle_index)
up_lip_index = np.arange(48, 55)
up_lip_index = np.hstack((up_lip_index, np.arange(60, 65))).tolist()
up_lip_landmarks = np.asarray(landmarks)[up_lip_index]
_, triangle_indices, _ = face_segmentation.get_triangle_landmarks(
up_lip_landmarks)
bottom_contour = [0]
bottom_contour.extend(np.arange(6, 12).tolist())
selected_triangle_index = list()
for i, triangle_index in enumerate(triangle_indices):
if (triangle_index[0] in bottom_contour) & \
(triangle_index[1] in bottom_contour) & \
(triangle_index[2] in bottom_contour):
continue
selected_triangle_index.append(i)
triangle_indices = np.asarray(
triangle_indices)[selected_triangle_index]
up_lip_triangle = []
for triangle_index in triangle_indices:
tmp = [
up_lip_index[triangle_index[0]],
up_lip_index[triangle_index[1]],
up_lip_index[triangle_index[2]]
]
up_lip_triangle.append(tmp)
tmp_triangle_indices.extend(up_lip_triangle)
bottom_lip_index = [48]
bottom_lip_index.extend(np.arange(54, 61).tolist())
bottom_lip_index.extend(np.arange(64, 68).tolist())
bottom_lip_landmarks = np.asarray(landmarks)[bottom_lip_index]
_, triangle_indices, _ = face_segmentation.get_triangle_landmarks(
bottom_lip_landmarks)
up_contour = np.arange(7, 12).tolist()
selected_triangle_index = list()
for i, triangle_index in enumerate(triangle_indices):
if (triangle_index[0] in up_contour) & \
(triangle_index[1] in up_contour) & \
(triangle_index[2] in up_contour):
continue
selected_triangle_index.append(i)
triangle_indices = np.asarray(
triangle_indices)[selected_triangle_index]
bottom_lip_triangle = []
for triangle_index in triangle_indices:
tmp = [
bottom_lip_index[triangle_index[0]],
bottom_lip_index[triangle_index[1]],
bottom_lip_index[triangle_index[2]]
]
bottom_lip_triangle.append(tmp)
tmp_triangle_indices.extend(bottom_lip_triangle)
# remove eyes
left_eyes = np.arange(36, 42).tolist()
selected_triangle_index = []
for i, tmp_triangle in enumerate(tmp_triangle_indices):
if (tmp_triangle[0] in left_eyes) & \
(tmp_triangle[1] in left_eyes) & \
(tmp_triangle[2] in left_eyes):
continue
else:
selected_triangle_index.append(i)
tmp_triangle_indices = np.asarray(
tmp_triangle_indices)[selected_triangle_index]
right_eyes = np.arange(42, 48).tolist()
selected_triangle_index = []
for i, tmp_triangle in enumerate(tmp_triangle_indices):
if (tmp_triangle[0] in right_eyes) & \
(tmp_triangle[1] in right_eyes) & \
(tmp_triangle[2] in right_eyes):
continue
else:
selected_triangle_index.append(i)
tmp_triangle_indices = np.asarray(
tmp_triangle_indices)[selected_triangle_index]
np.savetxt('landmark_triangle_index.txt',
tmp_triangle_indices,
fmt='%i')
triangles = face_segmentation.get_triangle_mesh(
landmarks, tmp_triangle_indices)
for triangle in triangles:
cv2.polylines(image, [np.asarray(triangle)], True, (0, 255, 255))
cv2.imwrite('test1.jpg', image)
pass