def test_faceswap(person, model_path, test_path, save_path):
mtcnn_weights_dir = "./mtcnn_weights/"
fd = MTCNNFaceDetector(sess=K.get_session(), model_path=mtcnn_weights_dir)
da_config, arch_config, loss_weights, loss_config = get_model_params()
model = FaceswapGANModel(**arch_config)
model.load_weights(path=model_path)
ftrans = FaceTransformer()
ftrans.set_model(model)
# Read input image
test_imgs = glob.glob(test_path + '/*.jpg')
Path(save_path).mkdir(parents=True, exist_ok=True)
for test_img in test_imgs:
input_img = plt.imread(test_img)[..., :3]
if input_img.dtype == np.float32:
print("input_img has dtype np.float32 (perhaps the image format is PNG). Scale it to uint8.")
input_img = (input_img * 255).astype(np.uint8)
# Display detected face
faces, lms = fd.detect_face(input_img)
if len(faces) == 0:
continue
x0, y1, x1, y0, _ = faces[0]
det_face_im = input_img[int(x0):int(x1), int(y0):int(y1), :]
try:
src_landmarks = get_src_landmarks(x0, x1, y0, y1, lms)
tar_landmarks = get_tar_landmarks(det_face_im)
aligned_det_face_im = landmarks_match_mtcnn(det_face_im, src_landmarks, tar_landmarks)
except:
print("An error occured during face alignment.")
aligned_det_face_im = det_face_im
# plt.imshow(aligned_det_face_im)
# Transform detected face
result_img, result_rgb, result_mask = ftrans.transform(
aligned_det_face_im,
direction="BtoA",
roi_coverage=0.93,
color_correction="adain_xyz",
IMAGE_SHAPE=(RESOLUTION, RESOLUTION, 3)
)
try:
result_img = landmarks_match_mtcnn(result_img, tar_landmarks, src_landmarks)
result_rgb = landmarks_match_mtcnn(result_rgb, tar_landmarks, src_landmarks)
result_mask = landmarks_match_mtcnn(result_mask, tar_landmarks, src_landmarks)
except:
print("An error occured during face alignment.")
pass
result_input_img = input_img.copy()
result_input_img[int(x0):int(x1), int(y0):int(y1), :] = result_mask.astype(np.float32) / 255 * result_rgb + \
(1 - result_mask.astype(
np.float32) / 255) * result_input_img[int(x0):int(x1),
int(y0):int(y1), :]
img_name = os.path.basename(test_img)
plt.imshow(result_input_img)
plt.imsave(f'{save_path}/{img_name}', result_input_img)
_recalPos.append(
[int(_pos[0] / 180 * imagew),
int(_pos[1] / 108 * imageh)])
return _recalPos
mtcnn_weights_dir = "./mtcnn_weights/"
fd = MTCNNFaceDetector(sess=K.get_session(), model_path=mtcnn_weights_dir)
model = KerasELG()
model.net.load_weights("./elg_weights/elg_keras.h5")
fn = "./240281904_1.jpg"
input_img = cv2.imread(fn)[..., ::-1]
face, lms = fd.detect_face(
input_img) # assuming there is only one face in input image
assert len(face) >= 1, "No face detected"
print(right_eye_xy)
if len(face) > 1:
left_eye_xy = np.array([lms[6][0], lms[1]][0])
right_eye_xy = np.array([lms[5][0], lms[0][0]])
else:
left_eye_xy = np.array([lms[6], lms[1]])
right_eye_xy = np.array([lms[5], lms[0]])
dist_eyes = np.linalg.norm(left_eye_xy - right_eye_xy)
# bounding box
eye_bbox_w = (dist_eyes / 1.25)
eye_bbox_h = (eye_bbox_w * 0.6)