def generator_demo_example_lips(img_path):
name = img_path.split('/')[-1]
landmark_path = os.path.join('../image/', name.replace('jpg', 'npy'))
region_path = os.path.join('../image/',
name.replace('.jpg', '_region.jpg'))
roi, landmark = crop_image(img_path)
if np.sum(landmark[37:39, 1] - landmark[40:42, 1]) < -9:
# pts2 = np.float32(np.array([template[36],template[45],template[30]]))
template = np.load('../basics/base_68.npy')
else:
template = np.load('../basics/base_68_close.npy')
# pts2 = np.float32(np.vstack((template[27:36,:], template[39,:],template[42,:],template[45,:])))
pts2 = np.float32(template[27:45, :])
# pts2 = np.float32(template[17:35,:])
# pts1 = np.vstack((landmark[27:36,:], landmark[39,:],landmark[42,:],landmark[45,:]))
pts1 = np.float32(landmark[27:45, :])
# pts1 = np.float32(landmark[17:35,:])
tform = tf.SimilarityTransform()
tform.estimate(pts2, pts1)
dst = tf.warp(roi, tform, output_shape=(163, 163))
dst = np.array(dst * 255, dtype=np.uint8)
dst = dst[1:129, 1:129, :]
cv2.imwrite(region_path, dst)
gray = cv2.cvtColor(dst, cv2.COLOR_BGR2GRAY)
# detect faces in the grayscale image
rects = detector(gray, 1)
for (i, rect) in enumerate(rects):
shape = predictor(gray, rect)
shape = utils.shape_to_np(shape)
shape, _, _ = normLmarks(shape)
np.save(landmark_path, shape)
lmark = shape.reshape(68, 2)
name = img_path[:-4] + 'lmark.png'
utils.plot_flmarks(lmark,
name, (-0.2, 0.2), (-0.2, 0.2),
'x',
'y',
figsize=(10, 10))
return dst, lmark
def crop_image(image_path):
image = cv2.imread(image_path)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
rects = detector(gray, 1)
for (i, rect) in enumerate(rects):
shape = predictor(gray, rect)
shape = utils.shape_to_np(shape)
(x, y, w, h) = utils.rect_to_bb(rect)
center_x = x + int(0.5 * w)
center_y = y + int(0.5 * h)
r = int(0.64 * h)
new_x = center_x - r
new_y = center_y - r
roi = image[new_y:new_y + 2 * r, new_x:new_x + 2 * r]
roi = cv2.resize(roi, (163, 163), interpolation=cv2.INTER_AREA)
scale = 163. / (2 * r)
shape = ((shape - np.array([new_x, new_y])) * scale)
return roi, shape
