ret, frame = cap.read()
if ret:
faces, landmarks = face_detector.detect(frame,
0.8,
scales=[1.0],
do_flip=False)
if len(faces) > 0:
face_crops = []
face_boxes = []
for i in range(len(faces)):
bbox = faces[i]
bbox = (int(bbox[0]), int(bbox[1]), int(bbox[2]), int(bbox[3]))
face_crop = utils.crop_face_loosely(
bbox, frame, (config["model"]["im_width"],
config["model"]["im_height"]))
face_crop = cv2.resize(face_crop,
(config["model"]["im_width"],
config["model"]["im_height"]))
face_box, _, _ = utils.get_loosen_bbox(
bbox, frame, (config["model"]["im_width"],
config["model"]["im_height"]))
face_boxes.append(face_box)
# Convert crop image to RGB color space
face_crop = cv2.cvtColor(face_crop, cv2.COLOR_BGR2RGB)
face_crops.append(face_crop)
cv2.imshow("crop", face_crop)
print("Unable to connect to camera.")
exit(-1)
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(face_landmark_path)
frames = []
while cap.isOpened():
ret, frame = cap.read()
if ret:
face_rects = detector(frame, 0)
if len(face_rects) > 0:
shape = predictor(frame, face_rects[0])
shape = face_utils.shape_to_np(shape)
face_crop = utils.crop_face_loosely(shape, frame, INPUT_SIZE)
frames.append(face_crop)
if len(frames) == 1:
print(shape[30])
pred_cont_yaw, pred_cont_pitch, pred_cont_roll = net.test_online(
frames)
cv2_img = utils.draw_axis(frame,
pred_cont_yaw,
pred_cont_pitch,
pred_cont_roll,
tdx=shape[30][0],
tdy=shape[30][1],
size=100)
cv2.imshow("cv2_img", cv2_img)
landmark = re_label(draw.copy())
# cv2.imshow("Label", draw)
# cv2.waitKey(0)
x_min = int(min(pt2d[0, :]))
y_min = int(min(pt2d[1, :]))
x_max = int(max(pt2d[0, :]))
y_max = int(max(pt2d[1, :]))
y_min = max(0, y_min - int(0.3 * (y_max - y_min))) # Extend face to top
# Crop face
bbox = (x_min, y_min, x_max, y_max)
bbox_loosen, scale_x, scale_y = utils.get_loosen_bbox(
bbox, img, (args.input_size, args.input_size))
crop = utils.crop_face_loosely(bbox, img,
(args.input_size, args.input_size))
example["face_bbox"] = bbox_loosen
# Adjust landmark points
example["landmark"] = []
points = []
for l in range(len(landmark)):
# Normalize landmark points
x = float(landmark[l]['x'] - bbox_loosen[0]) * scale_x
y = float(landmark[l]['y'] - bbox_loosen[1]) * scale_y
x, y = utils.normalize_landmark_point(
(x, y), (args.input_size, args.input_size))
example["landmark"].append([x, y])