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import os
import cv2
import time
import torch
import numpy as np
from argparse import ArgumentParser
from ibug.face_alignment import FANPredictor
from ibug.face_alignment.utils import plot_landmarks
from ibug.face_detection import RetinaFacePredictor, S3FDPredictor
try:
from scipy.interpolate import CubicSpline
except:
CubicSpline = None
def main() -> None:
# Parse command-line arguments
parser = ArgumentParser()
parser.add_argument('--input', '-i', help='Input video path or webcam index (default=0)', default=0)
parser.add_argument('--output', '-o', help='Output file path', default=None)
parser.add_argument('--fourcc', '-f', help='FourCC of the output video (default=mp4v)',
type=str, default='mp4v')
parser.add_argument('--benchmark', '-b', help='Enable benchmark mode for CUDNN',
action='store_true', default=False)
parser.add_argument('--no-display', '-n', help='No display if processing a video file',
action='store_true', default=False)
parser.add_argument('--eyes-of-ibad', '-eoi', help='A tribute to Dune', type=float, default=0.0)
parser.add_argument('--detection-threshold', '-dt', type=float, default=0.8,
help='Confidence threshold for face detection (default=0.8)')
parser.add_argument('--detection-method', '-dm', default='retinaface',
help='Face detection method, can be either RatinaFace or S3FD (default=RatinaFace)')
parser.add_argument('--detection-weights', '-dw', default=None,
help='Weights to be loaded for face detection, ' +
'can be either resnet50 or mobilenet0.25 when using RetinaFace')
parser.add_argument('--detection-alternative-pth', '-dp', default=None,
help='Alternative pth file to be loaded for face detection')
parser.add_argument('--detection-device', '-dd', default='cuda:0',
help='Device to be used for face detection (default=cuda:0)')
parser.add_argument('--hide-detection-results', '-hd', help='Do not visualise face detection results',
action='store_true', default=False)
parser.add_argument('--alignment-threshold', '-at', type=float, default=0.2,
help='Score threshold used when visualising detected landmarks (default=0.2)')
parser.add_argument('--alignment-method', '-am', default='fan',
help='Face alignment method, must be set to FAN')
parser.add_argument('--alignment-weights', '-aw', default='2dfan2_alt',
help='Weights to be loaded for face alignment, can be either 2DFAN2, 2DFAN4, ' +
'or 2DFAN2_ALT (default=2DFAN2_ALT)')
parser.add_argument('--alignment-alternative-pth', '-ap', default=None,
help='Alternative pth file to be loaded for face alignment')
parser.add_argument('--alignment-alternative-landmarks', '-al', default=None,
help='Alternative number of landmarks to detect')
parser.add_argument('--alignment-device', '-ad', default='cuda:0',
help='Device to be used for face alignment (default=cuda:0)')
parser.add_argument('--hide-alignment-results', '-ha', help='Do not visualise face alignment results',
action='store_true', default=False)
args = parser.parse_args()
# Set benchmark mode flag for CUDNN
torch.backends.cudnn.benchmark = args.benchmark
vid = None
out_vid = None
has_window = False
try:
# Create the face detector
args.detection_method = args.detection_method.lower()
if args.detection_method == 'retinaface':
face_detector_class = (RetinaFacePredictor, 'RetinaFace')
elif args.detection_method == 's3fd':
face_detector_class = (S3FDPredictor, 'S3FD')
else:
raise ValueError('detector-method must be set to either RetinaFace or S3FD')
if args.detection_weights is None:
fd_model = face_detector_class[0].get_model()
else:
fd_model = face_detector_class[0].get_model(args.detection_weights)
if args.detection_alternative_pth is not None:
fd_model.weights = args.detection_alternative_pth
face_detector = face_detector_class[0](
threshold=args.detection_threshold, device=args.detection_device, model=fd_model)
print(f"Face detector created using {face_detector_class[1]} ({fd_model.weights}).")
# Create the landmark detector
args.alignment_method = args.alignment_method.lower()
if args.alignment_method == 'fan':
if args.alignment_weights is None:
fa_model = FANPredictor.get_model()
else:
fa_model = FANPredictor.get_model(args.alignment_weights)
if args.alignment_alternative_pth is not None:
fa_model.weights = args.alignment_alternative_pth
if args.alignment_alternative_landmarks is not None:
fa_model.config.num_landmarks = int(args.alignment_alternative_landmarks)
landmark_detector = FANPredictor(device=args.alignment_device, model=fa_model)
print(f"Landmark detector created using FAN ({fa_model.weights}).")
else:
raise ValueError('alignment-method must be set to FAN')
# Open the input video
using_webcam = not os.path.exists(args.input)
vid = cv2.VideoCapture(int(args.input) if using_webcam else args.input)
assert vid.isOpened()
if using_webcam:
print(f'Webcam #{int(args.input)} opened.')
else:
print(f'Input video "{args.input}" opened.')
# Open the output video (if a path is given)
if args.output is not None:
out_vid = cv2.VideoWriter(args.output, fps=vid.get(cv2.CAP_PROP_FPS),
frameSize=(int(vid.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT))),
fourcc=cv2.VideoWriter_fourcc(*args.fourcc))
assert out_vid.isOpened()
# Process the frames
frame_number = 0
window_title = os.path.splitext(os.path.basename(__file__))[0]
print('Processing started, press \'Q\' to quit.')
while True:
# Get a new frame
_, frame = vid.read()
if frame is None:
break
else:
# Detect faces
start_time = time.time()
faces = face_detector(frame, rgb=False)
current_time = time.time()
elapsed_time = current_time - start_time
# Face alignment
start_time = current_time
landmarks, scores = landmark_detector(frame, faces, rgb=False)
current_time = time.time()
elapsed_time2 = current_time - start_time
# Textural output
print(f'Frame #{frame_number} processed in {elapsed_time * 1000.0:.04f} + ' +
f'{elapsed_time2 * 1000.0:.04f} ms: {len(faces)} faces analysed.')
# Rendering
if args.eyes_of_ibad > 0:
contours = []
for lm, sc in zip(landmarks, scores):
if CubicSpline is None:
if sc[36:42].min() >= args.alignment_threshold:
contours.append(lm[36:42].astype(int))
if sc[42:48].min() >= args.alignment_threshold:
contours.append(lm[42:48].astype(int))
else:
if sc[36:42].min() >= args.alignment_threshold:
upper_lid = CubicSpline(range(4), lm[36:40])
lower_lid = CubicSpline(range(4), np.vstack((lm[39:42], lm[36])))
upper_lid_pts = upper_lid(np.arange(0, 3.0, 0.1))
lower_lid_pts = lower_lid(np.arange(0, 3.0, 0.1))
contours.append(np.vstack((upper_lid_pts, lower_lid_pts)).astype(int))
if sc[42:48].min() >= args.alignment_threshold:
upper_lid = CubicSpline(range(4), lm[42:46])
lower_lid = CubicSpline(range(4), np.vstack((lm[45:48], lm[42])))
upper_lid_pts = upper_lid(np.arange(0, 3.0, 0.1))
lower_lid_pts = lower_lid(np.arange(0, 3.0, 0.1))
contours.append(np.vstack((upper_lid_pts, lower_lid_pts)).astype(int))
if len(contours) > 0:
hsv_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV_FULL)
temp_hsv_frame = hsv_frame.copy()
alpha = np.clip(args.eyes_of_ibad, 0, 1)
cv2.fillPoly(temp_hsv_frame, contours, (170, 255, 0), lineType=cv2.LINE_AA)
hsv_frame[..., :2] = temp_hsv_frame[..., :2]
temp_frame = cv2.cvtColor(hsv_frame, cv2.COLOR_HSV2BGR_FULL)
frame = (frame.astype(float) * (1 - alpha) +
temp_frame.astype(float) * alpha).astype(np.uint8)
for face, lm, sc in zip(faces, landmarks, scores):
if not args.hide_detection_results:
bbox = face[:4].astype(int)
cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color=(0, 0, 255), thickness=2)
if not args.hide_alignment_results:
plot_landmarks(frame, lm, sc, threshold=args.alignment_threshold)
if not args.hide_detection_results and len(face) > 5:
plot_landmarks(frame, face[5:].reshape((-1, 2)), pts_radius=3)
# Write the frame to output video (if recording)
if out_vid is not None:
out_vid.write(frame)
# Display the frame
if using_webcam or not args.no_display:
has_window = True
cv2.imshow(window_title, frame)
key = cv2.waitKey(1) % 2 ** 16
if key == ord('q') or key == ord('Q'):
print('\'Q\' pressed, we are done here.')
break
frame_number += 1
finally:
if has_window:
cv2.destroyAllWindows()
if out_vid is not None:
out_vid.release()
if vid is not None:
vid.release()
print('All done.')
if __name__ == '__main__':
main()
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