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Reduced the impact of eye blinks and opening the mouth on head pose and added new models to benchmark.

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Emiliana 2020-12-07 18:11:44 +01:00
parent 8459f8bd97
commit a0ccd9a162
2 changed files with 10 additions and 6 deletions
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6
facetracker.py
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@ -134,8 +134,8 @@ if args.benchmark > 0:
model_base_path = get_model_base_path(args.model_dir) model_base_path = get_model_base_path(args.model_dir)
im = cv2.imread(os.path.join(model_base_path, "benchmark.bin"), cv2.IMREAD_COLOR) im = cv2.imread(os.path.join(model_base_path, "benchmark.bin"), cv2.IMREAD_COLOR)
results = [] results = []
for model_type in [3, 2, 1, 0, -1, -2]: for model_type in [3, 2, 1, 0, -1, -2, -3]:
tracker = Tracker(224, 224, threshold=0.1, max_threads=args.max_threads, max_faces=1, discard_after=0, scan_every=0, silent=True, model_type=model_type, model_dir=args.model_dir, no_gaze=(model_type < 0), detection_threshold=0.1, use_retinaface=0, max_feature_updates=900, static_model=True if args.no_3d_adapt == 1 else False) tracker = Tracker(224, 224, threshold=0.1, max_threads=args.max_threads, max_faces=1, discard_after=0, scan_every=0, silent=True, model_type=model_type, model_dir=args.model_dir, no_gaze=(model_type == -1), detection_threshold=0.1, use_retinaface=0, max_feature_updates=900, static_model=True if args.no_3d_adapt == 1 else False)
tracker.detected = 1 tracker.detected = 1
tracker.faces = [(0, 0, 224, 224)] tracker.faces = [(0, 0, 224, 224)]
total = 0.0 total = 0.0
@ -245,7 +245,7 @@ try:
first = False first = False
height, width, channels = frame.shape height, width, channels = frame.shape
sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
tracker = Tracker(width, height, threshold=args.threshold, max_threads=args.max_threads, max_faces=args.faces, discard_after=args.discard_after, scan_every=args.scan_every, silent=False if args.silent == 0 else True, model_type=args.model, model_dir=args.model_dir, no_gaze=False if args.gaze_tracking != 0 and args.model >= 0 else True, detection_threshold=args.detection_threshold, use_retinaface=args.scan_retinaface, max_feature_updates=args.max_feature_updates, static_model=True if args.no_3d_adapt == 1 else False, try_hard=args.try_hard == 1) tracker = Tracker(width, height, threshold=args.threshold, max_threads=args.max_threads, max_faces=args.faces, discard_after=args.discard_after, scan_every=args.scan_every, silent=False if args.silent == 0 else True, model_type=args.model, model_dir=args.model_dir, no_gaze=False if args.gaze_tracking != 0 and args.model != -1 else True, detection_threshold=args.detection_threshold, use_retinaface=args.scan_retinaface, max_feature_updates=args.max_feature_updates, static_model=True if args.no_3d_adapt == 1 else False, try_hard=args.try_hard == 1)
if not args.video_out is None: if not args.video_out is None:
out = cv2.VideoWriter(args.video_out, cv2.VideoWriter_fourcc('F','F','V','1'), args.video_fps, (width * args.video_scale, height * args.video_scale)) out = cv2.VideoWriter(args.video_out, cv2.VideoWriter_fourcc('F','F','V','1'), args.video_fps, (width * args.video_scale, height * args.video_scale))

10
tracker.py
View file

@ -328,10 +328,10 @@ class FaceInfo():
self.id = id self.id = id
self.frame_count = -1 self.frame_count = -1
self.tracker = tracker self.tracker = tracker
self.contour_pts = [0,1,8,15,16,27,28,29,30,31,32,33,34,35,36,39,42,45] self.contour_pts = [0,1,15,16,27,28,29,30,31,32,33,34,35]
self.face_3d = copy.copy(self.tracker.face_3d) self.face_3d = copy.copy(self.tracker.face_3d)
if self.tracker.model_type == -1: if self.tracker.model_type == -1:
self.contour_pts = [0,2,8,14,16,27,30,33] self.contour_pts = [0,2,14,16,27,30,33]
self.reset() self.reset()
self.alive = False self.alive = False
self.coord = None self.coord = None
@ -767,9 +767,13 @@ class Tracker():
if not face_info.rotation is None: if not face_info.rotation is None:
success, face_info.rotation, face_info.translation = cv2.solvePnP(face_info.contour, image_pts, self.camera, self.dist_coeffs, useExtrinsicGuess=True, rvec=np.transpose(face_info.rotation), tvec=np.transpose(face_info.translation), flags=cv2.SOLVEPNP_ITERATIVE) success, face_info.rotation, face_info.translation = cv2.solvePnP(face_info.contour, image_pts, self.camera, self.dist_coeffs, useExtrinsicGuess=True, rvec=np.transpose(face_info.rotation), tvec=np.transpose(face_info.translation), flags=cv2.SOLVEPNP_ITERATIVE)
else: else:
# Include jaw point for initial estimate to increase stability
image_pts = np.array(lms)[face_info.contour_pts + [8], 0:2]
contour = np.array(face_info.face_3d[face_info.contour_pts + [8]])
rvec = np.array([0, 0, 0], np.float32) rvec = np.array([0, 0, 0], np.float32)
tvec = np.array([0, 0, 0], np.float32) tvec = np.array([0, 0, 0], np.float32)
success, face_info.rotation, face_info.translation = cv2.solvePnP(face_info.contour, image_pts, self.camera, self.dist_coeffs, useExtrinsicGuess=True, rvec=rvec, tvec=tvec, flags=cv2.SOLVEPNP_ITERATIVE) success, face_info.rotation, face_info.translation = cv2.solvePnP(contour, image_pts, self.camera, self.dist_coeffs, useExtrinsicGuess=True, rvec=rvec, tvec=tvec, flags=cv2.SOLVEPNP_ITERATIVE)
rotation = face_info.rotation rotation = face_info.rotation
translation = face_info.translation translation = face_info.translation