New and improved tracking model and confidence value visualization.
This commit is contained in:
Emiliana
parent
b0a61b41d5
commit
5c118347b6
4 changed files with 17 additions and 14 deletions
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@ -16,8 +16,8 @@ else:
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parser.add_argument("-H", "--height", type=int, help="Set raw RGB height", default=360)
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parser.add_argument("-c", "--capture", help="Set camera ID (0, 1...) or video file", default="0")
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parser.add_argument("-m", "--max-threads", type=int, help="Set the maximum number of threads", default=1)
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parser.add_argument("-t", "--threshold", type=float, help="Set minimum confidence threshold for face detection", default=0.4)
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parser.add_argument("-v", "--visualize", type=int, help="Set this to 1 to visualize the tracking, to 2 to also show face ids or to 3 to add numbers to the point display", default=0)
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parser.add_argument("-t", "--threshold", type=float, help="Set minimum confidence threshold for face detection", default=0.6)
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parser.add_argument("-v", "--visualize", type=int, help="Set this to 1 to visualize the tracking, to 2 to also show face ids, to 3 to add confidence values or to 4 to add numbers to the point display", default=0)
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parser.add_argument("-P", "--pnp-points", type=int, help="Set this to 1 to add the 3D fitting points to the visualization", default=0)
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parser.add_argument("-s", "--silent", type=int, help="Set this to 1 to prevent text output on the console", default=0)
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parser.add_argument("--faces", type=int, help="Set the maximum number of faces (slow)", default=1)
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@ -159,6 +159,10 @@ try:
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log.write(f"{frame_count},{now},{width},{height},{args.fps},{face_num},{f.id},{f.eye_blink[0]},{f.eye_blink[1]},{f.conf},{f.success},{f.pnp_error},{f.quaternion[0]},{f.quaternion[1]},{f.quaternion[2]},{f.quaternion[3]},{f.euler[0]},{f.euler[1]},{f.euler[2]},{f.rotation[0]},{f.rotation[1]},{f.rotation[2]},{f.translation[0]},{f.translation[1]},{f.translation[2]}")
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for (x,y,c) in f.lms:
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packet.extend(bytearray(struct.pack("f", c)))
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if args.visualize > 1:
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frame = cv2.putText(frame, str(f.id), (int(f.bbox[0]), int(f.bbox[1])), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (255,0,255))
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if args.visualize > 2:
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frame = cv2.putText(frame, f"{f.conf:.4f}", (int(f.bbox[0] + 18), int(f.bbox[1] - 6)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,255,0))
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for pt_num, (x,y,c) in enumerate(f.lms):
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packet.extend(bytearray(struct.pack("f", y)))
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packet.extend(bytearray(struct.pack("f", x)))
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@ -171,9 +175,7 @@ try:
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x = int(x + 0.5)
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y = int(y + 0.5)
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if args.visualize != 0 or not out is None:
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if args.visualize > 1:
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frame = cv2.putText(frame, str(f.id), (int(f.bbox[0]), int(f.bbox[1])), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (255,0,255))
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if args.visualize > 2:
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if args.visualize > 3:
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frame = cv2.putText(frame, str(pt_num), (int(y), int(x)), cv2.FONT_HERSHEY_SIMPLEX, 0.25, (255,255,0))
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color = (0, 0, 255)
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if pt_num >= 66:
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2
run.bat
2
run.bat
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@ -8,6 +8,6 @@ set /p width=Select the width:
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set /p height=Select the height:
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set /p fps=Select the FPS:
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facetracker -c %cameraNum% -W %width% -H %height% -F %fps% -v 1 -P 1
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facetracker -c %cameraNum% -W %width% -H %height% -F %fps% -v 3 -P 1
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pause
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17
tracker.py
17
tracker.py
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@ -405,10 +405,10 @@ class FaceInfo():
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class Tracker():
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def __init__(self, width, height, model_type=3, threshold=0.4, max_faces=1, discard_after=5, scan_every=3, bbox_growth=0.0, max_threads=4, silent=False, model_dir=None, no_gaze=False, use_retinaface=False):
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def __init__(self, width, height, model_type=3, threshold=0.6, max_faces=1, discard_after=5, scan_every=3, bbox_growth=0.0, max_threads=4, silent=False, model_dir=None, no_gaze=False, use_retinaface=False):
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options = onnxruntime.SessionOptions()
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options.inter_op_num_threads = 1
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options.intra_op_num_threads = max_threads
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options.intra_op_num_threads = max(max_threads,4)
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options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL
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options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
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options.log_severity_level = 3
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@ -427,7 +427,7 @@ class Tracker():
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else:
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model_base_path = model_dir
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self.retinaface = RetinaFaceDetector(model_path=os.path.join(model_base_path, "retinaface_640x640_opt.onnx"), json_path=os.path.join(model_base_path, "priorbox_640x640.json"), threads=max_threads, top_k=max_faces, res=(640, 640))
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self.retinaface = RetinaFaceDetector(model_path=os.path.join(model_base_path, "retinaface_640x640_opt.onnx"), json_path=os.path.join(model_base_path, "priorbox_640x640.json"), threads=max(max_threads,4), top_k=max_faces, res=(640, 640))
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self.retinaface_scan = RetinaFaceDetector(model_path=os.path.join(model_base_path, "retinaface_640x640_opt.onnx"), json_path=os.path.join(model_base_path, "priorbox_640x640.json"), threads=2, top_k=max_faces, res=(640, 640))
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self.use_retinaface = use_retinaface
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@ -453,7 +453,7 @@ class Tracker():
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options = onnxruntime.SessionOptions()
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options.inter_op_num_threads = 1
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options.intra_op_num_threads = max_threads
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options.intra_op_num_threads = max(max_threads,4)
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options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL
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options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
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options.log_severity_level = 3
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@ -597,18 +597,19 @@ class Tracker():
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crop_x1, crop_y1, scale_x, scale_y, _ = crop_info
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avg_conf = 0
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lms = []
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res = self.res - 1 if self.model_type == 3 else self.res
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for i in range(0, 66):
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m = int(tensor[i].argmax())
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x = m // 28
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y = m % 28
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conf = float(tensor[i][x,y])
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avg_conf = avg_conf + conf
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off_x = self.res * ((1. * logit(tensor[66 + i][x, y])) - 0.0)
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off_y = self.res * ((1. * logit(tensor[66 * 2 + i][x, y])) - 0.0)
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off_x = res * ((1. * logit(tensor[66 + i][x, y])) - 0.0)
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off_y = res * ((1. * logit(tensor[66 * 2 + i][x, y])) - 0.0)
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off_x = math.floor(off_x + 0.5)
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off_y = math.floor(off_y + 0.5)
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lm_x = crop_y1 + scale_y * (self.res * (float(x) / 28.) + off_x)
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lm_y = crop_x1 + scale_x * (self.res * (float(y) / 28.) + off_y)
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lm_x = crop_y1 + scale_y * (res * (float(x) / 27.) + off_x)
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lm_y = crop_x1 + scale_x * (res * (float(y) / 27.) + off_y)
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lms.append((lm_x,lm_y,conf))
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avg_conf = avg_conf / 66.
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return (avg_conf, lms)
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