101 lines
3.9 KiB
Python
101 lines
3.9 KiB
Python
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import argparse
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import numpy as np
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import cv2 as cv
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def str2bool(v):
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if v.lower() in ['on', 'yes', 'true', 'y', 't']:
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return True
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elif v.lower() in ['off', 'no', 'false', 'n', 'f']:
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return False
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else:
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raise NotImplementedError
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parser = argparse.ArgumentParser()
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parser.add_argument('--input', '-i', type=str, help='Path to the input image.')
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parser.add_argument('--model', '-m', type=str, default='yunet.onnx', help='Path to the model. Download the model at https://github.com/ShiqiYu/libfacedetection.train/tree/master/tasks/task1/onnx.')
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parser.add_argument('--score_threshold', type=float, default=0.9, help='Filtering out faces of score < score_threshold.')
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parser.add_argument('--nms_threshold', type=float, default=0.3, help='Suppress bounding boxes of iou >= nms_threshold.')
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parser.add_argument('--top_k', type=int, default=5000, help='Keep top_k bounding boxes before NMS.')
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parser.add_argument('--save', '-s', type=str2bool, default=False, help='Set true to save results. This flag is invalid when using camera.')
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parser.add_argument('--vis', '-v', type=str2bool, default=True, help='Set true to open a window for result visualization. This flag is invalid when using camera.')
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args = parser.parse_args()
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def visualize(input, faces, thickness=2):
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output = input.copy()
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if faces[1] is not None:
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for idx, face in enumerate(faces[1]):
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print('Face {}, top-left coordinates: ({:.0f}, {:.0f}), box width: {:.0f}, box height {:.0f}, score: {:.2f}'.format(idx, face[0], face[1], face[2], face[3], face[-1]))
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coords = face[:-1].astype(np.int32)
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cv.rectangle(output, (coords[0], coords[1]), (coords[0]+coords[2], coords[1]+coords[3]), (0, 255, 0), 2)
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cv.circle(output, (coords[4], coords[5]), 2, (255, 0, 0), 2)
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cv.circle(output, (coords[6], coords[7]), 2, (0, 0, 255), 2)
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cv.circle(output, (coords[8], coords[9]), 2, (0, 255, 0), 2)
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cv.circle(output, (coords[10], coords[11]), 2, (255, 0, 255), 2)
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cv.circle(output, (coords[12], coords[13]), 2, (0, 255, 255), 2)
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return output
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if __name__ == '__main__':
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# Instantiate FaceDetectorYN
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detector = cv.FaceDetectorYN.create(
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args.model,
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"",
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(320, 320),
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args.score_threshold,
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args.nms_threshold,
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args.top_k
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)
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# If input is an image
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if args.input is not None:
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image = cv.imread(args.input)
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# Set input size before inference
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detector.setInputSize((image.shape[1], image.shape[0]))
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# Inference
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faces = detector.detect(image)
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# Draw results on the input image
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result = visualize(image, faces)
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# Save results if save is true
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if args.save:
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print('Resutls saved to result.jpg\n')
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cv.imwrite('result.jpg', result)
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# Visualize results in a new window
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if args.vis:
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cv.namedWindow(args.input, cv.WINDOW_AUTOSIZE)
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cv.imshow(args.input, result)
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cv.waitKey(0)
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else: # Omit input to call default camera
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deviceId = 0
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cap = cv.VideoCapture(deviceId)
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frameWidth = int(cap.get(cv.CAP_PROP_FRAME_WIDTH))
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frameHeight = int(cap.get(cv.CAP_PROP_FRAME_HEIGHT))
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detector.setInputSize([frameWidth, frameHeight])
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tm = cv.TickMeter()
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while cv.waitKey(1) < 0:
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hasFrame, frame = cap.read()
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if not hasFrame:
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print('No frames grabbed!')
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break
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# Inference
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tm.start()
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faces = detector.detect(frame) # faces is a tuple
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tm.stop()
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# Draw results on the input image
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frame = visualize(frame, faces)
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cv.putText(frame, 'FPS: {}'.format(tm.getFPS()), (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))
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# Visualize results in a new Window
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cv.imshow('Live', frame)
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tm.reset()
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