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62 lines
2.3 KiB
62 lines
2.3 KiB
#!/usr/bin/env python3
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import cv2
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import sys
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import rospy
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import head_estimator as he
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import detection_node as dn
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import deep_learning_model_options as do
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if __name__ == '__main__':
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print('cv2.__version__ =', cv2.__version__)
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print('Python version (must be > 3.0):', sys.version)
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assert(int(sys.version[0]) >= 3)
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##############################################
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# Perform coarse filtering of 3D points using anthropometry
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#
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# 30cm should be significantly over the maximum dimensions of a human head
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# 10cm should be significantly smaller than the dimensions of an adult head
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# https://en.wikipedia.org/wiki/Human_head
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# children "attain 30% of adult head width by the middle of
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# prenatal life, 60% by birth, 80% by 6 postnatal months, 90%
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# by 3 years and 95% by 9 years" - GROWTH IN HEAD WIDTH DURING
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# THE FIRST TWELVE YEARS OF LIFE HOWARD V. MEREDITH, copyright
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# 1953 by the American Academy of Pediatrics
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# https://pediatrics.aappublications.org/content/12/4/411
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# Filtering for depths corresponding with heads with heights
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# or widths from 8cm to 40cm should be conservative.
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min_head_m = 0.08
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max_head_m = 0.4
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##############################################
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models_directory = do.get_directory()
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print('Using the following directory for deep learning models:', models_directory)
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use_neural_compute_stick = do.use_neural_compute_stick()
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if use_neural_compute_stick:
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print('Attempt to use an Intel Neural Compute Stick 2.')
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else:
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print('Not attempting to use an Intel Neural Compute Stick 2.')
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detector = he.HeadPoseEstimator(models_directory,
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use_neural_compute_stick=use_neural_compute_stick)
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default_marker_name = 'face'
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node_name = 'DetectFacesNode'
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topic_base_name = 'faces'
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fit_plane = False
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node = dn.DetectionNode(detector,
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default_marker_name,
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node_name,
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topic_base_name,
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fit_plane,
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min_box_side_m=min_head_m,
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max_box_side_m=max_head_m)
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node.main()
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try:
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rospy.spin()
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except KeyboardInterrupt:
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print('interrupt received, so shutting down')
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