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80 lines
2.2 KiB
Python
Executable File
80 lines
2.2 KiB
Python
Executable File
#!/usr/bin/env python
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'''
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face detection using haar cascades
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USAGE:
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facedetect.py [--cascade <cascade_fn>] [--nested-cascade <cascade_fn>] [<video_source>]
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'''
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# Python 2/3 compatibility
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from __future__ import print_function
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import numpy as np
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import cv2 as cv
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# local modules
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from video import create_capture
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from common import clock, draw_str
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def detect(img, cascade):
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rects = cascade.detectMultiScale(img, scaleFactor=1.3, minNeighbors=4, minSize=(30, 30),
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flags=cv.CASCADE_SCALE_IMAGE)
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if len(rects) == 0:
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return []
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rects[:,2:] += rects[:,:2]
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return rects
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def draw_rects(img, rects, color):
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for x1, y1, x2, y2 in rects:
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cv.rectangle(img, (x1, y1), (x2, y2), color, 2)
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def main():
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import sys, getopt
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args, video_src = getopt.getopt(sys.argv[1:], '', ['cascade=', 'nested-cascade='])
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try:
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video_src = video_src[0]
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except:
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video_src = 0
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args = dict(args)
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cascade_fn = args.get('--cascade', "data/haarcascades/haarcascade_frontalface_alt.xml")
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nested_fn = args.get('--nested-cascade', "data/haarcascades/haarcascade_eye.xml")
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cascade = cv.CascadeClassifier(cv.samples.findFile(cascade_fn))
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nested = cv.CascadeClassifier(cv.samples.findFile(nested_fn))
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cam = create_capture(video_src, fallback='synth:bg={}:noise=0.05'.format(cv.samples.findFile('samples/data/lena.jpg')))
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while True:
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_ret, img = cam.read()
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gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
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gray = cv.equalizeHist(gray)
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t = clock()
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rects = detect(gray, cascade)
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vis = img.copy()
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draw_rects(vis, rects, (0, 255, 0))
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if not nested.empty():
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for x1, y1, x2, y2 in rects:
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roi = gray[y1:y2, x1:x2]
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vis_roi = vis[y1:y2, x1:x2]
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subrects = detect(roi.copy(), nested)
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draw_rects(vis_roi, subrects, (255, 0, 0))
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dt = clock() - t
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draw_str(vis, (20, 20), 'time: %.1f ms' % (dt*1000))
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cv.imshow('facedetect', vis)
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if cv.waitKey(5) == 27:
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break
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print('Done')
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if __name__ == '__main__':
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print(__doc__)
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main()
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cv.destroyAllWindows()
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