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