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- #!/usr/bin/env python
- '''
- example to detect upright people in images using HOG features
- Usage:
- peopledetect.py <image_names>
- Press any key to continue, ESC to stop.
- '''
- # Python 2/3 compatibility
- from __future__ import print_function
- import numpy as np
- import cv2
- def inside(r, q):
- rx, ry, rw, rh = r
- qx, qy, qw, qh = q
- return rx > qx and ry > qy and rx + rw < qx + qw and ry + rh < qy + qh
- def draw_detections(img, rects, thickness = 1):
- for x, y, w, h in rects:
- # the HOG detector returns slightly larger rectangles than the real objects.
- # so we slightly shrink the rectangles to get a nicer output.
- pad_w, pad_h = int(0.15*w), int(0.05*h)
- cv2.rectangle(img, (x+pad_w, y+pad_h), (x+w-pad_w, y+h-pad_h), (0, 255, 0), thickness)
- if __name__ == '__main__':
- import sys
- from glob import glob
- import itertools as it
- print(__doc__)
- hog = cv2.HOGDescriptor()
- hog.setSVMDetector( cv2.HOGDescriptor_getDefaultPeopleDetector() )
- default = ['../data/basketball2.png '] if len(sys.argv[1:]) == 0 else []
- for fn in it.chain(*map(glob, default + sys.argv[1:])):
- print(fn, ' - ',)
- try:
- img = cv2.imread(fn)
- if img is None:
- print('Failed to load image file:', fn)
- continue
- except:
- print('loading error')
- continue
- found, w = hog.detectMultiScale(img, winStride=(8,8), padding=(32,32), scale=1.05)
- found_filtered = []
- for ri, r in enumerate(found):
- for qi, q in enumerate(found):
- if ri != qi and inside(r, q):
- break
- else:
- found_filtered.append(r)
- draw_detections(img, found)
- draw_detections(img, found_filtered, 3)
- print('%d (%d) found' % (len(found_filtered), len(found)))
- cv2.imshow('img', img)
- ch = 0xFF & cv2.waitKey()
- if ch == 27:
- break
- cv2.destroyAllWindows()
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