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#!/usr/bin/env python
'''
example to detect upright people in images using HOG features
'''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2 as cv
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
from tests_common import NewOpenCVTests, intersectionRate
class peopledetect_test(NewOpenCVTests):
def test_peopledetect(self):
hog = cv.HOGDescriptor()
hog.setSVMDetector( cv.HOGDescriptor_getDefaultPeopleDetector() )
dirPath = 'samples/data/'
samples = ['basketball1.png', 'basketball2.png']
testPeople = [
[[23, 76, 164, 477], [440, 22, 637, 478]],
[[23, 76, 164, 477], [440, 22, 637, 478]]
]
eps = 0.5
for sample in samples:
img = self.get_sample(dirPath + sample, 0)
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)
matches = 0
for i in range(len(found_filtered)):
for j in range(len(testPeople)):
found_rect = (found_filtered[i][0], found_filtered[i][1],
found_filtered[i][0] + found_filtered[i][2],
found_filtered[i][1] + found_filtered[i][3])
if intersectionRate(found_rect, testPeople[j][0]) > eps or intersectionRate(found_rect, testPeople[j][1]) > eps:
matches += 1
self.assertGreater(matches, 0)
if __name__ == '__main__':
NewOpenCVTests.bootstrap()
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