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Open Source Computer Vision Library
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62 lines
1.8 KiB
62 lines
1.8 KiB
#!/usr/bin/env python |
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''' |
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example to detect upright people in images using HOG features |
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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 |
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def inside(r, q): |
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rx, ry, rw, rh = r |
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qx, qy, qw, qh = q |
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return rx > qx and ry > qy and rx + rw < qx + qw and ry + rh < qy + qh |
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from tests_common import NewOpenCVTests, intersectionRate |
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class peopledetect_test(NewOpenCVTests): |
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def test_peopledetect(self): |
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hog = cv2.HOGDescriptor() |
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hog.setSVMDetector( cv2.HOGDescriptor_getDefaultPeopleDetector() ) |
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dirPath = 'samples/data/' |
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samples = ['basketball1.png', 'basketball2.png'] |
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testPeople = [ |
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[[23, 76, 164, 477], [440, 22, 637, 478]], |
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[[23, 76, 164, 477], [440, 22, 637, 478]] |
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] |
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eps = 0.5 |
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for sample in samples: |
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img = self.get_sample(dirPath + sample, 0) |
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found, w = hog.detectMultiScale(img, winStride=(8,8), padding=(32,32), scale=1.05) |
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found_filtered = [] |
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for ri, r in enumerate(found): |
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for qi, q in enumerate(found): |
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if ri != qi and inside(r, q): |
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break |
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else: |
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found_filtered.append(r) |
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matches = 0 |
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for i in range(len(found_filtered)): |
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for j in range(len(testPeople)): |
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found_rect = (found_filtered[i][0], found_filtered[i][1], |
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found_filtered[i][0] + found_filtered[i][2], |
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found_filtered[i][1] + found_filtered[i][3]) |
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if intersectionRate(found_rect, testPeople[j][0]) > eps or intersectionRate(found_rect, testPeople[j][1]) > eps: |
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matches += 1 |
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self.assertGreater(matches, 0) |