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Open Source Computer Vision Library
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36 lines
1.5 KiB
36 lines
1.5 KiB
#!/usr/bin/env python |
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# Python 2/3 compatibility |
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from __future__ import print_function |
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import cv2 |
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import numpy as np |
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from tests_common import NewOpenCVTests |
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class TestGoodFeaturesToTrack_test(NewOpenCVTests): |
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def test_goodFeaturesToTrack(self): |
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arr = self.get_sample('samples/data/lena.jpg', 0) |
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original = arr.copy(True) |
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threshes = [ x / 100. for x in range(1,10) ] |
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numPoints = 20000 |
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results = dict([(t, cv2.goodFeaturesToTrack(arr, numPoints, t, 2, useHarrisDetector=True)) for t in threshes]) |
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# Check that GoodFeaturesToTrack has not modified input image |
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self.assertTrue(arr.tostring() == original.tostring()) |
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# Check for repeatability |
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for i in range(1): |
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results2 = dict([(t, cv2.goodFeaturesToTrack(arr, numPoints, t, 2, useHarrisDetector=True)) for t in threshes]) |
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for t in threshes: |
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self.assertTrue(len(results2[t]) == len(results[t])) |
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for i in range(len(results[t])): |
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self.assertTrue(cv2.norm(results[t][i][0] - results2[t][i][0]) == 0) |
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for t0,t1 in zip(threshes, threshes[1:]): |
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r0 = results[t0] |
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r1 = results[t1] |
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# Increasing thresh should make result list shorter |
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self.assertTrue(len(r0) > len(r1)) |
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# Increasing thresh should monly truncate result list |
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for i in range(len(r1)): |
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self.assertTrue(cv2.norm(r1[i][0] - r0[i][0])==0) |