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