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#!/usr/bin/env python
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'''
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CUDA-accelerated Computer Vision functions
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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 as cv
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from tests_common import NewOpenCVTests
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class cuda_test(NewOpenCVTests):
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def setUp(self):
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super(cuda_test, self).setUp()
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if not cv.cuda.getCudaEnabledDeviceCount():
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self.skipTest("No CUDA-capable device is detected")
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def test_cuda_upload_download(self):
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npMat = (np.random.random((128, 128, 3)) * 255).astype(np.uint8)
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cuMat = cv.cuda_GpuMat()
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cuMat.upload(npMat)
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self.assertTrue(np.allclose(cuMat.download(), npMat))
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def test_cudaarithm_arithmetic(self):
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npMat1 = np.random.random((128, 128, 3)) - 0.5
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npMat2 = np.random.random((128, 128, 3)) - 0.5
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cuMat1 = cv.cuda_GpuMat()
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cuMat2 = cv.cuda_GpuMat()
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cuMat1.upload(npMat1)
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cuMat2.upload(npMat2)
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self.assertTrue(np.allclose(cv.cuda.add(cuMat1, cuMat2).download(),
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cv.add(npMat1, npMat2)))
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self.assertTrue(np.allclose(cv.cuda.subtract(cuMat1, cuMat2).download(),
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cv.subtract(npMat1, npMat2)))
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self.assertTrue(np.allclose(cv.cuda.multiply(cuMat1, cuMat2).download(),
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cv.multiply(npMat1, npMat2)))
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self.assertTrue(np.allclose(cv.cuda.divide(cuMat1, cuMat2).download(),
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cv.divide(npMat1, npMat2)))
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self.assertTrue(np.allclose(cv.cuda.absdiff(cuMat1, cuMat2).download(),
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cv.absdiff(npMat1, npMat2)))
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self.assertTrue(np.allclose(cv.cuda.compare(cuMat1, cuMat2, cv.CMP_GE).download(),
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cv.compare(npMat1, npMat2, cv.CMP_GE)))
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self.assertTrue(np.allclose(cv.cuda.abs(cuMat1).download(),
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np.abs(npMat1)))
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self.assertTrue(np.allclose(cv.cuda.sqrt(cv.cuda.sqr(cuMat1)).download(),
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cv.cuda.abs(cuMat1).download()))
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self.assertTrue(np.allclose(cv.cuda.log(cv.cuda.exp(cuMat1)).download(),
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npMat1))
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self.assertTrue(np.allclose(cv.cuda.pow(cuMat1, 2).download(),
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cv.pow(npMat1, 2)))
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def test_cudaarithm_logical(self):
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npMat1 = (np.random.random((128, 128)) * 255).astype(np.uint8)
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npMat2 = (np.random.random((128, 128)) * 255).astype(np.uint8)
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cuMat1 = cv.cuda_GpuMat()
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cuMat2 = cv.cuda_GpuMat()
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cuMat1.upload(npMat1)
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cuMat2.upload(npMat2)
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self.assertTrue(np.allclose(cv.cuda.bitwise_or(cuMat1, cuMat2).download(),
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cv.bitwise_or(npMat1, npMat2)))
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self.assertTrue(np.allclose(cv.cuda.bitwise_and(cuMat1, cuMat2).download(),
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cv.bitwise_and(npMat1, npMat2)))
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self.assertTrue(np.allclose(cv.cuda.bitwise_xor(cuMat1, cuMat2).download(),
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cv.bitwise_xor(npMat1, npMat2)))
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self.assertTrue(np.allclose(cv.cuda.bitwise_not(cuMat1).download(),
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cv.bitwise_not(npMat1)))
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self.assertTrue(np.allclose(cv.cuda.min(cuMat1, cuMat2).download(),
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cv.min(npMat1, npMat2)))
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self.assertTrue(np.allclose(cv.cuda.max(cuMat1, cuMat2).download(),
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cv.max(npMat1, npMat2)))
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def test_cudabgsegm_existence(self):
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#Test at least the existence of wrapped functions for now
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bgsub = cv.cuda.createBackgroundSubtractorMOG()
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bgsub = cv.cuda.createBackgroundSubtractorMOG2()
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self.assertTrue(True) #It is sufficient that no exceptions have been there
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def test_cudacodec_existence(self):
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#Test at least the existence of wrapped functions for now
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try:
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writer = cv.cudacodec.createVideoWriter("tmp", (128, 128), 30)
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reader = cv.cudacodec.createVideoReader("tmp")
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except cv.error as e:
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self.assertEqual(e.code, cv.Error.StsNotImplemented)
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self.skipTest("NVCUVENC is not installed")
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self.assertTrue(True) #It is sufficient that no exceptions have been there
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def test_cudafeatures2d(self):
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npMat1 = self.get_sample("samples/data/right01.jpg")
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npMat2 = self.get_sample("samples/data/right02.jpg")
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cuMat1 = cv.cuda_GpuMat()
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cuMat2 = cv.cuda_GpuMat()
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cuMat1.upload(npMat1)
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cuMat2.upload(npMat2)
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cuMat1 = cv.cuda.cvtColor(cuMat1, cv.COLOR_RGB2GRAY)
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cuMat2 = cv.cuda.cvtColor(cuMat2, cv.COLOR_RGB2GRAY)
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fast = cv.cuda_FastFeatureDetector.create()
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kps = fast.detectAsync(cuMat1)
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orb = cv.cuda_ORB.create()
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kps1, descs1 = orb.detectAndComputeAsync(cuMat1, None)
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kps2, descs2 = orb.detectAndComputeAsync(cuMat2, None)
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bf = cv.cuda_DescriptorMatcher.createBFMatcher(cv.NORM_HAMMING)
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matches = bf.match(descs1, descs2)
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self.assertGreater(len(matches), 0)
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matches = bf.knnMatch(descs1, descs2, 2)
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self.assertGreater(len(matches), 0)
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matches = bf.radiusMatch(descs1, descs2, 0.1)
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self.assertGreater(len(matches), 0)
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self.assertTrue(True) #It is sufficient that no exceptions have been there
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def test_cudafilters_existence(self):
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#Test at least the existence of wrapped functions for now
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filter = cv.cuda.createBoxFilter(cv.CV_8UC1, -1, (3, 3))
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filter = cv.cuda.createLinearFilter(cv.CV_8UC4, -1, np.eye(3))
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filter = cv.cuda.createLaplacianFilter(cv.CV_16UC1, -1, ksize=3)
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filter = cv.cuda.createSeparableLinearFilter(cv.CV_8UC1, -1, np.eye(3), np.eye(3))
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filter = cv.cuda.createDerivFilter(cv.CV_8UC1, -1, 1, 1, 3)
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filter = cv.cuda.createSobelFilter(cv.CV_8UC1, -1, 1, 1)
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filter = cv.cuda.createScharrFilter(cv.CV_8UC1, -1, 1, 0)
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filter = cv.cuda.createGaussianFilter(cv.CV_8UC1, -1, (3, 3), 16)
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filter = cv.cuda.createMorphologyFilter(cv.MORPH_DILATE, cv.CV_32FC1, np.eye(3))
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filter = cv.cuda.createBoxMaxFilter(cv.CV_8UC1, (3, 3))
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filter = cv.cuda.createBoxMinFilter(cv.CV_8UC1, (3, 3))
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filter = cv.cuda.createRowSumFilter(cv.CV_8UC1, cv.CV_32FC1, 3)
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filter = cv.cuda.createColumnSumFilter(cv.CV_8UC1, cv.CV_32FC1, 3)
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filter = cv.cuda.createMedianFilter(cv.CV_8UC1, 3)
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self.assertTrue(True) #It is sufficient that no exceptions have been there
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def test_cudafilters_laplacian(self):
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npMat = (np.random.random((128, 128)) * 255).astype(np.uint16)
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cuMat = cv.cuda_GpuMat()
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cuMat.upload(npMat)
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self.assertTrue(np.allclose(cv.cuda.createLaplacianFilter(cv.CV_16UC1, -1, ksize=3).apply(cuMat).download(),
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cv.Laplacian(npMat, cv.CV_16UC1, ksize=3)))
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def test_cudaimgproc(self):
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npC1 = (np.random.random((128, 128)) * 255).astype(np.uint8)
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npC3 = (np.random.random((128, 128, 3)) * 255).astype(np.uint8)
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npC4 = (np.random.random((128, 128, 4)) * 255).astype(np.uint8)
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cuC1 = cv.cuda_GpuMat()
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cuC3 = cv.cuda_GpuMat()
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cuC4 = cv.cuda_GpuMat()
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cuC1.upload(npC1)
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cuC3.upload(npC3)
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cuC4.upload(npC4)
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cv.cuda.cvtColor(cuC3, cv.COLOR_RGB2HSV)
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cv.cuda.demosaicing(cuC1, cv.cuda.COLOR_BayerGR2BGR_MHT)
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cv.cuda.gammaCorrection(cuC3)
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cv.cuda.alphaComp(cuC4, cuC4, cv.cuda.ALPHA_XOR)
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cv.cuda.calcHist(cuC1)
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cv.cuda.equalizeHist(cuC1)
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cv.cuda.evenLevels(3, 0, 255)
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cv.cuda.meanShiftFiltering(cuC4, 10, 5)
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cv.cuda.meanShiftProc(cuC4, 10, 5)
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cv.cuda.bilateralFilter(cuC3, 3, 16, 3)
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cv.cuda.blendLinear
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cv.cuda.meanShiftSegmentation(cuC4, 10, 5, 5).download()
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clahe = cv.cuda.createCLAHE()
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clahe.apply(cuC1, cv.cuda_Stream.Null());
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histLevels = cv.cuda.histEven(cuC3, 20, 0, 255)
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cv.cuda.histRange(cuC1, histLevels)
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detector = cv.cuda.createCannyEdgeDetector(0, 100)
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detector.detect(cuC1)
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detector = cv.cuda.createHoughLinesDetector(3, np.pi / 180, 20)
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detector.detect(cuC1)
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detector = cv.cuda.createHoughSegmentDetector(3, np.pi / 180, 20, 5)
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detector.detect(cuC1)
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detector = cv.cuda.createHoughCirclesDetector(3, 20, 10, 10, 20, 100)
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detector.detect(cuC1)
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detector = cv.cuda.createGeneralizedHoughBallard()
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#BUG: detect accept only Mat!
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#Even if generate_gpumat_decls is set to True, it only wraps overload CUDA functions.
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#The problem is that Mat and GpuMat are not fully compatible to enable system-wide overloading
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#detector.detect(cuC1, cuC1, cuC1)
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detector = cv.cuda.createGeneralizedHoughGuil()
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#BUG: same as above..
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#detector.detect(cuC1, cuC1, cuC1)
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detector = cv.cuda.createHarrisCorner(cv.CV_8UC1, 15, 5, 1)
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detector.compute(cuC1)
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detector = cv.cuda.createMinEigenValCorner(cv.CV_8UC1, 15, 5, 1)
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detector.compute(cuC1)
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detector = cv.cuda.createGoodFeaturesToTrackDetector(cv.CV_8UC1)
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detector.detect(cuC1)
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matcher = cv.cuda.createTemplateMatching(cv.CV_8UC1, cv.TM_CCOEFF_NORMED)
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matcher.match(cuC3, cuC3)
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self.assertTrue(True) #It is sufficient that no exceptions have been there
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def test_cudaimgproc_cvtColor(self):
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npMat = (np.random.random((128, 128, 3)) * 255).astype(np.uint8)
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cuMat = cv.cuda_GpuMat()
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cuMat.upload(npMat)
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self.assertTrue(np.allclose(cv.cuda.cvtColor(cuMat, cv.COLOR_BGR2HSV).download(),
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cv.cvtColor(npMat, cv.COLOR_BGR2HSV)))
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if __name__ == '__main__':
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NewOpenCVTests.bootstrap()
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