diff --git a/modules/python/test/test_cuda.py b/modules/python/test/test_cuda.py index 4d62491435..5c4d9606dd 100644 --- a/modules/python/test/test_cuda.py +++ b/modules/python/test/test_cuda.py @@ -26,319 +26,5 @@ class cuda_test(NewOpenCVTests): self.assertTrue(np.allclose(cuMat.download(), npMat)) - def test_cudaarithm_arithmetic(self): - npMat1 = np.random.random((128, 128, 3)) - 0.5 - npMat2 = np.random.random((128, 128, 3)) - 0.5 - - cuMat1 = cv.cuda_GpuMat() - cuMat2 = cv.cuda_GpuMat() - cuMat1.upload(npMat1) - cuMat2.upload(npMat2) - cuMatDst = cv.cuda_GpuMat(cuMat1.size(),cuMat1.type()) - - self.assertTrue(np.allclose(cv.cuda.add(cuMat1, cuMat2).download(), - cv.add(npMat1, npMat2))) - - cv.cuda.add(cuMat1, cuMat2, cuMatDst) - self.assertTrue(np.allclose(cuMatDst.download(),cv.add(npMat1, npMat2))) - - self.assertTrue(np.allclose(cv.cuda.subtract(cuMat1, cuMat2).download(), - cv.subtract(npMat1, npMat2))) - - cv.cuda.subtract(cuMat1, cuMat2, cuMatDst) - self.assertTrue(np.allclose(cuMatDst.download(),cv.subtract(npMat1, npMat2))) - - self.assertTrue(np.allclose(cv.cuda.multiply(cuMat1, cuMat2).download(), - cv.multiply(npMat1, npMat2))) - - cv.cuda.multiply(cuMat1, cuMat2, cuMatDst) - self.assertTrue(np.allclose(cuMatDst.download(),cv.multiply(npMat1, npMat2))) - - self.assertTrue(np.allclose(cv.cuda.divide(cuMat1, cuMat2).download(), - cv.divide(npMat1, npMat2))) - - cv.cuda.divide(cuMat1, cuMat2, cuMatDst) - self.assertTrue(np.allclose(cuMatDst.download(),cv.divide(npMat1, npMat2))) - - self.assertTrue(np.allclose(cv.cuda.absdiff(cuMat1, cuMat2).download(), - cv.absdiff(npMat1, npMat2))) - - cv.cuda.absdiff(cuMat1, cuMat2, cuMatDst) - self.assertTrue(np.allclose(cuMatDst.download(),cv.absdiff(npMat1, npMat2))) - - self.assertTrue(np.allclose(cv.cuda.compare(cuMat1, cuMat2, cv.CMP_GE).download(), - cv.compare(npMat1, npMat2, cv.CMP_GE))) - - cuMatDst1 = cv.cuda_GpuMat(cuMat1.size(),cv.CV_8UC3) - cv.cuda.compare(cuMat1, cuMat2, cv.CMP_GE, cuMatDst1) - self.assertTrue(np.allclose(cuMatDst1.download(),cv.compare(npMat1, npMat2, cv.CMP_GE))) - - self.assertTrue(np.allclose(cv.cuda.abs(cuMat1).download(), - np.abs(npMat1))) - - cv.cuda.abs(cuMat1, cuMatDst) - self.assertTrue(np.allclose(cuMatDst.download(),np.abs(npMat1))) - - self.assertTrue(np.allclose(cv.cuda.sqrt(cv.cuda.sqr(cuMat1)).download(), - cv.cuda.abs(cuMat1).download())) - - cv.cuda.sqr(cuMat1, cuMatDst) - cv.cuda.sqrt(cuMatDst, cuMatDst) - self.assertTrue(np.allclose(cuMatDst.download(),cv.cuda.abs(cuMat1).download())) - - self.assertTrue(np.allclose(cv.cuda.log(cv.cuda.exp(cuMat1)).download(), - npMat1)) - - cv.cuda.exp(cuMat1, cuMatDst) - cv.cuda.log(cuMatDst, cuMatDst) - self.assertTrue(np.allclose(cuMatDst.download(),npMat1)) - - self.assertTrue(np.allclose(cv.cuda.pow(cuMat1, 2).download(), - cv.pow(npMat1, 2))) - - cv.cuda.pow(cuMat1, 2, cuMatDst) - self.assertTrue(np.allclose(cuMatDst.download(),cv.pow(npMat1, 2))) - - def test_cudaarithm_logical(self): - npMat1 = (np.random.random((128, 128)) * 255).astype(np.uint8) - npMat2 = (np.random.random((128, 128)) * 255).astype(np.uint8) - - cuMat1 = cv.cuda_GpuMat() - cuMat2 = cv.cuda_GpuMat() - cuMat1.upload(npMat1) - cuMat2.upload(npMat2) - cuMatDst = cv.cuda_GpuMat(cuMat1.size(),cuMat1.type()) - - self.assertTrue(np.allclose(cv.cuda.bitwise_or(cuMat1, cuMat2).download(), - cv.bitwise_or(npMat1, npMat2))) - - cv.cuda.bitwise_or(cuMat1, cuMat2, cuMatDst) - self.assertTrue(np.allclose(cuMatDst.download(),cv.bitwise_or(npMat1, npMat2))) - - self.assertTrue(np.allclose(cv.cuda.bitwise_and(cuMat1, cuMat2).download(), - cv.bitwise_and(npMat1, npMat2))) - - cv.cuda.bitwise_and(cuMat1, cuMat2, cuMatDst) - self.assertTrue(np.allclose(cuMatDst.download(),cv.bitwise_and(npMat1, npMat2))) - - self.assertTrue(np.allclose(cv.cuda.bitwise_xor(cuMat1, cuMat2).download(), - cv.bitwise_xor(npMat1, npMat2))) - - cv.cuda.bitwise_xor(cuMat1, cuMat2, cuMatDst) - self.assertTrue(np.allclose(cuMatDst.download(),cv.bitwise_xor(npMat1, npMat2))) - - self.assertTrue(np.allclose(cv.cuda.bitwise_not(cuMat1).download(), - cv.bitwise_not(npMat1))) - - cv.cuda.bitwise_not(cuMat1, cuMatDst) - self.assertTrue(np.allclose(cuMatDst.download(),cv.bitwise_not(npMat1))) - - self.assertTrue(np.allclose(cv.cuda.min(cuMat1, cuMat2).download(), - cv.min(npMat1, npMat2))) - - cv.cuda.min(cuMat1, cuMat2, cuMatDst) - self.assertTrue(np.allclose(cuMatDst.download(),cv.min(npMat1, npMat2))) - - self.assertTrue(np.allclose(cv.cuda.max(cuMat1, cuMat2).download(), - cv.max(npMat1, npMat2))) - - cv.cuda.max(cuMat1, cuMat2, cuMatDst) - self.assertTrue(np.allclose(cuMatDst.download(),cv.max(npMat1, npMat2))) - - def test_cudaarithm_arithmetic(self): - npMat1 = (np.random.random((128, 128, 3)) * 255).astype(np.uint8) - - cuMat1 = cv.cuda_GpuMat(npMat1) - cuMatDst = cv.cuda_GpuMat(cuMat1.size(),cuMat1.type()) - cuMatB = cv.cuda_GpuMat(cuMat1.size(),cv.CV_8UC1) - cuMatG = cv.cuda_GpuMat(cuMat1.size(),cv.CV_8UC1) - cuMatR = cv.cuda_GpuMat(cuMat1.size(),cv.CV_8UC1) - - self.assertTrue(np.allclose(cv.cuda.merge(cv.cuda.split(cuMat1)),npMat1)) - - cv.cuda.split(cuMat1,[cuMatB,cuMatG,cuMatR]) - cv.cuda.merge([cuMatB,cuMatG,cuMatR],cuMatDst) - self.assertTrue(np.allclose(cuMatDst.download(),npMat1)) - - def test_cudabgsegm_existence(self): - #Test at least the existence of wrapped functions for now - - _bgsub = cv.cuda.createBackgroundSubtractorMOG() - _bgsub = cv.cuda.createBackgroundSubtractorMOG2() - - self.assertTrue(True) #It is sufficient that no exceptions have been there - - @unittest.skipIf('OPENCV_TEST_DATA_PATH' not in os.environ, - "OPENCV_TEST_DATA_PATH is not defined") - def test_cudacodec(self): - #Test the functionality but not the results of the video reader - - vid_path = os.environ['OPENCV_TEST_DATA_PATH'] + '/cv/video/1920x1080.avi' - try: - reader = cv.cudacodec.createVideoReader(vid_path) - ret, gpu_mat = reader.nextFrame() - self.assertTrue(ret) - self.assertTrue('GpuMat' in str(type(gpu_mat)), msg=type(gpu_mat)) - #TODO: print(cv.utils.dumpInputArray(gpu_mat)) # - no support for GpuMat - - # not checking output, therefore sepearate tests for different signatures is unnecessary - ret, _gpu_mat2 = reader.nextFrame(gpu_mat) - #TODO: self.assertTrue(gpu_mat == gpu_mat2) - self.assertTrue(ret) - except cv.error as e: - notSupported = (e.code == cv.Error.StsNotImplemented or e.code == cv.Error.StsUnsupportedFormat or e.code == cv.Error.GPU_API_CALL_ERROR) - self.assertTrue(notSupported) - if e.code == cv.Error.StsNotImplemented: - self.skipTest("NVCUVID is not installed") - elif e.code == cv.Error.StsUnsupportedFormat: - self.skipTest("GPU hardware video decoder missing or video format not supported") - elif e.code == cv.Error.GPU_API_CALL_ERRROR: - self.skipTest("GPU hardware video decoder is missing") - else: - self.skipTest(e.err) - - def test_cudacodec_writer_existence(self): - #Test at least the existence of wrapped functions for now - - try: - _writer = cv.cudacodec.createVideoWriter("tmp", (128, 128), 30) - except cv.error as e: - self.assertEqual(e.code, cv.Error.StsNotImplemented) - self.skipTest("NVCUVENC is not installed") - - self.assertTrue(True) #It is sufficient that no exceptions have been there - - def test_cudafeatures2d(self): - npMat1 = self.get_sample("samples/data/right01.jpg") - npMat2 = self.get_sample("samples/data/right02.jpg") - - cuMat1 = cv.cuda_GpuMat() - cuMat2 = cv.cuda_GpuMat() - cuMat1.upload(npMat1) - cuMat2.upload(npMat2) - - cuMat1 = cv.cuda.cvtColor(cuMat1, cv.COLOR_RGB2GRAY) - cuMat2 = cv.cuda.cvtColor(cuMat2, cv.COLOR_RGB2GRAY) - - fast = cv.cuda_FastFeatureDetector.create() - _kps = fast.detectAsync(cuMat1) - - orb = cv.cuda_ORB.create() - _kps1, descs1 = orb.detectAndComputeAsync(cuMat1, None) - _kps2, descs2 = orb.detectAndComputeAsync(cuMat2, None) - - bf = cv.cuda_DescriptorMatcher.createBFMatcher(cv.NORM_HAMMING) - matches = bf.match(descs1, descs2) - self.assertGreater(len(matches), 0) - matches = bf.knnMatch(descs1, descs2, 2) - self.assertGreater(len(matches), 0) - matches = bf.radiusMatch(descs1, descs2, 0.1) - self.assertGreater(len(matches), 0) - - self.assertTrue(True) #It is sufficient that no exceptions have been there - - def test_cudafilters_existence(self): - #Test at least the existence of wrapped functions for now - - _filter = cv.cuda.createBoxFilter(cv.CV_8UC1, -1, (3, 3)) - _filter = cv.cuda.createLinearFilter(cv.CV_8UC4, -1, np.eye(3)) - _filter = cv.cuda.createLaplacianFilter(cv.CV_16UC1, -1, ksize=3) - _filter = cv.cuda.createSeparableLinearFilter(cv.CV_8UC1, -1, np.eye(3), np.eye(3)) - _filter = cv.cuda.createDerivFilter(cv.CV_8UC1, -1, 1, 1, 3) - _filter = cv.cuda.createSobelFilter(cv.CV_8UC1, -1, 1, 1) - _filter = cv.cuda.createScharrFilter(cv.CV_8UC1, -1, 1, 0) - _filter = cv.cuda.createGaussianFilter(cv.CV_8UC1, -1, (3, 3), 16) - _filter = cv.cuda.createMorphologyFilter(cv.MORPH_DILATE, cv.CV_32FC1, np.eye(3)) - _filter = cv.cuda.createBoxMaxFilter(cv.CV_8UC1, (3, 3)) - _filter = cv.cuda.createBoxMinFilter(cv.CV_8UC1, (3, 3)) - _filter = cv.cuda.createRowSumFilter(cv.CV_8UC1, cv.CV_32FC1, 3) - _filter = cv.cuda.createColumnSumFilter(cv.CV_8UC1, cv.CV_32FC1, 3) - _filter = cv.cuda.createMedianFilter(cv.CV_8UC1, 3) - - self.assertTrue(True) #It is sufficient that no exceptions have been there - - def test_cudafilters_laplacian(self): - npMat = (np.random.random((128, 128)) * 255).astype(np.uint16) - cuMat = cv.cuda_GpuMat() - cuMat.upload(npMat) - - self.assertTrue(np.allclose(cv.cuda.createLaplacianFilter(cv.CV_16UC1, -1, ksize=3).apply(cuMat).download(), - cv.Laplacian(npMat, cv.CV_16UC1, ksize=3))) - - def test_cudaimgproc(self): - npC1 = (np.random.random((128, 128)) * 255).astype(np.uint8) - npC3 = (np.random.random((128, 128, 3)) * 255).astype(np.uint8) - npC4 = (np.random.random((128, 128, 4)) * 255).astype(np.uint8) - cuC1 = cv.cuda_GpuMat() - cuC3 = cv.cuda_GpuMat() - cuC4 = cv.cuda_GpuMat() - cuC1.upload(npC1) - cuC3.upload(npC3) - cuC4.upload(npC4) - - cv.cuda.cvtColor(cuC3, cv.COLOR_RGB2HSV) - cv.cuda.demosaicing(cuC1, cv.cuda.COLOR_BayerGR2BGR_MHT) - cv.cuda.gammaCorrection(cuC3) - cv.cuda.alphaComp(cuC4, cuC4, cv.cuda.ALPHA_XOR) - cv.cuda.calcHist(cuC1) - cv.cuda.equalizeHist(cuC1) - cv.cuda.evenLevels(3, 0, 255) - cv.cuda.meanShiftFiltering(cuC4, 10, 5) - cv.cuda.meanShiftProc(cuC4, 10, 5) - cv.cuda.bilateralFilter(cuC3, 3, 16, 3) - cv.cuda.blendLinear - - cv.cuda.meanShiftSegmentation(cuC4, 10, 5, 5).download() - - clahe = cv.cuda.createCLAHE() - clahe.apply(cuC1, cv.cuda_Stream.Null()) - - histLevels = cv.cuda.histEven(cuC3, 20, 0, 255) - cv.cuda.histRange(cuC1, histLevels) - - detector = cv.cuda.createCannyEdgeDetector(0, 100) - detector.detect(cuC1) - - detector = cv.cuda.createHoughLinesDetector(3, np.pi / 180, 20) - detector.detect(cuC1) - - detector = cv.cuda.createHoughSegmentDetector(3, np.pi / 180, 20, 5) - detector.detect(cuC1) - - detector = cv.cuda.createHoughCirclesDetector(3, 20, 10, 10, 20, 100) - detector.detect(cuC1) - - detector = cv.cuda.createGeneralizedHoughBallard() - #BUG: detect accept only Mat! - #Even if generate_gpumat_decls is set to True, it only wraps overload CUDA functions. - #The problem is that Mat and GpuMat are not fully compatible to enable system-wide overloading - #detector.detect(cuC1, cuC1, cuC1) - - detector = cv.cuda.createGeneralizedHoughGuil() - #BUG: same as above.. - #detector.detect(cuC1, cuC1, cuC1) - - detector = cv.cuda.createHarrisCorner(cv.CV_8UC1, 15, 5, 1) - detector.compute(cuC1) - - detector = cv.cuda.createMinEigenValCorner(cv.CV_8UC1, 15, 5, 1) - detector.compute(cuC1) - - detector = cv.cuda.createGoodFeaturesToTrackDetector(cv.CV_8UC1) - detector.detect(cuC1) - - matcher = cv.cuda.createTemplateMatching(cv.CV_8UC1, cv.TM_CCOEFF_NORMED) - matcher.match(cuC3, cuC3) - - self.assertTrue(True) #It is sufficient that no exceptions have been there - - def test_cudaimgproc_cvtColor(self): - npMat = (np.random.random((128, 128, 3)) * 255).astype(np.uint8) - cuMat = cv.cuda_GpuMat() - cuMat.upload(npMat) - - self.assertTrue(np.allclose(cv.cuda.cvtColor(cuMat, cv.COLOR_BGR2HSV).download(), - cv.cvtColor(npMat, cv.COLOR_BGR2HSV))) - if __name__ == '__main__': NewOpenCVTests.bootstrap()