#!/usr/bin/env python ''' CUDA-accelerated Computer Vision functions ''' # Python 2/3 compatibility from __future__ import print_function import numpy as np import cv2 as cv from tests_common import NewOpenCVTests class cuda_test(NewOpenCVTests): def setUp(self): super(cuda_test, self).setUp() if not cv.cuda.getCudaEnabledDeviceCount(): self.skipTest("No CUDA-capable device is detected") def test_cuda_upload_download(self): npMat = (np.random.random((128, 128, 3)) * 255).astype(np.uint8) cuMat = cv.cuda_GpuMat() cuMat.upload(npMat) 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) self.assertTrue(np.allclose(cv.cuda.add(cuMat1, cuMat2).download(), cv.add(npMat1, npMat2))) self.assertTrue(np.allclose(cv.cuda.subtract(cuMat1, cuMat2).download(), cv.subtract(npMat1, npMat2))) self.assertTrue(np.allclose(cv.cuda.multiply(cuMat1, cuMat2).download(), cv.multiply(npMat1, npMat2))) self.assertTrue(np.allclose(cv.cuda.divide(cuMat1, cuMat2).download(), cv.divide(npMat1, npMat2))) self.assertTrue(np.allclose(cv.cuda.absdiff(cuMat1, cuMat2).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))) self.assertTrue(np.allclose(cv.cuda.abs(cuMat1).download(), np.abs(npMat1))) self.assertTrue(np.allclose(cv.cuda.sqrt(cv.cuda.sqr(cuMat1)).download(), cv.cuda.abs(cuMat1).download())) self.assertTrue(np.allclose(cv.cuda.log(cv.cuda.exp(cuMat1)).download(), npMat1)) self.assertTrue(np.allclose(cv.cuda.pow(cuMat1, 2).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) self.assertTrue(np.allclose(cv.cuda.bitwise_or(cuMat1, cuMat2).download(), cv.bitwise_or(npMat1, npMat2))) self.assertTrue(np.allclose(cv.cuda.bitwise_and(cuMat1, cuMat2).download(), cv.bitwise_and(npMat1, npMat2))) self.assertTrue(np.allclose(cv.cuda.bitwise_xor(cuMat1, cuMat2).download(), cv.bitwise_xor(npMat1, npMat2))) self.assertTrue(np.allclose(cv.cuda.bitwise_not(cuMat1).download(), cv.bitwise_not(npMat1))) self.assertTrue(np.allclose(cv.cuda.min(cuMat1, cuMat2).download(), cv.min(npMat1, npMat2))) self.assertTrue(np.allclose(cv.cuda.max(cuMat1, cuMat2).download(), cv.max(npMat1, npMat2))) 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 def test_cudacodec_existence(self): #Test at least the existence of wrapped functions for now try: writer = cv.cudacodec.createVideoWriter("tmp", (128, 128), 30) reader = cv.cudacodec.createVideoReader("tmp") 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()