mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
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277 lines
11 KiB
277 lines
11 KiB
#!/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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import os |
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from tests_common import NewOpenCVTests, unittest |
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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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@unittest.skipIf('OPENCV_TEST_DATA_PATH' not in os.environ, |
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"OPENCV_TEST_DATA_PATH is not defined") |
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def test_cudacodec(self): |
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#Test the functionality but not the results of the video reader |
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vid_path = os.environ['OPENCV_TEST_DATA_PATH'] + '/cv/video/1920x1080.avi' |
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try: |
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reader = cv.cudacodec.createVideoReader(vid_path) |
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ret, gpu_mat = reader.nextFrame() |
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self.assertTrue(ret) |
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self.assertTrue('GpuMat' in str(type(gpu_mat)), msg=type(gpu_mat)) |
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#TODO: print(cv.utils.dumpInputArray(gpu_mat)) # - no support for GpuMat |
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# not checking output, therefore sepearate tests for different signatures is unecessary |
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ret, _gpu_mat2 = reader.nextFrame(gpu_mat) |
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#TODO: self.assertTrue(gpu_mat == gpu_mat2) |
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self.assertTrue(ret) |
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except cv.error as e: |
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notSupported = (e.code == cv.Error.StsNotImplemented or e.code == cv.Error.StsUnsupportedFormat or e.code == cv.Error.GPU_API_CALL_ERROR) |
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self.assertTrue(notSupported) |
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if e.code == cv.Error.StsNotImplemented: |
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self.skipTest("NVCUVID is not installed") |
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elif e.code == cv.Error.StsUnsupportedFormat: |
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self.skipTest("GPU hardware video decoder missing or video format not supported") |
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elif e.code == cv.Error.GPU_API_CALL_ERRROR: |
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self.skipTest("GPU hardware video decoder is missing") |
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else: |
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self.skipTest(e.err) |
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def test_cudacodec_writer_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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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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