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Open Source Computer Vision Library
https://opencv.org/
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147 lines
6.0 KiB
147 lines
6.0 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_cuda_upload_download_stream(self): |
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stream = cv.cuda_Stream() |
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npMat = (np.random.random((128, 128, 3)) * 255).astype(np.uint8) |
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cuMat = cv.cuda_GpuMat(128,128, cv.CV_8UC3) |
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cuMat.upload(npMat, stream) |
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npMat2 = cuMat.download(stream=stream) |
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stream.waitForCompletion() |
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self.assertTrue(np.allclose(npMat2, npMat)) |
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def test_cuda_interop(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(cuMat.cudaPtr() != 0) |
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cuMatFromPtrSz = cv.cuda.createGpuMatFromCudaMemory(cuMat.size(),cuMat.type(),cuMat.cudaPtr(), cuMat.step) |
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self.assertTrue(cuMat.cudaPtr() == cuMatFromPtrSz.cudaPtr()) |
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cuMatFromPtrRc = cv.cuda.createGpuMatFromCudaMemory(cuMat.size()[1],cuMat.size()[0],cuMat.type(),cuMat.cudaPtr(), cuMat.step) |
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self.assertTrue(cuMat.cudaPtr() == cuMatFromPtrRc.cudaPtr()) |
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stream = cv.cuda_Stream() |
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self.assertTrue(stream.cudaPtr() != 0) |
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streamFromPtr = cv.cuda.wrapStream(stream.cudaPtr()) |
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self.assertTrue(stream.cudaPtr() == streamFromPtr.cudaPtr()) |
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asyncstream = cv.cuda_Stream(1) # cudaStreamNonBlocking |
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self.assertTrue(asyncstream.cudaPtr() != 0) |
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def test_cuda_buffer_pool(self): |
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cv.cuda.setBufferPoolUsage(True) |
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cv.cuda.setBufferPoolConfig(cv.cuda.getDevice(), 1024 * 1024 * 64, 2) |
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stream_a = cv.cuda.Stream() |
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pool_a = cv.cuda.BufferPool(stream_a) |
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cuMat = pool_a.getBuffer(1024, 1024, cv.CV_8UC3) |
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cv.cuda.setBufferPoolUsage(False) |
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self.assertEqual(cuMat.size(), (1024, 1024)) |
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self.assertEqual(cuMat.type(), cv.CV_8UC3) |
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def test_cuda_release(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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cuMat.release() |
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self.assertTrue(cuMat.cudaPtr() == 0) |
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self.assertTrue(cuMat.step == 0) |
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self.assertTrue(cuMat.size() == (0, 0)) |
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def test_cuda_convertTo(self): |
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# setup |
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npMat_8UC4 = (np.random.random((128, 128, 4)) * 255).astype(np.uint8) |
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npMat_32FC4 = npMat_8UC4.astype(np.single) |
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new_type = cv.CV_32FC4 |
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# sync |
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# in/out |
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cuMat_8UC4 = cv.cuda_GpuMat(npMat_8UC4) |
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cuMat_32FC4 = cv.cuda_GpuMat(cuMat_8UC4.size(), new_type) |
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cuMat_32FC4_out = cuMat_8UC4.convertTo(new_type, cuMat_32FC4) |
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self.assertTrue(cuMat_32FC4.cudaPtr() == cuMat_32FC4_out.cudaPtr()) |
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npMat_32FC4_out = cuMat_32FC4.download() |
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self.assertTrue(np.array_equal(npMat_32FC4, npMat_32FC4_out)) |
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# out |
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cuMat_32FC4_out = cuMat_8UC4.convertTo(new_type) |
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npMat_32FC4_out = cuMat_32FC4.download() |
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self.assertTrue(np.array_equal(npMat_32FC4, npMat_32FC4_out)) |
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# async |
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stream = cv.cuda.Stream() |
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cuMat_32FC4 = cv.cuda_GpuMat(cuMat_8UC4.size(), new_type) |
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cuMat_32FC4_out = cuMat_8UC4.convertTo(new_type, cuMat_32FC4) |
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# in/out |
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cuMat_32FC4_out = cuMat_8UC4.convertTo(new_type, 1, 0, stream, cuMat_32FC4) |
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self.assertTrue(cuMat_32FC4.cudaPtr() == cuMat_32FC4_out.cudaPtr()) |
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npMat_32FC4_out = cuMat_32FC4.download(stream) |
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stream.waitForCompletion() |
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self.assertTrue(np.array_equal(npMat_32FC4, npMat_32FC4_out)) |
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# out |
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cuMat_32FC4_out = cuMat_8UC4.convertTo(new_type, 1, 0, stream) |
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npMat_32FC4_out = cuMat_32FC4.download(stream) |
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stream.waitForCompletion() |
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self.assertTrue(np.array_equal(npMat_32FC4, npMat_32FC4_out)) |
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def test_cuda_copyTo(self): |
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# setup |
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npMat_8UC4 = (np.random.random((128, 128, 4)) * 255).astype(np.uint8) |
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# sync |
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# in/out |
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cuMat_8UC4 = cv.cuda_GpuMat(npMat_8UC4) |
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cuMat_8UC4_dst = cv.cuda_GpuMat(cuMat_8UC4.size(), cuMat_8UC4.type()) |
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cuMat_8UC4_out = cuMat_8UC4.copyTo(cuMat_8UC4_dst) |
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self.assertTrue(cuMat_8UC4_out.cudaPtr() == cuMat_8UC4_dst.cudaPtr()) |
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npMat_8UC4_out = cuMat_8UC4_out.download() |
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self.assertTrue(np.array_equal(npMat_8UC4, npMat_8UC4_out)) |
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# out |
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cuMat_8UC4_out = cuMat_8UC4.copyTo() |
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npMat_8UC4_out = cuMat_8UC4_out.download() |
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self.assertTrue(np.array_equal(npMat_8UC4, npMat_8UC4_out)) |
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# async |
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stream = cv.cuda.Stream() |
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# in/out |
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cuMat_8UC4 = cv.cuda_GpuMat(npMat_8UC4) |
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cuMat_8UC4_dst = cv.cuda_GpuMat(cuMat_8UC4.size(), cuMat_8UC4.type()) |
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cuMat_8UC4_out = cuMat_8UC4.copyTo(cuMat_8UC4_dst, stream) |
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self.assertTrue(cuMat_8UC4_out.cudaPtr() == cuMat_8UC4_out.cudaPtr()) |
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npMat_8UC4_out = cuMat_8UC4_dst.download(stream) |
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stream.waitForCompletion() |
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self.assertTrue(np.array_equal(npMat_8UC4, npMat_8UC4_out)) |
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# out |
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cuMat_8UC4_out = cuMat_8UC4.copyTo(stream) |
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npMat_8UC4_out = cuMat_8UC4_out.download(stream) |
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stream.waitForCompletion() |
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self.assertTrue(np.array_equal(npMat_8UC4, npMat_8UC4_out)) |
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def test_cuda_denoising(self): |
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self.assertEqual(True, hasattr(cv.cuda, 'fastNlMeansDenoising')) |
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self.assertEqual(True, hasattr(cv.cuda, 'fastNlMeansDenoisingColored')) |
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self.assertEqual(True, hasattr(cv.cuda, 'nonLocalMeans')) |
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if __name__ == '__main__': |
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NewOpenCVTests.bootstrap()
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