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60 lines
1.9 KiB
Python
60 lines
1.9 KiB
Python
#!/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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stream = cv.cuda_Stream()
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self.assertTrue(stream.cudaPtr() != 0)
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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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if __name__ == '__main__':
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NewOpenCVTests.bootstrap()
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