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178 lines
7.7 KiB
178 lines
7.7 KiB
#!/usr/bin/env python |
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import os |
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import cv2 as cv |
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import numpy as np |
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from tests_common import NewOpenCVTests, unittest |
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class cudaarithm_test(NewOpenCVTests): |
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def setUp(self): |
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super(cudaarithm_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_cudaarithm(self): |
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npMat = (np.random.random((128, 128, 3)) * 255).astype(np.uint8) |
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cuMat = cv.cuda_GpuMat(npMat) |
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cuMatDst = cv.cuda_GpuMat(cuMat.size(),cuMat.type()) |
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cuMatB = cv.cuda_GpuMat(cuMat.size(),cv.CV_8UC1) |
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cuMatG = cv.cuda_GpuMat(cuMat.size(),cv.CV_8UC1) |
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cuMatR = cv.cuda_GpuMat(cuMat.size(),cv.CV_8UC1) |
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self.assertTrue(np.allclose(cv.cuda.merge(cv.cuda.split(cuMat)),npMat)) |
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cv.cuda.split(cuMat,[cuMatB,cuMatG,cuMatR]) |
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cv.cuda.merge([cuMatB,cuMatG,cuMatR],cuMatDst) |
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self.assertTrue(np.allclose(cuMatDst.download(),npMat)) |
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shift = (np.random.random((cuMat.channels(),)) * 8).astype(np.uint8).tolist() |
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self.assertTrue(np.allclose(cv.cuda.rshift(cuMat,shift).download(),npMat >> shift)) |
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cv.cuda.rshift(cuMat,shift,cuMatDst) |
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self.assertTrue(np.allclose(cuMatDst.download(),npMat >> shift)) |
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self.assertTrue(np.allclose(cv.cuda.lshift(cuMat,shift).download(),(npMat << shift).astype('uint8'))) |
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cv.cuda.lshift(cuMat,shift,cuMatDst) |
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self.assertTrue(np.allclose(cuMatDst.download(),(npMat << shift).astype('uint8'))) |
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def test_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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cuMatDst = cv.cuda_GpuMat(cuMat1.size(),cuMat1.type()) |
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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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cv.cuda.add(cuMat1, cuMat2, cuMatDst) |
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self.assertTrue(np.allclose(cuMatDst.download(),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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cv.cuda.subtract(cuMat1, cuMat2, cuMatDst) |
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self.assertTrue(np.allclose(cuMatDst.download(),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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cv.cuda.multiply(cuMat1, cuMat2, cuMatDst) |
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self.assertTrue(np.allclose(cuMatDst.download(),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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cv.cuda.divide(cuMat1, cuMat2, cuMatDst) |
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self.assertTrue(np.allclose(cuMatDst.download(),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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cv.cuda.absdiff(cuMat1, cuMat2, cuMatDst) |
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self.assertTrue(np.allclose(cuMatDst.download(),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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cuMatDst1 = cv.cuda_GpuMat(cuMat1.size(),cv.CV_8UC3) |
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cv.cuda.compare(cuMat1, cuMat2, cv.CMP_GE, cuMatDst1) |
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self.assertTrue(np.allclose(cuMatDst1.download(),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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cv.cuda.abs(cuMat1, cuMatDst) |
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self.assertTrue(np.allclose(cuMatDst.download(),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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cv.cuda.sqr(cuMat1, cuMatDst) |
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cv.cuda.sqrt(cuMatDst, cuMatDst) |
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self.assertTrue(np.allclose(cuMatDst.download(),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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cv.cuda.exp(cuMat1, cuMatDst) |
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cv.cuda.log(cuMatDst, cuMatDst) |
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self.assertTrue(np.allclose(cuMatDst.download(),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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cv.cuda.pow(cuMat1, 2, cuMatDst) |
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self.assertTrue(np.allclose(cuMatDst.download(),cv.pow(npMat1, 2))) |
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def test_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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cuMatDst = cv.cuda_GpuMat(cuMat1.size(),cuMat1.type()) |
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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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cv.cuda.bitwise_or(cuMat1, cuMat2, cuMatDst) |
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self.assertTrue(np.allclose(cuMatDst.download(),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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cv.cuda.bitwise_and(cuMat1, cuMat2, cuMatDst) |
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self.assertTrue(np.allclose(cuMatDst.download(),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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cv.cuda.bitwise_xor(cuMat1, cuMat2, cuMatDst) |
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self.assertTrue(np.allclose(cuMatDst.download(),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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cv.cuda.bitwise_not(cuMat1, cuMatDst) |
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self.assertTrue(np.allclose(cuMatDst.download(),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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cv.cuda.min(cuMat1, cuMat2, cuMatDst) |
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self.assertTrue(np.allclose(cuMatDst.download(),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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cv.cuda.max(cuMat1, cuMat2, cuMatDst) |
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self.assertTrue(np.allclose(cuMatDst.download(),cv.max(npMat1, npMat2))) |
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def test_convolution(self): |
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npMat = (np.random.random((128, 128)) * 255).astype(np.float32) |
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npDims = np.array(npMat.shape) |
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kernel = (np.random.random((3, 3)) * 1).astype(np.float32) |
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kernelDims = np.array(kernel.shape) |
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iS = (kernelDims/2).astype(int) |
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iE = npDims - kernelDims + iS |
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cuMat = cv.cuda_GpuMat(npMat) |
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cuKernel= cv.cuda_GpuMat(kernel) |
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cuMatDst = cv.cuda_GpuMat(tuple(npDims - kernelDims + 1), cuMat.type()) |
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conv = cv.cuda.createConvolution() |
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self.assertTrue(np.allclose(conv.convolve(cuMat,cuKernel,ccorr=True).download(), |
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cv.filter2D(npMat,-1,kernel,anchor=(-1,-1))[iS[0]:iE[0]+1,iS[1]:iE[1]+1])) |
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conv.convolve(cuMat,cuKernel,cuMatDst,True) |
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self.assertTrue(np.allclose(cuMatDst.download(), |
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cv.filter2D(npMat,-1,kernel,anchor=(-1,-1))[iS[0]:iE[0]+1,iS[1]:iE[1]+1])) |
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if __name__ == '__main__': |
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NewOpenCVTests.bootstrap() |