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Merge pull request #16248 from cudawarped:fix_python_cudawarping_cudaarithm

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* Move python CUDA  tests to the contrib repo.
pull/16478/head
cudawarped 5 years ago committed by GitHub
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commit 5542dcb2bc
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  1. 314
      modules/python/test/test_cuda.py

314
modules/python/test/test_cuda.py
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@ -26,319 +26,5 @@ class cuda_test(NewOpenCVTests):
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)
cuMatDst = cv.cuda_GpuMat(cuMat1.size(),cuMat1.type())
self.assertTrue(np.allclose(cv.cuda.add(cuMat1, cuMat2).download(),
cv.add(npMat1, npMat2)))
cv.cuda.add(cuMat1, cuMat2, cuMatDst)
self.assertTrue(np.allclose(cuMatDst.download(),cv.add(npMat1, npMat2)))
self.assertTrue(np.allclose(cv.cuda.subtract(cuMat1, cuMat2).download(),
cv.subtract(npMat1, npMat2)))
cv.cuda.subtract(cuMat1, cuMat2, cuMatDst)
self.assertTrue(np.allclose(cuMatDst.download(),cv.subtract(npMat1, npMat2)))
self.assertTrue(np.allclose(cv.cuda.multiply(cuMat1, cuMat2).download(),
cv.multiply(npMat1, npMat2)))
cv.cuda.multiply(cuMat1, cuMat2, cuMatDst)
self.assertTrue(np.allclose(cuMatDst.download(),cv.multiply(npMat1, npMat2)))
self.assertTrue(np.allclose(cv.cuda.divide(cuMat1, cuMat2).download(),
cv.divide(npMat1, npMat2)))
cv.cuda.divide(cuMat1, cuMat2, cuMatDst)
self.assertTrue(np.allclose(cuMatDst.download(),cv.divide(npMat1, npMat2)))
self.assertTrue(np.allclose(cv.cuda.absdiff(cuMat1, cuMat2).download(),
cv.absdiff(npMat1, npMat2)))
cv.cuda.absdiff(cuMat1, cuMat2, cuMatDst)
self.assertTrue(np.allclose(cuMatDst.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)))
cuMatDst1 = cv.cuda_GpuMat(cuMat1.size(),cv.CV_8UC3)
cv.cuda.compare(cuMat1, cuMat2, cv.CMP_GE, cuMatDst1)
self.assertTrue(np.allclose(cuMatDst1.download(),cv.compare(npMat1, npMat2, cv.CMP_GE)))
self.assertTrue(np.allclose(cv.cuda.abs(cuMat1).download(),
np.abs(npMat1)))
cv.cuda.abs(cuMat1, cuMatDst)
self.assertTrue(np.allclose(cuMatDst.download(),np.abs(npMat1)))
self.assertTrue(np.allclose(cv.cuda.sqrt(cv.cuda.sqr(cuMat1)).download(),
cv.cuda.abs(cuMat1).download()))
cv.cuda.sqr(cuMat1, cuMatDst)
cv.cuda.sqrt(cuMatDst, cuMatDst)
self.assertTrue(np.allclose(cuMatDst.download(),cv.cuda.abs(cuMat1).download()))
self.assertTrue(np.allclose(cv.cuda.log(cv.cuda.exp(cuMat1)).download(),
npMat1))
cv.cuda.exp(cuMat1, cuMatDst)
cv.cuda.log(cuMatDst, cuMatDst)
self.assertTrue(np.allclose(cuMatDst.download(),npMat1))
self.assertTrue(np.allclose(cv.cuda.pow(cuMat1, 2).download(),
cv.pow(npMat1, 2)))
cv.cuda.pow(cuMat1, 2, cuMatDst)
self.assertTrue(np.allclose(cuMatDst.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)
cuMatDst = cv.cuda_GpuMat(cuMat1.size(),cuMat1.type())
self.assertTrue(np.allclose(cv.cuda.bitwise_or(cuMat1, cuMat2).download(),
cv.bitwise_or(npMat1, npMat2)))
cv.cuda.bitwise_or(cuMat1, cuMat2, cuMatDst)
self.assertTrue(np.allclose(cuMatDst.download(),cv.bitwise_or(npMat1, npMat2)))
self.assertTrue(np.allclose(cv.cuda.bitwise_and(cuMat1, cuMat2).download(),
cv.bitwise_and(npMat1, npMat2)))
cv.cuda.bitwise_and(cuMat1, cuMat2, cuMatDst)
self.assertTrue(np.allclose(cuMatDst.download(),cv.bitwise_and(npMat1, npMat2)))
self.assertTrue(np.allclose(cv.cuda.bitwise_xor(cuMat1, cuMat2).download(),
cv.bitwise_xor(npMat1, npMat2)))
cv.cuda.bitwise_xor(cuMat1, cuMat2, cuMatDst)
self.assertTrue(np.allclose(cuMatDst.download(),cv.bitwise_xor(npMat1, npMat2)))
self.assertTrue(np.allclose(cv.cuda.bitwise_not(cuMat1).download(),
cv.bitwise_not(npMat1)))
cv.cuda.bitwise_not(cuMat1, cuMatDst)
self.assertTrue(np.allclose(cuMatDst.download(),cv.bitwise_not(npMat1)))
self.assertTrue(np.allclose(cv.cuda.min(cuMat1, cuMat2).download(),
cv.min(npMat1, npMat2)))
cv.cuda.min(cuMat1, cuMat2, cuMatDst)
self.assertTrue(np.allclose(cuMatDst.download(),cv.min(npMat1, npMat2)))
self.assertTrue(np.allclose(cv.cuda.max(cuMat1, cuMat2).download(),
cv.max(npMat1, npMat2)))
cv.cuda.max(cuMat1, cuMat2, cuMatDst)
self.assertTrue(np.allclose(cuMatDst.download(),cv.max(npMat1, npMat2)))
def test_cudaarithm_arithmetic(self):
npMat1 = (np.random.random((128, 128, 3)) * 255).astype(np.uint8)
cuMat1 = cv.cuda_GpuMat(npMat1)
cuMatDst = cv.cuda_GpuMat(cuMat1.size(),cuMat1.type())
cuMatB = cv.cuda_GpuMat(cuMat1.size(),cv.CV_8UC1)
cuMatG = cv.cuda_GpuMat(cuMat1.size(),cv.CV_8UC1)
cuMatR = cv.cuda_GpuMat(cuMat1.size(),cv.CV_8UC1)
self.assertTrue(np.allclose(cv.cuda.merge(cv.cuda.split(cuMat1)),npMat1))
cv.cuda.split(cuMat1,[cuMatB,cuMatG,cuMatR])
cv.cuda.merge([cuMatB,cuMatG,cuMatR],cuMatDst)
self.assertTrue(np.allclose(cuMatDst.download(),npMat1))
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
@unittest.skipIf('OPENCV_TEST_DATA_PATH' not in os.environ,
"OPENCV_TEST_DATA_PATH is not defined")
def test_cudacodec(self):
#Test the functionality but not the results of the video reader
vid_path = os.environ['OPENCV_TEST_DATA_PATH'] + '/cv/video/1920x1080.avi'
try:
reader = cv.cudacodec.createVideoReader(vid_path)
ret, gpu_mat = reader.nextFrame()
self.assertTrue(ret)
self.assertTrue('GpuMat' in str(type(gpu_mat)), msg=type(gpu_mat))
#TODO: print(cv.utils.dumpInputArray(gpu_mat)) # - no support for GpuMat
# not checking output, therefore sepearate tests for different signatures is unnecessary
ret, _gpu_mat2 = reader.nextFrame(gpu_mat)
#TODO: self.assertTrue(gpu_mat == gpu_mat2)
self.assertTrue(ret)
except cv.error as e:
notSupported = (e.code == cv.Error.StsNotImplemented or e.code == cv.Error.StsUnsupportedFormat or e.code == cv.Error.GPU_API_CALL_ERROR)
self.assertTrue(notSupported)
if e.code == cv.Error.StsNotImplemented:
self.skipTest("NVCUVID is not installed")
elif e.code == cv.Error.StsUnsupportedFormat:
self.skipTest("GPU hardware video decoder missing or video format not supported")
elif e.code == cv.Error.GPU_API_CALL_ERRROR:
self.skipTest("GPU hardware video decoder is missing")
else:
self.skipTest(e.err)
def test_cudacodec_writer_existence(self):
#Test at least the existence of wrapped functions for now
try:
_writer = cv.cudacodec.createVideoWriter("tmp", (128, 128), 30)
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()

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