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Fix mistake introcuded in previous PR and increase test coverage to avod this happening again

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pull/16177/head
cudawarped 5 years ago
parent ba7b0f4c54
commit d427cebd12
2 changed files with 68 additions and 1 deletions
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  1. 2
      modules/python/src2/gen2.py
  2. 67
      modules/python/test/test_cuda.py

2
modules/python/src2/gen2.py
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@ -349,7 +349,7 @@ class ArgInfo(object):
self.py_outputarg = False
def isbig(self):
return self.tp in ["Mat", "vector_Mat", "GpuMat", "UMat", "vector_UMat"] # or self.tp.startswith("vector")
return self.tp in ["Mat", "vector_Mat", "cuda::GpuMat", "GpuMat", "vector_GpuMat", "UMat", "vector_UMat"] # or self.tp.startswith("vector")
def crepr(self):
return "ArgInfo(\"%s\", %d)" % (self.name, self.outputarg)

67
modules/python/test/test_cuda.py
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@ -34,38 +34,71 @@ class cuda_test(NewOpenCVTests):
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)
@ -74,25 +107,59 @@ class cuda_test(NewOpenCVTests):
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

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