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opencv_contrib/modules/cudaarithm/misc/python/test/test_cudaarithm.py

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#!/usr/bin/env python
import os
import cv2 as cv
import numpy as np
from tests_common import NewOpenCVTests, unittest
class cudaarithm_test(NewOpenCVTests):
def setUp(self):
super(cudaarithm_test, self).setUp()
if not cv.cuda.getCudaEnabledDeviceCount():
self.skipTest("No CUDA-capable device is detected")
def test_cudaarithm(self):
npMat = (np.random.random((128, 128, 3)) * 255).astype(np.uint8)
cuMat = cv.cuda_GpuMat(npMat)
cuMatDst = cv.cuda_GpuMat(cuMat.size(),cuMat.type())
cuMatB = cv.cuda_GpuMat(cuMat.size(),cv.CV_8UC1)
cuMatG = cv.cuda_GpuMat(cuMat.size(),cv.CV_8UC1)
cuMatR = cv.cuda_GpuMat(cuMat.size(),cv.CV_8UC1)
self.assertTrue(np.allclose(cv.cuda.merge(cv.cuda.split(cuMat)),npMat))
cv.cuda.split(cuMat,[cuMatB,cuMatG,cuMatR])
cv.cuda.merge([cuMatB,cuMatG,cuMatR],cuMatDst)
self.assertTrue(np.allclose(cuMatDst.download(),npMat))
shift = (np.random.random((cuMat.channels(),)) * 8).astype(np.uint8).tolist()
self.assertTrue(np.allclose(cv.cuda.rshift(cuMat,shift).download(),npMat >> shift))
cv.cuda.rshift(cuMat,shift,cuMatDst)
self.assertTrue(np.allclose(cuMatDst.download(),npMat >> shift))
self.assertTrue(np.allclose(cv.cuda.lshift(cuMat,shift).download(),(npMat << shift).astype('uint8')))
cv.cuda.lshift(cuMat,shift,cuMatDst)
self.assertTrue(np.allclose(cuMatDst.download(),(npMat << shift).astype('uint8')))
def test_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_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)))
self.assertTrue(cv.cuda.minMax(cuMat1),cv.minMaxLoc(npMat1)[:2])
self.assertTrue(cv.cuda.minMaxLoc(cuMat1),cv.minMaxLoc(npMat1))
def test_convolution(self):
npMat = (np.random.random((128, 128)) * 255).astype(np.float32)
npDims = np.array(npMat.shape)
kernel = (np.random.random((3, 3)) * 1).astype(np.float32)
kernelDims = np.array(kernel.shape)
iS = (kernelDims/2).astype(int)
iE = npDims - kernelDims + iS
cuMat = cv.cuda_GpuMat(npMat)
cuKernel= cv.cuda_GpuMat(kernel)
cuMatDst = cv.cuda_GpuMat(tuple(npDims - kernelDims + 1), cuMat.type())
conv = cv.cuda.createConvolution()
self.assertTrue(np.allclose(conv.convolve(cuMat,cuKernel,ccorr=True).download(),
cv.filter2D(npMat,-1,kernel,anchor=(-1,-1))[iS[0]:iE[0]+1,iS[1]:iE[1]+1]))
conv.convolve(cuMat,cuKernel,cuMatDst,True)
self.assertTrue(np.allclose(cuMatDst.download(),
cv.filter2D(npMat,-1,kernel,anchor=(-1,-1))[iS[0]:iE[0]+1,iS[1]:iE[1]+1]))
def test_inrange(self):
npMat = (np.random.random((128, 128, 3)) * 255).astype(np.float32)
bound1 = np.random.random((4,)) * 255
bound2 = np.random.random((4,)) * 255
lowerb = np.minimum(bound1, bound2).tolist()
upperb = np.maximum(bound1, bound2).tolist()
cuMat = cv.cuda_GpuMat()
cuMat.upload(npMat)
self.assertTrue((cv.cuda.inRange(cuMat, lowerb, upperb).download() ==
cv.inRange(npMat, np.array(lowerb), np.array(upperb))).all())
cuMatDst = cv.cuda_GpuMat(cuMat.size(), cv.CV_8UC1)
cv.cuda.inRange(cuMat, lowerb, upperb, cuMatDst)
self.assertTrue((cuMatDst.download() ==
cv.inRange(npMat, np.array(lowerb), np.array(upperb))).all())
if __name__ == '__main__':
NewOpenCVTests.bootstrap()
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