mirror of
https://github.com/opencv/opencv_contrib.git
synced 2025-10-20 04:25:42 +08:00
Merge pull request #2396 from cudawarped:fix_python_cudawarping_cudaarithm
Add python bindings to cudaobjdetect, cudawarping and cudaarithm * Overload cudawarping functions to generate correct python bindings. Add python wrapper to convolution funciton. * Added shift and hog. * Moved cuda python tests to this repo and added python bindings to SURF. * Fix SURF documentation and allow meanshiftsegmention to create GpuMat internaly if not passed for python bindings consistency. * Add correct cuda SURF test case. * Fix python mog and mog2 python bindings, add tests and correct cudawarping documentation. * Updated KeyPoints in cuda::ORB::Convert python wrapper to be an output argument. * Add changes suggested by alalek * Added changes suggested by asmorkalov
This commit is contained in:
cudawarped
178
modules/cudaarithm/misc/python/test/test_cudaarithm.py
Normal file
178
modules/cudaarithm/misc/python/test/test_cudaarithm.py
Normal file
@@ -0,0 +1,178 @@
|
||||
#!/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)))
|
||||
|
||||
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]))
|
||||
|
||||
if __name__ == '__main__':
|
||||
NewOpenCVTests.bootstrap()
|
||||
Reference in New Issue
Block a user