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#!/usr/bin/env python
from __future__ import print_function
import numpy as np
import cv2 as cv
from tests_common import NewOpenCVTests
class UMat(NewOpenCVTests):
def test_umat_construct(self):
data = np.random.random([512, 512])
# UMat constructors
data_um = cv.UMat(data) # from ndarray
data_sub_um = cv.UMat(data_um, [128, 256], [128, 256]) # from UMat
data_dst_um = cv.UMat(128, 128, cv.CV_64F) # from size/type
# test continuous and submatrix flags
assert data_um.isContinuous() and not data_um.isSubmatrix()
assert not data_sub_um.isContinuous() and data_sub_um.isSubmatrix()
# test operation on submatrix
cv.multiply(data_sub_um, 2., dst=data_dst_um)
assert np.allclose(2. * data[128:256, 128:256], data_dst_um.get())
def test_umat_handle(self):
a_um = cv.UMat(256, 256, cv.CV_32F)
_ctx_handle = cv.UMat.context() # obtain context handle
_queue_handle = cv.UMat.queue() # obtain queue handle
_a_handle = a_um.handle(cv.ACCESS_READ) # obtain buffer handle
_offset = a_um.offset # obtain buffer offset
def test_umat_matching(self):
img1 = self.get_sample("samples/data/right01.jpg")
img2 = self.get_sample("samples/data/right02.jpg")
orb = cv.ORB_create()
img1, img2 = cv.UMat(img1), cv.UMat(img2)
ps1, descs_umat1 = orb.detectAndCompute(img1, None)
ps2, descs_umat2 = orb.detectAndCompute(img2, None)
self.assertIsInstance(descs_umat1, cv.UMat)
self.assertIsInstance(descs_umat2, cv.UMat)
self.assertGreater(len(ps1), 0)
self.assertGreater(len(ps2), 0)
bf = cv.BFMatcher(cv.NORM_HAMMING, crossCheck=True)
res_umats = bf.match(descs_umat1, descs_umat2)
res = bf.match(descs_umat1.get(), descs_umat2.get())
self.assertGreater(len(res), 0)
self.assertEqual(len(res_umats), len(res))
def test_umat_optical_flow(self):
img1 = self.get_sample("samples/data/right01.jpg", cv.IMREAD_GRAYSCALE)
img2 = self.get_sample("samples/data/right02.jpg", cv.IMREAD_GRAYSCALE)
# Note, that if you want to see performance boost by OCL implementation - you need enough data
# For example you can increase maxCorners param to 10000 and increase img1 and img2 in such way:
# img = np.hstack([np.vstack([img] * 6)] * 6)
feature_params = dict(maxCorners=239,
qualityLevel=0.3,
minDistance=7,
blockSize=7)
p0 = cv.goodFeaturesToTrack(img1, mask=None, **feature_params)
p0_umat = cv.goodFeaturesToTrack(cv.UMat(img1), mask=None, **feature_params)
self.assertEqual(p0_umat.get().shape, p0.shape)
p0 = np.array(sorted(p0, key=lambda p: tuple(p[0])))
p0_umat = cv.UMat(np.array(sorted(p0_umat.get(), key=lambda p: tuple(p[0]))))
self.assertTrue(np.allclose(p0_umat.get(), p0))
_p1_mask_err = cv.calcOpticalFlowPyrLK(img1, img2, p0, None)
_p1_mask_err_umat0 = map(cv.UMat.get, cv.calcOpticalFlowPyrLK(img1, img2, p0_umat, None))
_p1_mask_err_umat1 = map(cv.UMat.get, cv.calcOpticalFlowPyrLK(cv.UMat(img1), img2, p0_umat, None))
_p1_mask_err_umat2 = map(cv.UMat.get, cv.calcOpticalFlowPyrLK(img1, cv.UMat(img2), p0_umat, None))
# # results of OCL optical flow differs from CPU implementation, so result can not be easily compared
# for p1_mask_err_umat in [p1_mask_err_umat0, p1_mask_err_umat1, p1_mask_err_umat2]:
# for data, data_umat in zip(p1_mask_err, p1_mask_err_umat):
# self.assertTrue(np.allclose(data, data_umat))
if __name__ == '__main__':
NewOpenCVTests.bootstrap()