mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
86 lines
3.7 KiB
86 lines
3.7 KiB
#!/usr/bin/env python |
|
from __future__ import print_function |
|
|
|
import numpy as np |
|
import cv2 |
|
|
|
from tests_common import NewOpenCVTests |
|
|
|
class UMat(NewOpenCVTests): |
|
|
|
def test_umat_construct(self): |
|
data = np.random.random([512, 512]) |
|
# UMat constructors |
|
data_um = cv2.UMat(data) # from ndarray |
|
data_sub_um = cv2.UMat(data_um, [128, 256], [128, 256]) # from UMat |
|
data_dst_um = cv2.UMat(128, 128, cv2.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 |
|
cv2.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 = cv2.UMat(256, 256, cv2.CV_32F) |
|
_ctx_handle = cv2.UMat.context() # obtain context handle |
|
_queue_handle = cv2.UMat.queue() # obtain queue handle |
|
_a_handle = a_um.handle(cv2.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 = cv2.ORB_create() |
|
|
|
img1, img2 = cv2.UMat(img1), cv2.UMat(img2) |
|
ps1, descs_umat1 = orb.detectAndCompute(img1, None) |
|
ps2, descs_umat2 = orb.detectAndCompute(img2, None) |
|
|
|
self.assertIsInstance(descs_umat1, cv2.UMat) |
|
self.assertIsInstance(descs_umat2, cv2.UMat) |
|
self.assertGreater(len(ps1), 0) |
|
self.assertGreater(len(ps2), 0) |
|
|
|
bf = cv2.BFMatcher(cv2.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", cv2.IMREAD_GRAYSCALE) |
|
img2 = self.get_sample("samples/data/right02.jpg", cv2.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 = cv2.goodFeaturesToTrack(img1, mask=None, **feature_params) |
|
p0_umat = cv2.goodFeaturesToTrack(cv2.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 = cv2.UMat(np.array(sorted(p0_umat.get(), key=lambda p: tuple(p[0])))) |
|
self.assertTrue(np.allclose(p0_umat.get(), p0)) |
|
|
|
_p1_mask_err = cv2.calcOpticalFlowPyrLK(img1, img2, p0, None) |
|
|
|
_p1_mask_err_umat0 = map(cv2.UMat.get, cv2.calcOpticalFlowPyrLK(img1, img2, p0_umat, None)) |
|
_p1_mask_err_umat1 = map(cv2.UMat.get, cv2.calcOpticalFlowPyrLK(cv2.UMat(img1), img2, p0_umat, None)) |
|
_p1_mask_err_umat2 = map(cv2.UMat.get, cv2.calcOpticalFlowPyrLK(img1, cv2.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()
|
|
|