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import os
import cv2 as cv
import numpy as np
from tests_common import NewOpenCVTests, unittest
class nvidiaopticalflow_test(NewOpenCVTests):
def setUp(self):
super(nvidiaopticalflow_test, self).setUp()
if not cv.cuda.getCudaEnabledDeviceCount():
self.skipTest("No CUDA-capable device is detected")
@unittest.skipIf('OPENCV_TEST_DATA_PATH' not in os.environ,
"OPENCV_TEST_DATA_PATH is not defined")
def test_calc(self):
frame1 = os.environ['OPENCV_TEST_DATA_PATH'] + '/gpu/opticalflow/frame0.png'
frame2 = os.environ['OPENCV_TEST_DATA_PATH'] + '/gpu/opticalflow/frame1.png'
npMat1 = cv.cvtColor(cv.imread(frame1),cv.COLOR_BGR2GRAY)
npMat2 = cv.cvtColor(cv.imread(frame2),cv.COLOR_BGR2GRAY)
cuMat1 = cv.cuda_GpuMat(npMat1)
cuMat2 = cv.cuda_GpuMat(npMat2)
try:
nvof = cv.cuda_NvidiaOpticalFlow_1_0.create((npMat1.shape[1], npMat1.shape[0]), 5, False, False, False, 0)
flow, cost = nvof.calc(cuMat1, cuMat2, None)
self.assertTrue(flow.size()[1] > 0 and flow.size()[0] > 0)
flowUpSampled = nvof.upSampler(flow, (npMat1.shape[1], npMat1.shape[0]), nvof.getGridSize(), None)
nvof.collectGarbage()
self.assertTrue(flowUpSampled.size()[1] > 0 and flowUpSampled.size()[0] > 0)
except cv.error as e:
if e.code == cv.Error.StsBadFunc or e.code == cv.Error.StsBadArg or e.code == cv.Error.StsNullPtr:
self.skipTest("Algorithm is not supported in the current environment")
if __name__ == '__main__':
NewOpenCVTests.bootstrap()
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