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Open Source Computer Vision Library
https://opencv.org/
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129 lines
3.4 KiB
129 lines
3.4 KiB
#!/usr/bin/env python |
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import numpy as np |
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import cv2 as cv |
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import os |
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from tests_common import NewOpenCVTests |
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class test_gapi_streaming(NewOpenCVTests): |
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def test_image_input(self): |
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sz = (1280, 720) |
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in_mat = np.random.randint(0, 100, sz).astype(np.uint8) |
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# OpenCV |
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expected = cv.medianBlur(in_mat, 3) |
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# G-API |
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g_in = cv.GMat() |
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g_out = cv.gapi.medianBlur(g_in, 3) |
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c = cv.GComputation(g_in, g_out) |
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ccomp = c.compileStreaming(cv.descr_of(cv.gin(in_mat))) |
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ccomp.setSource(cv.gin(in_mat)) |
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ccomp.start() |
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_, actual = ccomp.pull() |
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# Assert |
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self.assertEqual(0.0, cv.norm(expected, actual, cv.NORM_INF)) |
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def test_video_input(self): |
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ksize = 3 |
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path = self.find_file('cv/video/768x576.avi', [os.environ['OPENCV_TEST_DATA_PATH']]) |
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# OpenCV |
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cap = cv.VideoCapture(path) |
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# G-API |
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g_in = cv.GMat() |
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g_out = cv.gapi.medianBlur(g_in, ksize) |
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c = cv.GComputation(g_in, g_out) |
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ccomp = c.compileStreaming() |
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source = cv.gapi.wip.make_capture_src(path) |
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ccomp.setSource(source) |
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ccomp.start() |
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# Assert |
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while cap.isOpened(): |
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has_expected, expected = cap.read() |
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has_actual, actual = ccomp.pull() |
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self.assertEqual(has_expected, has_actual) |
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if not has_actual: |
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break |
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self.assertEqual(0.0, cv.norm(cv.medianBlur(expected, ksize), actual, cv.NORM_INF)) |
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def test_video_split3(self): |
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path = self.find_file('cv/video/768x576.avi', [os.environ['OPENCV_TEST_DATA_PATH']]) |
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# OpenCV |
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cap = cv.VideoCapture(path) |
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# G-API |
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g_in = cv.GMat() |
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b, g, r = cv.gapi.split3(g_in) |
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c = cv.GComputation(cv.GIn(g_in), cv.GOut(b, g, r)) |
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ccomp = c.compileStreaming() |
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source = cv.gapi.wip.make_capture_src(path) |
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ccomp.setSource(source) |
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ccomp.start() |
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# Assert |
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while cap.isOpened(): |
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has_expected, frame = cap.read() |
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has_actual, actual = ccomp.pull() |
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self.assertEqual(has_expected, has_actual) |
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if not has_actual: |
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break |
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expected = cv.split(frame) |
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for e, a in zip(expected, actual): |
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self.assertEqual(0.0, cv.norm(e, a, cv.NORM_INF)) |
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def test_video_add(self): |
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sz = (576, 768, 3) |
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in_mat = np.random.randint(0, 100, sz).astype(np.uint8) |
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path = self.find_file('cv/video/768x576.avi', [os.environ['OPENCV_TEST_DATA_PATH']]) |
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# OpenCV |
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cap = cv.VideoCapture(path) |
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# G-API |
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g_in1 = cv.GMat() |
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g_in2 = cv.GMat() |
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out = cv.gapi.add(g_in1, g_in2) |
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c = cv.GComputation(cv.GIn(g_in1, g_in2), cv.GOut(out)) |
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ccomp = c.compileStreaming() |
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source = cv.gapi.wip.make_capture_src(path) |
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ccomp.setSource(cv.gin(source, in_mat)) |
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ccomp.start() |
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# Assert |
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while cap.isOpened(): |
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has_expected, frame = cap.read() |
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has_actual, actual = ccomp.pull() |
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self.assertEqual(has_expected, has_actual) |
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if not has_actual: |
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break |
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expected = cv.add(frame, in_mat) |
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self.assertEqual(0.0, cv.norm(expected, actual, cv.NORM_INF)) |
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if __name__ == '__main__': |
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NewOpenCVTests.bootstrap()
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