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eb82ba36a3
[G-API] Introduce cv.gin/cv.descr_of for python * Implement cv.gin/cv.descr_of * Fix macos build * Fix gcomputation tests * Add test * Add using to a void exceeded length for windows build * Add using to a void exceeded length for windows build * Fix comments to review * Fix comments to review * Update from latest master * Avoid graph compilation to obtain in/out info * Fix indentation * Fix comments to review * Avoid using default in switches * Post output meta for giebackend
107 lines
3.5 KiB
Python
107 lines
3.5 KiB
Python
#!/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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# Plaidml is an optional backend
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pkgs = [
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('ocl' , cv.gapi.core.ocl.kernels()),
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('cpu' , cv.gapi.core.cpu.kernels()),
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('fluid' , cv.gapi.core.fluid.kernels())
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# ('plaidml', cv.gapi.core.plaidml.kernels())
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]
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class gapi_imgproc_test(NewOpenCVTests):
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def test_good_features_to_track(self):
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# TODO: Extend to use any type and size here
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img_path = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')])
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in1 = cv.cvtColor(cv.imread(img_path), cv.COLOR_RGB2GRAY)
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# NB: goodFeaturesToTrack configuration
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max_corners = 50
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quality_lvl = 0.01
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min_distance = 10
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block_sz = 3
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use_harris_detector = True
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k = 0.04
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mask = None
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# OpenCV
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expected = cv.goodFeaturesToTrack(in1, max_corners, quality_lvl,
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min_distance, mask=mask,
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blockSize=block_sz, useHarrisDetector=use_harris_detector, k=k)
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# G-API
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g_in = cv.GMat()
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g_out = cv.gapi.goodFeaturesToTrack(g_in, max_corners, quality_lvl,
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min_distance, mask, block_sz, use_harris_detector, k)
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comp = cv.GComputation(cv.GIn(g_in), cv.GOut(g_out))
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for pkg_name, pkg in pkgs:
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actual = comp.apply(cv.gin(in1), args=cv.compile_args(pkg))
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# NB: OpenCV & G-API have different output shapes:
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# OpenCV - (num_points, 1, 2)
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# G-API - (num_points, 2)
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# Comparison
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self.assertEqual(0.0, cv.norm(expected.flatten(),
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np.array(actual, dtype=np.float32).flatten(),
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cv.NORM_INF),
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'Failed on ' + pkg_name + ' backend')
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def test_rgb2gray(self):
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# TODO: Extend to use any type and size here
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img_path = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')])
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in1 = cv.imread(img_path)
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# OpenCV
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expected = cv.cvtColor(in1, cv.COLOR_RGB2GRAY)
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# G-API
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g_in = cv.GMat()
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g_out = cv.gapi.RGB2Gray(g_in)
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comp = cv.GComputation(cv.GIn(g_in), cv.GOut(g_out))
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for pkg_name, pkg in pkgs:
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actual = comp.apply(cv.gin(in1), args=cv.compile_args(pkg))
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# Comparison
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self.assertEqual(0.0, cv.norm(expected, actual, cv.NORM_INF),
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'Failed on ' + pkg_name + ' backend')
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def test_bounding_rect(self):
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sz = 1280
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fscale = 256
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def sample_value(fscale):
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return np.random.uniform(0, 255 * fscale) / fscale
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points = np.array([(sample_value(fscale), sample_value(fscale)) for _ in range(1280)], np.float32)
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# OpenCV
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expected = cv.boundingRect(points)
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# G-API
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g_in = cv.GMat()
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g_out = cv.gapi.boundingRect(g_in)
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comp = cv.GComputation(cv.GIn(g_in), cv.GOut(g_out))
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for pkg_name, pkg in pkgs:
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actual = comp.apply(cv.gin(points), args=cv.compile_args(pkg))
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# Comparison
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self.assertEqual(0.0, cv.norm(expected, actual, cv.NORM_INF),
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'Failed on ' + pkg_name + ' backend')
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if __name__ == '__main__':
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NewOpenCVTests.bootstrap()
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