718c41634f
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试 2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程 3.重整权利声明文件，重整代码工程，确保最小化侵权风险 Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
48 lines
1.2 KiB
Python
48 lines
1.2 KiB
Python
#!/usr/bin/env python
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'''
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Texture flow direction estimation.
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Sample shows how cv.cornerEigenValsAndVecs function can be used
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to estimate image texture flow direction.
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'''
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# Python 2/3 compatibility
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from __future__ import print_function
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import numpy as np
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import cv2 as cv
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import sys
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from tests_common import NewOpenCVTests
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class texture_flow_test(NewOpenCVTests):
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def test_texture_flow(self):
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img = self.get_sample('samples/data/chessboard.png')
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gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
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h, w = img.shape[:2]
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eigen = cv.cornerEigenValsAndVecs(gray, 5, 3)
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eigen = eigen.reshape(h, w, 3, 2) # [[e1, e2], v1, v2]
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flow = eigen[:,:,2]
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d = 300
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eps = d / 30
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points = np.dstack( np.mgrid[d/2:w:d, d/2:h:d] ).reshape(-1, 2)
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textureVectors = []
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for x, y in np.int32(points):
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textureVectors.append(np.int32(flow[y, x]*d))
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for i in range(len(textureVectors)):
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self.assertTrue(cv.norm(textureVectors[i], cv.NORM_L2) < eps
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or abs(cv.norm(textureVectors[i], cv.NORM_L2) - d) < eps)
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if __name__ == '__main__':
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NewOpenCVTests.bootstrap()
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