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#!/usr/bin/env python
'''
Texture flow direction estimation.
Sample shows how cv2.cornerEigenValsAndVecs function can be used
to estimate image texture flow direction.
'''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2
import sys
from tests_common import NewOpenCVTests
class texture_flow_test(NewOpenCVTests):
def test_texture_flow(self):
img = self.get_sample('samples/data/chessboard.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
h, w = img.shape[:2]
eigen = cv2.cornerEigenValsAndVecs(gray, 5, 3)
eigen = eigen.reshape(h, w, 3, 2) # [[e1, e2], v1, v2]
flow = eigen[:,:,2]
d = 300
eps = d / 30
points = np.dstack( np.mgrid[d/2:w:d, d/2:h:d] ).reshape(-1, 2)
textureVectors = []
for x, y in np.int32(points):
textureVectors.append(np.int32(flow[y, x]*d))
for i in range(len(textureVectors)):
self.assertTrue(cv2.norm(textureVectors[i], cv2.NORM_L2) < eps
or abs(cv2.norm(textureVectors[i], cv2.NORM_L2) - d) < eps)
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