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Open Source Computer Vision Library
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65 lines
3.6 KiB
65 lines
3.6 KiB
9 years ago
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#!/usr/bin/env python
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'''
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MSER detector test
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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
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from tests_common import NewOpenCVTests
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class mser_test(NewOpenCVTests):
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def test_mser(self):
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img = self.get_sample('cv/mser/puzzle.png', 0)
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smallImg = [
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[255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
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[255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
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[255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
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[255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
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[255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
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[255, 255, 255, 255, 255, 0, 0, 0, 0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 0, 0, 255, 255, 255, 255],
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[255, 255, 255, 255, 255, 0, 0, 0, 0, 0, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 0, 0, 255, 255, 255, 255],
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[255, 255, 255, 255, 255, 0, 0, 0, 0, 0, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 0, 0, 255, 255, 255, 255],
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[255, 255, 255, 255, 255, 0, 0, 0, 0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 0, 0, 255, 255, 255, 255],
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[255, 255, 255, 255, 255, 255, 0, 0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 255, 255, 255, 255, 255],
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[255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
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[255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
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[255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
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[255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255]
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]
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thresharr = [ 0, 70, 120, 180, 255 ]
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kDelta = 5
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np.random.seed(10)
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for i in range(100):
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use_big_image = int(np.random.rand(1,1)*7) != 0
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invert = int(np.random.rand(1,1)*2) != 0
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binarize = int(np.random.rand(1,1)*5) != 0 if use_big_image else False
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blur = int(np.random.rand(1,1)*2) != 0
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thresh = thresharr[int(np.random.rand(1,1)*5)]
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src0 = img if use_big_image else np.array(smallImg).astype('uint8')
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src = src0.copy()
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kMinArea = 256 if use_big_image else 10
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kMaxArea = int(src.shape[0]*src.shape[1]/4)
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mserExtractor = cv2.MSER(kDelta, kMinArea, kMaxArea)
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if invert:
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cv2.bitwise_not(src, src)
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if binarize:
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_, src = cv2.threshold(src, thresh, 255, cv2.THRESH_BINARY)
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if blur:
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src = cv2.GaussianBlur(src, (5, 5), 1.5, 1.5)
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minRegs = 7 if use_big_image else 2
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maxRegs = 1000 if use_big_image else 15
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if binarize and (thresh == 0 or thresh == 255):
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minRegs = maxRegs = 0
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msers = mserExtractor.detect(src)
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nmsers = len(msers)
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self.assertLessEqual(minRegs, nmsers)
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self.assertGreaterEqual(maxRegs, nmsers)
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