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#!/usr/bin/env python
'''
MSER detector test
'''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2
from tests_common import NewOpenCVTests
class mser_test(NewOpenCVTests):
def test_mser(self):
img = self.get_sample('cv/mser/puzzle.png', 0)
smallImg = [
[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],
[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],
[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],
[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],
[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],
[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],
[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],
[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],
[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],
[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],
[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],
[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],
[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],
[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]
]
thresharr = [ 0, 70, 120, 180, 255 ]
kDelta = 5
mserExtractor = cv2.MSER_create()
mserExtractor.setDelta(kDelta)
np.random.seed(10)
for i in range(100):
use_big_image = int(np.random.rand(1,1)*7) != 0
invert = int(np.random.rand(1,1)*2) != 0
binarize = int(np.random.rand(1,1)*5) != 0 if use_big_image else False
blur = int(np.random.rand(1,1)*2) != 0
thresh = thresharr[int(np.random.rand(1,1)*5)]
src0 = img if use_big_image else np.array(smallImg).astype('uint8')
src = src0.copy()
kMinArea = 256 if use_big_image else 10
kMaxArea = int(src.shape[0]*src.shape[1]/4)
mserExtractor.setMinArea(kMinArea)
mserExtractor.setMaxArea(kMaxArea)
if invert:
cv2.bitwise_not(src, src)
if binarize:
_, src = cv2.threshold(src, thresh, 255, cv2.THRESH_BINARY)
if blur:
src = cv2.GaussianBlur(src, (5, 5), 1.5, 1.5)
minRegs = 7 if use_big_image else 2
maxRegs = 1000 if use_big_image else 20
if binarize and (thresh == 0 or thresh == 255):
minRegs = maxRegs = 0
msers, boxes = mserExtractor.detectRegions(src)
nmsers = len(msers)
self.assertEqual(nmsers, len(boxes))
self.assertLessEqual(minRegs, nmsers)
self.assertGreaterEqual(maxRegs, nmsers)
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