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Merge pull request #8020 from alalek:fix_4976

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Alexander Alekhin 2017-01-19 15:40:07 +00:00
commit a46adbfcbc
4 changed files with 56 additions and 0 deletions
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23
modules/python/test/test_shape.py Normal file
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@ -0,0 +1,23 @@
#!/usr/bin/env python
import cv2
from tests_common import NewOpenCVTests
class shape_test(NewOpenCVTests):
def test_computeDistance(self):
a = self.get_sample('samples/data/shape_sample/1.png', cv2.IMREAD_GRAYSCALE);
b = self.get_sample('samples/data/shape_sample/2.png', cv2.IMREAD_GRAYSCALE);
_, ca, _ = cv2.findContours(a, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_KCOS)
_, cb, _ = cv2.findContours(b, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_KCOS)
hd = cv2.createHausdorffDistanceExtractor()
sd = cv2.createShapeContextDistanceExtractor()
d1 = hd.computeDistance(ca[0], cb[0])
d2 = sd.computeDistance(ca[0], cb[0])
self.assertAlmostEqual(d1, 26.4196891785, 3, "HausdorffDistanceExtractor")
self.assertAlmostEqual(d2, 0.25804194808, 3, "ShapeContextDistanceExtractor")

7
modules/shape/src/haus_dis.cpp
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@ -138,6 +138,13 @@ float HausdorffDistanceExtractorImpl::computeDistance(InputArray contour1, Input
set2.convertTo(set2, CV_32F);
CV_Assert((set1.channels()==2) && (set1.cols>0));
CV_Assert((set2.channels()==2) && (set2.cols>0));
// Force vectors column-based
if (set1.dims > 1)
set1 = set1.reshape(2, 1);
if (set2.dims > 1)
set2 = set2.reshape(2, 1);
return std::max( _apply(set1, set2, distanceFlag, rankProportion),
_apply(set2, set1, distanceFlag, rankProportion) );
}

7
modules/shape/src/sc_dis.cpp
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@ -202,6 +202,13 @@ float ShapeContextDistanceExtractorImpl::computeDistance(InputArray contour1, In
CV_Assert((set1.channels()==2) && (set1.cols>0));
CV_Assert((set2.channels()==2) && (set2.cols>0));
// Force vectors column-based
if (set1.dims > 1)
set1 = set1.reshape(2, 1);
if (set2.dims > 1)
set2 = set2.reshape(2, 1);
if (imageAppearanceWeight!=0)
{
CV_Assert((!image1.empty()) && (!image2.empty()));

19
modules/shape/test/test_shape.cpp
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@ -299,3 +299,22 @@ TEST(Hauss, regression)
ShapeBaseTest<int, computeShapeDistance_Haussdorf> test(NSN_val, NP_val, CURRENT_MAX_ACCUR_val);
test.safe_run();
}
TEST(computeDistance, regression_4976)
{
Mat a = imread(cvtest::findDataFile("shape/samples/1.png"), 0);
Mat b = imread(cvtest::findDataFile("shape/samples/2.png"), 0);
vector<vector<Point> > ca,cb;
findContours(a, ca, cv::RETR_CCOMP, cv::CHAIN_APPROX_TC89_KCOS);
findContours(b, cb, cv::RETR_CCOMP, cv::CHAIN_APPROX_TC89_KCOS);
Ptr<HausdorffDistanceExtractor> hd = createHausdorffDistanceExtractor();
Ptr<ShapeContextDistanceExtractor> sd = createShapeContextDistanceExtractor();
double d1 = hd->computeDistance(ca[0],cb[0]);
double d2 = sd->computeDistance(ca[0],cb[0]);
EXPECT_NEAR(d1, 26.4196891785, 1e-3) << "HausdorffDistanceExtractor";
EXPECT_NEAR(d2, 0.25804194808, 1e-3) << "ShapeContextDistanceExtractor";
}