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factor conditional save

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This commit is contained in:
Adrien BAK 2014-10-08 11:15:41 +09:00
parent 11d89ad7c1
commit 0f0bda9722
1 changed files with 24 additions and 1 deletions
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25
modules/photo/test/test_cloning.cpp
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@ -39,6 +39,15 @@
//
//M*/
#define OUTPUT_SAVING 0
#if OUTPUT_SAVING
#define SAVE(x) std::vector<int> params;\
params.push_back(16);\
params.push_back(0);\
imwrite(folder + "output.png", x ,params);
#else
#define SAVE(x)
#endif
#include "test_precomp.hpp"
#include "opencv2/photo.hpp"
@ -70,8 +79,12 @@ TEST(Photo_SeamlessClone_normal, regression)
p.y = destination.size().height/2;
seamlessClone(source, destination, mask, p, result, 1);
Mat reference = imread(folder + "reference.png");
double error = norm(reference, result, NORM_L1);
SAVE(result);
double error = cvtest::norm(reference, result, NORM_L1);
EXPECT_LE(error, numerical_precision);
}
@ -96,6 +109,8 @@ TEST(Photo_SeamlessClone_mixed, regression)
p.y = destination.size().height/2;
seamlessClone(source, destination, mask, p, result, 2);
SAVE(result);
Mat reference = imread(folder + "reference.png");
double error = norm(reference, result, NORM_L1);
EXPECT_LE(error, numerical_precision);
@ -123,6 +138,8 @@ TEST(Photo_SeamlessClone_featureExchange, regression)
p.y = destination.size().height/2;
seamlessClone(source, destination, mask, p, result, 3);
SAVE(result);
Mat reference = imread(folder + "reference.png");
double error = norm(reference, result, NORM_L1);
EXPECT_LE(error, numerical_precision);
@ -144,6 +161,8 @@ TEST(Photo_SeamlessClone_colorChange, regression)
Mat result;
colorChange(source, mask, result, 1.5, .5, .5);
SAVE(result);
Mat reference = imread(folder + "reference.png");
double error = norm(reference, result, NORM_L1);
EXPECT_LE(error, numerical_precision);
@ -165,6 +184,8 @@ TEST(Photo_SeamlessClone_illuminationChange, regression)
Mat result;
illuminationChange(source, mask, result, 0.2f, 0.4f);
SAVE(result);
Mat reference = imread(folder + "reference.png");
double error = norm(reference, result, NORM_L1);
EXPECT_LE(error, numerical_precision);
@ -186,6 +207,8 @@ TEST(Photo_SeamlessClone_textureFlattening, regression)
Mat result;
textureFlattening(source, mask, result, 30, 45, 3);
SAVE(result);
Mat reference = imread(folder + "reference.png");
double error = norm(reference, result, NORM_L1);
EXPECT_LE(error, numerical_precision);