#include "test_precomp.hpp" using namespace cv; using namespace std; using namespace std::tr1; #ifdef HAVE_JPEG /** * Test for check whether reading exif orientation tag was processed successfully or not * The test info is the set of 8 images named testExifRotate_{1 to 8}.jpg * The test image is the square 10x10 points divided by four sub-squares: * (R corresponds to Red, G to Green, B to Blue, W to white) * --------- --------- * | R | G | | G | R | * |-------| - (tag 1) |-------| - (tag 2) * | B | W | | W | B | * --------- --------- * * --------- --------- * | W | B | | B | W | * |-------| - (tag 3) |-------| - (tag 4) * | G | R | | R | G | * --------- --------- * * --------- --------- * | R | B | | G | W | * |-------| - (tag 5) |-------| - (tag 6) * | G | W | | R | B | * --------- --------- * * --------- --------- * | W | G | | B | R | * |-------| - (tag 7) |-------| - (tag 8) * | B | R | | W | G | * --------- --------- * * * Every image contains exif field with orientation tag (0x112) * After reading each image the corresponding matrix must be read as * --------- * | R | G | * |-------| * | B | W | * --------- * */ typedef testing::TestWithParam Imgcodecs_Jpeg_Exif; TEST_P(Imgcodecs_Jpeg_Exif, exif_orientation) { const string root = cvtest::TS::ptr()->get_data_path(); const string filename = root + GetParam(); const int colorThresholdHigh = 250; const int colorThresholdLow = 5; Mat m_img = imread(filename); ASSERT_FALSE(m_img.empty()); Vec3b vec; //Checking the first quadrant (with supposed red) vec = m_img.at(2, 2); //some point inside the square EXPECT_LE(vec.val[0], colorThresholdLow); EXPECT_LE(vec.val[1], colorThresholdLow); EXPECT_GE(vec.val[2], colorThresholdHigh); //Checking the second quadrant (with supposed green) vec = m_img.at(2, 7); //some point inside the square EXPECT_LE(vec.val[0], colorThresholdLow); EXPECT_GE(vec.val[1], colorThresholdHigh); EXPECT_LE(vec.val[2], colorThresholdLow); //Checking the third quadrant (with supposed blue) vec = m_img.at(7, 2); //some point inside the square EXPECT_GE(vec.val[0], colorThresholdHigh); EXPECT_LE(vec.val[1], colorThresholdLow); EXPECT_LE(vec.val[2], colorThresholdLow); } const string exif_files[] = { "readwrite/testExifOrientation_1.jpg", "readwrite/testExifOrientation_2.jpg", "readwrite/testExifOrientation_3.jpg", "readwrite/testExifOrientation_4.jpg", "readwrite/testExifOrientation_5.jpg", "readwrite/testExifOrientation_6.jpg", "readwrite/testExifOrientation_7.jpg", "readwrite/testExifOrientation_8.jpg" }; INSTANTIATE_TEST_CASE_P(ExifFiles, Imgcodecs_Jpeg_Exif, testing::ValuesIn(exif_files)); //================================================================================================== TEST(Imgcodecs_Jpeg, encode_empty) { cv::Mat img; std::vector jpegImg; ASSERT_THROW(cv::imencode(".jpg", img, jpegImg), cv::Exception); } TEST(Imgcodecs_Jpeg, encode_decode_progressive_jpeg) { cvtest::TS& ts = *cvtest::TS::ptr(); string input = string(ts.get_data_path()) + "../cv/shared/lena.png"; cv::Mat img = cv::imread(input); ASSERT_FALSE(img.empty()); std::vector params; params.push_back(IMWRITE_JPEG_PROGRESSIVE); params.push_back(1); string output_progressive = cv::tempfile(".jpg"); EXPECT_NO_THROW(cv::imwrite(output_progressive, img, params)); cv::Mat img_jpg_progressive = cv::imread(output_progressive); string output_normal = cv::tempfile(".jpg"); EXPECT_NO_THROW(cv::imwrite(output_normal, img)); cv::Mat img_jpg_normal = cv::imread(output_normal); EXPECT_EQ(0, cvtest::norm(img_jpg_progressive, img_jpg_normal, NORM_INF)); EXPECT_EQ(0, remove(output_progressive.c_str())); EXPECT_EQ(0, remove(output_normal.c_str())); } TEST(Imgcodecs_Jpeg, encode_decode_optimize_jpeg) { cvtest::TS& ts = *cvtest::TS::ptr(); string input = string(ts.get_data_path()) + "../cv/shared/lena.png"; cv::Mat img = cv::imread(input); ASSERT_FALSE(img.empty()); std::vector params; params.push_back(IMWRITE_JPEG_OPTIMIZE); params.push_back(1); string output_optimized = cv::tempfile(".jpg"); EXPECT_NO_THROW(cv::imwrite(output_optimized, img, params)); cv::Mat img_jpg_optimized = cv::imread(output_optimized); string output_normal = cv::tempfile(".jpg"); EXPECT_NO_THROW(cv::imwrite(output_normal, img)); cv::Mat img_jpg_normal = cv::imread(output_normal); EXPECT_EQ(0, cvtest::norm(img_jpg_optimized, img_jpg_normal, NORM_INF)); EXPECT_EQ(0, remove(output_optimized.c_str())); EXPECT_EQ(0, remove(output_normal.c_str())); } TEST(Imgcodecs_Jpeg, encode_decode_rst_jpeg) { cvtest::TS& ts = *cvtest::TS::ptr(); string input = string(ts.get_data_path()) + "../cv/shared/lena.png"; cv::Mat img = cv::imread(input); ASSERT_FALSE(img.empty()); std::vector params; params.push_back(IMWRITE_JPEG_RST_INTERVAL); params.push_back(1); string output_rst = cv::tempfile(".jpg"); EXPECT_NO_THROW(cv::imwrite(output_rst, img, params)); cv::Mat img_jpg_rst = cv::imread(output_rst); string output_normal = cv::tempfile(".jpg"); EXPECT_NO_THROW(cv::imwrite(output_normal, img)); cv::Mat img_jpg_normal = cv::imread(output_normal); EXPECT_EQ(0, cvtest::norm(img_jpg_rst, img_jpg_normal, NORM_INF)); EXPECT_EQ(0, remove(output_rst.c_str())); EXPECT_EQ(0, remove(output_normal.c_str())); } #endif // HAVE_JPEG