Open Source Computer Vision Library https://opencv.org/
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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html
#include "test_precomp.hpp"
namespace opencv_test { namespace {
#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<string> 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<Vec3b>(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<Vec3b>(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<Vec3b>(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<uchar> 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<int> 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<int> 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<int> 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()));
}
//==================================================================================================
static const uint32_t default_sampling_factor = static_cast<uint32_t>(0x221111);
static uint32_t test_jpeg_subsampling( const Mat src, const vector<int> param )
{
vector<uint8_t> jpeg;
if ( cv::imencode(".jpg", src, jpeg, param ) == false )
{
return 0;
}
if ( src.channels() != 3 )
{
return 0;
}
// Find SOF Marker(FFC0)
int sof_offset = 0; // not found.
int jpeg_size = static_cast<int>( jpeg.size() );
for ( int i = 0 ; i < jpeg_size - 1; i++ )
{
if ( (jpeg[i] == 0xff ) && ( jpeg[i+1] == 0xC0 ) )
{
sof_offset = i;
break;
}
}
if ( sof_offset == 0 )
{
return 0;
}
// Extract Subsampling Factor from SOF.
return ( jpeg[sof_offset + 0x0A + 3 * 0 + 1] << 16 ) +
( jpeg[sof_offset + 0x0A + 3 * 1 + 1] << 8 ) +
( jpeg[sof_offset + 0x0A + 3 * 2 + 1] ) ;
}
TEST(Imgcodecs_Jpeg, encode_subsamplingfactor_default)
{
vector<int> param;
Mat src( 48, 64, CV_8UC3, cv::Scalar::all(0) );
EXPECT_EQ( default_sampling_factor, test_jpeg_subsampling(src, param) );
}
TEST(Imgcodecs_Jpeg, encode_subsamplingfactor_usersetting_valid)
{
Mat src( 48, 64, CV_8UC3, cv::Scalar::all(0) );
const uint32_t sampling_factor_list[] = {
IMWRITE_JPEG_SAMPLING_FACTOR_411,
IMWRITE_JPEG_SAMPLING_FACTOR_420,
IMWRITE_JPEG_SAMPLING_FACTOR_422,
IMWRITE_JPEG_SAMPLING_FACTOR_440,
IMWRITE_JPEG_SAMPLING_FACTOR_444,
};
const int sampling_factor_list_num = 5;
for ( int i = 0 ; i < sampling_factor_list_num; i ++ )
{
vector<int> param;
param.push_back( IMWRITE_JPEG_SAMPLING_FACTOR );
param.push_back( sampling_factor_list[i] );
EXPECT_EQ( sampling_factor_list[i], test_jpeg_subsampling(src, param) );
}
}
TEST(Imgcodecs_Jpeg, encode_subsamplingfactor_usersetting_invalid)
{
Mat src( 48, 64, CV_8UC3, cv::Scalar::all(0) );
const uint32_t sampling_factor_list[] = { // Invalid list
0x111112,
0x000000,
0x001111,
0xFF1111,
0x141111, // 1x4,1x1,1x1 - unknown
0x241111, // 2x4,1x1,1x1 - unknown
0x421111, // 4x2,1x1,1x1 - unknown
0x441111, // 4x4,1x1,1x1 - 410(libjpeg cannot handle it)
};
const int sampling_factor_list_num = 8;
for ( int i = 0 ; i < sampling_factor_list_num; i ++ )
{
vector<int> param;
param.push_back( IMWRITE_JPEG_SAMPLING_FACTOR );
param.push_back( sampling_factor_list[i] );
EXPECT_EQ( default_sampling_factor, test_jpeg_subsampling(src, param) );
}
}
#endif // HAVE_JPEG
}} // namespace