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modules/dnn_superres/samples/butterfly.png
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modules/dnn_superres/samples/dnn_superres.cpp
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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 <iostream>
#include <opencv2/dnn_superres.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
using namespace std;
using namespace cv;
using namespace dnn;
using namespace dnn_superres;
int main(int argc, char *argv[])
{
// Check for valid command line arguments, print usage
// if insufficient arguments were given.
if ( argc < 4 ) {
cout << "usage: Arg 1: image | Path to image" << endl;
cout << "\t Arg 2: algorithm | bilinear, bicubic, edsr, espcn, fsrcnn or lapsrn" << endl;
cout << "\t Arg 3: scale | 2, 3 or 4 \n";
cout << "\t Arg 4: path to model file \n";
return -1;
}
string img_path = string(argv[1]);
string algorithm = string(argv[2]);
int scale = atoi(argv[3]);
string path = "";
if( argc > 4)
path = string(argv[4]);
// Load the image
Mat img = cv::imread(img_path);
Mat original_img(img);
if ( img.empty() )
{
std::cerr << "Couldn't load image: " << img << "\n";
return -2;
}
//Make dnn super resolution instance
DnnSuperResImpl sr;
Mat img_new;
if( algorithm == "bilinear" ){
resize(img, img_new, Size(), scale, scale, 2);
}
else if( algorithm == "bicubic" )
{
resize(img, img_new, Size(), scale, scale, 3);
}
else if( algorithm == "edsr" || algorithm == "espcn" || algorithm == "fsrcnn" || algorithm == "lapsrn" )
{
sr.readModel(path);
sr.setModel(algorithm, scale);
sr.upsample(img, img_new);
}
else{
std::cerr << "Algorithm not recognized. \n";
}
if ( img_new.empty() )
{
std::cerr << "Upsampling failed. \n";
return -3;
}
cout << "Upsampling succeeded. \n";
// Display image
cv::namedWindow("Initial Image", WINDOW_AUTOSIZE);
cv::imshow("Initial Image", img_new);
//cv::imwrite("./saved.jpg", img_new);
cv::waitKey(0);
return 0;
}
modules/dnn_superres/samples/dnn_superres_benchmark_quality.cpp
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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 <iostream>
#include <opencv2/opencv_modules.hpp>
#ifdef HAVE_OPENCV_QUALITY
#include <opencv2/dnn_superres.hpp>
#include <opencv2/quality.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
using namespace std;
using namespace cv;
using namespace dnn_superres;
static void showBenchmark(vector<Mat> images, string title, Size imageSize,
const vector<String> imageTitles,
const vector<double> psnrValues,
const vector<double> ssimValues)
{
int fontFace = FONT_HERSHEY_COMPLEX_SMALL;
int fontScale = 1;
Scalar fontColor = Scalar(255, 255, 255);
int len = static_cast<int>(images.size());
int cols = 2, rows = 2;
Mat fullImage = Mat::zeros(Size((cols * 10) + imageSize.width * cols, (rows * 10) + imageSize.height * rows),
images[0].type());
stringstream ss;
int h_ = -1;
for (int i = 0; i < len; i++) {
int fontStart = 15;
int w_ = i % cols;
if (i % cols == 0)
h_++;
Rect ROI((w_ * (10 + imageSize.width)), (h_ * (10 + imageSize.height)), imageSize.width, imageSize.height);
Mat tmp;
resize(images[i], tmp, Size(ROI.width, ROI.height));
ss << imageTitles[i];
putText(tmp,
ss.str(),
Point(5, fontStart),
fontFace,
fontScale,
fontColor,
1,
16);
ss.str("");
fontStart += 20;
ss << "PSNR: " << psnrValues[i];
putText(tmp,
ss.str(),
Point(5, fontStart),
fontFace,
fontScale,
fontColor,
1,
16);
ss.str("");
fontStart += 20;
ss << "SSIM: " << ssimValues[i];
putText(tmp,
ss.str(),
Point(5, fontStart),
fontFace,
fontScale,
fontColor,
1,
16);
ss.str("");
fontStart += 20;
tmp.copyTo(fullImage(ROI));
}
namedWindow(title, 1);
imshow(title, fullImage);
waitKey();
}
static Vec2d getQualityValues(Mat orig, Mat upsampled)
{
double psnr = PSNR(upsampled, orig);
Scalar q = quality::QualitySSIM::compute(upsampled, orig, noArray());
double ssim = mean(Vec3d((q[0]), q[1], q[2]))[0];
return Vec2d(psnr, ssim);
}
int main(int argc, char *argv[])
{
// Check for valid command line arguments, print usage
// if insufficient arguments were given.
if (argc < 4) {
cout << "usage: Arg 1: image path | Path to image" << endl;
cout << "\t Arg 2: algorithm | edsr, espcn, fsrcnn or lapsrn" << endl;
cout << "\t Arg 3: path to model file 2 \n";
cout << "\t Arg 4: scale | 2, 3, 4 or 8 \n";
return -1;
}
string path = string(argv[1]);
string algorithm = string(argv[2]);
string model = string(argv[3]);
int scale = atoi(argv[4]);
Mat img = imread(path);
if (img.empty()) {
cerr << "Couldn't load image: " << img << "\n";
return -2;
}
//Crop the image so the images will be aligned
int width = img.cols - (img.cols % scale);
int height = img.rows - (img.rows % scale);
Mat cropped = img(Rect(0, 0, width, height));
//Downscale the image for benchmarking
Mat img_downscaled;
resize(cropped, img_downscaled, Size(), 1.0 / scale, 1.0 / scale);
//Make dnn super resolution instance
DnnSuperResImpl sr;
vector <Mat> allImages;
Mat img_new;
//Read and set the dnn model
sr.readModel(model);
sr.setModel(algorithm, scale);
sr.upsample(img_downscaled, img_new);
vector<double> psnrValues = vector<double>();
vector<double> ssimValues = vector<double>();
//DL MODEL
Vec2f quality = getQualityValues(cropped, img_new);
psnrValues.push_back(quality[0]);
ssimValues.push_back(quality[1]);
cout << sr.getAlgorithm() << ":" << endl;
cout << "PSNR: " << quality[0] << " SSIM: " << quality[1] << endl;
cout << "----------------------" << endl;
//BICUBIC
Mat bicubic;
resize(img_downscaled, bicubic, Size(), scale, scale, INTER_CUBIC);
quality = getQualityValues(cropped, bicubic);
psnrValues.push_back(quality[0]);
ssimValues.push_back(quality[1]);
cout << "Bicubic " << endl;
cout << "PSNR: " << quality[0] << " SSIM: " << quality[1] << endl;
cout << "----------------------" << endl;
//NEAREST NEIGHBOR
Mat nearest;
resize(img_downscaled, nearest, Size(), scale, scale, INTER_NEAREST);
quality = getQualityValues(cropped, nearest);
psnrValues.push_back(quality[0]);
ssimValues.push_back(quality[1]);
cout << "Nearest neighbor" << endl;
cout << "PSNR: " << quality[0] << " SSIM: " << quality[1] << endl;
cout << "----------------------" << endl;
//LANCZOS
Mat lanczos;
resize(img_downscaled, lanczos, Size(), scale, scale, INTER_LANCZOS4);
quality = getQualityValues(cropped, lanczos);
psnrValues.push_back(quality[0]);
ssimValues.push_back(quality[1]);
cout << "Lanczos" << endl;
cout << "PSNR: " << quality[0] << " SSIM: " << quality[1] << endl;
cout << "-----------------------------------------------" << endl;
vector <Mat> imgs{img_new, bicubic, nearest, lanczos};
vector <String> titles{sr.getAlgorithm(), "Bicubic", "Nearest neighbor", "Lanczos"};
showBenchmark(imgs, "Quality benchmark", Size(bicubic.cols, bicubic.rows), titles, psnrValues, ssimValues);
waitKey(0);
return 0;
}
#else
int main()
{
std::cout << "This sample requires the OpenCV Quality module." << std::endl;
return 0;
}
#endif
modules/dnn_superres/samples/dnn_superres_benchmark_time.cpp
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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 <iostream>
#include <opencv2/dnn_superres.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
using namespace std;
using namespace cv;
using namespace dnn_superres;
static void showBenchmark(vector<Mat> images, string title, Size imageSize,
const vector<String> imageTitles,
const vector<double> perfValues)
{
int fontFace = FONT_HERSHEY_COMPLEX_SMALL;
int fontScale = 1;
Scalar fontColor = Scalar(255, 255, 255);
int len = static_cast<int>(images.size());
int cols = 2, rows = 2;
Mat fullImage = Mat::zeros(Size((cols * 10) + imageSize.width * cols, (rows * 10) + imageSize.height * rows),
images[0].type());
stringstream ss;
int h_ = -1;
for (int i = 0; i < len; i++) {
int fontStart = 15;
int w_ = i % cols;
if (i % cols == 0)
h_++;
Rect ROI((w_ * (10 + imageSize.width)), (h_ * (10 + imageSize.height)), imageSize.width, imageSize.height);
Mat tmp;
resize(images[i], tmp, Size(ROI.width, ROI.height));
ss << imageTitles[i];
putText(tmp,
ss.str(),
Point(5, fontStart),
fontFace,
fontScale,
fontColor,
1,
16);
ss.str("");
fontStart += 20;
ss << perfValues[i];
putText(tmp,
ss.str(),
Point(5, fontStart),
fontFace,
fontScale,
fontColor,
1,
16);
tmp.copyTo(fullImage(ROI));
}
namedWindow(title, 1);
imshow(title, fullImage);
waitKey();
}
int main(int argc, char *argv[])
{
// Check for valid command line arguments, print usage
// if insufficient arguments were given.
if (argc < 4) {
cout << "usage: Arg 1: image path | Path to image" << endl;
cout << "\t Arg 2: algorithm | edsr, espcn, fsrcnn or lapsrn" << endl;
cout << "\t Arg 3: path to model file 2 \n";
cout << "\t Arg 4: scale | 2, 3, 4 or 8 \n";
return -1;
}
string path = string(argv[1]);
string algorithm = string(argv[2]);
string model = string(argv[3]);
int scale = atoi(argv[4]);
Mat img = imread(path);
if (img.empty()) {
cerr << "Couldn't load image: " << img << "\n";
return -2;
}
//Crop the image so the images will be aligned
int width = img.cols - (img.cols % scale);
int height = img.rows - (img.rows % scale);
Mat cropped = img(Rect(0, 0, width, height));
//Downscale the image for benchmarking
Mat img_downscaled;
resize(cropped, img_downscaled, Size(), 1.0 / scale, 1.0 / scale);
//Make dnn super resolution instance
DnnSuperResImpl sr;
Mat img_new;
//Read and set the dnn model
sr.readModel(model);
sr.setModel(algorithm, scale);
double elapsed = 0.0;
vector<double> perf;
TickMeter tm;
//DL MODEL
tm.start();
sr.upsample(img_downscaled, img_new);
tm.stop();
elapsed = tm.getTimeSec() / tm.getCounter();
perf.push_back(elapsed);
cout << sr.getAlgorithm() << " : " << elapsed << endl;
//BICUBIC
Mat bicubic;
tm.start();
resize(img_downscaled, bicubic, Size(), scale, scale, INTER_CUBIC);
tm.stop();
elapsed = tm.getTimeSec() / tm.getCounter();
perf.push_back(elapsed);
cout << "Bicubic" << " : " << elapsed << endl;
//NEAREST NEIGHBOR
Mat nearest;
tm.start();
resize(img_downscaled, nearest, Size(), scale, scale, INTER_NEAREST);
tm.stop();
elapsed = tm.getTimeSec() / tm.getCounter();
perf.push_back(elapsed);
cout << "Nearest" << " : " << elapsed << endl;
//LANCZOS
Mat lanczos;
tm.start();
resize(img_downscaled, lanczos, Size(), scale, scale, INTER_LANCZOS4);
tm.stop();
elapsed = tm.getTimeSec() / tm.getCounter();
perf.push_back(elapsed);
cout << "Lanczos" << " : " << elapsed << endl;
vector <Mat> imgs{img_new, bicubic, nearest, lanczos};
vector <String> titles{sr.getAlgorithm(), "Bicubic", "Nearest neighbor", "Lanczos"};
showBenchmark(imgs, "Time benchmark", Size(bicubic.cols, bicubic.rows), titles, perf);
waitKey(0);
return 0;
}
modules/dnn_superres/samples/dnn_superres_multioutput.cpp
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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 <iostream>
#include <sstream>
#include <opencv2/dnn_superres.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
using namespace std;
using namespace cv;
using namespace dnn_superres;
int main(int argc, char *argv[])
{
// Check for valid command line arguments, print usage
// if insufficient arguments were given.
if (argc < 4) {
cout << "usage: Arg 1: image | Path to image" << endl;
cout << "\t Arg 2: scales in a format of 2,4,8\n";
cout << "\t Arg 3: output node names in a format of nchw_output_0,nchw_output_1\n";
cout << "\t Arg 4: path to model file \n";
return -1;
}
string img_path = string(argv[1]);
string scales_str = string(argv[2]);
string output_names_str = string(argv[3]);
std::string path = string(argv[4]);
//Parse the scaling factors
std::vector<int> scales;
char delim = ',';
{
std::stringstream ss(scales_str);
std::string token;
while (std::getline(ss, token, delim)) {
scales.push_back(atoi(token.c_str()));
}
}
//Parse the output node names
std::vector<String> node_names;
{
std::stringstream ss(output_names_str);
std::string token;
while (std::getline(ss, token, delim)) {
node_names.push_back(token);
}
}
// Load the image
Mat img = cv::imread(img_path);
Mat original_img(img);
if (img.empty())
{
std::cerr << "Couldn't load image: " << img << "\n";
return -2;
}
//Make dnn super resolution instance
DnnSuperResImpl sr;
int scale = *max_element(scales.begin(), scales.end());
std::vector<Mat> outputs;
sr.readModel(path);
sr.setModel("lapsrn", scale);
sr.upsampleMultioutput(img, outputs, scales, node_names);
for(unsigned int i = 0; i < outputs.size(); i++)
{
cv::namedWindow("Upsampled image", WINDOW_AUTOSIZE);
cv::imshow("Upsampled image", outputs[i]);
//cv::imwrite("./saved.jpg", img_new);
cv::waitKey(0);
}
return 0;
}
modules/dnn_superres/samples/dnn_superres_video.cpp
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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 <iostream>
#include <opencv2/dnn_superres.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
using namespace std;
using namespace cv;
using namespace dnn_superres;
int main(int argc, char *argv[])
{
// Check for valid command line arguments, print usage
// if insufficient arguments were given.
if (argc < 4) {
cout << "usage: Arg 1: input video path" << endl;
cout << "\t Arg 2: output video path" << endl;
cout << "\t Arg 3: algorithm | edsr, espcn, fsrcnn or lapsrn" << endl;
cout << "\t Arg 4: scale | 2, 3, 4 or 8 \n";
cout << "\t Arg 5: path to model file \n";
return -1;
}
string input_path = string(argv[1]);
string output_path = string(argv[2]);
string algorithm = string(argv[3]);
int scale = atoi(argv[4]);
string path = string(argv[5]);
VideoCapture input_video(input_path);
int ex = static_cast<int>(input_video.get(CAP_PROP_FOURCC));
Size S = Size((int) input_video.get(CAP_PROP_FRAME_WIDTH) * scale,
(int) input_video.get(CAP_PROP_FRAME_HEIGHT) * scale);
VideoWriter output_video;
output_video.open(output_path, ex, input_video.get(CAP_PROP_FPS), S, true);
if (!input_video.isOpened())
{
std::cerr << "Could not open the video." << std::endl;
return -1;
}
DnnSuperResImpl sr;
sr.readModel(path);
sr.setModel(algorithm, scale);
for(;;)
{
Mat frame, output_frame;
input_video >> frame;
if ( frame.empty() )
break;
sr.upsample(frame, output_frame);
output_video << output_frame;
namedWindow("Upsampled video", WINDOW_AUTOSIZE);
imshow("Upsampled video", output_frame);
namedWindow("Original video", WINDOW_AUTOSIZE);
imshow("Original video", frame);
char c=(char)waitKey(25);
if(c==27)
break;
}
input_video.release();
output_video.release();
return 0;
}