OpenCV  4.9.0-dev
Open Source Computer Vision
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Super-resolution benchmarking

Benchmarking

The super-resolution module contains sample codes for benchmarking, in order to compare different models and algorithms. Here is presented a sample code for performing benchmarking, and then a few benchmarking results are collected. It was performed on an Intel i7-9700K CPU on an Ubuntu 18.04.02 OS.

Source Code of the sample

1// This file is part of OpenCV project.
2// It is subject to the license terms in the LICENSE file found in the top-level directory
3// of this distribution and at http://opencv.org/license.html.
4
5#include <iostream>
6#include <opencv2/opencv_modules.hpp>
7
8#ifdef HAVE_OPENCV_QUALITY
9#include <opencv2/dnn_superres.hpp>
10#include <opencv2/quality.hpp>
11#include <opencv2/imgproc.hpp>
12#include <opencv2/highgui.hpp>
13
14using namespace std;
15using namespace cv;
16using namespace dnn_superres;
17
18static void showBenchmark(vector<Mat> images, string title, Size imageSize,
19 const vector<String> imageTitles,
20 const vector<double> psnrValues,
21 const vector<double> ssimValues)
22{
23 int fontFace = FONT_HERSHEY_COMPLEX_SMALL;
24 int fontScale = 1;
25 Scalar fontColor = Scalar(255, 255, 255);
26
27 int len = static_cast<int>(images.size());
28
29 int cols = 2, rows = 2;
30
31 Mat fullImage = Mat::zeros(Size((cols * 10) + imageSize.width * cols, (rows * 10) + imageSize.height * rows),
32 images[0].type());
33
34 stringstream ss;
35 int h_ = -1;
36 for (int i = 0; i < len; i++) {
37
38 int fontStart = 15;
39 int w_ = i % cols;
40 if (i % cols == 0)
41 h_++;
42
43 Rect ROI((w_ * (10 + imageSize.width)), (h_ * (10 + imageSize.height)), imageSize.width, imageSize.height);
44 Mat tmp;
45 resize(images[i], tmp, Size(ROI.width, ROI.height));
46
47 ss << imageTitles[i];
48 putText(tmp,
49 ss.str(),
50 Point(5, fontStart),
51 fontFace,
52 fontScale,
53 fontColor,
54 1,
55 16);
56
57 ss.str("");
58 fontStart += 20;
59
60 ss << "PSNR: " << psnrValues[i];
61 putText(tmp,
62 ss.str(),
63 Point(5, fontStart),
64 fontFace,
65 fontScale,
66 fontColor,
67 1,
68 16);
69
70 ss.str("");
71 fontStart += 20;
72
73 ss << "SSIM: " << ssimValues[i];
74 putText(tmp,
75 ss.str(),
76 Point(5, fontStart),
77 fontFace,
78 fontScale,
79 fontColor,
80 1,
81 16);
82
83 ss.str("");
84 fontStart += 20;
85
86 tmp.copyTo(fullImage(ROI));
87 }
88
89 namedWindow(title, 1);
90 imshow(title, fullImage);
91 waitKey();
92}
93
94static Vec2d getQualityValues(Mat orig, Mat upsampled)
95{
96 double psnr = PSNR(upsampled, orig);
97 Scalar q = quality::QualitySSIM::compute(upsampled, orig, noArray());
98 double ssim = mean(Vec3d((q[0]), q[1], q[2]))[0];
99 return Vec2d(psnr, ssim);
100}
101
102int main(int argc, char *argv[])
103{
104 // Check for valid command line arguments, print usage
105 // if insufficient arguments were given.
106 if (argc < 4) {
107 cout << "usage: Arg 1: image path | Path to image" << endl;
108 cout << "\t Arg 2: algorithm | edsr, espcn, fsrcnn or lapsrn" << endl;
109 cout << "\t Arg 3: path to model file 2 \n";
110 cout << "\t Arg 4: scale | 2, 3, 4 or 8 \n";
111 return -1;
112 }
113
114 string path = string(argv[1]);
115 string algorithm = string(argv[2]);
116 string model = string(argv[3]);
117 int scale = atoi(argv[4]);
118
119 Mat img = imread(path);
120 if (img.empty()) {
121 cerr << "Couldn't load image: " << img << "\n";
122 return -2;
123 }
124
125 //Crop the image so the images will be aligned
126 int width = img.cols - (img.cols % scale);
127 int height = img.rows - (img.rows % scale);
128 Mat cropped = img(Rect(0, 0, width, height));
129
130 //Downscale the image for benchmarking
131 Mat img_downscaled;
132 resize(cropped, img_downscaled, Size(), 1.0 / scale, 1.0 / scale);
133
134 //Make dnn super resolution instance
135 DnnSuperResImpl sr;
136
137 vector <Mat> allImages;
138 Mat img_new;
139
140 //Read and set the dnn model
141 sr.readModel(model);
142 sr.setModel(algorithm, scale);
143 sr.upsample(img_downscaled, img_new);
144
145 vector<double> psnrValues = vector<double>();
146 vector<double> ssimValues = vector<double>();
147
148 //DL MODEL
149 Vec2f quality = getQualityValues(cropped, img_new);
150
151 psnrValues.push_back(quality[0]);
152 ssimValues.push_back(quality[1]);
153
154 cout << sr.getAlgorithm() << ":" << endl;
155 cout << "PSNR: " << quality[0] << " SSIM: " << quality[1] << endl;
156 cout << "----------------------" << endl;
157
158 //BICUBIC
159 Mat bicubic;
160 resize(img_downscaled, bicubic, Size(), scale, scale, INTER_CUBIC);
161 quality = getQualityValues(cropped, bicubic);
162
163 psnrValues.push_back(quality[0]);
164 ssimValues.push_back(quality[1]);
165
166 cout << "Bicubic " << endl;
167 cout << "PSNR: " << quality[0] << " SSIM: " << quality[1] << endl;
168 cout << "----------------------" << endl;
169
170 //NEAREST NEIGHBOR
171 Mat nearest;
172 resize(img_downscaled, nearest, Size(), scale, scale, INTER_NEAREST);
173 quality = getQualityValues(cropped, nearest);
174
175 psnrValues.push_back(quality[0]);
176 ssimValues.push_back(quality[1]);
177
178 cout << "Nearest neighbor" << endl;
179 cout << "PSNR: " << quality[0] << " SSIM: " << quality[1] << endl;
180 cout << "----------------------" << endl;
181
182 //LANCZOS
183 Mat lanczos;
184 resize(img_downscaled, lanczos, Size(), scale, scale, INTER_LANCZOS4);
185 quality = getQualityValues(cropped, lanczos);
186
187 psnrValues.push_back(quality[0]);
188 ssimValues.push_back(quality[1]);
189
190 cout << "Lanczos" << endl;
191 cout << "PSNR: " << quality[0] << " SSIM: " << quality[1] << endl;
192 cout << "-----------------------------------------------" << endl;
193
194 vector <Mat> imgs{img_new, bicubic, nearest, lanczos};
195 vector <String> titles{sr.getAlgorithm(), "Bicubic", "Nearest neighbor", "Lanczos"};
196 showBenchmark(imgs, "Quality benchmark", Size(bicubic.cols, bicubic.rows), titles, psnrValues, ssimValues);
197
198 waitKey(0);
199
200 return 0;
201}
202#else
203int main()
204{
205 std::cout << "This sample requires the OpenCV Quality module." << std::endl;
206 return 0;
207}
208#endif
cv::Mat
n-dimensional dense array class
Definition mat.hpp:812
cv::Mat::copyTo
void copyTo(OutputArray m) const
Copies the matrix to another one.
cv::Mat::cols
int cols
Definition mat.hpp:2138
cv::Mat::empty
bool empty() const
Returns true if the array has no elements.
cv::Mat::rows
int rows
the number of rows and columns or (-1, -1) when the matrix has more than 2 dimensions
Definition mat.hpp:2138
cv::Point_< int >
cv::Rect_
Template class for 2D rectangles.
Definition types.hpp:444
cv::Scalar_< double >
cv::Size_
Template class for specifying the size of an image or rectangle.
Definition types.hpp:335
cv::Size_::height
_Tp height
the height
Definition types.hpp:363
cv::Size_::width
_Tp width
the width
Definition types.hpp:362
dnn_superres.hpp
cv::waitKey
int waitKey(int delay=0)
Waits for a pressed key.
cv::imread
CV_EXPORTS_W Mat imread(const String &filename, int flags=IMREAD_COLOR)
Loads an image from a file.
cv::resize
void resize(InputArray src, OutputArray dst, Size dsize, double fx=0, double fy=0, int interpolation=INTER_LINEAR)
Resizes an image.
main
int main(int argc, char *argv[])
Definition highgui_qt.cpp:3
cv::quality::quality_utils::scale
void scale(cv::Mat &mat, const cv::Mat &range, const T min, const T max)
Definition quality_utils.hpp:90
cv
"black box" representation of the file storage associated with a file on disk.
Definition core.hpp:102
std
STL namespace.

Explanation

  1. Read and downscale the image

    int width = img.cols - (img.cols % scale);
    int height = img.rows - (img.rows % scale);
    Mat cropped = img(Rect(0, 0, width, height));
    Mat img_downscaled;
    cv::resize(cropped, img_downscaled, cv::Size(), 1.0 / scale, 1.0 / scale);

    Resize the image by the scaling factor. Before that a cropping is necessary, so the images will align.

  2. Set the model

    DnnSuperResImpl sr;
    sr.readModel(path);
    sr.setModel(algorithm, scale);
    sr.upsample(img_downscaled, img_new);

    Instantiate a dnn super-resolution object. Read and set the algorithm and scaling factor.

  3. Perform benchmarking

    double psnr = PSNR(img_new, cropped);
    Scalar q = cv::quality::QualitySSIM::compute(img_new, cropped, cv::noArray());
    double ssim = mean(cv::Vec3f(q[0], q[1], q[2]))[0];
    cv::quality::QualitySSIM::compute
    cv::Scalar compute(InputArray cmp) CV_OVERRIDE
    Computes SSIM.
    cv::noArray
    InputOutputArray noArray()

    Calculate PSNR and SSIM. Use OpenCVs PSNR (core opencv) and SSIM (contrib) functions to compare the images. Repeat it with other upscaling algorithms, such as other DL models or interpolation methods (eg. bicubic, nearest neighbor).

Benchmarking results

General100 dataset

2x scaling factor

Avg inference time in sec (CPU) Avg PSNR Avg SSIM
ESPCN 0.008795 32.7059 0.9276
EDSR 5.923450 34.1300 0.9447
FSRCNN 0.021741 32.8886 0.9301
LapSRN 0.114812 32.2681 0.9248
Bicubic 0.000208 32.1638 0.9305
Nearest neighbor 0.000114 29.1665 0.9049
Lanczos 0.001094 32.4687 0.9327

3x scaling factor

Avg inference time in sec (CPU) Avg PSNR Avg SSIM
ESPCN 0.005495 28.4229 0.8474
EDSR 2.455510 29.9828 0.8801
FSRCNN 0.008807 28.3068 0.8429
LapSRN 0.282575 26.7330 0.8862
Bicubic 0.000311 26.0635 0.8754
Nearest neighbor 0.000148 23.5628 0.8174
Lanczos 0.001012 25.9115 0.8706

4x scaling factor

Avg inference time in sec (CPU) Avg PSNR Avg SSIM
ESPCN 0.004311 26.6870 0.7891
EDSR 1.607570 28.1552 0.8317
FSRCNN 0.005302 26.6088 0.7863
LapSRN 0.121229 26.7383 0.7896
Bicubic 0.000311 26.0635 0.8754
Nearest neighbor 0.000148 23.5628 0.8174
Lanczos 0.001012 25.9115 0.8706

Images

2x scaling factor

Set5: butterfly.png size: 256x256
PSRN / SSIM / Speed (CPU) 26.6645 / 0.9048 / 0.000201 23.6854 / 0.8698 / 0.000075 26.9476 / 0.9075 / 0.001039
29.0341 / 0.9354 / 0.004157 29.0077 / 0.9345 / 0.006325 27.8212 / 0.9230 / 0.037937 30.0347 / 0.9453 / 2.077280

3x scaling factor

Urban100: img_001.png size: 1024x644
PSRN / SSIM / Speed (CPU) 27.0474 / 0.8484 / 0.000391 26.0842 / 0.8353 / 0.000236 27.0704 / 0.8483 / 0.002234
LapSRN is not trained for 3x
because of its architecture
28.0118 / 0.8588 / 0.030748 28.0184 / 0.8597 / 0.094173 30.5671 / 0.9019 / 9.517580

4x scaling factor

Set14: comic.png size: 250x361
PSRN / SSIM / Speed (CPU) 19.6766 / 0.6413 / 0.000262 18.5106 / 0.5879 / 0.000085 19.4948 / 0.6317 / 0.001098
20.0417 / 0.6302 / 0.001894 20.0885 / 0.6384 / 0.002103 20.0676 / 0.6339 / 0.061640 20.5233 / 0.6901 / 0.665876

8x scaling factor

Div2K: 0006.png size: 1356x2040
PSRN / SSIM / Speed (CPU) 26.3139 / 0.8033 / 0.001107 23.8291 / 0.7340 / 0.000611
26.1565 / 0.7962 / 0.004782 26.7046 / 0.7987 / 2.274290
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