如何利用OpenCV实现基于深度学习的超分辨率处理

#include #include #include 
using namespace std;using namespace cv;using namespace dnn;using namespace dnn_superres;
int main(int argc, char *argv[]){    //Create the module's object    DnnSuperResImpl sr;
    //Set the image you would like to upscale    string img_path = "image.png";    Mat img = cv::imread(img_path);
    //Read the desired model    string path = "FSRCNN_x2.pb";    sr.readModel(path);
    //Set the desired model and scale to get correct pre- and post-processing    sr.setModel("fsrcnn", 2);
    //Upscale    Mat img_new;    sr.upsample(img, img_new);    cv::imwrite( "upscaled.png", img_new);
    return 0;}

//Read the desired modelstring path = "FSRCNN_x2.pb";sr.readModel(path);

//Set the desired model and scale to get correct pre- and post-processingsr.setModel("fsrcnn", 2);

//UpscaleMat img_new;sr.upsample(img, img_new);cv::imwrite( "upscaled.png", img_new);

import cv2from cv2 import dnn_superres
# Create an SR objectsr = dnn_superres.DnnSuperResImpl_create()
# Read imageimage = cv2.imread('./input.png')
# Read the desired modelpath = "EDSR_x3.pb"sr.readModel(path)
# Set the desired model and scale to get correct pre- and post-processingsr.setModel("edsr", 3)
# Upscale the imageresult = sr.upsample(image)
# Save the imagecv2.imwrite("./upscaled.png", result)

# Create an SR objectsr = dnn_superres.DnnSuperResImpl_create()