Документация

% Copyright 2018 The MathWorks, Inc.

function out = resnet_predict(in) 
%#codegen

% A persistent object mynet is used to load the series network object.
% At the first call to this function, the persistent object is constructed and
% setup. When the function is called subsequent times, the same object is reused 
% to call predict on inputs, avoiding reconstructing and reloading the
% network object.

persistent mynet;

if isempty(mynet)
    % Call the function resnet50 that returns a DAG network
    % for ResNet-50 model.
    mynet = coder.loadDeepLearningNetwork('resnet50','resnet');
end

% pass in input   
out = mynet.predict(in);

Code generation successful: To view the report, open('codegen\mex\resnet_predict\html\report.mldatx').

Code generation successful: To view the report, open('codegen\lib\resnet_predict\html\report.mldatx').

/* Copyright 2018 The MathWorks, Inc. */

#include "resnet_predict.h"

#include <stdio.h>
#include <string.h>
#include <math.h>
#include <iostream>
#include "opencv2/opencv.hpp"
using namespace cv;

int readData(void* inputBuffer, char* inputImage) {

    Mat inpImage, intermImage;
    inpImage = imread(inputImage, 1);
    Size size(224, 224);
    resize(inpImage, intermImage, size);
    if (!intermImage.data) {
        printf(" No image data \n ");
        exit(1);
    }
    float* input = (float*)inputBuffer;

    for (int j = 0; j < 224 * 224; j++) {
        // BGR to RGB
        input[2 * 224 * 224 + j] = (float)(intermImage.data[j * 3 + 0]);
        input[1 * 224 * 224 + j] = (float)(intermImage.data[j * 3 + 1]);
        input[0 * 224 * 224 + j] = (float)(intermImage.data[j * 3 + 2]);
    }
    return 1;
}

#if defined(WIN32) || defined(_WIN32) || defined(__WIN32) || defined(_WIN64)

int cmpfunc(void* r, const void* a, const void* b) {
    float x = ((float*)r)[*(int*)b] - ((float*)r)[*(int*)a];
    return (x > 0 ? ceil(x) : floor(x));
}
#else

int cmpfunc(const void* a, const void* b, void* r) {
    float x = ((float*)r)[*(int*)b] - ((float*)r)[*(int*)a];
    return (x > 0 ? ceil(x) : floor(x));
}

#endif

void top(float* r, int* top5) {
    int t[1000];
    for (int i = 0; i < 1000; i++) {
        t[i] = i;
    }
#if defined(WIN32) || defined(_WIN32) || defined(__WIN32) || defined(_WIN64)
    qsort_s(t, 1000, sizeof(int), cmpfunc, r);
#else
    qsort_r(t, 1000, sizeof(int), cmpfunc, r);
#endif
    top5[0] = t[0];
    top5[1] = t[1];
    top5[2] = t[2];
    top5[3] = t[3];
    top5[4] = t[4];
    return;
}


int prepareSynset(char synsets[1000][100]) {
    FILE* fp1 = fopen("synsetWords.txt", "r");
    if (fp1 == 0) {
        return -1;
    }

    for (int i = 0; i < 1000; i++) {
        if (fgets(synsets[i], 100, fp1) != NULL)
            ;
        strtok(synsets[i], "\n");
    }
    fclose(fp1);
    return 0;
}

void writeData(float* output, char synsetWords[1000][100], Mat &frame) {
    int top5[5], j;
    
    top(output, top5);
    
    copyMakeBorder(frame, frame, 0, 0, 400, 0, BORDER_CONSTANT, CV_RGB(0,0,0));
    char strbuf[50];
    sprintf(strbuf, "%4.1f%% %s", output[top5[0]]*100, synsetWords[top5[0]]);
    putText(frame, strbuf, cvPoint(30,80), CV_FONT_HERSHEY_DUPLEX, 1.0, CV_RGB(220,220,220), 1);
    sprintf(strbuf, "%4.1f%% %s", output[top5[1]]*100, synsetWords[top5[1]]);
    putText(frame, strbuf, cvPoint(30,130), CV_FONT_HERSHEY_DUPLEX, 1.0, CV_RGB(220,220,220), 1);
    sprintf(strbuf, "%4.1f%% %s", output[top5[2]]*100, synsetWords[top5[2]]);
    putText(frame, strbuf, cvPoint(30,180), CV_FONT_HERSHEY_DUPLEX, 1.0, CV_RGB(220,220,220), 1);
    sprintf(strbuf, "%4.1f%% %s", output[top5[3]]*100, synsetWords[top5[3]]);
    putText(frame, strbuf, cvPoint(30,230), CV_FONT_HERSHEY_DUPLEX, 1.0, CV_RGB(220,220,220), 1);
    sprintf(strbuf, "%4.1f%% %s", output[top5[4]]*100, synsetWords[top5[4]]);
    putText(frame, strbuf, cvPoint(30,280), CV_FONT_HERSHEY_DUPLEX, 1.0, CV_RGB(220,220,220), 1);

}

// Main function
int main(int argc, char* argv[]) {
    int n = 1;
    char synsetWords[1000][100];
    
    namedWindow("Classification with ResNet-50",CV_WINDOW_NORMAL);
    resizeWindow("Classification with ResNet-50",440,224);

    Mat im;
    im = imread(argv[1], 1);
    
    float* ipfBuffer = (float*)calloc(sizeof(float), 224*224*3);
    
    float* opBuffer = (float*)calloc(sizeof(float), 1000);
    if (argc != 2) {
        printf("Input image missing \nSample Usage-./resnet_exe image.png\n");
        exit(1);
    }
    if (prepareSynset(synsetWords) == -1) {
        printf("ERROR: Unable to find synsetWords.txt\n");
        return -1;
    }

    //read input imaget to the ipfBuffer
    readData(ipfBuffer, argv[1]);
    
    //run prediction on image stored in ipfBuffer
    resnet_predict(ipfBuffer, opBuffer);
    
    //write predictions on input image
    writeData(opBuffer, synsetWords, im);

    //show predictions on input image
    imshow("Classification with ResNet-50", im);
    waitKey(5000);
    destroyWindow("Classification with ResNet-50");
    return 0;
}