darknet训练完成后批量测试图片

darknet训练完成后批量测试图片

参考博客https://blog.csdn.net/fqlovetb/article/details/84393806

darknet训练自己的数据集，以及训练完成后对单张图片的检测见如下地址https://blog.csdn.net/qq_38915710/article/details/97112788

下面介绍darknet批量测试图片并保存在自己的文件夹，亲测成功。

首先在darknet目录下新建myData_test.txt，其中存放测试图片集的绝对路径。

然后找到/home/.../darknet/examples/detector.c，进行如下修改：

将开头进行如下替换：

#include "darknet.h"
#include 
#include
#include
#include
static int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90};
 
char *GetFilename(char *p)
{ 
    static char name[20]={""};
    char *q = strrchr(p,'/') + 1;
    strncpy(name,q,6);//后面的6是图片名长度（不包括后缀），根据自己的需要进行修改
    return name;
}

再对test_detector()函数进行如下替换：（其中有三处地址为图片输出地址，根据自己的情况进行修改）

void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh, char *outfile, int fullscreen)
{
    list *options = read_data_cfg(datacfg);
    char *name_list = option_find_str(options, "names", "data/names.list");
    char **names = get_labels(name_list);
 
    image **alphabet = load_alphabet();
    network *net = load_network(cfgfile, weightfile, 0);
    set_batch_network(net, 1);
    srand(2222222);
    double time;
    char buff[256];
    char *input = buff;
    float nms=.45;
    int i=0;
    while(1){
        if(filename){
            strncpy(input, filename, 256);
            image im = load_image_color(input,0,0);
            image sized = letterbox_image(im, net->w, net->h);
        //image sized = resize_image(im, net->w, net->h);
        //image sized2 = resize_max(im, net->w);
        //image sized = crop_image(sized2, -((net->w - sized2.w)/2), -((net->h - sized2.h)/2), net->w, net->h);
        //resize_network(net, sized.w, sized.h);
            layer l = net->layers[net->n-1];
 
 
            float *X = sized.data;
            time=what_time_is_it_now();
            network_predict(net, X);
            printf("%s: Predicted in %f seconds.\n", input, what_time_is_it_now()-time);
            int nboxes = 0;
            detection *dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes);
            //printf("%d\n", nboxes);
            //if (nms) do_nms_obj(boxes, probs, l.w*l.h*l.n, l.classes, nms);
            if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
                draw_detections(im, dets, nboxes, thresh, names, alphabet, l.classes);
                free_detections(dets, nboxes);
            if(outfile)
             {
                save_image(im, outfile);
             }
            else{
                save_image(im, "predictions");
#ifdef OPENCV
                /*cvNamedWindow("predictions", CV_WINDOW_NORMAL); 
                if(fullscreen){
                cvSetWindowProperty("predictions", CV_WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN);
                }
                show_image(im, "predictions");
                cvWaitKey(0);
                cvDestroyAllWindows();*/
#endif
            }
            free_image(im);
            free_image(sized);
            if (filename) break;
         } 
        else {
            printf("Enter Image Path: ");
            fflush(stdout);
            input = fgets(input, 256, stdin);
            if(!input) return;
            strtok(input, "\n");
   
            list *plist = get_paths(input);
            char **paths = (char **)list_to_array(plist);
             printf("Start Testing!\n");
            int m = plist->size;
            if(access("/home/.../darknet/data/out",0)==-1)//"/home/.../darknet/data"根据自己的路径修改
            {
              if (mkdir("/home/.../darknet/data/out",0777))//"/home/.../darknet/data"根据自己的路径修改
               {
                 printf("creat file bag failed!!!");
               }
            }
            for(i = 0; i < m; ++i){
             char *path = paths[i];
             image im = load_image_color(path,0,0);
             image sized = letterbox_image(im, net->w, net->h);
        //image sized = resize_image(im, net->w, net->h);
        //image sized2 = resize_max(im, net->w);
        //image sized = crop_image(sized2, -((net->w - sized2.w)/2), -((net->h - sized2.h)/2), net->w, net->h);
        //resize_network(net, sized.w, sized.h);
        layer l = net->layers[net->n-1];
 
 
        float *X = sized.data;
        time=what_time_is_it_now();
        network_predict(net, X);
        printf("Try Very Hard:");
        printf("%s: Predicted in %f seconds.\n", path, what_time_is_it_now()-time);
        int nboxes = 0;
        detection *dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes);
        //printf("%d\n", nboxes);
        //if (nms) do_nms_obj(boxes, probs, l.w*l.h*l.n, l.classes, nms);
        if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
        draw_detections(im, dets, nboxes, thresh, names, alphabet, l.classes);
        free_detections(dets, nboxes);
        if(outfile){
            save_image(im, outfile);
        }
        else{
             
             char b[2048];
            sprintf(b,"/home/.../darknet/data/out/%s",GetFilename(path));//"/home/.../darknet/data"根据自己的路径修改
            
            save_image(im, b);
            printf("save %s successfully!\n",GetFilename(path));
#ifdef OPENCV
            /*cvNamedWindow("predictions", CV_WINDOW_NORMAL); 
            if(fullscreen){
                cvSetWindowProperty("predictions", CV_WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN);
            }
            show_image(im, "predictions");
            cvWaitKey(0);
            cvDestroyAllWindows();*/
#endif
        }
 
        free_image(im);
        free_image(sized);
        if (filename) break;
        }
      }
    }
}

做完以上的准备工作之后，返回darknet文件夹，先make clean，再make，然后输入：./darknet detector test cfg/voc.data cfg/yolov3.cfg cfg/yolov3-voc_final.weights

成功之后会打印Enter Image Path：

在后面输入myData_test.txt的绝对路径（如/home/.../darknet/myData_test.txt），等待图片输出即可。

需要注意的是，测试集图片的地址一定要和myData_test.txt里面的图片地址一样，voc.data文件下的valid也要修改为/home/.../darknet/myData.txt。