之前写过一篇文章将Yolo-v2检测到的目标单独保存成图像，其实内容差不多少，只是最近总有人问我对于YOLO-V3的怎么改，下面就详细的说一下，在YOLO-V3的源码中修改具有保存子图像的方法。
打开darknet_no_gpu.sln，可以看到很多的代码，其实对我们有用的只有detector.c，image.c,image.h这三个文件。
下面就具体介绍一下怎么改。
在YOLO的源码中，画框的函数通常会命名成draw_detectionsxxx之类的，所以通常都会draw_detections后面可能有别的什么的用来区分，比如在YOLO-V2的源码中就用draw_detections函数，而在YOLO-V3中调用的是draw_detections_v3函数，只是将draw_detections的功能进行了优化，优化了什么我等会再说。先去看看draw_detections_v3函数
void draw_detections_v3(image im, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output)
{
int selected_detections_num;
detection_with_class* selected_detections = get_actual_detections(dets, num, thresh, &selected_detections_num);
//added by fyj start
m_img = copy_image(im);
//added by fyj end
// text output
qsort(selected_detections, selected_detections_num, sizeof(*selected_detections), compare_by_lefts);
int i;
for (i = 0; i < selected_detections_num; ++i) {
const int best_class = selected_detections[i].best_class;
printf("%s: %.0f%%", names[best_class], selected_detections[i].det.prob[best_class] * 100);
if (ext_output)
printf("\t(left_x: %4.0f top_y: %4.0f width: %4.0f height: %4.0f)\n",
(selected_detections[i].det.bbox.x - selected_detections[i].det.bbox.w / 2)*im.w,
(selected_detections[i].det.bbox.y - selected_detections[i].det.bbox.h / 2)*im.h,
selected_detections[i].det.bbox.w*im.w, selected_detections[i].det.bbox.h*im.h);
else
printf("\n");
int j;
for (j = 0; j < classes; ++j) {
if (selected_detections[i].det.prob[j] > thresh && j != best_class) {
printf("%s: %.0f%%\n", names[j], selected_detections[i].det.prob[j] * 100);
}
}
}
// image output
qsort(selected_detections, selected_detections_num, sizeof(*selected_detections), compare_by_probs);
for (i = 0; i < selected_detections_num; ++i) {
int width = im.h * .006;
if (width < 1)
width = 1;
/*
if(0){
width = pow(prob, 1./2.)*10+1;
alphabet = 0;
}
*/
//printf("%d %s: %.0f%%\n", i, names[selected_detections[i].best_class], prob*100);
int offset = selected_detections[i].best_class * 123457 % classes;
float red = get_color(2, offset, classes);
float green = get_color(1, offset, classes);
float blue = get_color(0, offset, classes);
float rgb[3];
//width = prob*20+2;
rgb[0] = red;
rgb[1] = green;
rgb[2] = blue;
box b = selected_detections[i].det.bbox;
//printf("%f %f %f %f\n", b.x, b.y, b.w, b.h);
int left = (b.x - b.w / 2.)*im.w;
int right = (b.x + b.w / 2.)*im.w;
int top = (b.y - b.h / 2.)*im.h;
int bot = (b.y + b.h / 2.)*im.h;
//add by fyj start
//printf("That is it!");
pre_x = left;
pre_y = top;
pre_h = bot - top;
pre_w = right - left;
save_cut_image(pre_x, pre_y, pre_h, pre_w, i);
//added by fyj end
if (left < 0) left = 0;
if (right > im.w - 1) right = im.w - 1;
if (top < 0) top = 0;
if (bot > im.h - 1) bot = im.h - 1;
//int b_x_center = (left + right) / 2;
//int b_y_center = (top + bot) / 2;
//int b_width = right - left;
//int b_height = bot - top;
//sprintf(labelstr, "%d x %d - w: %d, h: %d", b_x_center, b_y_center, b_width, b_height);
draw_box_width(im, left, top, right, bot, width, red, green, blue);
if (alphabet) {
char labelstr[4096] = { 0 };
strcat(labelstr, names[selected_detections[i].best_class]);
int j;
for (j = 0; j < classes; ++j) {
if (selected_detections[i].det.prob[j] > thresh && j != selected_detections[i].best_class) {
strcat(labelstr, ", ");
strcat(labelstr, names[j]);
}
}
image label = get_label_v3(alphabet, labelstr, (im.h*.03));
draw_label(im, top + width, left, label, rgb);
free_image(label);
}
if (selected_detections[i].det.mask) {
image mask = float_to_image(14, 14, 1, selected_detections[i].det.mask);
image resized_mask = resize_image(mask, b.w*im.w, b.h*im.h);
image tmask = threshold_image(resized_mask, .5);
embed_image(tmask, im, left, top);
free_image(mask);
free_image(resized_mask);
free_image(tmask);
}
}
free(selected_detections);
}
这个函数的输入有image im, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output这么多，added by fyj xxx 就是我自己加的。
opencv保存子图像需要x,y,h,w这几个参数，因为用的c语言版本的opencv，具体的用法可以参考C语言与C++版本的opencv实现截取图像中的一部分显示这篇文章。
这四个参数明显还不够，还需要一个能区分不同子图像的num，用来保存。这个num可以直接调用for循环里的i，简单吧。然后就是我自己加的save_cut_image函数了。
void save_cut_image(int px, int py, int ph, int pw, int no)
{
image copy = copy_image(m_img);
if (m_img.c == 3) rgbgr_image(copy);
int x, y, k;
char buff[256];
//F://darknet-v3//darknet-master//build//darknet//x64//results//%d.jpg
sprintf(buff, "F://darknet-v3//darknet-master//build//darknet//x64//results//%d.jpg", no);
IplImage *disp = cvCreateImage(cvSize(m_img.w, m_img.h), IPL_DEPTH_8U, m_img.c);
int step = disp->widthStep;
for (y = 0; y < m_img.h; ++y) {
for (x = 0; x < m_img.w; ++x) {
for (k = 0; k < m_img.c; ++k) {
disp->imageData[y*step + x*m_img.c + k] = (unsigned char)(get_pixel(copy, x, y, k) * 255);
}
}
}
CvMat *pMat = cvCreateMatHeader(m_img.w, m_img.h, IPL_DEPTH_8U);
//char rect_name[256];
//sprintf(rect_name, "%d_rect", no);
CvRect rect = cvRect(px, py, pw, ph);
cvGetSubRect(disp, pMat, rect);
IplImage *pSubImg = cvCreateImage(cvSize(pw, ph), IPL_DEPTH_8U, m_img.c);
cvGetImage(pMat, pSubImg);
//printf("x=%d,y=%d,h=%d,w=%d\n", px, py, ph, pw);
cvSaveImage(buff, pSubImg, 0);
//cvReleaseImage(&disp);
//cvReleaseImage(&pMat);
//cvReleaseImage(&rect);
//memset(&rect, 0, sizeof(rect));
//cvReleaseImage(&pSubImg);
//free(&rect);
free_image(copy);
}
请注意这里我用的是我自己的路径，你们可以改成相对路径"results//%d.jpg"
void save_cut_image(int px, int py, int ph, int pw, int no)
{
image copy = copy_image(m_img);
if (m_img.c == 3) rgbgr_image(copy);
int x, y, k;
char buff[256];
//F://darknet-v3//darknet-master//build//darknet//x64//results//%d.jpg
sprintf(buff, "results//%d.jpg", no);
IplImage *disp = cvCreateImage(cvSize(m_img.w, m_img.h), IPL_DEPTH_8U, m_img.c);
int step = disp->widthStep;
for (y = 0; y < m_img.h; ++y) {
for (x = 0; x < m_img.w; ++x) {
for (k = 0; k < m_img.c; ++k) {
disp->imageData[y*step + x*m_img.c + k] = (unsigned char)(get_pixel(copy, x, y, k) * 255);
}
}
}
CvMat *pMat = cvCreateMatHeader(m_img.w, m_img.h, IPL_DEPTH_8U);
//char rect_name[256];
//sprintf(rect_name, "%d_rect", no);
CvRect rect = cvRect(px, py, pw, ph);
cvGetSubRect(disp, pMat, rect);
IplImage *pSubImg = cvCreateImage(cvSize(pw, ph), IPL_DEPTH_8U, m_img.c);
cvGetImage(pMat, pSubImg);
//printf("x=%d,y=%d,h=%d,w=%d\n", px, py, ph, pw);
cvSaveImage(buff, pSubImg, 0);
//cvReleaseImage(&disp);
//cvReleaseImage(&pMat);
//cvReleaseImage(&rect);
//memset(&rect, 0, sizeof(rect));
//cvReleaseImage(&pSubImg);
//free(&rect);
free_image(copy);
}
其实都可以的。
还有就是在image.c中声明的全局变量和静态变量
image m_img;
pre_x = 0;
pre_y = 0;
pre_h = 0;
pre_w = 0;
pre_x ，pre_y ，pre_h ，pre_w 需要在image.h中进行声明
static int pre_x, pre_y, pre_h, pre_w;
然后再说一下YOLO-V3版本的draw_detections_v3函数，最大的优点就是，保存的图像名称是有序的1.jpg,2.jpg....
而在YOLO-V2版本的子图像是无序的数字。，代码我将会上传到github上（可以的话给个星星⭐）。
改完以后，点击生成---生成解决方案
然后就会看到生成的darknet_no_gpu.exe，在x64文件夹里，shift+右键，打开Windows PowerShell输入命令
darknet_no_gpu.exe detector test data/coco.data cfg/yolov3.cfg yolov3.weights -i 0 -thresh 0.25 dog.jpg
可以看到结果
训练是正常的，通过命令
darknet_no_gpu.exe detector train data/voc.data cfg/yolov3-voc.cfg
或者使用预训练好的权重文件
darknet_no_gpu.exe detector train data/voc.data cfg/yolov3-voc.cfg darknet53.conv.74
训练的细节在这篇文章里详细说YOLO-V3训练中会遇到的问题 。