我正在使用 YOLO 进行对象检测，并想编辑代码文件以对文件夹中的所有图像运行检测，我发现此功能 Github link to the function
我编辑了 here 中的“test_detector”函数，如下所示：
while (1) {
folder = opendir("./result_img/");
char str1[100] = "./result_img/";
while( (entry=readdir(folder)) != NULL)
{
if((strcmp(entry->d_name,".")==0 || strcmp(entry->d_name,"..")==0 || (entry->d_name) == '.' ) || (strcmp(entry->d_name,"Server_v1.py")==0))
{
printf(".");
sleep(0.5);
continue;
}
if (filename) {
strcat(str1, entry->d_name);
strncpy(input, str1, 256);
closedir(folder);
if (strlen(input) > 0)
if (input[strlen(input) - 1] == 0x0d) input[strlen(input) - 1] = 0;
}
else {
printf("Enter Image Path: ");
fflush(stdout);
input = fgets(input, 256, stdin);
if (!input) break;
strtok(input, "\n");
}
//image im;
//image sized = load_image_resize(input, net.w, net.h, net.c, &im);
image im = load_image(input, 0, 0, net.c);
image sized;
if(letter_box) sized = letterbox_image(im, net.w, net.h);
else sized = resize_image(im, net.w, net.h);
layer l = net.layers[net.n - 1];
int k;
for (k = 0; k < net.n; ++k) {
layer lk = net.layers[k];
if (lk.type == YOLO || lk.type == GAUSSIAN_YOLO || lk.type == REGION) {
l = lk;
printf(" Detection layer: %d - type = %d \n", k, l.type);
}
}
//box *boxes = calloc(l.w*l.h*l.n, sizeof(box));
//float **probs = calloc(l.w*l.h*l.n, sizeof(float*));
//for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = (float*)xcalloc(l.classes, sizeof(float));
float *X = sized.data;
//time= what_time_is_it_now();
double time = get_time_point();
network_predict(net, X);
//network_predict_image(&net, im); letterbox = 1;
printf("%s: Predicted in %lf milli-seconds.\n", input, ((double)get_time_point() - time) / 1000);
//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, letter_box);
if (nms) {
if (l.nms_kind == DEFAULT_NMS) do_nms_sort(dets, nboxes, l.classes, nms);
else diounms_sort(dets, nboxes, l.classes, nms, l.nms_kind, l.beta_nms);
}
draw_detections_v3(im, dets, nboxes, thresh, names, alphabet, l.classes, ext_output, input);
save_image(im, "predictions");
if (!dont_show) {
show_image(im, "predictions");
}
if (json_file) {
if (json_buf) {
char *tmp = ", \n";
fwrite(tmp, sizeof(char), strlen(tmp), json_file);
}
++json_image_id;
json_buf = detection_to_json(dets, nboxes, l.classes, names, json_image_id, input);
fwrite(json_buf, sizeof(char), strlen(json_buf), json_file);
free(json_buf);
}
// pseudo labeling concept - fast.ai
if (save_labels)
{
char labelpath[4096];
replace_image_to_label(input, labelpath);
FILE* fw = fopen(labelpath, "wb");
int i;
for (i = 0; i < nboxes; ++i) {
char buff[1024];
int class_id = -1;
float prob = 0;
for (j = 0; j < l.classes; ++j) {
if (dets[i].prob[j] > thresh && dets[i].prob[j] > prob) {
prob = dets[i].prob[j];
class_id = j;
}
}
if (class_id >= 0) {
sprintf(buff, "%d %2.4f %2.4f %2.4f %2.4f\n", class_id, dets[i].bbox.x, dets[i].bbox.y, dets[i].bbox.w, dets[i].bbox.h);
fwrite(buff, sizeof(char), strlen(buff), fw);
}
}
fclose(fw);
}
free_detections(dets, nboxes);
free_image(im);
free_image(sized);
if (dont_show) {
wait_until_press_key_cv();
destroy_all_windows_cv();
}
if (filename) break;
}
sleep(1);
printf("outside the loop");
char newname[100];
removeSubstrr(str1, "./result_img/");
sprintf(newname, "./pfiles/%s",str1);
//remove(input);
printf("newname %s\n",newname);
rename (input, newname);
//sleep(1);
}
if (json_file) {
char *tmp = "\n]";
fwrite(tmp, sizeof(char), strlen(tmp), json_file);
fclose(json_file);
}
// free memory
free_ptrs((void**)names, net.layers[net.n - 1].classes);
free_list_contents_kvp(options);
free_list(options);
int i;
const int nsize = 8;
for (j = 0; j < nsize; ++j) {
for (i = 32; i < 127; ++i) {
free_image(alphabet[j][i]);
}
free(alphabet[j]);
}
free(alphabet);
free_network(net);
}
当我运行以下代码时，如果文件夹不为空，它运行良好。一旦文件夹在一段时间后为空，我就会收到分段错误（核心转储）错误。如果我将“sleep(1)”放在第一个循环的末尾，代码运行良好，但每次检测需要 1 秒，这对应用程序来说很慢。
我发现如果我删除“if(filename)break;”这一行即使文件夹不为空，代码也会在循环结束时停止。
filename 始终为真，因为它通过命令行传递