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windows环境下，需要用到VS
1.下载Darknet和yolov3
下载Darknet
如果有git的话 git clone https://github.com/AlexeyAB/darknet
下载master版本的
首先打开darknet_no_gpu.sln
打开后修改detector.c
替换原先的detector.c文件
void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh,
float hier_thresh, int dont_show, int ext_output, int save_labels, char *outfile, int letter_box, int benchmark_layers)
{
list *options = read_data_cfg(datacfg);
char *name_list = option_find_str(options, "names", "data/names.list");
int names_size = 0;
char **names = get_labels_custom(name_list, &names_size);
image **alphabet = load_alphabet();
network net = parse_network_cfg_custom(cfgfile, 1, 1);
if (weightfile) {
load_weights(&net, weightfile);
}
if (net.letter_box) letter_box = 1;
net.benchmark_layers = benchmark_layers;
fuse_conv_batchnorm(net);
calculate_binary_weights(net);
if (net.layers[net.n - 1].classes != names_size) {
printf("\n Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n",
name_list, names_size, net.layers[net.n - 1].classes, cfgfile);
if (net.layers[net.n - 1].classes > names_size) getchar();
}
srand(2222222);
char buff[256];
char *input = buff;
char *json_buf = NULL;
int json_image_id = 0;
FILE* json_file = NULL;
if (outfile) {
json_file = fopen(outfile, "wb");
if (!json_file) {
error("fopen failed", DARKNET_LOC);
}
char *tmp = "[\n";
fwrite(tmp, sizeof(char), strlen(tmp), json_file);
}
int j;
float nms = .45;
while (1) {
if (filename) {
strncpy(input, filename, 256);
list *plist = get_paths(input);
char **paths = (char **)list_to_array(plist);
printf("Start Testing!\n");
int m = plist->size;
int i;
for (i = 0; i < m; ++i) {
char *path = paths[i];
image im = load_image(path, 0, 0, net.c);
int letterbox = 0;
image sized = resize_image(im, net.w, net.h);
layer l = net.layers[net.n - 1];
float *X = sized.data;
double time = get_time_point();
network_predict(net, X);
printf("%s: Predicted in %lf milli-seconds.\n", input, ((double)get_time_point() - time) / 1000);
printf("Try Very Hard:");
printf("%s: Predicted in %lf milli-seconds.\n", path, ((double)get_time_point() - time) / 1000);
int nboxes = 0;
detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letterbox);
if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
draw_detections_v3(im, dets, nboxes, thresh, names, alphabet, l.classes, ext_output);
char b[2048];
sprintf(b, "E:\\darknet-Yolo_v3\\train\\test_result\\%d", i);
save_image(im, b);
printf("save %s successfully!\n", b);
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);
}
}
}
else {
}
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);
}
}
float *X = sized.data;
double time = get_time_point();
network_predict(net, X);
printf("%s: Predicted in %lf milli-seconds.\n", input, ((double)get_time_point() - time) / 1000);
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);
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);
}
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;
}
if (json_file) {
char *tmp = "\n]";
fwrite(tmp, sizeof(char), strlen(tmp), json_file);
fclose(json_file);
}
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);
}
2、然后编译生成exe文件
3、生成包括批量的图片txt，txt包括每一张代检测图片的绝对路径
最后执行下面命令来批量检测：
darknet_no_gpu.exe detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights D:\Ctext\input_image_list.txt
得到检测结果！
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