darknet批量测试并保存图片
darknet源代码是AB大神版本的，将darknet下detector.c的test_detector函数整体替换，注意中间的更改保存输出图片的路径为自己路径；重新编译darknet。
控制台测试命令：darknet.exe detector test data/img.data yolo-obj-test.cfg yolo-obj_best.weights data/valid.txt
注意将yolo-obj-test.cfg中的batch及subdivison设为1， data/valid.txt为待测试图片路径
1 void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, 2 float hier_thresh, int dont_show, int ext_output, int save_labels, char *outfile, int letter_box) 3 { 4 list *options = read_data_cfg(datacfg); 5 char *name_list = option_find_str(options, "names", "data/names.list"); 6 int names_size = 0; 7 char **names = get_labels_custom(name_list, &names_size); //get_labels(name_list); 8 9 image **alphabet = load_alphabet(); 10 network net = parse_network_cfg_custom(cfgfile, 1, 1); // set batch=1 11 if (weightfile) { 12 load_weights(&net, weightfile); 13 } 14 fuse_conv_batchnorm(net); 15 calculate_binary_weights(net); 16 if (net.layers[net.n - 1].classes != names_size) { 17 printf(" Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n", 18 name_list, names_size, net.layers[net.n - 1].classes, cfgfile); 19 if (net.layers[net.n - 1].classes > names_size) getchar(); 20 } 21 srand(2222222); 22 double time; 23 char buff[256]; 24 char *input = buff; 25 char *json_buf = NULL; 26 int json_image_id = 0; 27 FILE* json_file = NULL; 28 if (outfile) { 29 json_file = fopen(outfile, "wb"); 30 char *tmp = "[\n"; 31 fwrite(tmp, sizeof(char), strlen(tmp), json_file); 32 } 33 int j, i; 34 float nms = .45; // 0.4F 35 if (filename) { 36 strncpy(input, filename, 256); 37 list *plist = get_paths(input); 38 char **paths = (char **)list_to_array(plist); 39 printf("Start Testing!\n"); 40 int m = plist->size; 41 42 for (i = 0; i < m; ++i) { 43 char *path = paths[i]; 44 image im = load_image(path, 0, 0, net.c); 45 int letterbox = 0; 46 image sized = resize_image(im, net.w, net.h); 47 //image sized = letterbox_image(im, net.w, net.h); letterbox = 1; 48 layer l = net.layers[net.n - 1]; 49 float *X = sized.data; 50 double time = get_time_point(); 51 network_predict(net, X); 52 printf("%s: Predicted in %lf milli-seconds.\n", input, ((double)get_time_point() - time) / 1000); 53 printf("Try Very Hard:"); 54 printf("%s: Predicted in %lf milli-seconds.\n", path, ((double)get_time_point() - time) / 1000); 55 int nboxes = 0; 56 detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letterbox); 57 if (nms) do_nms_sort(dets, nboxes, l.classes, nms); 58 // draw_detections_v3(basecfg(input), im, dets, nboxes, thresh, names, alphabet, l.classes, ext_output); 59 draw_detections_v3(im, dets, nboxes, thresh, names, alphabet, l.classes, ext_output); 60 61 char b[2048]; 62 sprintf(b, "data/output/%d", i); // data/output/ 改成保存文件夹的路径 63 save_image(im, b); 64 printf("save %s successfully!\n", b); 65 66 if (save_labels) 67 { 68 char labelpath[4096]; 69 replace_image_to_label(input, labelpath); 70 FILE* fw = fopen(labelpath, "wb"); 71 int i; 72 for (i = 0; i < nboxes; ++i) { 73 char buff[1024]; 74 int class_id = -1; 75 float prob = 0; 76 for (j = 0; j < l.classes; ++j) { 77 if (dets[i].prob[j] > thresh && dets[i].prob[j] > prob) { 78 prob = dets[i].prob[j]; 79 class_id = j; 80 } 81 } 82 if (class_id >= 0) { 83 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); 84 fwrite(buff, sizeof(char), strlen(buff), fw); 85 } 86 } 87 fclose(fw); 88 } 89 90 free_detections(dets, nboxes); 91 free_image(im); 92 free_image(sized); 93 } 94 } 95 printf("All Done!\n"); 96 exit(0); 97 free_ptrs(names, net.layers[net.n - 1].classes); 98 free_list_contents_kvp(options); 99 free_list(options); 100 101 const int nsize = 8; 102 for (j = 0; j < nsize; ++j) { 103 for (i = 32; i < 127; ++i) { 104 free_image(alphabet[j][i]); 105 } 106 free(alphabet[j]); 107 } 108 109 free(alphabet); 110 free_network(net); 111 printf("All Done!\n"); 112 }
