darknet一括テストと画像保存

darknetソースコードはAB大神バージョンで、darknetをdetectorします.cのtest_detector関数は全体的に置き換えられ、中間の変更に注意して出力ピクチャのパスを自分のパスとして保存します.darknetを再コンパイルします.
コンソールテストコマンド:darknet.exe  detector test data/img.data yolo-obj-test.cfg yolo-obj_best.weights data/valid.txt
注意yolo-obj-test.cfgにおけるbatchおよびsubdivisonを1,data/validとする.txtはテスト対象ピクチャパス

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)
{
	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); //get_labels(name_list);

	image **alphabet = load_alphabet();
	network net = parse_network_cfg_custom(cfgfile, 1, 1); // set batch=1
	if (weightfile) {
		load_weights(&net, weightfile);
	}
	fuse_conv_batchnorm(net);
	calculate_binary_weights(net);
	if (net.layers[net.n - 1].classes != names_size) {
		printf(" Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s 
",
			name_list, names_size, net.layers[net.n - 1].classes, cfgfile);
		if (net.layers[net.n - 1].classes > names_size) getchar();
	}
	srand(2222222);
	double time;
	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");
		char *tmp = "[
";
		fwrite(tmp, sizeof(char), strlen(tmp), json_file);
	}
	int j, i;
	float nms = .45;    // 0.4F
	if (filename) {
		strncpy(input, filename, 256);
		list *plist = get_paths(input);
		char **paths = (char **)list_to_array(plist);
		printf("Start Testing!
");
		int m = plist->size;

		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);
			//image sized = letterbox_image(im, net.w, net.h); letterbox = 1;
			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.
", input, ((double)get_time_point() - time) / 1000);
			printf("Try Very Hard:");
			printf("%s: Predicted in %lf milli-seconds.
", 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(basecfg(input), im, dets, nboxes, thresh, names, alphabet, l.classes, ext_output);
			draw_detections_v3(im, dets, nboxes, thresh, names, alphabet, l.classes, ext_output);

			char b[2048];
			sprintf(b, "data/output/%d", i);     //   data/output/  
			save_image(im, b);
			printf("save %s successfully!
", 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
", 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);
		}
	}
	printf("All Done!
");
	exit(0);
	free_ptrs(names, net.layers[net.n - 1].classes);
	free_list_contents_kvp(options);
	free_list(options);

	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);
	printf("All Done!
");
}