本文主要是介绍yolo2 检测到的物体输出2，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
说明
检测到的物体被box所标记，输出box所在的位置，并存储到txt中。
上次是在src/image.c的代码中修改的，相当于是在画出检测出的物体的框的同时把坐标存入了txt，这次修改的是examples/detector.c文件。
先来看一下主函数 examples/darknet.c
官网上运行的示例测试代码为：
其中运行的主函数的源码就是examples/darknet.c，后面的detector test等参数通过命令行传给了主函数。
darknet.c的代码为(代码比较长，省略了其他只看main)：
#include "darknet.h"#include <time.h>
#include <stdlib.h>
#include <stdio.h>
int main(int argc, char **argv)
{//test_resize("data/bad.jpg");//test_box();//test_convolutional_layer();if(argc < 2){fprintf(stderr, "usage: %s <function>\n", argv[0]);return 0;}gpu_index = find_int_arg(argc, argv, "-i", 0);if(find_arg(argc, argv, "-nogpu")) {gpu_index = -1;}#ifndef GPUgpu_index = -1;
#elseif(gpu_index >= 0){cuda_set_device(gpu_index);}
#endifif (0 == strcmp(argv[1], "average")){average(argc, argv);} else if (0 == strcmp(argv[1], "yolo")){run_yolo(argc, argv);} else if (0 == strcmp(argv[1], "voxel")){run_voxel(argc, argv);} else if (0 == strcmp(argv[1], "super")){run_super(argc, argv);} else if (0 == strcmp(argv[1], "lsd")){run_lsd(argc, argv);} else if (0 == strcmp(argv[1], "detector")){ //*********************************run_detector(argc, argv);} else if (0 == strcmp(argv[1], "detect")){float thresh = find_float_arg(argc, argv, "-thresh", .24);char *filename = (argc > 4) ? argv[4]: 0;char *outfile = find_char_arg(argc, argv, "-out", 0);int fullscreen = find_arg(argc, argv, "-fullscreen");test_detector("cfg/coco.data", argv[2], argv[3], filename, thresh, .5, outfile, fullscreen);} else if (0 == strcmp(argv[1], "cifar")){run_cifar(argc, argv);} else if (0 == strcmp(argv[1], "go")){run_go(argc, argv);} else if (0 == strcmp(argv[1], "rnn")){run_char_rnn(argc, argv);} else if (0 == strcmp(argv[1], "vid")){run_vid_rnn(argc, argv);} else if (0 == strcmp(argv[1], "coco")){run_coco(argc, argv);} else if (0 == strcmp(argv[1], "classify")){predict_classifier("cfg/imagenet1k.data", argv[2], argv[3], argv[4], 5);} else if (0 == strcmp(argv[1], "classifier")){run_classifier(argc, argv);} else if (0 == strcmp(argv[1], "regressor")){run_regressor(argc, argv);} else if (0 == strcmp(argv[1], "segmenter")){run_segmenter(argc, argv);} else if (0 == strcmp(argv[1], "art")){run_art(argc, argv);} else if (0 == strcmp(argv[1], "tag")){run_tag(argc, argv);} else if (0 == strcmp(argv[1], "compare")){run_compare(argc, argv);} else if (0 == strcmp(argv[1], "dice")){run_dice(argc, argv);} else if (0 == strcmp(argv[1], "writing")){run_writing(argc, argv);} else if (0 == strcmp(argv[1], "3d")){composite_3d(argv[2], argv[3], argv[4], (argc > 5) ? atof(argv[5]) : 0);} else if (0 == strcmp(argv[1], "test")){test_resize(argv[2]);} else if (0 == strcmp(argv[1], "captcha")){run_captcha(argc, argv);} else if (0 == strcmp(argv[1], "nightmare")){run_nightmare(argc, argv);} else if (0 == strcmp(argv[1], "rgbgr")){rgbgr_net(argv[2], argv[3], argv[4]);} else if (0 == strcmp(argv[1], "reset")){reset_normalize_net(argv[2], argv[3], argv[4]);} else if (0 == strcmp(argv[1], "denormalize")){denormalize_net(argv[2], argv[3], argv[4]);} else if (0 == strcmp(argv[1], "statistics")){statistics_net(argv[2], argv[3]);} else if (0 == strcmp(argv[1], "normalize")){normalize_net(argv[2], argv[3], argv[4]);} else if (0 == strcmp(argv[1], "rescale")){rescale_net(argv[2], argv[3], argv[4]);} else if (0 == strcmp(argv[1], "ops")){operations(argv[2]);} else if (0 == strcmp(argv[1], "speed")){speed(argv[2], (argc > 3 && argv[3]) ? atoi(argv[3]) : 0);} else if (0 == strcmp(argv[1], "oneoff")){oneoff(argv[2], argv[3], argv[4]);} else if (0 == strcmp(argv[1], "oneoff2")){oneoff2(argv[2], argv[3], argv[4], atoi(argv[5]));} else if (0 == strcmp(argv[1], "partial")){partial(argv[2], argv[3], argv[4], atoi(argv[5]));} else if (0 == strcmp(argv[1], "average")){average(argc, argv);} else if (0 == strcmp(argv[1], "visualize")){visualize(argv[2], (argc > 3) ? argv[3] : 0);} else if (0 == strcmp(argv[1], "mkimg")){mkimg(argv[2], argv[3], atoi(argv[4]), atoi(argv[5]), atoi(argv[6]), argv[7]);} else if (0 == strcmp(argv[1], "imtest")){test_resize(argv[2]);} else {fprintf(stderr, "Not an option: %s\n", argv[1]);}return 0;
}
可以发现调用了run_detector(argc, argv);这个函数，这个函数位于 examples/detector.c中。查看detector.c,发现调用了test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh, char *outfile, int fullscreen)。
于是考虑在这个函数中存储输出文件。
编写了一个sava_txt函数。
#include "darknet.h"static int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90};void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear)
{list *options = read_data_cfg(datacfg);char *train_images = option_find_str(options, "train", "/home/maqy/darknet/darknet-master/scripts/train.txt");char *backup_directory = option_find_str(options, "backup", "/backup/");srand(time(0));char *base = basecfg(cfgfile);printf("%s\n", base);float avg_loss = -1;network *nets = calloc(ngpus, sizeof(network));srand(time(0));int seed = rand();int i;for(i = 0; i < ngpus; ++i){srand(seed);
#ifdef GPUcuda_set_device(gpus[i]);
#endifnets[i] = load_network(cfgfile, weightfile, clear);nets[i].learning_rate *= ngpus;}srand(time(0));network net = nets[0];int imgs = net.batch * net.subdivisions * ngpus;printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);data train, buffer;layer l = net.layers[net.n - 1];int classes = l.classes;float jitter = l.jitter;list *plist = get_paths(train_images);//int N = plist->size;char **paths = (char **)list_to_array(plist);load_args args = get_base_args(net);args.coords = l.coords;args.paths = paths;args.n = imgs;args.m = plist->size;args.classes = classes;args.jitter = jitter;args.num_boxes = l.max_boxes;args.d = &buffer;args.type = DETECTION_DATA;//args.type = INSTANCE_DATA;args.threads = 8;pthread_t load_thread = load_data(args);clock_t time;int count = 0;//while(i*imgs < N*120){while(get_current_batch(net) < net.max_batches){if(l.random && count++%10 == 0){printf("Resizing\n");int dim = (rand() % 10 + 10) * 32;if (get_current_batch(net)+200 > net.max_batches) dim = 608;//int dim = (rand() % 4 + 16) * 32;printf("%d\n", dim);args.w = dim;args.h = dim;pthread_join(load_thread, 0);train = buffer;free_data(train);load_thread = load_data(args);for(i = 0; i < ngpus; ++i){resize_network(nets + i, dim, dim);}net = nets[0];}time=clock();pthread_join(load_thread, 0);train = buffer;load_thread = load_data(args);/*int k;for(k = 0; k < l.max_boxes; ++k){box b = float_to_box(train.y.vals[10] + 1 + k*5);if(!b.x) break;printf("loaded: %f %f %f %f\n", b.x, b.y, b.w, b.h);}*//*int zz;for(zz = 0; zz < train.X.cols; ++zz){image im = float_to_image(net.w, net.h, 3, train.X.vals[zz]);int k;for(k = 0; k < l.max_boxes; ++k){box b = float_to_box(train.y.vals[zz] + k*5, 1);printf("%f %f %f %f\n", b.x, b.y, b.w, b.h);draw_bbox(im, b, 1, 1,0,0);}show_image(im, "truth11");cvWaitKey(0);save_image(im, "truth11");}*/printf("Loaded: %lf seconds\n", sec(clock()-time));time=clock();float loss = 0;
#ifdef GPUif(ngpus == 1){loss = train_network(net, train);} else {loss = train_networks(nets, ngpus, train, 4);}
#elseloss = train_network(net, train);
#endifif (avg_loss < 0) avg_loss = loss;avg_loss = avg_loss*.9 + loss*.1;i = get_current_batch(net);printf("%ld: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs);if(i%100==0){
#ifdef GPUif(ngpus != 1) sync_nets(nets, ngpus, 0);
#endifchar buff[256];sprintf(buff, "%s/%s.backup", backup_directory, base);save_weights(net, buff);}if(i%10000==0 || (i < 1000 && i%100 == 0)){
#ifdef GPUif(ngpus != 1) sync_nets(nets, ngpus, 0);
#endifchar buff[256];sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);save_weights(net, buff);}free_data(train);}
#ifdef GPUif(ngpus != 1) sync_nets(nets, ngpus, 0);
#endifchar buff[256];sprintf(buff, "%s/%s_final.weights", backup_directory, base);save_weights(net, buff);
}static int get_coco_image_id(char *filename)
{char *p = strrchr(filename, '_');return atoi(p+1);
}static void print_cocos(FILE *fp, char *image_path, box *boxes, float **probs, int num_boxes, int classes, int w, int h)
{int i, j;int image_id = get_coco_image_id(image_path);for(i = 0; i < num_boxes; ++i){float xmin = boxes[i].x - boxes[i].w/2.;float xmax = boxes[i].x + boxes[i].w/2.;float ymin = boxes[i].y - boxes[i].h/2.;float ymax = boxes[i].y + boxes[i].h/2.;if (xmin < 0) xmin = 0;if (ymin < 0) ymin = 0;if (xmax > w) xmax = w;if (ymax > h) ymax = h;float bx = xmin;float by = ymin;float bw = xmax - xmin;float bh = ymax - ymin;for(j = 0; j < classes; ++j){if (probs[i][j]) fprintf(fp, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f},\n", image_id, coco_ids[j], bx, by, bw, bh, probs[i][j]);}}
}void print_detector_detections(FILE **fps, char *id, box *boxes, float **probs, int total, int classes, int w, int h)
{int i, j;for(i = 0; i < total; ++i){float xmin = boxes[i].x - boxes[i].w/2. + 1;float xmax = boxes[i].x + boxes[i].w/2. + 1;float ymin = boxes[i].y - boxes[i].h/2. + 1;float ymax = boxes[i].y + boxes[i].h/2. + 1;if (xmin < 1) xmin = 1;if (ymin < 1) ymin = 1;if (xmax > w) xmax = w;if (ymax > h) ymax = h;for(j = 0; j < classes; ++j){if (probs[i][j]) fprintf(fps[j], "%s %f %f %f %f %f\n", id, probs[i][j],xmin, ymin, xmax, ymax);}}
}void print_imagenet_detections(FILE *fp, int id, box *boxes, float **probs, int total, int classes, int w, int h)
{int i, j;for(i = 0; i < total; ++i){float xmin = boxes[i].x - boxes[i].w/2.;float xmax = boxes[i].x + boxes[i].w/2.;float ymin = boxes[i].y - boxes[i].h/2.;float ymax = boxes[i].y + boxes[i].h/2.;if (xmin < 0) xmin = 0;if (ymin < 0) ymin = 0;if (xmax > w) xmax = w;if (ymax > h) ymax = h;for(j = 0; j < classes; ++j){int class = j;if (probs[i][class]) fprintf(fp, "%d %d %f %f %f %f %f\n", id, j+1, probs[i][class],xmin, ymin, xmax, ymax);}}
}void validate_detector_flip(char *datacfg, char *cfgfile, char *weightfile, char *outfile)
{int j;list *options = read_data_cfg(datacfg);char *valid_images = option_find_str(options, "valid", "/home/maqy/darknet/darknet-master/scripts/2007_test.txt");char *name_list = option_find_str(options, "names", "data/names.list");char *prefix = option_find_str(options, "results", "results");char **names = get_labels(name_list);char *mapf = option_find_str(options, "map", 0);int *map = 0;if (mapf) map = read_map(mapf);network net = parse_network_cfg(cfgfile);if(weightfile){load_weights(&net, weightfile);}set_batch_network(&net, 2);fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);srand(time(0));list *plist = get_paths(valid_images);char **paths = (char **)list_to_array(plist);layer l = net.layers[net.n-1];int classes = l.classes;char buff[1024];char *type = option_find_str(options, "eval", "voc");FILE *fp = 0;FILE **fps = 0;int coco = 0;int imagenet = 0;if(0==strcmp(type, "coco")){if(!outfile) outfile = "coco_results";snprintf(buff, 1024, "%s/%s.json", prefix, outfile);fp = fopen(buff, "w");fprintf(fp, "[\n");coco = 1;} else if(0==strcmp(type, "imagenet")){if(!outfile) outfile = "imagenet-detection";snprintf(buff, 1024, "%s/%s.txt", prefix, outfile);fp = fopen(buff, "w");imagenet = 1;classes = 200;} else {if(!outfile) outfile = "comp4_det_test_";fps = calloc(classes, sizeof(FILE *));for(j = 0; j < classes; ++j){snprintf(buff, 1024, "%s/%s%s.txt", prefix, outfile, names[j]);fps[j] = fopen(buff, "w");}}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] = calloc(classes+1, sizeof(float *));int m = plist->size;int i=0;int t;float thresh = .005;float nms = .45;int nthreads = 4;image *val = calloc(nthreads, sizeof(image));image *val_resized = calloc(nthreads, sizeof(image));image *buf = calloc(nthreads, sizeof(image));image *buf_resized = calloc(nthreads, sizeof(image));pthread_t *thr = calloc(nthreads, sizeof(pthread_t));image input = make_image(net.w, net.h, net.c*2);load_args args = {0};args.w = net.w;args.h = net.h;//args.type = IMAGE_DATA;args.type = LETTERBOX_DATA;for(t = 0; t < nthreads; ++t){args.path = paths[i+t];args.im = &buf[t];args.resized = &buf_resized[t];thr[t] = load_data_in_thread(args);}time_t start = time(0);for(i = nthreads; i < m+nthreads; i += nthreads){fprintf(stderr, "%d\n", i);for(t = 0; t < nthreads && i+t-nthreads < m; ++t){pthread_join(thr[t], 0);val[t] = buf[t];val_resized[t] = buf_resized[t];}for(t = 0; t < nthreads && i+t < m; ++t){args.path = paths[i+t];args.im = &buf[t];args.resized = &buf_resized[t];thr[t] = load_data_in_thread(args);}for(t = 0; t < nthreads && i+t-nthreads < m; ++t){char *path = paths[i+t-nthreads];char *id = basecfg(path);copy_cpu(net.w*net.h*net.c, val_resized[t].data, 1, input.data, 1);flip_image(val_resized[t]);copy_cpu(net.w*net.h*net.c, val_resized[t].data, 1, input.data + net.w*net.h*net.c, 1);network_predict(net, input.data);int w = val[t].w;int h = val[t].h;get_region_boxes(l, w, h, net.w, net.h, thresh, probs, boxes, 0, 0, map, .5, 0);if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, classes, nms);if (coco){print_cocos(fp, path, boxes, probs, l.w*l.h*l.n, classes, w, h);} else if (imagenet){print_imagenet_detections(fp, i+t-nthreads+1, boxes, probs, l.w*l.h*l.n, classes, w, h);} else {print_detector_detections(fps, id, boxes, probs, l.w*l.h*l.n, classes, w, h);}free(id);free_image(val[t]);free_image(val_resized[t]);}}for(j = 0; j < classes; ++j){if(fps) fclose(fps[j]);}if(coco){fseek(fp, -2, SEEK_CUR); fprintf(fp, "\n]\n");fclose(fp);}fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
}void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *outfile)
{int j;list *options = read_data_cfg(datacfg);char *valid_images = option_find_str(options, "valid", "/home/maqy/darknet/darknet-master/scripts/2007_test.txt");//这里应该是train还是test？？char *name_list = option_find_str(options, "names", "data/names.list");char *prefix = option_find_str(options, "results", "results");char **names = get_labels(name_list);char *mapf = option_find_str(options, "map", 0);int *map = 0;if (mapf) map = read_map(mapf);network net = parse_network_cfg(cfgfile);if(weightfile){load_weights(&net, weightfile);}set_batch_network(&net, 1);fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);srand(time(0));list *plist = get_paths(valid_images);char **paths = (char **)list_to_array(plist);layer l = net.layers[net.n-1];int classes = l.classes;char buff[1024];char *type = option_find_str(options, "eval", "voc");FILE *fp = 0;FILE **fps = 0;int coco = 0;int imagenet = 0;if(0==strcmp(type, "coco")){if(!outfile) outfile = "coco_results";snprintf(buff, 1024, "%s/%s.json", prefix, outfile);fp = fopen(buff, "w");fprintf(fp, "[\n");coco = 1;} else if(0==strcmp(type, "imagenet")){if(!outfile) outfile = "imagenet-detection";snprintf(buff, 1024, "%s/%s.txt", prefix, outfile);fp = fopen(buff, "w");imagenet = 1;classes = 200;} else {if(!outfile) outfile = "comp4_det_test_";fps = calloc(classes, sizeof(FILE *));for(j = 0; j < classes; ++j){snprintf(buff, 1024, "%s/%s%s.txt", prefix, outfile, names[j]);fps[j] = fopen(buff, "w");}}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] = calloc(classes+1, sizeof(float *));int m = plist->size;int i=0;int t;float thresh = .005;float nms = .45;int nthreads = 4;image *val = calloc(nthreads, sizeof(image));image *val_resized = calloc(nthreads, sizeof(image));image *buf = calloc(nthreads, sizeof(image));image *buf_resized = calloc(nthreads, sizeof(image));pthread_t *thr = calloc(nthreads, sizeof(pthread_t));load_args args = {0};args.w = net.w;args.h = net.h;//args.type = IMAGE_DATA;args.type = LETTERBOX_DATA;for(t = 0; t < nthreads; ++t){args.path = paths[i+t];args.im = &buf[t];args.resized = &buf_resized[t];thr[t] = load_data_in_thread(args);}time_t start = time(0);for(i = nthreads; i < m+nthreads; i += nthreads){fprintf(stderr, "%d\n", i);for(t = 0; t < nthreads && i+t-nthreads < m; ++t){pthread_join(thr[t], 0);val[t] = buf[t];val_resized[t] = buf_resized[t];}for(t = 0; t < nthreads && i+t < m; ++t){args.path = paths[i+t];args.im = &buf[t];args.resized = &buf_resized[t];thr[t] = load_data_in_thread(args);}for(t = 0; t < nthreads && i+t-nthreads < m; ++t){char *path = paths[i+t-nthreads];char *id = basecfg(path);float *X = val_resized[t].data;network_predict(net, X);int w = val[t].w;int h = val[t].h;get_region_boxes(l, w, h, net.w, net.h, thresh, probs, boxes, 0, 0, map, .5, 0);if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, classes, nms);if (coco){print_cocos(fp, path, boxes, probs, l.w*l.h*l.n, classes, w, h);} else if (imagenet){print_imagenet_detections(fp, i+t-nthreads+1, boxes, probs, l.w*l.h*l.n, classes, w, h);} else {print_detector_detections(fps, id, boxes, probs, l.w*l.h*l.n, classes, w, h);}free(id);free_image(val[t]);free_image(val_resized[t]);}}for(j = 0; j < classes; ++j){if(fps) fclose(fps[j]);}if(coco){fseek(fp, -2, SEEK_CUR); fprintf(fp, "\n]\n");fclose(fp);}fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
}void validate_detector_recall(char *cfgfile, char *weightfile)
{network net = parse_network_cfg(cfgfile);if(weightfile){load_weights(&net, weightfile);}set_batch_network(&net, 1);fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);srand(time(0));list *plist = get_paths("/home/maqy/darknet/darknet-master/scripts/2007_test.txt"); //测试？？？数据char **paths = (char **)list_to_array(plist);layer l = net.layers[net.n-1];int classes = l.classes;int j, k;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] = calloc(classes+1, sizeof(float *));int m = plist->size;int i=0;float thresh = .001;float iou_thresh = .5;float nms = .4;int total = 0;int correct = 0;int proposals = 0;float avg_iou = 0;for(i = 0; i < m; ++i){char *path = paths[i];image orig = load_image_color(path, 0, 0);image sized = resize_image(orig, net.w, net.h);char *id = basecfg(path);network_predict(net, sized.data);get_region_boxes(l, sized.w, sized.h, net.w, net.h, thresh, probs, boxes, 0, 1, 0, .5, 1);if (nms) do_nms(boxes, probs, l.w*l.h*l.n, 1, nms);char labelpath[4096];find_replace(path, "images", "labels", labelpath);find_replace(labelpath, "JPEGImages", "labels", labelpath);find_replace(labelpath, ".jpg", ".txt", labelpath);find_replace(labelpath, ".JPEG", ".txt", labelpath);int num_labels = 0;box_label *truth = read_boxes(labelpath, &num_labels);for(k = 0; k < l.w*l.h*l.n; ++k){if(probs[k][0] > thresh){++proposals;}}for (j = 0; j < num_labels; ++j) {++total;box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h};float best_iou = 0;for(k = 0; k < l.w*l.h*l.n; ++k){float iou = box_iou(boxes[k], t);if(probs[k][0] > thresh && iou > best_iou){best_iou = iou;}}avg_iou += best_iou;if(best_iou > iou_thresh){++correct;}}fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total);free(id);free_image(orig);free_image(sized);}
}
//保存船的位置和数量
void save_txt(char *filename,box *boxes,int num,float **probs,float thresh,image im,int classes)
{char a[100]; //存储每行的字符串char name[100]; //存储txt的路径strcpy(name,filename);int k=0;while(name[k]!='\0'){k++;}k=k-3;name[k]='t';k++;name[k]='x';k++;name[k]='t';k++;printf("txt_name:%s\n",name);int shipnum=0; //船的数量FILE *fp;printf("save_txt filename:%s \n",filename);if((fp=fopen(name,"w"))==NULL){printf("%s can't open\n",name);exit(1);}int left;//距离图片左边界的值 right-left为box宽度int right;int top;//距离图片上边界的值 bot-top为box高度int bot;int i;for(i=0;i<num;i++){int class = max_index(probs[i], classes); float prob = probs[i][class];if(prob > thresh){left = (boxes[i].x-boxes[i].w/2.)*im.w;//距离图片左边界的值 right-left为box宽度right = (boxes[i].x+boxes[i].w/2.)*im.w;top = (boxes[i].y-boxes[i].h/2.)*im.h;//距离图片上边界的值 bot-top为box高度bot = (boxes[i].y+boxes[i].h/2.)*im.h;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;++shipnum;printf("boxes[%d]: left:%d right:%d top:%d bot:%d prob:%f \n",i,left,right,top,bot,prob); //只有1类的话概率存在probs[i][1]中sprintf(a,"%d %d %d %d\n",left,right,top,bot);//将每个box的数据打印到 a中fputs(a,fp); //写入文件}}sprintf(a,"%d\n",shipnum);printf("save shipnum:%s \n",a);fputs(a,fp);fclose(fp); //关闭文件
}void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh, char *outfile, int fullscreen)
{list *options = read_data_cfg(datacfg);char *name_list = option_find_str(options, "names", "data/names.list");char **names = get_labels(name_list);image **alphabet = load_alphabet();network net = parse_network_cfg(cfgfile);if(weightfile){load_weights(&net, weightfile);}set_batch_network(&net, 1);srand(2222222);double time;char buff[256];char *input = buff;int j;float nms=.3;while(1){if(filename){strncpy(input, filename, 256);} else {printf("Enter Image Path: ");fflush(stdout);input = fgets(input, 256, stdin);if(!input) return;strtok(input, "\n");}image im = load_image_color(input,0,0);image sized = letterbox_image(im, net.w, net.h);//image sized = resize_image(im, net.w, net.h);//image sized2 = resize_max(im, net.w);//image sized = crop_image(sized2, -((net.w - sized2.w)/2), -((net.h - sized2.h)/2), net.w, net.h);//resize_network(&net, sized.w, sized.h);layer l = net.layers[net.n-1];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] = calloc(l.classes + 1, sizeof(float *));float **masks = 0;if (l.coords > 4){masks = calloc(l.w*l.h*l.n, sizeof(float*));for(j = 0; j < l.w*l.h*l.n; ++j) masks[j] = calloc(l.coords-4, sizeof(float *));}float *X = sized.data;time=what_time_is_it_now();network_predict(net, X);printf("%s: Predicted in %f seconds.\n", input, what_time_is_it_now()-time);get_region_boxes(l, im.w, im.h, net.w, net.h, thresh, probs, boxes, masks, 0, 0, hier_thresh, 1); //这里得到了box未转化前的区域以及probs//maqy添加，打印出boxes//打印出l.w,l.h,l.nprintf("l.w:%d l.h:%d l.n:%d l.classes:%d \n",l.w,l.h,l.n,l.classes);printf("maqy filename:%s\n",filename);//for(kkk=0;kkk<l.w*l.h*l.n;kkk++)//{// printf("boxes[%d]: x:%f y:%f w:%f h:%f prob:%f ---%f\n",kkk,boxes[kkk].x,boxes[kkk].y,boxes[kkk].w,boxes[kkk].h,probs[kkk][0],probs[kkk][1]);//}
// int left;//距离图片左边界的值 right-left为box宽度
// int right;
// int top;//距离图片上边界的值 bot-top为box高度
// int bot;
// int kkk;
// for(kkk=0;kkk<l.w*l.h*l.n;kkk++)
// {
// left = (boxes[kkk].x-boxes[kkk].w/2.)*im.w;//距离图片左边界的值 right-left为box宽度
// right = (boxes[kkk].x+boxes[kkk].w/2.)*im.w;
// top = (boxes[kkk].y-boxes[kkk].h/2.)*im.h;//距离图片上边界的值 bot-top为box高度
// bot = (boxes[kkk].y+boxes[kkk].h/2.)*im.h;
// 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;
// if(probs[kkk][1]>thresh)
// {
// printf("boxes[%d]: left:%d right:%d top:%d bot:%d prob:%f \n",kkk,left,right,top,bot,probs[kkk][1]); //只有1类的话概率存在probs[kkk][1]中
// }
// }if (nms) do_nms_obj(boxes, probs, l.w*l.h*l.n, l.classes, nms);//else if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, l.classes, nms);save_txt(filename, boxes, l.w*l.h*l.n, probs, thresh,im,l.classes);//必须放在上个if后面，不然会有重复的框出现draw_detections(im, l.w*l.h*l.n, thresh, boxes, probs, masks, names, alphabet, l.classes);//maqyif(outfile){save_image(im, outfile);}else{save_image(im, "predictions");
#ifdef OPENCVcvNamedWindow("predictions", CV_WINDOW_NORMAL); if(fullscreen){cvSetWindowProperty("predictions", CV_WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN);}show_image(im, "predictions");cvWaitKey(0);cvDestroyAllWindows();
#endif}free_image(im);free_image(sized);free(boxes);free_ptrs((void **)probs, l.w*l.h*l.n);if (filename) break;}
}void run_detector(int argc, char **argv)
{char *prefix = find_char_arg(argc, argv, "-prefix", 0);float thresh = find_float_arg(argc, argv, "-thresh", .24);float hier_thresh = find_float_arg(argc, argv, "-hier", .5);int cam_index = find_int_arg(argc, argv, "-c", 0);int frame_skip = find_int_arg(argc, argv, "-s", 0);int avg = find_int_arg(argc, argv, "-avg", 3);if(argc < 4){fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);return;}char *gpu_list = find_char_arg(argc, argv, "-gpus", 0);char *outfile = find_char_arg(argc, argv, "-out", 0);int *gpus = 0;int gpu = 0;int ngpus = 0;if(gpu_list){printf("%s\n", gpu_list);int len = strlen(gpu_list);ngpus = 1;int i;for(i = 0; i < len; ++i){if (gpu_list[i] == ',') ++ngpus;}gpus = calloc(ngpus, sizeof(int));for(i = 0; i < ngpus; ++i){gpus[i] = atoi(gpu_list);gpu_list = strchr(gpu_list, ',')+1;}} else {gpu = gpu_index;gpus = &gpu;ngpus = 1;}int clear = find_arg(argc, argv, "-clear");int fullscreen = find_arg(argc, argv, "-fullscreen");int width = find_int_arg(argc, argv, "-w", 0);int height = find_int_arg(argc, argv, "-h", 0);int fps = find_int_arg(argc, argv, "-fps", 0);char *datacfg = argv[3];char *cfg = argv[4];char *weights = (argc > 5) ? argv[5] : 0;char *filename = (argc > 6) ? argv[6]: 0;if(0==strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, outfile, fullscreen);else if(0==strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear);else if(0==strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile);else if(0==strcmp(argv[2], "valid2")) validate_detector_flip(datacfg, cfg, weights, outfile);else if(0==strcmp(argv[2], "recall")) validate_detector_recall(cfg, weights);else if(0==strcmp(argv[2], "demo")) {list *options = read_data_cfg(datacfg);int classes = option_find_int(options, "classes", 1); //classchar *name_list = option_find_str(options, "names", "data/names.list");char **names = get_labels(name_list);demo(cfg, weights, thresh, cam_index, filename, names, classes, frame_skip, prefix, avg, hier_thresh, width, height, fps, fullscreen);}
}
写的有点乱，以后再做修改。
功能就是在待检测图片路径下多了一个同名的txt文件，存储的是框的位置和数量
这篇关于yolo2 检测到的物体输出2的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
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