我是靠谱客的博主 激动小笼包，最近开发中收集的这篇文章主要介绍YOLO-V3 把玩 image.c demo.cdetector.cimage.cdemo.c，觉得挺不错的，现在分享给大家，希望可以做个参考。
概述
detector.c
#include "network.h"
#include "region_layer.h"
#include "cost_layer.h"
#include "utils.h"
#include "parser.h"
#include "box.h"
#include "demo.h"
#include "option_list.h"
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#include "opencv2/core/core_c.h"
//#include "opencv2/core/core.hpp"
#include "opencv2/core/version.hpp"
#include "opencv2/imgproc/imgproc_c.h"
#ifndef CV_VERSION_EPOCH
#include "opencv2/videoio/videoio_c.h"
#define OPENCV_VERSION CVAUX_STR(CV_VERSION_MAJOR)"" CVAUX_STR(CV_VERSION_MINOR)"" CVAUX_STR(CV_VERSION_REVISION)
#pragma comment(lib, "opencv_world" OPENCV_VERSION ".lib")
#else
#define OPENCV_VERSION CVAUX_STR(CV_VERSION_EPOCH)"" CVAUX_STR(CV_VERSION_MAJOR)"" CVAUX_STR(CV_VERSION_MINOR)
#pragma comment(lib, "opencv_core" OPENCV_VERSION ".lib")
#pragma comment(lib, "opencv_imgproc" OPENCV_VERSION ".lib")
#pragma comment(lib, "opencv_highgui" OPENCV_VERSION ".lib")
#endif
IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size);
void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches);
#endif // OPENCV
#include "http_stream.h"
int check_mistakes;
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, int dont_show)
{
list *options = read_data_cfg(datacfg);
char *train_images = option_find_str(options, "train", "data/train.list");
char *backup_directory = option_find_str(options, "backup", "/backup/");
srand(time(0));
char *base = basecfg(cfgfile);
printf("%sn", 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 GPU
cuda_set_device(gpus[i]);
#endif
nets[i] = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&nets[i], weightfile);
}
if(clear) *nets[i].seen = 0;
nets[i].learning_rate *= ngpus;
}
srand(time(0));
network net = nets[0];
const int actual_batch_size = net.batch * net.subdivisions;
if (actual_batch_size == 1) {
printf("n Error: You set incorrect value batch=1 for Training! You should set batch=64 subdivision=64 n");
getchar();
}
else if (actual_batch_size < 64) {
printf("n Warning: You set batch=%d lower than 64! It is recommended to set batch=64 subdivision=64 n", actual_batch_size);
}
int imgs = net.batch * net.subdivisions * ngpus;
printf("Learning Rate: %g, Momentum: %g, Decay: %gn", 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);
int init_w = net.w;
int init_h = net.h;
int iter_save;
iter_save = get_current_batch(net);
load_args args = {0};
args.w = net.w;
args.h = net.h;
args.c = net.c;
args.paths = paths;
args.n = imgs;
args.m = plist->size;
args.classes = classes;
args.flip = net.flip;
args.jitter = jitter;
args.num_boxes = l.max_boxes;
args.small_object = net.small_object;
args.d = &buffer;
args.type = DETECTION_DATA;
args.threads = 16; // 64
args.angle = net.angle;
args.exposure = net.exposure;
args.saturation = net.saturation;
args.hue = net.hue;
#ifdef OPENCV
args.threads = 3 * ngpus;
IplImage* img = NULL;
float max_img_loss = 5;
int number_of_lines = 100;
int img_size = 1000;
if (!dont_show)
img = draw_train_chart(max_img_loss, net.max_batches, number_of_lines, img_size);
#endif //OPENCV
pthread_t load_thread = load_data(args);
double time;
int count = 0;
//while(i*imgs < N*120){
while(get_current_batch(net) < net.max_batches){
if(l.random && count++%10 == 0){
printf("Resizingn");
//int dim = (rand() % 12 + (init_w/32 - 5)) * 32; // +-160
//int dim = (rand() % 4 + 16) * 32;
//if (get_current_batch(net)+100 > net.max_batches) dim = 544;
//int random_val = rand() % 12;
//int dim_w = (random_val + (init_w / 32 - 5)) * 32; // +-160
//int dim_h = (random_val + (init_h / 32 - 5)) * 32; // +-160
float random_val = rand_scale(1.4); // *x or /x
int dim_w = roundl(random_val*init_w / 32) * 32;
int dim_h = roundl(random_val*init_h / 32) * 32;
if (dim_w < 32) dim_w = 32;
if (dim_h < 32) dim_h = 32;
printf("%d x %d n", dim_w, dim_h);
args.w = dim_w;
args.h = dim_h;
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_w, dim_h);
}
net = nets[0];
}
time=what_time_is_it_now();
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 %fn", b.x, b.y, b.w, b.h);
}
image im = float_to_image(448, 448, 3, train.X.vals[10]);
int k;
for(k = 0; k < l.max_boxes; ++k){
box b = float_to_box(train.y.vals[10] + 1 + k*5);
printf("%d %d %d %dn", truth.x, truth.y, truth.w, truth.h);
draw_bbox(im, b, 8, 1,0,0);
}
save_image(im, "truth11");
*/
printf("Loaded: %lf secondsn", (what_time_is_it_now()-time));
time=what_time_is_it_now();
float loss = 0;
#ifdef GPU
if(ngpus == 1){
loss = train_network(net, train);
} else {
loss = train_networks(nets, ngpus, train, 4);
}
#else
loss = train_network(net, train);
#endif
if (avg_loss < 0 || avg_loss != avg_loss) avg_loss = loss; // if(-inf or nan)
avg_loss = avg_loss*.9 + loss*.1;
i = get_current_batch(net);
printf("n %d: %f, %f avg loss, %f rate, %lf seconds, %d imagesn", get_current_batch(net), loss, avg_loss, get_current_rate(net), (what_time_is_it_now()-time), i*imgs);
#ifdef OPENCV
if(!dont_show)
draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches);
#endif // OPENCV
//if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) {
//if (i % 100 == 0) {
if(i >= (iter_save + 100)) {
iter_save = i;
#ifdef GPU
if (ngpus != 1) sync_nets(nets, ngpus, 0);
#endif
char buff[256];
sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
save_weights(net, buff);
}
free_data(train);
}
#ifdef GPU
if(ngpus != 1) sync_nets(nets, ngpus, 0);
#endif
char buff[256];
sprintf(buff, "%s/%s_final.weights", backup_directory, base);
save_weights(net, buff);
#ifdef OPENCV
cvReleaseImage(&img);
cvDestroyAllWindows();
#endif
// free memory
pthread_join(load_thread, 0);
free_data(buffer);
free(base);
free(paths);
free_list_contents(plist);
free_list(plist);
free_list_contents_kvp(options);
free_list(options);
free(nets);
free_network(net);
}
static int get_coco_image_id(char *filename)
{
char *p = strrchr(filename, '/');
char *c = strrchr(filename, '_');
if (c) p = c;
return atoi(p + 1);
}
static void print_cocos(FILE *fp, char *image_path, detection *dets, 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 = dets[i].bbox.x - dets[i].bbox.w / 2.;
float xmax = dets[i].bbox.x + dets[i].bbox.w / 2.;
float ymin = dets[i].bbox.y - dets[i].bbox.h / 2.;
float ymax = dets[i].bbox.y + dets[i].bbox.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 (dets[i].prob[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, dets[i].prob[j]);
}
}
}
void print_detector_detections(FILE **fps, char *id, detection *dets, int total, int classes, int w, int h)
{
int i, j;
for (i = 0; i < total; ++i) {
float xmin = dets[i].bbox.x - dets[i].bbox.w / 2. + 1;
float xmax = dets[i].bbox.x + dets[i].bbox.w / 2. + 1;
float ymin = dets[i].bbox.y - dets[i].bbox.h / 2. + 1;
float ymax = dets[i].bbox.y + dets[i].bbox.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 (dets[i].prob[j]) fprintf(fps[j], "%s %f %f %f %f %fn", id, dets[i].prob[j],
xmin, ymin, xmax, ymax);
}
}
}
void print_imagenet_detections(FILE *fp, int id, detection *dets, int total, int classes, int w, int h)
{
int i, j;
for (i = 0; i < total; ++i) {
float xmin = dets[i].bbox.x - dets[i].bbox.w / 2.;
float xmax = dets[i].bbox.x + dets[i].bbox.w / 2.;
float ymin = dets[i].bbox.y - dets[i].bbox.h / 2.;
float ymax = dets[i].bbox.y + dets[i].bbox.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 (dets[i].prob[class]) fprintf(fp, "%d %d %f %f %f %f %fn", id, j + 1, dets[i].prob[class],
xmin, ymin, xmax, ymax);
}
}
}
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", "data/train.list");
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_custom(cfgfile, 1); // set batch=1
if (weightfile) {
load_weights(&net, weightfile);
}
//set_batch_network(&net, 1);
fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %gn", 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");
}
}
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.c = net.c;
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, "%dn", 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;
int nboxes = 0;
int letterbox = (args.type == LETTERBOX_DATA);
detection *dets = get_network_boxes(&net, w, h, thresh, .5, map, 0, &nboxes, letterbox);
if (nms) do_nms_sort(dets, nboxes, classes, nms);
if (coco) {
print_cocos(fp, path, dets, nboxes, classes, w, h);
}
else if (imagenet) {
print_imagenet_detections(fp, i + t - nthreads + 1, dets, nboxes, classes, w, h);
}
else {
print_detector_detections(fps, id, dets, nboxes, classes, w, h);
}
free_detections(dets, nboxes);
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 Secondsn", (double)time(0) - start);
}
void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile)
{
network net = parse_network_cfg_custom(cfgfile, 1); // set batch=1
if (weightfile) {
load_weights(&net, weightfile);
}
//set_batch_network(&net, 1);
fuse_conv_batchnorm(net);
srand(time(0));
//list *plist = get_paths("data/coco_val_5k.list");
list *options = read_data_cfg(datacfg);
char *valid_images = option_find_str(options, "valid", "data/train.txt");
list *plist = get_paths(valid_images);
char **paths = (char **)list_to_array(plist);
layer l = net.layers[net.n - 1];
int j, k;
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(path, 0, 0, net.c);
image sized = resize_image(orig, net.w, net.h);
char *id = basecfg(path);
network_predict(net, sized.data);
int nboxes = 0;
int letterbox = 0;
detection *dets = get_network_boxes(&net, sized.w, sized.h, thresh, .5, 0, 1, &nboxes, letterbox);
if (nms) do_nms_obj(dets, nboxes, 1, nms);
char labelpath[4096];
replace_image_to_label(path, labelpath);
int num_labels = 0;
box_label *truth = read_boxes(labelpath, &num_labels);
for (k = 0; k < nboxes; ++k) {
if (dets[k].objectness > 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 < nboxes; ++k) {
float iou = box_iou(dets[k].bbox, t);
if (dets[k].objectness > thresh && iou > best_iou) {
best_iou = iou;
}
}
avg_iou += best_iou;
if (best_iou > iou_thresh) {
++correct;
}
}
//fprintf(stderr, " %s - %s - ", paths[i], labelpath);
fprintf(stderr, "%5d %5d %5dtRPs/Img: %.2ftIOU: %.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);
}
}
typedef struct {
box b;
float p;
int class_id;
int image_index;
int truth_flag;
int unique_truth_index;
} box_prob;
int detections_comparator(const void *pa, const void *pb)
{
box_prob a = *(box_prob *)pa;
box_prob b = *(box_prob *)pb;
float diff = a.p - b.p;
if (diff < 0) return 1;
else if (diff > 0) return -1;
return 0;
}
void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou)
{
int j;
list *options = read_data_cfg(datacfg);
char *valid_images = option_find_str(options, "valid", "data/train.txt");
char *difficult_valid_images = option_find_str(options, "difficult", NULL);
char *name_list = option_find_str(options, "names", "data/names.list");
char **names = get_labels(name_list);
char *mapf = option_find_str(options, "map", 0);
int *map = 0;
if (mapf) map = read_map(mapf);
FILE* reinforcement_fd = NULL;
network net = parse_network_cfg_custom(cfgfile, 1); // set batch=1
if (weightfile) {
load_weights(&net, weightfile);
}
//set_batch_network(&net, 1);
fuse_conv_batchnorm(net);
calculate_binary_weights(net);
srand(time(0));
list *plist = get_paths(valid_images);
char **paths = (char **)list_to_array(plist);
char **paths_dif = NULL;
if (difficult_valid_images) {
list *plist_dif = get_paths(difficult_valid_images);
paths_dif = (char **)list_to_array(plist_dif);
}
layer l = net.layers[net.n - 1];
int classes = l.classes;
int m = plist->size;
int i = 0;
int t;
const float thresh = .005;
const float nms = .45;
const float iou_thresh = 0.5;
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.c = net.c;
args.type = IMAGE_DATA;
//args.type = LETTERBOX_DATA;
//const float thresh_calc_avg_iou = 0.24;
float avg_iou = 0;
int tp_for_thresh = 0;
int fp_for_thresh = 0;
box_prob *detections = calloc(1, sizeof(box_prob));
int detections_count = 0;
int unique_truth_count = 0;
int *truth_classes_count = calloc(classes, sizeof(int));
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, "%dn", 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) {
const int image_index = i + t - nthreads;
char *path = paths[image_index];
char *id = basecfg(path);
float *X = val_resized[t].data;
network_predict(net, X);
int nboxes = 0;
float hier_thresh = 0;
detection *dets;
if (args.type == LETTERBOX_DATA) {
int letterbox = 1;
dets = get_network_boxes(&net, val[t].w, val[t].h, thresh, hier_thresh, 0, 1, &nboxes, letterbox);
}
else {
int letterbox = 0;
dets = get_network_boxes(&net, 1, 1, thresh, hier_thresh, 0, 0, &nboxes, letterbox);
}
//detection *dets = get_network_boxes(&net, val[t].w, val[t].h, thresh, hier_thresh, 0, 1, &nboxes, letterbox); // for letterbox=1
if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
char labelpath[4096];
replace_image_to_label(path, labelpath);
int num_labels = 0;
box_label *truth = read_boxes(labelpath, &num_labels);
int i, j;
for (j = 0; j < num_labels; ++j) {
truth_classes_count[truth[j].id]++;
}
// difficult
box_label *truth_dif = NULL;
int num_labels_dif = 0;
if (paths_dif)
{
char *path_dif = paths_dif[image_index];
char labelpath_dif[4096];
replace_image_to_label(path_dif, labelpath_dif);
truth_dif = read_boxes(labelpath_dif, &num_labels_dif);
}
const int checkpoint_detections_count = detections_count;
for (i = 0; i < nboxes; ++i) {
int class_id;
for (class_id = 0; class_id < classes; ++class_id) {
float prob = dets[i].prob[class_id];
if (prob > 0) {
detections_count++;
detections = realloc(detections, detections_count * sizeof(box_prob));
detections[detections_count - 1].b = dets[i].bbox;
detections[detections_count - 1].p = prob;
detections[detections_count - 1].image_index = image_index;
detections[detections_count - 1].class_id = class_id;
detections[detections_count - 1].truth_flag = 0;
detections[detections_count - 1].unique_truth_index = -1;
int truth_index = -1;
float max_iou = 0;
for (j = 0; j < num_labels; ++j)
{
box t = { truth[j].x, truth[j].y, truth[j].w, truth[j].h };
//printf(" IoU = %f, prob = %f, class_id = %d, truth[j].id = %d n",
// box_iou(dets[i].bbox, t), prob, class_id, truth[j].id);
float current_iou = box_iou(dets[i].bbox, t);
if (current_iou > iou_thresh && class_id == truth[j].id) {
if (current_iou > max_iou) {
max_iou = current_iou;
truth_index = unique_truth_count + j;
}
}
}
// best IoU
if (truth_index > -1) {
detections[detections_count - 1].truth_flag = 1;
detections[detections_count - 1].unique_truth_index = truth_index;
}
else {
// if object is difficult then remove detection
for (j = 0; j < num_labels_dif; ++j) {
box t = { truth_dif[j].x, truth_dif[j].y, truth_dif[j].w, truth_dif[j].h };
float current_iou = box_iou(dets[i].bbox, t);
if (current_iou > iou_thresh && class_id == truth_dif[j].id) {
--detections_count;
break;
}
}
}
// calc avg IoU, true-positives, false-positives for required Threshold
if (prob > thresh_calc_avg_iou) {
int z, found = 0;
for (z = checkpoint_detections_count; z < detections_count-1; ++z)
if (detections[z].unique_truth_index == truth_index) {
found = 1; break;
}
if(truth_index > -1 && found == 0) {
avg_iou += max_iou;
++tp_for_thresh;
}
else
fp_for_thresh++;
}
}
}
}
unique_truth_count += num_labels;
//static int previous_errors = 0;
//int total_errors = fp_for_thresh + (unique_truth_count - tp_for_thresh);
//int errors_in_this_image = total_errors - previous_errors;
//previous_errors = total_errors;
//if(reinforcement_fd == NULL) reinforcement_fd = fopen("reinforcement.txt", "wb");
//char buff[1000];
//sprintf(buff, "%sn", path);
//if(errors_in_this_image > 0) fwrite(buff, sizeof(char), strlen(buff), reinforcement_fd);
free_detections(dets, nboxes);
free(id);
free_image(val[t]);
free_image(val_resized[t]);
}
}
if((tp_for_thresh + fp_for_thresh) > 0)
avg_iou = avg_iou / (tp_for_thresh + fp_for_thresh);
// SORT(detections)
qsort(detections, detections_count, sizeof(box_prob), detections_comparator);
typedef struct {
double precision;
double recall;
int tp, fp, fn;
} pr_t;
// for PR-curve
pr_t **pr = calloc(classes, sizeof(pr_t*));
for (i = 0; i < classes; ++i) {
pr[i] = calloc(detections_count, sizeof(pr_t));
}
printf("detections_count = %d, unique_truth_count = %d n", detections_count, unique_truth_count);
int *truth_flags = calloc(unique_truth_count, sizeof(int));
int rank;
for (rank = 0; rank < detections_count; ++rank) {
if(rank % 100 == 0)
printf(" rank = %d of ranks = %d r", rank, detections_count);
if (rank > 0) {
int class_id;
for (class_id = 0; class_id < classes; ++class_id) {
pr[class_id][rank].tp = pr[class_id][rank - 1].tp;
pr[class_id][rank].fp = pr[class_id][rank - 1].fp;
}
}
box_prob d = detections[rank];
// if (detected && isn't detected before)
if (d.truth_flag == 1) {
if (truth_flags[d.unique_truth_index] == 0)
{
truth_flags[d.unique_truth_index] = 1;
pr[d.class_id][rank].tp++; // true-positive
}
}
else {
pr[d.class_id][rank].fp++; // false-positive
}
for (i = 0; i < classes; ++i)
{
const int tp = pr[i][rank].tp;
const int fp = pr[i][rank].fp;
const int fn = truth_classes_count[i] - tp; // false-negative = objects - true-positive
pr[i][rank].fn = fn;
if ((tp + fp) > 0) pr[i][rank].precision = (double)tp / (double)(tp + fp);
else pr[i][rank].precision = 0;
if ((tp + fn) > 0) pr[i][rank].recall = (double)tp / (double)(tp + fn);
else pr[i][rank].recall = 0;
}
}
free(truth_flags);
double mean_average_precision = 0;
for (i = 0; i < classes; ++i) {
double avg_precision = 0;
int point;
for (point = 0; point < 11; ++point) {
double cur_recall = point * 0.1;
double cur_precision = 0;
for (rank = 0; rank < detections_count; ++rank)
{
if (pr[i][rank].recall >= cur_recall) { // > or >=
if (pr[i][rank].precision > cur_precision) {
cur_precision = pr[i][rank].precision;
}
}
}
//printf("class_id = %d, point = %d, cur_recall = %.4f, cur_precision = %.4f n", i, point, cur_recall, cur_precision);
avg_precision += cur_precision;
}
avg_precision = avg_precision / 11;
printf("class_id = %d, name = %s, t ap = %2.2f %% n", i, names[i], avg_precision*100);
mean_average_precision += avg_precision;
}
const float cur_precision = (float)tp_for_thresh / ((float)tp_for_thresh + (float)fp_for_thresh);
const float cur_recall = (float)tp_for_thresh / ((float)tp_for_thresh + (float)(unique_truth_count - tp_for_thresh));
const float f1_score = 2.F * cur_precision * cur_recall / (cur_precision + cur_recall);
printf(" for thresh = %1.2f, precision = %1.2f, recall = %1.2f, F1-score = %1.2f n",
thresh_calc_avg_iou, cur_precision, cur_recall, f1_score);
printf(" for thresh = %0.2f, TP = %d, FP = %d, FN = %d, average IoU = %2.2f %% n",
thresh_calc_avg_iou, tp_for_thresh, fp_for_thresh, unique_truth_count - tp_for_thresh, avg_iou * 100);
mean_average_precision = mean_average_precision / classes;
printf("n mean average precision (mAP) = %f, or %2.2f %% n", mean_average_precision, mean_average_precision*100);
for (i = 0; i < classes; ++i) {
free(pr[i]);
}
free(pr);
free(detections);
free(truth_classes_count);
fprintf(stderr, "Total Detection Time: %f Secondsn", (double)(time(0) - start));
if (reinforcement_fd != NULL) fclose(reinforcement_fd);
}
#ifdef OPENCV
typedef struct {
float w, h;
} anchors_t;
int anchors_comparator(const void *pa, const void *pb)
{
anchors_t a = *(anchors_t *)pa;
anchors_t b = *(anchors_t *)pb;
float diff = b.w*b.h - a.w*a.h;
if (diff < 0) return 1;
else if (diff > 0) return -1;
return 0;
}
void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int show)
{
printf("n num_of_clusters = %d, width = %d, height = %d n", num_of_clusters, width, height);
if (width < 0 || height < 0) {
printf("Usage: darknet detector calc_anchors data/voc.data -num_of_clusters 9 -width 416 -height 416 n");
printf("Error: set width and height n");
return;
}
//float pointsdata[] = { 1,1, 2,2, 6,6, 5,5, 10,10 };
float *rel_width_height_array = calloc(1000, sizeof(float));
list *options = read_data_cfg(datacfg);
char *train_images = option_find_str(options, "train", "data/train.list");
list *plist = get_paths(train_images);
int number_of_images = plist->size;
char **paths = (char **)list_to_array(plist);
int number_of_boxes = 0;
printf(" read labels from %d images n", number_of_images);
int i, j;
for (i = 0; i < number_of_images; ++i) {
char *path = paths[i];
char labelpath[4096];
replace_image_to_label(path, labelpath);
int num_labels = 0;
box_label *truth = read_boxes(labelpath, &num_labels);
//printf(" new path: %s n", labelpath);
char buff[1024];
for (j = 0; j < num_labels; ++j)
{
if (truth[j].x > 1 || truth[j].x <= 0 || truth[j].y > 1 || truth[j].y <= 0 ||
truth[j].w > 1 || truth[j].w <= 0 || truth[j].h > 1 || truth[j].h <= 0)
{
printf("nnWrong label: %s - j = %d, x = %f, y = %f, width = %f, height = %f n",
labelpath, j, truth[j].x, truth[j].y, truth[j].w, truth[j].h);
sprintf(buff, "echo "Wrong label: %s - j = %d, x = %f, y = %f, width = %f, height = %f" >> bad_label.list",
labelpath, j, truth[j].x, truth[j].y, truth[j].w, truth[j].h);
system(buff);
if (check_mistakes) getchar();
}
number_of_boxes++;
rel_width_height_array = realloc(rel_width_height_array, 2 * number_of_boxes * sizeof(float));
rel_width_height_array[number_of_boxes * 2 - 2] = truth[j].w * width;
rel_width_height_array[number_of_boxes * 2 - 1] = truth[j].h * height;
printf("r loaded t image: %d t box: %d", i+1, number_of_boxes);
}
}
printf("n all loaded. n");
CvMat* points = cvCreateMat(number_of_boxes, 2, CV_32FC1);
CvMat* centers = cvCreateMat(num_of_clusters, 2, CV_32FC1);
CvMat* labels = cvCreateMat(number_of_boxes, 1, CV_32SC1);
for (i = 0; i < number_of_boxes; ++i) {
points->data.fl[i * 2] = rel_width_height_array[i * 2];
points->data.fl[i * 2 + 1] = rel_width_height_array[i * 2 + 1];
//cvSet1D(points, i * 2, cvScalar(rel_width_height_array[i * 2], 0, 0, 0));
//cvSet1D(points, i * 2 + 1, cvScalar(rel_width_height_array[i * 2 + 1], 0, 0, 0));
}
const int attemps = 10;
double compactness;
enum {
KMEANS_RANDOM_CENTERS = 0,
KMEANS_USE_INITIAL_LABELS = 1,
KMEANS_PP_CENTERS = 2
};
printf("n calculating k-means++ ...");
// Should be used: distance(box, centroid) = 1 - IoU(box, centroid)
cvKMeans2(points, num_of_clusters, labels,
cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 10000, 0), attemps,
0, KMEANS_PP_CENTERS,
centers, &compactness);
// sort anchors
qsort(centers->data.fl, num_of_clusters, 2*sizeof(float), anchors_comparator);
//orig 2.0 anchors = 1.08,1.19, 3.42,4.41, 6.63,11.38, 9.42,5.11, 16.62,10.52
//float orig_anch[] = { 1.08,1.19, 3.42,4.41, 6.63,11.38, 9.42,5.11, 16.62,10.52 };
// worse than ours (even for 19x19 final size - for input size 608x608)
//orig anchors = 1.3221,1.73145, 3.19275,4.00944, 5.05587,8.09892, 9.47112,4.84053, 11.2364,10.0071
//float orig_anch[] = { 1.3221,1.73145, 3.19275,4.00944, 5.05587,8.09892, 9.47112,4.84053, 11.2364,10.0071 };
// orig (IoU=59.90%) better than ours (59.75%)
//gen_anchors.py = 1.19, 1.99, 2.79, 4.60, 4.53, 8.92, 8.06, 5.29, 10.32, 10.66
//float orig_anch[] = { 1.19, 1.99, 2.79, 4.60, 4.53, 8.92, 8.06, 5.29, 10.32, 10.66 };
// ours: anchors = 9.3813,6.0095, 3.3999,5.3505, 10.9476,11.1992, 5.0161,9.8314, 1.5003,2.1595
//float orig_anch[] = { 9.3813,6.0095, 3.3999,5.3505, 10.9476,11.1992, 5.0161,9.8314, 1.5003,2.1595 };
//for (i = 0; i < num_of_clusters * 2; ++i) centers->data.fl[i] = orig_anch[i];
//for (i = 0; i < number_of_boxes; ++i)
// printf("%2.2f,%2.2f, ", points->data.fl[i * 2], points->data.fl[i * 2 + 1]);
printf("n");
float avg_iou = 0;
for (i = 0; i < number_of_boxes; ++i) {
float box_w = points->data.fl[i * 2];
float box_h = points->data.fl[i * 2 + 1];
//int cluster_idx = labels->data.i[i];
int cluster_idx = 0;
float min_dist = FLT_MAX;
for (j = 0; j < num_of_clusters; ++j) {
float anchor_w = centers->data.fl[j * 2];
float anchor_h = centers->data.fl[j * 2 + 1];
float w_diff = anchor_w - box_w;
float h_diff = anchor_h - box_h;
float distance = sqrt(w_diff*w_diff + h_diff*h_diff);
if (distance < min_dist) min_dist = distance, cluster_idx = j;
}
float anchor_w = centers->data.fl[cluster_idx * 2];
float anchor_h = centers->data.fl[cluster_idx * 2 + 1];
float min_w = (box_w < anchor_w) ? box_w : anchor_w;
float min_h = (box_h < anchor_h) ? box_h : anchor_h;
float box_intersect = min_w*min_h;
float box_union = box_w*box_h + anchor_w*anchor_h - box_intersect;
float iou = box_intersect / box_union;
if (iou > 1 || iou < 0) { // || box_w > width || box_h > height) {
printf(" Wrong label: i = %d, box_w = %d, box_h = %d, anchor_w = %d, anchor_h = %d, iou = %f n",
i, box_w, box_h, anchor_w, anchor_h, iou);
}
else avg_iou += iou;
}
avg_iou = 100 * avg_iou / number_of_boxes;
printf("n avg IoU = %2.2f %% n", avg_iou);
char buff[1024];
FILE* fw = fopen("anchors.txt", "wb");
if (fw) {
printf("nSaving anchors to the file: anchors.txt n");
printf("anchors = ");
for (i = 0; i < num_of_clusters; ++i) {
sprintf(buff, "%2.4f,%2.4f", centers->data.fl[i * 2], centers->data.fl[i * 2 + 1]);
printf("%s", buff);
fwrite(buff, sizeof(char), strlen(buff), fw);
if (i + 1 < num_of_clusters) {
fwrite(", ", sizeof(char), 2, fw);
printf(", ");
}
}
printf("n");
fclose(fw);
}
else {
printf(" Error: file anchors.txt can't be open n");
}
if (show) {
size_t img_size = 700;
IplImage* img = cvCreateImage(cvSize(img_size, img_size), 8, 3);
cvZero(img);
for (j = 0; j < num_of_clusters; ++j) {
CvPoint pt1, pt2;
pt1.x = pt1.y = 0;
pt2.x = centers->data.fl[j * 2] * img_size / width;
pt2.y = centers->data.fl[j * 2 + 1] * img_size / height;
cvRectangle(img, pt1, pt2, CV_RGB(255, 255, 255), 1, 8, 0);
}
for (i = 0; i < number_of_boxes; ++i) {
CvPoint pt;
pt.x = points->data.fl[i * 2] * img_size / width;
pt.y = points->data.fl[i * 2 + 1] * img_size / height;
int cluster_idx = labels->data.i[i];
int red_id = (cluster_idx * (uint64_t)123 + 55) % 255;
int green_id = (cluster_idx * (uint64_t)321 + 33) % 255;
int blue_id = (cluster_idx * (uint64_t)11 + 99) % 255;
cvCircle(img, pt, 1, CV_RGB(red_id, green_id, blue_id), CV_FILLED, 8, 0);
//if(pt.x > img_size || pt.y > img_size) printf("n pt.x = %d, pt.y = %d n", pt.x, pt.y);
}
cvShowImage("clusters", img);
cvWaitKey(0);
cvReleaseImage(&img);
cvDestroyAllWindows();
}
free(rel_width_height_array);
cvReleaseMat(&points);
cvReleaseMat(¢ers);
cvReleaseMat(&labels);
}
#else
void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int show) {
printf(" k-means++ can't be used without OpenCV, because there is used cvKMeans2 implementation n");
}
#endif // OPENCV
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)
{
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); // set batch=1
if(weightfile){
load_weights(&net, weightfile);
}
//set_batch_network(&net, 1);
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 n",
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;
int j;
float nms=.45; // 0.4F
while(1){
if(filename){
strncpy(input, filename, 256);
if(strlen(input) > 0)
if (input[strlen(input) - 1] == 0x0d) input[strlen(input) - 1] = 0;
} else {
printf("****detector.c 1133****** Enter Image Path: "); //dspeia
fflush(stdout);
input = fgets(input, 256, stdin);
if(!input) return;
strtok(input, "n");
}
image im = load_image(input,0,0,net.c);
printf("****detector.c 1140****** input: %c",input); //dspeia
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];
//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, 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;
//图片载入完成
printf("%s: Predicted in %lf milli-seconds. **detecotr.c 1162 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, letterbox);
if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
printf("**** l.classes == %c ** n", l.classes); //test classes
draw_detections_v3(im, dets, nboxes, thresh, names, alphabet, l.classes, ext_output); //draw_detections_v3,不是image.c的draw_detections
save_image(im, "pre-img//predictions");
if (!dont_show) {
show_image(im, "predictions");
}
// 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 %2.4fn", 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);
//free(boxes);
//free_ptrs((void **)probs, l.w*l.h*l.n);
#ifdef OPENCV
if (!dont_show) {
cvWaitKey(0);
cvDestroyAllWindows();
}
#endif
if (filename) break;
}
// free memory
free_ptrs(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);
}
void run_detector(int argc, char **argv)
{ //输入中有第四位参数的函数，要求cmd中跟上参数如："-out_filename result/out.mp4" 其他的不跟参数
int dont_show = find_arg(argc, argv, "-dont_show"); //不展示窗口，find_arg()有匹配为1，无匹配则0
int show = find_arg(argc, argv, "-show");
check_mistakes = find_arg(argc, argv, "-check_mistakes");
int http_stream_port = find_int_arg(argc, argv, "-http_port", -1); //浏览器展示结果的端口号
char *out_filename = find_char_arg(argc, argv, "-out_filename", 0); //如：-out_filename out.mp4，有视频文件输入才能有输出，无法用摄像头保存输出
char *outfile = find_char_arg(argc, argv, "-out", 0);
char *prefix = find_char_arg(argc, argv, "-prefix", 0);
float thresh = find_float_arg(argc, argv, "-thresh", .25); // 0.24 //单一阈值
float hier_thresh = find_float_arg(argc, argv, "-hier", .5); //多类别显示阈值
int cam_index = find_int_arg(argc, argv, "-c", 0); //摄像头选择
int frame_skip = find_int_arg(argc, argv, "-s", 0); //在框中跳帧显示
int num_of_clusters = find_int_arg(argc, argv, "-num_of_clusters", 5); //簇个数？
int width = find_int_arg(argc, argv, "-width", -1);
int height = find_int_arg(argc, argv, "-height", -1);
// extended output in test mode (output of rect bound coords)
// and for recall mode (extended output table-like format with results for best_class fit)
int ext_output = find_arg(argc, argv, "-ext_output"); //输出目标坐标
int save_labels = find_arg(argc, argv, "-save_labels");
if(argc < 4){
fprintf(stderr, "usage: %s %s [train/test/valid/demo/map] [data] [cfg] [weights (optional)]n", argv[0], argv[1]);
return;
}
char *gpu_list = find_char_arg(argc, argv, "-gpus", 0); //选择gpu
int *gpus = 0;
int gpu = 0;
int ngpus = 0;
if(gpu_list){
printf("%sn", 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");
char *datacfg = argv[3];
char *cfg = argv[4];
char *weights = (argc > 5) ? argv[5] : 0;
if(weights)
if(strlen(weights) > 0)
if (weights[strlen(weights) - 1] == 0x0d) weights[strlen(weights) - 1] = 0;
char *filename = (argc > 6) ? argv[6]: 0;
if(0==strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show, ext_output, save_labels);
else if(0==strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show);
else if(0==strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile);
else if(0==strcmp(argv[2], "recall")) validate_detector_recall(datacfg, cfg, weights);
else if(0==strcmp(argv[2], "map")) validate_detector_map(datacfg, cfg, weights, thresh);
else if(0==strcmp(argv[2], "calc_anchors")) calc_anchors(datacfg, num_of_clusters, width, height, show);
else if(0==strcmp(argv[2], "demo")) {
list *options = read_data_cfg(datacfg);
int classes = option_find_int(options, "classes", 20); //class可能是一帧最大类别数？？
char *name_list = option_find_str(options, "names", "data/names.list");
char **names = get_labels(name_list);
if(filename)
if(strlen(filename) > 0)
if (filename[strlen(filename) - 1] == 0x0d) filename[strlen(filename) - 1] = 0;
demo(cfg, weights, thresh, hier_thresh, cam_index, filename, names, classes, frame_skip, prefix, out_filename,
http_stream_port, dont_show, ext_output);
free_list_contents_kvp(options);
free_list(options);
}
else printf(" There isn't such command: %s", argv[2]);
}
image.c
#include "image.h"
#include "utils.h"
#include "blas.h"
#include "cuda.h"
#include <stdio.h>
#include <math.h>
#define STB_IMAGE_IMPLEMENTATION
#include "stb_image.h"
#define STB_IMAGE_WRITE_IMPLEMENTATION
#include "stb_image_write.h"
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/core/types_c.h"
#include "opencv2/core/version.hpp"
#ifndef CV_VERSION_EPOCH
#include "opencv2/videoio/videoio_c.h"
#include "opencv2/imgcodecs/imgcodecs_c.h"
#include "http_stream.h"
#endif
#include "http_stream.h"
#endif
extern int check_mistakes;
int windows = 0;
float colors[6][3] = { {1,0,1}, {0,0,1},{0,1,1},{0,1,0},{1,1,0},{1,0,0} };
float get_color(int c, int x, int max)
{
float ratio = ((float)x/max)*5;
int i = floor(ratio);
int j = ceil(ratio);
ratio -= i;
float r = (1-ratio) * colors[i][c] + ratio*colors[j][c];
//printf("%fn", r);
return r;
}
static float get_pixel(image m, int x, int y, int c)
{
assert(x < m.w && y < m.h && c < m.c);
return m.data[c*m.h*m.w + y*m.w + x];
}
static float get_pixel_extend(image m, int x, int y, int c)
{
if (x < 0 || x >= m.w || y < 0 || y >= m.h) return 0;
/*
if(x < 0) x = 0;
if(x >= m.w) x = m.w-1;
if(y < 0) y = 0;
if(y >= m.h) y = m.h-1;
*/
if (c < 0 || c >= m.c) return 0;
return get_pixel(m, x, y, c);
}
static void set_pixel(image m, int x, int y, int c, float val)
{
if (x < 0 || y < 0 || c < 0 || x >= m.w || y >= m.h || c >= m.c) return;
assert(x < m.w && y < m.h && c < m.c);
m.data[c*m.h*m.w + y*m.w + x] = val;
}
static void add_pixel(image m, int x, int y, int c, float val)
{
assert(x < m.w && y < m.h && c < m.c);
m.data[c*m.h*m.w + y*m.w + x] += val;
}
void composite_image(image source, image dest, int dx, int dy)
{
int x,y,k;
for(k = 0; k < source.c; ++k){
for(y = 0; y < source.h; ++y){
for(x = 0; x < source.w; ++x){
float val = get_pixel(source, x, y, k);
float val2 = get_pixel_extend(dest, dx+x, dy+y, k);
set_pixel(dest, dx+x, dy+y, k, val * val2);
}
}
}
}
image border_image(image a, int border)
{
image b = make_image(a.w + 2*border, a.h + 2*border, a.c);
int x,y,k;
for(k = 0; k < b.c; ++k){
for(y = 0; y < b.h; ++y){
for(x = 0; x < b.w; ++x){
float val = get_pixel_extend(a, x - border, y - border, k);
if(x - border < 0 || x - border >= a.w || y - border < 0 || y - border >= a.h) val = 1;
set_pixel(b, x, y, k, val);
}
}
}
return b;
}
image tile_images(image a, image b, int dx)
{
if(a.w == 0) return copy_image(b);
image c = make_image(a.w + b.w + dx, (a.h > b.h) ? a.h : b.h, (a.c > b.c) ? a.c : b.c);
fill_cpu(c.w*c.h*c.c, 1, c.data, 1);
embed_image(a, c, 0, 0);
composite_image(b, c, a.w + dx, 0);
return c;
}
image get_label(image **characters, char *string, int size)
{
if(size > 7) size = 7;
image label = make_empty_image(0,0,0);
while(*string){
image l = characters[size][(int)*string];
image n = tile_images(label, l, -size - 1 + (size+1)/2);
free_image(label);
label = n;
++string;
}
image b = border_image(label, label.h*.25);
free_image(label);
return b;
}
image get_label_v3(image **characters, char *string, int size)
{
size = size / 10;
if (size > 7) size = 7;
image label = make_empty_image(0, 0, 0);
while (*string) {
image l = characters[size][(int)*string];
image n = tile_images(label, l, -size - 1 + (size + 1) / 2);
free_image(label);
label = n;
++string;
}
image b = border_image(label, label.h*.25);
free_image(label);
return b;
}
void draw_label(image a, int r, int c, image label, const float *rgb)
{
int w = label.w;
int h = label.h;
if (r - h >= 0) r = r - h;
int i, j, k;
for(j = 0; j < h && j + r < a.h; ++j){
for(i = 0; i < w && i + c < a.w; ++i){
for(k = 0; k < label.c; ++k){
float val = get_pixel(label, i, j, k);
set_pixel(a, i+c, j+r, k, rgb[k] * val);
}
}
}
}
void draw_box(image a, int x1, int y1, int x2, int y2, float r, float g, float b)
{
//normalize_image(a);
int i;
if(x1 < 0) x1 = 0;
if(x1 >= a.w) x1 = a.w-1;
if(x2 < 0) x2 = 0;
if(x2 >= a.w) x2 = a.w-1;
if(y1 < 0) y1 = 0;
if(y1 >= a.h) y1 = a.h-1;
if(y2 < 0) y2 = 0;
if(y2 >= a.h) y2 = a.h-1;
for(i = x1; i <= x2; ++i){
a.data[i + y1*a.w + 0*a.w*a.h] = r;
a.data[i + y2*a.w + 0*a.w*a.h] = r;
a.data[i + y1*a.w + 1*a.w*a.h] = g;
a.data[i + y2*a.w + 1*a.w*a.h] = g;
a.data[i + y1*a.w + 2*a.w*a.h] = b;
a.data[i + y2*a.w + 2*a.w*a.h] = b;
}
for(i = y1; i <= y2; ++i){
a.data[x1 + i*a.w + 0*a.w*a.h] = r;
a.data[x2 + i*a.w + 0*a.w*a.h] = r;
a.data[x1 + i*a.w + 1*a.w*a.h] = g;
a.data[x2 + i*a.w + 1*a.w*a.h] = g;
a.data[x1 + i*a.w + 2*a.w*a.h] = b;
a.data[x2 + i*a.w + 2*a.w*a.h] = b;
}
}
void draw_box_width(image a, int x1, int y1, int x2, int y2, int w, float r, float g, float b)
{
int i;
for(i = 0; i < w; ++i){
draw_box(a, x1+i, y1+i, x2-i, y2-i, r, g, b);
}
}
void draw_bbox(image a, box bbox, int w, float r, float g, float b)
{
int left = (bbox.x-bbox.w/2)*a.w;
int right = (bbox.x+bbox.w/2)*a.w;
int top = (bbox.y-bbox.h/2)*a.h;
int bot = (bbox.y+bbox.h/2)*a.h;
int i;
for(i = 0; i < w; ++i){
draw_box(a, left+i, top+i, right-i, bot-i, r, g, b);
}
}
image **load_alphabet()
{
int i, j;
const int nsize = 8;
image **alphabets = calloc(nsize, sizeof(image));
for(j = 0; j < nsize; ++j){
alphabets[j] = calloc(128, sizeof(image));
for(i = 32; i < 127; ++i){
char buff[256];
sprintf(buff, "data/labels/%d_%d.png", i, j);
alphabets[j][i] = load_image_color(buff, 0, 0);
}
}
return alphabets;
}
// Creates array of detections with prob > thresh and fills best_class for them
detection_with_class* get_actual_detections(detection *dets, int dets_num, float thresh, int* selected_detections_num)
{
int selected_num = 0;
detection_with_class* result_arr = calloc(dets_num, sizeof(detection_with_class));
int i;
for (i = 0; i < dets_num; ++i) { //提取到的特征目标循环判断
int best_class = -1;
float best_class_prob = thresh;
int j;
for (j = 0; j < dets[i].classes; ++j) { //对检测到的目标的类别概率判断，赋予最大的概率类别
if (dets[i].prob[j] > best_class_prob ) {
best_class = j;
best_class_prob = dets[i].prob[j];
}
}
if (best_class >= 0) { //如果最大的类别概率大于0，该detction结构体赋给result_arr返回
result_arr[selected_num].det = dets[i];
result_arr[selected_num].best_class = best_class;
++selected_num;
}
}
if (selected_detections_num)
*selected_detections_num = selected_num;
return result_arr;
}
// compare to sort detection** by bbox.x
int compare_by_lefts(const void *a_ptr, const void *b_ptr) {
const detection_with_class* a = (detection_with_class*)a_ptr;
const detection_with_class* b = (detection_with_class*)b_ptr;
const float delta = (a->det.bbox.x - a->det.bbox.w/2) - (b->det.bbox.x - b->det.bbox.w/2);
return delta < 0 ? -1 : delta > 0 ? 1 : 0;
}
// compare to sort detection** by best_class probability
int compare_by_probs(const void *a_ptr, const void *b_ptr) {
const detection_with_class* a = (detection_with_class*)a_ptr;
const detection_with_class* b = (detection_with_class*)b_ptr;
float delta = a->det.prob[a->best_class] - b->det.prob[b->best_class];
return delta < 0 ? -1 : delta > 0 ? 1 : 0;
}
void draw_detections_v3(image im, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output)
{
int selected_detections_num;
//实例化一个结构体并得到卷积后的各个特征的类名和最大概率
detection_with_class* selected_detections = get_actual_detections(dets, num, thresh, &selected_detections_num);
// text output
qsort(selected_detections, selected_detections_num, sizeof(*selected_detections), compare_by_lefts);
int i;
for (i = 0; i < selected_detections_num; ++i) { //对于检测到的目标的循环
int best_class = selected_detections[i].best_class; //上面返回的结构体中的best_class 原为const int
/*************dspeia 20181026 修改label上的显示***************/
if (best_class != 1 && best_class != 2 && best_class != 3 && best_class != 0){
printf("%s: %.0f%% ******* image.c 292 *******", names[79], selected_detections[i].det.prob[best_class] * 100); //增加这一句，cmd上显示other，调用的coco.name文件，如何图片上显示other？？
}
else {
printf("%s: %.0f%% ******* image.c 292 *******", names[best_class], selected_detections[i].det.prob[best_class] * 100);
}
if (ext_output) //如果cmd的命令该位置为真，可打印出各个目标的box位置
printf("t(left_x: %4.0f top_y: %4.0f width: %4.0f height: %4.0f)n",
(selected_detections[i].det.bbox.x - selected_detections[i].det.bbox.w / 2)*im.w,
(selected_detections[i].det.bbox.y - selected_detections[i].det.bbox.h / 2)*im.h,
selected_detections[i].det.bbox.w*im.w, selected_detections[i].det.bbox.h*im.h);
else
printf("n");
//int j;
//for (j = 0; j < classes; ++j) { //一个object有多个预测类别时进入，输出大于给定阈值的同一个目标的不同类别信息。控制是否进入for的是classes
// if (selected_detections[i].det.prob[j] > thresh && j != best_class) {
// printf("%s: %.0f%% ******image.c 303 ***** n", names[j], selected_detections[i].det.prob[j] * 100);
// }
//}
}
/*******************dspeia 20181017***********************/
// image output
qsort(selected_detections, selected_detections_num, sizeof(*selected_detections), compare_by_probs);
for (i = 0; i < selected_detections_num; ++i) {
int width = im.h * .006;
if (width < 1)
width = 1;
/*
if(0){
width = pow(prob, 1./2.)*10+1;
alphabet = 0;
}
*/
//printf("%d %s: %.0f%%n", i, names[selected_detections[i].best_class], prob*100);
int offset = selected_detections[i].best_class * 123457 % classes;
float red = get_color(2, offset, classes);
float green = get_color(1, offset, classes);
float blue = get_color(0, offset, classes);
float rgb[3];
//width = prob*20+2;
//rgb值为了定义label框的颜色
rgb[0] = red;
rgb[1] = green;
rgb[2] = blue;
box b = selected_detections[i].det.bbox;
//printf("%f %f %f %fn", b.x, b.y, b.w, b.h);
int left = (b.x - b.w / 2.)*im.w;
int right = (b.x + b.w / 2.)*im.w;
int top = (b.y - b.h / 2.)*im.h;
int bot = (b.y + b.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;
/*******************************/
int the_class = selected_detections[i].best_class;
//char cut_class[20] = { 0 }; //为了传类名到cut函数的方法一
//strcpy(cut_class, names[the_class]);
//char cut_class = names[the_class];
float cut_pro = selected_detections[i].det.prob[the_class] * 100;
printf("******355 **cut_class :%s ...........cut_class:%.0f n", names[the_class], cut_pro);
//printf(cut_class);
//printf("%f", cut_pro); //
/******dspeia ****/
int pre_x = left;
int pre_y = top;
int pre_h = bot - top;
int pre_w = right - left;
save_cut_image(pre_x, pre_y, pre_h, pre_w, i, im, names, cut_pro, the_class);
printf("/************ cut and save over *****************/ n");
/********************************/
//int b_x_center = (left + right) / 2;
//int b_y_center = (top + bot) / 2;
//int b_width = right - left;
//int b_height = bot - top;
//sprintf(labelstr, "%d x %d - w: %d, h: %d", b_x_center, b_y_center, b_width, b_height);
draw_box_width(im, left, top, right, bot, width, red, green, blue);
if (alphabet) {
char labelstr[4096] = { 0 };
if (selected_detections[i].best_class != 0 && selected_detections[i].best_class != 1 && selected_detections[i].best_class != 2 && selected_detections[i].best_class != 3){
strcat(labelstr, names[79]); //加入这一句if，在label上写other
}
else
{
strcat(labelstr, names[selected_detections[i].best_class]);
}
//int j;
//for (j = 0; j < classes; ++j) { //同一个object多个预测时，画多个框,注释了图片上仍然画第二框，只是cmd上不打印第二预测
// if (selected_detections[i].det.prob[j] > thresh && j != selected_detections[i].best_class) {
// strcat(labelstr, ", ");
// strcat(labelstr, names[j]);
// }
//}
image label = get_label_v3(alphabet, labelstr, (im.h*.03)); //画出框，复制到im上
draw_label(im, top + width, left, label, rgb);
//image* pic -> label;
//const CvArr* label_copy = (CvArr*)&label; //**********************
//cvShowImage("*ima 394 label", label_copy); //***********dspeia plus test
free_image(label);
}
if (selected_detections[i].det.mask) {
image mask = float_to_image(14, 14, 1, selected_detections[i].det.mask);
image resized_mask = resize_image(mask, b.w*im.w, b.h*im.h);
image tmask = threshold_image(resized_mask, .5);
embed_image(tmask, im, left, top);
free_image(mask);
free_image(resized_mask);
free_image(tmask);
}
}
free(selected_detections);
}
/********************dspeia 20181017********************/
/*********** darknet.exe detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights */
void save_cut_image(int px, int py, int ph, int pw, int no, image m_img, char **names, float cut_pro, int the_class)
{
//cvShowImage("the enter", *m_img);
image copy = copy_image(m_img);
if (m_img.c == 3) rgbgr_image(copy);
int x, y, k;
char buff[256];
/*****************************/
//printf("%s: %.0f%% ******* image.c 292 *******", cut_clas, selected_detections[i].det.prob[best_class] * 100);
/**********************************/
sprintf(buff, "results//%s%.0f%%%d.jpg", names[the_class], cut_pro, no);
printf("****411** cut_class :%s ...........cut_class:%.0f ", names[the_class], cut_pro);//
printf(names[the_class]);
printf("%f",cut_pro); //
IplImage *disp = cvCreateImage(cvSize(m_img.w, m_img.h), IPL_DEPTH_8U, m_img.c);
//cvShowImage("**the enter", disp); //disp 为黑框乱码
int step = disp->widthStep;
for (y = 0; y < m_img.h; ++y) {
for (x = 0; x < m_img.w; ++x) {
for (k = 0; k < m_img.c; ++k) {
disp->imageData[y*step + x*m_img.c + k] = (unsigned char)(get_pixel(copy, x, y, k) * 255);
}
}
}
CvMat *pMat = cvCreateMatHeader(m_img.w, m_img.h, IPL_DEPTH_8U);
//char rect_name[256];
//sprintf(rect_name, "%d_rect", no);
CvRect rect = cvRect(px, py, pw, ph);
cvGetSubRect(disp, pMat, rect);
IplImage *pSubImg = cvCreateImage(cvSize(pw, ph), IPL_DEPTH_8U, m_img.c);
cvGetImage(pMat, pSubImg);
//printf("x=%d,y=%d,h=%d,w=%dn", px, py, ph, pw);
cvSaveImage(buff, pSubImg, 0);
//cvShowImage("average loss", pSubImg); //pSubImg为分割的子目标
//cvReleaseImage(&disp);
//cvReleaseImage(&pMat);
//cvReleaseImage(&rect);
//memset(&rect, 0, sizeof(rect));
//cvReleaseImage(&pSubImg);
//free(&rect);
free_image(copy);
}
/**********************20181017******************************/
void draw_detections(image im, int num, float thresh, box *boxes, float **probs, char **names, image **alphabet, int classes)
{
int i;
for(i = 0; i < num; ++i){
int class_id = max_index(probs[i], classes);
float prob = probs[i][class_id];
if(prob > thresh){
for comparison with OpenCV version of DNN Darknet Yolo v2
//printf("n %f, %f, %f, %f, ", boxes[i].x, boxes[i].y, boxes[i].w, boxes[i].h);
// int k;
//for (k = 0; k < classes; ++k) {
// printf("%f, ", probs[i][k]);
//}
//printf("n");
int width = im.h * .012;
if(0){
width = pow(prob, 1./2.)*10+1;
alphabet = 0;
}
int offset = class_id*123457 % classes;
float red = get_color(2,offset,classes);
float green = get_color(1,offset,classes);
float blue = get_color(0,offset,classes);
float rgb[3];
//width = prob*20+2;
rgb[0] = red;
rgb[1] = green;
rgb[2] = blue;
box b = boxes[i];
int left = (b.x-b.w/2.)*im.w;
int right = (b.x+b.w/2.)*im.w;
int top = (b.y-b.h/2.)*im.h;
int bot = (b.y+b.h/2.)*im.h;
/*******************dspeia 20181017***********************/
int pre_x = left;
int pre_y = top;
int pre_h = bot - top;
int pre_w = right - left;
/*******************dspeia 20181017***********************/
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;
printf("%s: %.0f%% test*******************", names[class_id], prob * 100);
/**********************/
printf("/**********image.c 505*** test和demo 出不来，不走这一步？****************/");
//save_cut_image(pre_x, pre_y, pre_h, pre_w, i, im);
/*******************/
//printf(" - id: %d, x_center: %d, y_center: %d, width: %d, height: %d",
// class_id, (right + left) / 2, (bot - top) / 2, right - left, bot - top);
printf("n");
draw_box_width(im, left, top, right, bot, width, red, green, blue);
if (alphabet) {
image label = get_label(alphabet, names[class_id], (im.h*.03)/10);
draw_label(im, top + width, left, label, rgb);
}
/*****************dspeia 20181017************************/
/*save_cut_image(pre_x, pre_y, pre_h, pre_w, i, im);
printf("/");*/
/*****************dspeia 20181017************************/
/*********** darknet.exe detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights */
}
}
}
#ifdef OPENCV
**************1019最新版本，改动此函数,demo 视频用
void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output)
{
int i, j;
if (!show_img) return;
static int frame_id = 0;
frame_id++;
/**********dspeia 20181019***** 新加视频裁剪小目标 *******/
/*int selected_detections_num;
detection_with_class* selected_detections = get_actual_detections(dets, num, thresh, &selected_detections_num);*/
/***********************/
for (i = 0; i < num; ++i) {
char labelstr[4096] = { 0 };
int class_id = -1;
for (j = 0; j < classes; ++j) {
if (dets[i].prob[j] > thresh) {
if (class_id < 0) {
strcat(labelstr, names[j]);
class_id = j;
}
else {
strcat(labelstr, ", ");
strcat(labelstr, names[j]);
}
printf(" **** image.c 562 ***** %s: %.0f%% ", names[j], dets[i].prob[j] * 100);
///***************************新加，视频裁剪小目标*************/
IplImage* cut = show_img;
//box b = selected_detections[i].det.bbox;
//int left = (b.x - b.w / 2.)* show_img->width;
//int right = (b.x + b.w / 2.)*show_img->width;
//int top = (b.y - b.h / 2.)*show_img->height;
//int bot = (b.y + b.h / 2.)*show_img->height;
//if (left < 0) left = 0;
//if (right > show_img->width - 1) right = show_img->width - 1;
//if (top < 0) top = 0;
//if (bot > show_img->height - 1) bot = show_img->height - 1;
//int pre_x = left;
//int pre_y = top;
//int pre_h = bot - top;
//int pre_w = right - left;
image m = show_img;
network net = parse_network_cfg_custom(cfgfile, 1);
//image im = load_image(show_img, 0, 0, 3);
//int the_class = names[j];
char cut_class[20] = { 0 }; //为了传类名到cut函数的方法一
strcpy(cut_class, names[the_class]);
char cut_class = names[the_class];
//float cut_pro = dets[i].prob[j] * 100;
//save_cut_image(pre_x, pre_y, pre_h, pre_w, i, im, names, cut_pro, the_class);
//printf("/************ cut and save over *****************/");
///********************************/
}
}
if (class_id >= 0) {
int width = show_img->height * .006;
//if(0){
//width = pow(prob, 1./2.)*10+1;
//alphabet = 0;
//}
//printf("%d %s: %.0f%%n", i, names[class_id], prob*100);
int offset = class_id * 123457 % classes;
float red = get_color(2, offset, classes);
float green = get_color(1, offset, classes);
float blue = get_color(0, offset, classes);
float rgb[3];
//width = prob*20+2;
rgb[0] = red;
rgb[1] = green;
rgb[2] = blue;
box b = dets[i].bbox;
b.w = (b.w < 1) ? b.w : 1;
b.h = (b.h < 1) ? b.h : 1;
b.x = (b.x < 1) ? b.x : 1;
b.y = (b.y < 1) ? b.y : 1;
//printf("%f %f %f %fn", b.x, b.y, b.w, b.h);
int left = (b.x - b.w / 2.)*show_img->width;
int right = (b.x + b.w / 2.)*show_img->width;
int top = (b.y - b.h / 2.)*show_img->height;
int bot = (b.y + b.h / 2.)*show_img->height;
if (left < 0) left = 0;
if (right > show_img->width - 1) right = show_img->width - 1;
if (top < 0) top = 0;
if (bot > show_img->height - 1) bot = show_img->height - 1;
//int b_x_center = (left + right) / 2;
//int b_y_center = (top + bot) / 2;
//int b_width = right - left;
//int b_height = bot - top;
//sprintf(labelstr, "%d x %d - w: %d, h: %d", b_x_center, b_y_center, b_width, b_height);
float const font_size = show_img->height / 1000.F;
CvPoint pt1, pt2, pt_text, pt_text_bg1, pt_text_bg2;
pt1.x = left;
pt1.y = top;
pt2.x = right;
pt2.y = bot;
pt_text.x = left;
pt_text.y = top - 12;
pt_text_bg1.x = left;
pt_text_bg1.y = top - (10 + 25 * font_size);
pt_text_bg2.x = right;
pt_text_bg2.y = top;
CvScalar color;
color.val[0] = red * 256;
color.val[1] = green * 256;
color.val[2] = blue * 256;
// you should create directory: result_img
//static int copied_frame_id = -1;
//static IplImage* copy_img = NULL;
//if (copied_frame_id != frame_id) {
// copied_frame_id = frame_id;
// if(copy_img == NULL) copy_img = cvCreateImage(cvSize(show_img->width, show_img->height), show_img->depth, show_img->nChannels);
// cvCopy(show_img, copy_img, 0);
//}
//static int img_id = 0;
//img_id++;
//char image_name[1024];
//sprintf(image_name, "result_img/img_%d_%d_%d.jpg", frame_id, img_id, class_id);
//CvRect rect = cvRect(pt1.x, pt1.y, pt2.x - pt1.x, pt2.y - pt1.y);
//cvSetImageROI(copy_img, rect);
//cvSaveImage(image_name, copy_img, 0);
//cvResetImageROI(copy_img);
cvRectangle(show_img, pt1, pt2, color, width, 8, 0);
if (ext_output)
printf("t(****image.c 644**left_x: %4.0f top_y: %4.0f width: %4.0f height: %4.0f)n",
(float)left, (float)top, b.w*show_img->width, b.h*show_img->height);
else
printf("n");
cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, width, 8, 0);
cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, CV_FILLED, 8, 0); // filled
CvScalar black_color;
black_color.val[0] = 0;
CvFont font;
cvInitFont(&font, CV_FONT_HERSHEY_SIMPLEX, font_size, font_size, 0, font_size * 3, 8);
cvPutText(show_img, labelstr, pt_text, &font, black_color);
}
}
if (ext_output) {
fflush(stdout);
}
}
void draw_detections_cv(IplImage* show_img, int num, float thresh, box *boxes, float **probs, char **names, image **alphabet, int classes)
{
int i;
for (i = 0; i < num; ++i) {
int class_id = max_index(probs[i], classes);
float prob = probs[i][class_id];
if (prob > thresh) {
int width = show_img->height * .012;
if (0) {
width = pow(prob, 1. / 2.) * 10 + 1;
alphabet = 0;
}
printf("%s: %.0f%%n", names[class_id], prob * 100);
int offset = class_id * 123457 % classes;
float red = get_color(2, offset, classes);
float green = get_color(1, offset, classes);
float blue = get_color(0, offset, classes);
float rgb[3];
//width = prob*20+2;
rgb[0] = red;
rgb[1] = green;
rgb[2] = blue;
box b = boxes[i];
int left = (b.x - b.w / 2.)*show_img->width;
int right = (b.x + b.w / 2.)*show_img->width;
int top = (b.y - b.h / 2.)*show_img->height;
int bot = (b.y + b.h / 2.)*show_img->height;
if (left < 0) left = 0;
if (right > show_img->width - 1) right = show_img->width - 1;
if (top < 0) top = 0;
if (bot > show_img->height - 1) bot = show_img->height - 1;
float const font_size = show_img->height / 1000.F;
CvPoint pt1, pt2, pt_text, pt_text_bg1, pt_text_bg2;
pt1.x = left;
pt1.y = top;
pt2.x = right;
pt2.y = bot;
pt_text.x = left;
pt_text.y = top - 12;
pt_text_bg1.x = left;
pt_text_bg1.y = top - (10+25*font_size);
pt_text_bg2.x = right;
pt_text_bg2.y = top;
CvScalar color;
color.val[0] = red * 256;
color.val[1] = green * 256;
color.val[2] = blue * 256;
cvRectangle(show_img, pt1, pt2, color, width, 8, 0);
//printf("left=%d, right=%d, top=%d, bottom=%d, obj_id=%d, obj=%s n", left, right, top, bot, class_id, names[class_id]);
cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, width, 8, 0);
cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, CV_FILLED, 8, 0); // filled
CvScalar black_color;
black_color.val[0] = 0;
CvFont font;
cvInitFont(&font, CV_FONT_HERSHEY_SIMPLEX, font_size, font_size, 0, font_size * 3, 8);
cvPutText(show_img, names[class_id], pt_text, &font, black_color);
}
}
}
IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size)
{
int img_offset = 50;
int draw_size = img_size - img_offset;
IplImage* img = cvCreateImage(cvSize(img_size, img_size), 8, 3);
cvSet(img, CV_RGB(255, 255, 255), 0);
CvPoint pt1, pt2, pt_text;
CvFont font;
cvInitFont(&font, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 1, CV_AA);
char char_buff[100];
int i;
// vertical lines
pt1.x = img_offset; pt2.x = img_size, pt_text.x = 10;
for (i = 1; i <= number_of_lines; ++i) {
pt1.y = pt2.y = (float)i * draw_size / number_of_lines;
cvLine(img, pt1, pt2, CV_RGB(224, 224, 224), 1, 8, 0);
if (i % 10 == 0) {
sprintf(char_buff, "%2.1f", max_img_loss*(number_of_lines - i) / number_of_lines);
pt_text.y = pt1.y + 5;
cvPutText(img, char_buff, pt_text, &font, CV_RGB(0, 0, 0));
cvLine(img, pt1, pt2, CV_RGB(128, 128, 128), 1, 8, 0);
}
}
// horizontal lines
pt1.y = draw_size; pt2.y = 0, pt_text.y = draw_size + 15;
for (i = 0; i <= number_of_lines; ++i) {
pt1.x = pt2.x = img_offset + (float)i * draw_size / number_of_lines;
cvLine(img, pt1, pt2, CV_RGB(224, 224, 224), 1, 8, 0);
if (i % 10 == 0) {
sprintf(char_buff, "%d", max_batches * i / number_of_lines);
pt_text.x = pt1.x - 20;
cvPutText(img, char_buff, pt_text, &font, CV_RGB(0, 0, 0));
cvLine(img, pt1, pt2, CV_RGB(128, 128, 128), 1, 8, 0);
}
}
cvPutText(img, "Iteration number", cvPoint(draw_size / 2, img_size - 10), &font, CV_RGB(0, 0, 0));
cvPutText(img, "Press 's' to save: chart.jpg", cvPoint(5, img_size - 10), &font, CV_RGB(0, 0, 0));
printf(" If error occurs - run training with flag: -dont_show n");
cvNamedWindow("average loss", CV_WINDOW_NORMAL);
cvMoveWindow("average loss", 0, 0);
cvResizeWindow("average loss", img_size, img_size);
cvShowImage("average loss", img);
cvWaitKey(20);
return img;
}
void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches)
{
int img_offset = 50;
int draw_size = img_size - img_offset;
CvFont font;
cvInitFont(&font, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 1, CV_AA);
char char_buff[100];
CvPoint pt1, pt2;
pt1.x = img_offset + draw_size * (float)current_batch / max_batches;
pt1.y = draw_size * (1 - avg_loss / max_img_loss);
if (pt1.y < 0) pt1.y = 1;
cvCircle(img, pt1, 1, CV_RGB(0, 0, 255), CV_FILLED, 8, 0);
sprintf(char_buff, "current avg loss = %2.4f", avg_loss);
pt1.x = img_size / 2, pt1.y = 30;
pt2.x = pt1.x + 250, pt2.y = pt1.y + 20;
cvRectangle(img, pt1, pt2, CV_RGB(255, 255, 255), CV_FILLED, 8, 0);
pt1.y += 15;
cvPutText(img, char_buff, pt1, &font, CV_RGB(0, 0, 0));
cvShowImage("average loss", img);
int k = cvWaitKey(20);
if (k == 's' || current_batch == (max_batches-1)) cvSaveImage("chart.jpg", img, 0);
}
#endif // OPENCV
void transpose_image(image im)
{
assert(im.w == im.h);
int n, m;
int c;
for(c = 0; c < im.c; ++c){
for(n = 0; n < im.w-1; ++n){
for(m = n + 1; m < im.w; ++m){
float swap = im.data[m + im.w*(n + im.h*c)];
im.data[m + im.w*(n + im.h*c)] = im.data[n + im.w*(m + im.h*c)];
im.data[n + im.w*(m + im.h*c)] = swap;
}
}
}
}
void rotate_image_cw(image im, int times)
{
assert(im.w == im.h);
times = (times + 400) % 4;
int i, x, y, c;
int n = im.w;
for(i = 0; i < times; ++i){
for(c = 0; c < im.c; ++c){
for(x = 0; x < n/2; ++x){
for(y = 0; y < (n-1)/2 + 1; ++y){
float temp = im.data[y + im.w*(x + im.h*c)];
im.data[y + im.w*(x + im.h*c)] = im.data[n-1-x + im.w*(y + im.h*c)];
im.data[n-1-x + im.w*(y + im.h*c)] = im.data[n-1-y + im.w*(n-1-x + im.h*c)];
im.data[n-1-y + im.w*(n-1-x + im.h*c)] = im.data[x + im.w*(n-1-y + im.h*c)];
im.data[x + im.w*(n-1-y + im.h*c)] = temp;
}
}
}
}
}
void flip_image(image a)
{
int i,j,k;
for(k = 0; k < a.c; ++k){
for(i = 0; i < a.h; ++i){
for(j = 0; j < a.w/2; ++j){
int index = j + a.w*(i + a.h*(k));
int flip = (a.w - j - 1) + a.w*(i + a.h*(k));
float swap = a.data[flip];
a.data[flip] = a.data[index];
a.data[index] = swap;
}
}
}
}
image image_distance(image a, image b)
{
int i,j;
image dist = make_image(a.w, a.h, 1);
for(i = 0; i < a.c; ++i){
for(j = 0; j < a.h*a.w; ++j){
dist.data[j] += pow(a.data[i*a.h*a.w+j]-b.data[i*a.h*a.w+j],2);
}
}
for(j = 0; j < a.h*a.w; ++j){
dist.data[j] = sqrt(dist.data[j]);
}
return dist;
}
void embed_image(image source, image dest, int dx, int dy)
{
int x,y,k;
for(k = 0; k < source.c; ++k){
for(y = 0; y < source.h; ++y){
for(x = 0; x < source.w; ++x){
float val = get_pixel(source, x,y,k);
set_pixel(dest, dx+x, dy+y, k, val);
}
}
}
}
image collapse_image_layers(image source, int border)
{
int h = source.h;
h = (h+border)*source.c - border;
image dest = make_image(source.w, h, 1);
int i;
for(i = 0; i < source.c; ++i){
image layer = get_image_layer(source, i);
int h_offset = i*(source.h+border);
embed_image(layer, dest, 0, h_offset);
free_image(layer);
}
return dest;
}
void constrain_image(image im)
{
int i;
for(i = 0; i < im.w*im.h*im.c; ++i){
if(im.data[i] < 0) im.data[i] = 0;
if(im.data[i] > 1) im.data[i] = 1;
}
}
void normalize_image(image p)
{
int i;
float min = 9999999;
float max = -999999;
for(i = 0; i < p.h*p.w*p.c; ++i){
float v = p.data[i];
if(v < min) min = v;
if(v > max) max = v;
}
if(max - min < .000000001){
min = 0;
max = 1;
}
for(i = 0; i < p.c*p.w*p.h; ++i){
p.data[i] = (p.data[i] - min)/(max-min);
}
}
void normalize_image2(image p)
{
float *min = calloc(p.c, sizeof(float));
float *max = calloc(p.c, sizeof(float));
int i,j;
for(i = 0; i < p.c; ++i) min[i] = max[i] = p.data[i*p.h*p.w];
for(j = 0; j < p.c; ++j){
for(i = 0; i < p.h*p.w; ++i){
float v = p.data[i+j*p.h*p.w];
if(v < min[j]) min[j] = v;
if(v > max[j]) max[j] = v;
}
}
for(i = 0; i < p.c; ++i){
if(max[i] - min[i] < .000000001){
min[i] = 0;
max[i] = 1;
}
}
for(j = 0; j < p.c; ++j){
for(i = 0; i < p.w*p.h; ++i){
p.data[i+j*p.h*p.w] = (p.data[i+j*p.h*p.w] - min[j])/(max[j]-min[j]);
}
}
free(min);
free(max);
}
image copy_image(image p)
{
image copy = p;
copy.data = calloc(p.h*p.w*p.c, sizeof(float));
memcpy(copy.data, p.data, p.h*p.w*p.c*sizeof(float));
return copy;
}
void rgbgr_image(image im)
{
int i;
for(i = 0; i < im.w*im.h; ++i){
float swap = im.data[i];
im.data[i] = im.data[i+im.w*im.h*2];
im.data[i+im.w*im.h*2] = swap;
}
}
#ifdef OPENCV
void show_image_cv(image p, const char *name)
{
int x,y,k;
image copy = copy_image(p);
constrain_image(copy);
if(p.c == 3) rgbgr_image(copy);
//normalize_image(copy);
char buff[256];
//sprintf(buff, "%s (%d)", name, windows);
sprintf(buff, "%s", name);
IplImage *disp = cvCreateImage(cvSize(p.w,p.h), IPL_DEPTH_8U, p.c);
int step = disp->widthStep;
cvNamedWindow(buff, CV_WINDOW_NORMAL);
//cvMoveWindow(buff, 100*(windows%10) + 200*(windows/10), 100*(windows%10));
++windows;
for(y = 0; y < p.h; ++y){
for(x = 0; x < p.w; ++x){
for(k= 0; k < p.c; ++k){
disp->imageData[y*step + x*p.c + k] = (unsigned char)(get_pixel(copy,x,y,k)*255);
}
}
}
free_image(copy);
if(0){
int w = 448;
int h = w*p.h/p.w;
if(h > 1000){
h = 1000;
w = h*p.w/p.h;
}
IplImage *buffer = disp;
disp = cvCreateImage(cvSize(w, h), buffer->depth, buffer->nChannels);
cvResize(buffer, disp, CV_INTER_LINEAR);
cvReleaseImage(&buffer);
}
cvShowImage(buff, disp);
cvReleaseImage(&disp);
}
void show_image_cv_ipl(IplImage *disp, const char *name) //demo.c 传过来的，*disp为检测后的图片 *name为窗口名称字符串
{
if (disp == NULL) return;
char buff[256];
//sprintf(buff, "%s (%d)", name, windows);
sprintf(buff, "%s", name); //name写入buff 显示合成视频在demo窗口上
cvNamedWindow(buff, CV_WINDOW_NORMAL);
//cvMoveWindow(buff, 100*(windows%10) + 200*(windows/10), 100*(windows%10));
++windows;
cvShowImage(buff, disp); //框合成到demo，展示
//cvReleaseImage(&disp);
}
#endif
void show_image(image p, const char *name)
{
#ifdef OPENCV
show_image_cv(p, name);
#else
fprintf(stderr, "Not compiled with OpenCV, saving to %s.png insteadn", name);
save_image(p, name);
#endif
}
#ifdef OPENCV
image ipl_to_image(IplImage* src)
{
unsigned char *data = (unsigned char *)src->imageData;
int h = src->height;
int w = src->width;
int c = src->nChannels;
int step = src->widthStep;
image out = make_image(w, h, c);
int i, j, k, count=0;;
for(k= 0; k < c; ++k){
for(i = 0; i < h; ++i){
for(j = 0; j < w; ++j){
out.data[count++] = data[i*step + j*c + k]/255.;
}
}
}
return out;
}
image load_image_cv(char *filename, int channels)
{
IplImage* src = 0;
int flag = -1;
if (channels == 0) flag = 1;
else if (channels == 1) flag = 0;
else if (channels == 3) flag = 1;
else {
fprintf(stderr, "OpenCV can't force load with %d channelsn", channels);
}
if( (src = cvLoadImage(filename, flag)) == 0 )
{
char shrinked_filename[1024];
if (strlen(filename) >= 1024) sprintf(shrinked_filename, "name is too long");
else sprintf(shrinked_filename, "%s", filename);
fprintf(stderr, "*image.c 1132*Cannot load image "%s"n", shrinked_filename);
FILE* fw = fopen("bad.list", "a");
fwrite(shrinked_filename, sizeof(char), strlen(shrinked_filename), fw);
char *new_line = "n";
fwrite(new_line, sizeof(char), strlen(new_line), fw);
fclose(fw);
if (check_mistakes) getchar();
return make_image(10,10,3);
//exit(EXIT_FAILURE);
}
image out = ipl_to_image(src);
cvReleaseImage(&src);
if (out.c > 1)
rgbgr_image(out);
return out;
}
image get_image_from_stream(CvCapture *cap)
{
IplImage* src = cvQueryFrame(cap);
if (!src) return make_empty_image(0,0,0);
image im = ipl_to_image(src);
rgbgr_image(im);
return im;
}
image get_image_from_stream_cpp(CvCapture *cap)
{
//IplImage* src = cvQueryFrame(cap);
IplImage* src;
static int once = 1;
if (once) {
once = 0;
do {
src = get_webcam_frame(cap);
if (!src) return make_empty_image(0, 0, 0);
} while (src->width < 1 || src->height < 1 || src->nChannels < 1);
printf("Video stream: %d x %d n", src->width, src->height);
}
else
src = get_webcam_frame(cap);
if (!src) return make_empty_image(0, 0, 0);
image im = ipl_to_image(src);
rgbgr_image(im);
return im;
}
int wait_for_stream(CvCapture *cap, IplImage* src, int dont_close) {
if (!src) {
if (dont_close) src = cvCreateImage(cvSize(416, 416), IPL_DEPTH_8U, 3);
else return 0;
}
if (src->width < 1 || src->height < 1 || src->nChannels < 1) {
if (dont_close) {
cvReleaseImage(&src);
int z = 0;
for (z = 0; z < 20; ++z) {
get_webcam_frame(cap);
cvReleaseImage(&src);
}
src = cvCreateImage(cvSize(416, 416), IPL_DEPTH_8U, 3);
}
else return 0;
}
return 1;
}
image get_image_from_stream_resize(CvCapture *cap, int w, int h, int c, IplImage** in_img, int cpp_video_capture, int dont_close)
{
c = c ? c : 3;
IplImage* src;
if (cpp_video_capture) {
static int once = 1;
if (once) {
once = 0;
do {
src = get_webcam_frame(cap);
if (!src) return make_empty_image(0, 0, 0);
} while (src->width < 1 || src->height < 1 || src->nChannels < 1);
printf("Video stream: %d x %d n", src->width, src->height);
} else
src = get_webcam_frame(cap);
}
else src = cvQueryFrame(cap);
if (cpp_video_capture)
if(!wait_for_stream(cap, src, dont_close)) return make_empty_image(0, 0, 0);
IplImage* new_img = cvCreateImage(cvSize(w, h), IPL_DEPTH_8U, c);
*in_img = cvCreateImage(cvSize(src->width, src->height), IPL_DEPTH_8U, c);
cvResize(src, *in_img, CV_INTER_LINEAR);
cvResize(src, new_img, CV_INTER_LINEAR);
image im = ipl_to_image(new_img);
cvReleaseImage(&new_img);
if (cpp_video_capture) cvReleaseImage(&src);
if (c>1)
rgbgr_image(im);
return im;
}
image get_image_from_stream_letterbox(CvCapture *cap, int w, int h, int c, IplImage** in_img, int cpp_video_capture, int dont_close)
{
c = c ? c : 3;
IplImage* src;
if (cpp_video_capture) {
static int once = 1;
if (once) {
once = 0;
do {
src = get_webcam_frame(cap);
if (!src) return make_empty_image(0, 0, 0);
} while (src->width < 1 || src->height < 1 || src->nChannels < 1);
printf("Video stream: %d x %d n", src->width, src->height);
}
else
src = get_webcam_frame(cap);
}
else src = cvQueryFrame(cap);
if (cpp_video_capture)
if (!wait_for_stream(cap, src, dont_close)) return make_empty_image(0, 0, 0);
*in_img = cvCreateImage(cvSize(src->width, src->height), IPL_DEPTH_8U, c);
cvResize(src, *in_img, CV_INTER_LINEAR);
image tmp = ipl_to_image(src);
image im = letterbox_image(tmp, w, h);
free_image(tmp);
if (cpp_video_capture) cvReleaseImage(&src);
if (c>1) rgbgr_image(im);
return im;
}
int get_stream_fps(CvCapture *cap, int cpp_video_capture)
{
int fps = 25;
if (cpp_video_capture) {
fps = get_stream_fps_cpp(cap);
}
else {
fps = cvGetCaptureProperty(cap, CV_CAP_PROP_FPS);
}
return fps;
}
void save_image_jpg(image p, const char *name)
{
image copy = copy_image(p);
if(p.c == 3) rgbgr_image(copy);
int x,y,k;
char buff[256];
sprintf(buff, "%s.jpg", name);
IplImage *disp = cvCreateImage(cvSize(p.w,p.h), IPL_DEPTH_8U, p.c);
int step = disp->widthStep;
for(y = 0; y < p.h; ++y){
for(x = 0; x < p.w; ++x){
for(k= 0; k < p.c; ++k){
disp->imageData[y*step + x*p.c + k] = (unsigned char)(get_pixel(copy,x,y,k)*255);
}
}
}
cvSaveImage(buff, disp,0);
cvReleaseImage(&disp);
free_image(copy);
}
#endif
void save_image_png(image im, const char *name)
{
char buff[256];
//sprintf(buff, "%s (%d)", name, windows);
sprintf(buff, "%s.png", name);
unsigned char *data = calloc(im.w*im.h*im.c, sizeof(char));
int i,k;
for(k = 0; k < im.c; ++k){
for(i = 0; i < im.w*im.h; ++i){
data[i*im.c+k] = (unsigned char) (255*im.data[i + k*im.w*im.h]);
}
}
int success = stbi_write_png(buff, im.w, im.h, im.c, data, im.w*im.c);
free(data);
if(!success) fprintf(stderr, "Failed to write image %sn", buff);
}
void save_image(image im, const char *name)
{
#ifdef OPENCV
save_image_jpg(im, name);
#else
save_image_png(im, name);
#endif
}
void show_image_layers(image p, char *name)
{
int i;
char buff[256];
for(i = 0; i < p.c; ++i){
sprintf(buff, "%s - Layer %d", name, i);
image layer = get_image_layer(p, i);
show_image(layer, buff);
free_image(layer);
}
}
void show_image_collapsed(image p, char *name)
{
image c = collapse_image_layers(p, 1);
show_image(c, name);
free_image(c);
}
image make_empty_image(int w, int h, int c)
{
image out;
out.data = 0;
out.h = h;
out.w = w;
out.c = c;
return out;
}
image make_image(int w, int h, int c)
{
image out = make_empty_image(w,h,c);
out.data = calloc(h*w*c, sizeof(float));
return out;
}
image make_random_image(int w, int h, int c)
{
image out = make_empty_image(w,h,c);
out.data = calloc(h*w*c, sizeof(float));
int i;
for(i = 0; i < w*h*c; ++i){
out.data[i] = (rand_normal() * .25) + .5;
}
return out;
}
image float_to_image(int w, int h, int c, float *data)
{
image out = make_empty_image(w,h,c);
out.data = data;
return out;
}
image rotate_crop_image(image im, float rad, float s, int w, int h, float dx, float dy, float aspect)
{
int x, y, c;
float cx = im.w/2.;
float cy = im.h/2.;
image rot = make_image(w, h, im.c);
for(c = 0; c < im.c; ++c){
for(y = 0; y < h; ++y){
for(x = 0; x < w; ++x){
float rx = cos(rad)*((x - w/2.)/s*aspect + dx/s*aspect) - sin(rad)*((y - h/2.)/s + dy/s) + cx;
float ry = sin(rad)*((x - w/2.)/s*aspect + dx/s*aspect) + cos(rad)*((y - h/2.)/s + dy/s) + cy;
float val = bilinear_interpolate(im, rx, ry, c);
set_pixel(rot, x, y, c, val);
}
}
}
return rot;
}
image rotate_image(image im, float rad)
{
int x, y, c;
float cx = im.w/2.;
float cy = im.h/2.;
image rot = make_image(im.w, im.h, im.c);
for(c = 0; c < im.c; ++c){
for(y = 0; y < im.h; ++y){
for(x = 0; x < im.w; ++x){
float rx = cos(rad)*(x-cx) - sin(rad)*(y-cy) + cx;
float ry = sin(rad)*(x-cx) + cos(rad)*(y-cy) + cy;
float val = bilinear_interpolate(im, rx, ry, c);
set_pixel(rot, x, y, c, val);
}
}
}
return rot;
}
void translate_image(image m, float s)
{
int i;
for(i = 0; i < m.h*m.w*m.c; ++i) m.data[i] += s;
}
void scale_image(image m, float s)
{
int i;
for(i = 0; i < m.h*m.w*m.c; ++i) m.data[i] *= s;
}
image crop_image(image im, int dx, int dy, int w, int h)
{
image cropped = make_image(w, h, im.c);
int i, j, k;
for(k = 0; k < im.c; ++k){
for(j = 0; j < h; ++j){
for(i = 0; i < w; ++i){
int r = j + dy;
int c = i + dx;
float val = 0;
r = constrain_int(r, 0, im.h-1);
c = constrain_int(c, 0, im.w-1);
if (r >= 0 && r < im.h && c >= 0 && c < im.w) {
val = get_pixel(im, c, r, k);
}
set_pixel(cropped, i, j, k, val);
}
}
}
return cropped;
}
int best_3d_shift_r(image a, image b, int min, int max)
{
if(min == max) return min;
int mid = floor((min + max) / 2.);
image c1 = crop_image(b, 0, mid, b.w, b.h);
image c2 = crop_image(b, 0, mid+1, b.w, b.h);
float d1 = dist_array(c1.data, a.data, a.w*a.h*a.c, 10);
float d2 = dist_array(c2.data, a.data, a.w*a.h*a.c, 10);
free_image(c1);
free_image(c2);
if(d1 < d2) return best_3d_shift_r(a, b, min, mid);
else return best_3d_shift_r(a, b, mid+1, max);
}
int best_3d_shift(image a, image b, int min, int max)
{
int i;
int best = 0;
float best_distance = FLT_MAX;
for(i = min; i <= max; i += 2){
image c = crop_image(b, 0, i, b.w, b.h);
float d = dist_array(c.data, a.data, a.w*a.h*a.c, 100);
if(d < best_distance){
best_distance = d;
best = i;
}
printf("%d %fn", i, d);
free_image(c);
}
return best;
}
void composite_3d(char *f1, char *f2, char *out, int delta)
{
if(!out) out = "out";
image a = load_image(f1, 0,0,0);
image b = load_image(f2, 0,0,0);
int shift = best_3d_shift_r(a, b, -a.h/100, a.h/100);
image c1 = crop_image(b, 10, shift, b.w, b.h);
float d1 = dist_array(c1.data, a.data, a.w*a.h*a.c, 100);
image c2 = crop_image(b, -10, shift, b.w, b.h);
float d2 = dist_array(c2.data, a.data, a.w*a.h*a.c, 100);
if(d2 < d1 && 0){
image swap = a;
a = b;
b = swap;
shift = -shift;
printf("swapped, %dn", shift);
}
else{
printf("%dn", shift);
}
image c = crop_image(b, delta, shift, a.w, a.h);
int i;
for(i = 0; i < c.w*c.h; ++i){
c.data[i] = a.data[i];
}
#ifdef OPENCV
save_image_jpg(c, out);
#else
save_image(c, out);
#endif
}
void fill_image(image m, float s)
{
int i;
for (i = 0; i < m.h*m.w*m.c; ++i) m.data[i] = s;
}
void letterbox_image_into(image im, int w, int h, image boxed)
{
int new_w = im.w;
int new_h = im.h;
if (((float)w / im.w) < ((float)h / im.h)) {
new_w = w;
new_h = (im.h * w) / im.w;
}
else {
new_h = h;
new_w = (im.w * h) / im.h;
}
image resized = resize_image(im, new_w, new_h);
embed_image(resized, boxed, (w - new_w) / 2, (h - new_h) / 2);
free_image(resized);
}
image letterbox_image(image im, int w, int h)
{
int new_w = im.w;
int new_h = im.h;
if (((float)w / im.w) < ((float)h / im.h)) {
new_w = w;
new_h = (im.h * w) / im.w;
}
else {
new_h = h;
new_w = (im.w * h) / im.h;
}
image resized = resize_image(im, new_w, new_h);
image boxed = make_image(w, h, im.c);
fill_image(boxed, .5);
//int i;
//for(i = 0; i < boxed.w*boxed.h*boxed.c; ++i) boxed.data[i] = 0;
embed_image(resized, boxed, (w - new_w) / 2, (h - new_h) / 2);
free_image(resized);
return boxed;
}
image resize_max(image im, int max)
{
int w = im.w;
int h = im.h;
if(w > h){
h = (h * max) / w;
w = max;
} else {
w = (w * max) / h;
h = max;
}
if(w == im.w && h == im.h) return im;
image resized = resize_image(im, w, h);
return resized;
}
image resize_min(image im, int min)
{
int w = im.w;
int h = im.h;
if(w < h){
h = (h * min) / w;
w = min;
} else {
w = (w * min) / h;
h = min;
}
if(w == im.w && h == im.h) return im;
image resized = resize_image(im, w, h);
return resized;
}
image random_crop_image(image im, int w, int h)
{
int dx = rand_int(0, im.w - w);
int dy = rand_int(0, im.h - h);
image crop = crop_image(im, dx, dy, w, h);
return crop;
}
image random_augment_image(image im, float angle, float aspect, int low, int high, int size)
{
aspect = rand_scale(aspect);
int r = rand_int(low, high);
int min = (im.h < im.w*aspect) ? im.h : im.w*aspect;
float scale = (float)r / min;
float rad = rand_uniform(-angle, angle) * TWO_PI / 360.;
float dx = (im.w*scale/aspect - size) / 2.;
float dy = (im.h*scale - size) / 2.;
if(dx < 0) dx = 0;
if(dy < 0) dy = 0;
dx = rand_uniform(-dx, dx);
dy = rand_uniform(-dy, dy);
image crop = rotate_crop_image(im, rad, scale, size, size, dx, dy, aspect);
return crop;
}
float three_way_max(float a, float b, float c)
{
return (a > b) ? ( (a > c) ? a : c) : ( (b > c) ? b : c) ;
}
float three_way_min(float a, float b, float c)
{
return (a < b) ? ( (a < c) ? a : c) : ( (b < c) ? b : c) ;
}
// http://www.cs.rit.edu/~ncs/color/t_convert.html
void rgb_to_hsv(image im)
{
assert(im.c == 3);
int i, j;
float r, g, b;
float h, s, v;
for(j = 0; j < im.h; ++j){
for(i = 0; i < im.w; ++i){
r = get_pixel(im, i , j, 0);
g = get_pixel(im, i , j, 1);
b = get_pixel(im, i , j, 2);
float max = three_way_max(r,g,b);
float min = three_way_min(r,g,b);
float delta = max - min;
v = max;
if(max == 0){
s = 0;
h = 0;
}else{
s = delta/max;
if(r == max){
h = (g - b) / delta;
} else if (g == max) {
h = 2 + (b - r) / delta;
} else {
h = 4 + (r - g) / delta;
}
if (h < 0) h += 6;
h = h/6.;
}
set_pixel(im, i, j, 0, h);
set_pixel(im, i, j, 1, s);
set_pixel(im, i, j, 2, v);
}
}
}
void hsv_to_rgb(image im)
{
assert(im.c == 3);
int i, j;
float r, g, b;
float h, s, v;
float f, p, q, t;
for(j = 0; j < im.h; ++j){
for(i = 0; i < im.w; ++i){
h = 6 * get_pixel(im, i , j, 0);
s = get_pixel(im, i , j, 1);
v = get_pixel(im, i , j, 2);
if (s == 0) {
r = g = b = v;
} else {
int index = floor(h);
f = h - index;
p = v*(1-s);
q = v*(1-s*f);
t = v*(1-s*(1-f));
if(index == 0){
r = v; g = t; b = p;
} else if(index == 1){
r = q; g = v; b = p;
} else if(index == 2){
r = p; g = v; b = t;
} else if(index == 3){
r = p; g = q; b = v;
} else if(index == 4){
r = t; g = p; b = v;
} else {
r = v; g = p; b = q;
}
}
set_pixel(im, i, j, 0, r);
set_pixel(im, i, j, 1, g);
set_pixel(im, i, j, 2, b);
}
}
}
image grayscale_image(image im)
{
assert(im.c == 3);
int i, j, k;
image gray = make_image(im.w, im.h, 1);
float scale[] = {0.587, 0.299, 0.114};
for(k = 0; k < im.c; ++k){
for(j = 0; j < im.h; ++j){
for(i = 0; i < im.w; ++i){
gray.data[i+im.w*j] += scale[k]*get_pixel(im, i, j, k);
}
}
}
return gray;
}
image threshold_image(image im, float thresh)
{
int i;
image t = make_image(im.w, im.h, im.c);
for(i = 0; i < im.w*im.h*im.c; ++i){
t.data[i] = im.data[i]>thresh ? 1 : 0;
}
return t;
}
image blend_image(image fore, image back, float alpha)
{
assert(fore.w == back.w && fore.h == back.h && fore.c == back.c);
image blend = make_image(fore.w, fore.h, fore.c);
int i, j, k;
for(k = 0; k < fore.c; ++k){
for(j = 0; j < fore.h; ++j){
for(i = 0; i < fore.w; ++i){
float val = alpha * get_pixel(fore, i, j, k) +
(1 - alpha)* get_pixel(back, i, j, k);
set_pixel(blend, i, j, k, val);
}
}
}
return blend;
}
void scale_image_channel(image im, int c, float v)
{
int i, j;
for(j = 0; j < im.h; ++j){
for(i = 0; i < im.w; ++i){
float pix = get_pixel(im, i, j, c);
pix = pix*v;
set_pixel(im, i, j, c, pix);
}
}
}
void translate_image_channel(image im, int c, float v)
{
int i, j;
for(j = 0; j < im.h; ++j){
for(i = 0; i < im.w; ++i){
float pix = get_pixel(im, i, j, c);
pix = pix+v;
set_pixel(im, i, j, c, pix);
}
}
}
image binarize_image(image im)
{
image c = copy_image(im);
int i;
for(i = 0; i < im.w * im.h * im.c; ++i){
if(c.data[i] > .5) c.data[i] = 1;
else c.data[i] = 0;
}
return c;
}
void saturate_image(image im, float sat)
{
rgb_to_hsv(im);
scale_image_channel(im, 1, sat);
hsv_to_rgb(im);
constrain_image(im);
}
void hue_image(image im, float hue)
{
rgb_to_hsv(im);
int i;
for(i = 0; i < im.w*im.h; ++i){
im.data[i] = im.data[i] + hue;
if (im.data[i] > 1) im.data[i] -= 1;
if (im.data[i] < 0) im.data[i] += 1;
}
hsv_to_rgb(im);
constrain_image(im);
}
void exposure_image(image im, float sat)
{
rgb_to_hsv(im);
scale_image_channel(im, 2, sat);
hsv_to_rgb(im);
constrain_image(im);
}
void distort_image(image im, float hue, float sat, float val)
{
if (im.c >= 3)
{
rgb_to_hsv(im);
scale_image_channel(im, 1, sat);
scale_image_channel(im, 2, val);
int i;
for(i = 0; i < im.w*im.h; ++i){
im.data[i] = im.data[i] + hue;
if (im.data[i] > 1) im.data[i] -= 1;
if (im.data[i] < 0) im.data[i] += 1;
}
hsv_to_rgb(im);
}
else
{
scale_image_channel(im, 0, val);
}
constrain_image(im);
}
void random_distort_image(image im, float hue, float saturation, float exposure)
{
float dhue = rand_uniform_strong(-hue, hue);
float dsat = rand_scale(saturation);
float dexp = rand_scale(exposure);
distort_image(im, dhue, dsat, dexp);
}
void saturate_exposure_image(image im, float sat, float exposure)
{
rgb_to_hsv(im);
scale_image_channel(im, 1, sat);
scale_image_channel(im, 2, exposure);
hsv_to_rgb(im);
constrain_image(im);
}
float bilinear_interpolate(image im, float x, float y, int c)
{
int ix = (int) floorf(x);
int iy = (int) floorf(y);
float dx = x - ix;
float dy = y - iy;
float val = (1-dy) * (1-dx) * get_pixel_extend(im, ix, iy, c) +
dy * (1-dx) * get_pixel_extend(im, ix, iy+1, c) +
(1-dy) * dx * get_pixel_extend(im, ix+1, iy, c) +
dy * dx * get_pixel_extend(im, ix+1, iy+1, c);
return val;
}
image resize_image(image im, int w, int h)
{
image resized = make_image(w, h, im.c);
image part = make_image(w, im.h, im.c);
int r, c, k;
float w_scale = (float)(im.w - 1) / (w - 1);
float h_scale = (float)(im.h - 1) / (h - 1);
for(k = 0; k < im.c; ++k){
for(r = 0; r < im.h; ++r){
for(c = 0; c < w; ++c){
float val = 0;
if(c == w-1 || im.w == 1){
val = get_pixel(im, im.w-1, r, k);
} else {
float sx = c*w_scale;
int ix = (int) sx;
float dx = sx - ix;
val = (1 - dx) * get_pixel(im, ix, r, k) + dx * get_pixel(im, ix+1, r, k);
}
set_pixel(part, c, r, k, val);
}
}
}
for(k = 0; k < im.c; ++k){
for(r = 0; r < h; ++r){
float sy = r*h_scale;
int iy = (int) sy;
float dy = sy - iy;
for(c = 0; c < w; ++c){
float val = (1-dy) * get_pixel(part, c, iy, k);
set_pixel(resized, c, r, k, val);
}
if(r == h-1 || im.h == 1) continue;
for(c = 0; c < w; ++c){
float val = dy * get_pixel(part, c, iy+1, k);
add_pixel(resized, c, r, k, val);
}
}
}
free_image(part);
return resized;
}
void test_resize(char *filename)
{
image im = load_image(filename, 0,0, 3);
float mag = mag_array(im.data, im.w*im.h*im.c);
printf("L2 Norm: %fn", mag);
image gray = grayscale_image(im);
image c1 = copy_image(im);
image c2 = copy_image(im);
image c3 = copy_image(im);
image c4 = copy_image(im);
distort_image(c1, .1, 1.5, 1.5);
distort_image(c2, -.1, .66666, .66666);
distort_image(c3, .1, 1.5, .66666);
distort_image(c4, .1, .66666, 1.5);
show_image(im, "Original");
show_image(gray, "Gray");
show_image(c1, "C1");
show_image(c2, "C2");
show_image(c3, "C3");
show_image(c4, "C4");
#ifdef OPENCV
while(1){
image aug = random_augment_image(im, 0, .75, 320, 448, 320);
show_image(aug, "aug");
free_image(aug);
float exposure = 1.15;
float saturation = 1.15;
float hue = .05;
image c = copy_image(im);
float dexp = rand_scale(exposure);
float dsat = rand_scale(saturation);
float dhue = rand_uniform(-hue, hue);
distort_image(c, dhue, dsat, dexp);
show_image(c, "rand");
printf("%f %f %fn", dhue, dsat, dexp);
free_image(c);
cvWaitKey(0);
}
#endif
}
image load_image_stb(char *filename, int channels)
{
int w, h, c;
unsigned char *data = stbi_load(filename, &w, &h, &c, channels);
if (!data) {
char shrinked_filename[1024];
if (strlen(filename) >= 1024) sprintf(shrinked_filename, "name is too long");
else sprintf(shrinked_filename, "%s", filename);
fprintf(stderr, "*image.c 1979*Cannot load image "%s"nSTB Reason: %sn", shrinked_filename, stbi_failure_reason());
FILE* fw = fopen("bad.list", "a");
fwrite(shrinked_filename, sizeof(char), strlen(shrinked_filename), fw);
char *new_line = "n";
fwrite(new_line, sizeof(char), strlen(new_line), fw);
fclose(fw);
if (check_mistakes) getchar();
return make_image(10, 10, 3);
//exit(EXIT_FAILURE);
}
if(channels) c = channels;
int i,j,k;
image im = make_image(w, h, c);
for(k = 0; k < c; ++k){
for(j = 0; j < h; ++j){
for(i = 0; i < w; ++i){
int dst_index = i + w*j + w*h*k;
int src_index = k + c*i + c*w*j;
im.data[dst_index] = (float)data[src_index]/255.;
}
}
}
free(data);
return im;
}
image load_image(char *filename, int w, int h, int c)
{
#ifdef OPENCV
#ifndef CV_VERSION_EPOCH
//image out = load_image_stb(filename, c); // OpenCV 3.x
image out = load_image_cv(filename, c);
#else
image out = load_image_cv(filename, c); // OpenCV 2.4.x
#endif
#else
image out = load_image_stb(filename, c); // without OpenCV
#endif
if((h && w) && (h != out.h || w != out.w)){
image resized = resize_image(out, w, h);
free_image(out);
out = resized;
}
return out;
}
image load_image_color(char *filename, int w, int h)
{
return load_image(filename, w, h, 3);
}
image get_image_layer(image m, int l)
{
image out = make_image(m.w, m.h, 1);
int i;
for(i = 0; i < m.h*m.w; ++i){
out.data[i] = m.data[i+l*m.h*m.w];
}
return out;
}
void print_image(image m)
{
int i, j, k;
for(i =0 ; i < m.c; ++i){
for(j =0 ; j < m.h; ++j){
for(k = 0; k < m.w; ++k){
printf("%.2lf, ", m.data[i*m.h*m.w + j*m.w + k]);
if(k > 30) break;
}
printf("n");
if(j > 30) break;
}
printf("n");
}
printf("n");
}
image collapse_images_vert(image *ims, int n)
{
int color = 1;
int border = 1;
int h,w,c;
w = ims[0].w;
h = (ims[0].h + border) * n - border;
c = ims[0].c;
if(c != 3 || !color){
w = (w+border)*c - border;
c = 1;
}
image filters = make_image(w, h, c);
int i,j;
for(i = 0; i < n; ++i){
int h_offset = i*(ims[0].h+border);
image copy = copy_image(ims[i]);
//normalize_image(copy);
if(c == 3 && color){
embed_image(copy, filters, 0, h_offset);
}
else{
for(j = 0; j < copy.c; ++j){
int w_offset = j*(ims[0].w+border);
image layer = get_image_layer(copy, j);
embed_image(layer, filters, w_offset, h_offset);
free_image(layer);
}
}
free_image(copy);
}
return filters;
}
image collapse_images_horz(image *ims, int n)
{
int color = 1;
int border = 1;
int h,w,c;
int size = ims[0].h;
h = size;
w = (ims[0].w + border) * n - border;
c = ims[0].c;
if(c != 3 || !color){
h = (h+border)*c - border;
c = 1;
}
image filters = make_image(w, h, c);
int i,j;
for(i = 0; i < n; ++i){
int w_offset = i*(size+border);
image copy = copy_image(ims[i]);
//normalize_image(copy);
if(c == 3 && color){
embed_image(copy, filters, w_offset, 0);
}
else{
for(j = 0; j < copy.c; ++j){
int h_offset = j*(size+border);
image layer = get_image_layer(copy, j);
embed_image(layer, filters, w_offset, h_offset);
free_image(layer);
}
}
free_image(copy);
}
return filters;
}
void show_image_normalized(image im, const char *name)
{
image c = copy_image(im);
normalize_image(c);
show_image(c, name);
free_image(c);
}
void show_images(image *ims, int n, char *window)
{
image m = collapse_images_vert(ims, n);
/*
int w = 448;
int h = ((float)m.h/m.w) * 448;
if(h > 896){
h = 896;
w = ((float)m.w/m.h) * 896;
}
image sized = resize_image(m, w, h);
*/
normalize_image(m);
save_image(m, window);
show_image(m, window);
free_image(m);
}
void free_image(image m)
{
if(m.data){
free(m.data);
}
}
demo.c
#include "network.h"
#include "detection_layer.h"
#include "region_layer.h"
#include "cost_layer.h"
#include "utils.h"
#include "parser.h"
#include "box.h"
#include "image.h"
#include "demo.h"
#ifdef WIN32
#include <time.h>
#include <winsock.h>
#include "gettimeofday.h"
#else
#include <sys/time.h>
#endif
#define FRAMES 3
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/core/version.hpp"
#ifndef CV_VERSION_EPOCH
#include "opencv2/videoio/videoio_c.h"
#endif
#include "http_stream.h"
image get_image_from_stream(CvCapture *cap);
static char **demo_names;
static image **demo_alphabet;
static int demo_classes;
static float **probs;
static box *boxes;
static network net;
static image in_s ;
static image det_s;
static CvCapture * cap;
static int cpp_video_capture = 0;
static float fps = 0;
static float demo_thresh = 0;
static int demo_ext_output = 0;
static float *predictions[FRAMES];
static int demo_index = 0;
static image images[FRAMES];
static IplImage* ipl_images[FRAMES];
static float *avg;
void draw_detections_cv(IplImage* show_img, int num, float thresh, box *boxes, float **probs, char **names, image **alphabet, int classes);
void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output);
void show_image_cv_ipl(IplImage *disp, const char *name);
image get_image_from_stream_resize(CvCapture *cap, int w, int h, int c, IplImage** in_img, int cpp_video_capture, int dont_close);
image get_image_from_stream_letterbox(CvCapture *cap, int w, int h, int c, IplImage** in_img, int cpp_video_capture, int dont_close);
int get_stream_fps(CvCapture *cap, int cpp_video_capture);
IplImage* in_img;
IplImage* det_img;
IplImage* show_img;
static int flag_exit;
static int letter_box = 0;
void *fetch_in_thread(void *ptr)
{
//in = get_image_from_stream(cap);
int dont_close_stream = 0; // set 1 if your IP-camera periodically turns off and turns on video-stream
if(letter_box)
in_s = get_image_from_stream_letterbox(cap, net.w, net.h, net.c, &in_img, cpp_video_capture, dont_close_stream);
else
in_s = get_image_from_stream_resize(cap, net.w, net.h, net.c, &in_img, cpp_video_capture, dont_close_stream);
if(!in_s.data){
//error("Stream closed.");
printf("Stream closed.n");
flag_exit = 1;
return EXIT_FAILURE;
}
//in_s = resize_image(in, net.w, net.h);
return 0;
}
void *detect_in_thread(void *ptr)
{
float nms = .45; // 0.4F
layer l = net.layers[net.n-1];
float *X = det_s.data;
float *prediction = network_predict(net, X);
memcpy(predictions[demo_index], prediction, l.outputs*sizeof(float));
mean_arrays(predictions, FRAMES, l.outputs, avg);
l.output = avg;
free_image(det_s);
int nboxes = 0;
detection *dets = NULL;
if (letter_box)
dets = get_network_boxes(&net, in_img->width, in_img->height, demo_thresh, demo_thresh, 0, 1, &nboxes, 1); // letter box
else
dets = get_network_boxes(&net, det_s.w, det_s.h, demo_thresh, demo_thresh, 0, 1, &nboxes, 0); // resized
//if (nms) do_nms_obj(dets, nboxes, l.classes, nms); // bad results
if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
printf(" 33[2J");
printf(" 33[1;1H");
printf("nFPS:%.1fn",fps);
printf("Objects:nn");
ipl_images[demo_index] = det_img;
det_img = ipl_images[(demo_index + FRAMES / 2 + 1) % FRAMES];
demo_index = (demo_index + 1)%FRAMES;
draw_detections_cv_v3(det_img, dets, nboxes, demo_thresh, demo_names, demo_alphabet, demo_classes, demo_ext_output);
free_detections(dets, nboxes);
return 0;
}
double get_wall_time()
{
struct timeval time;
if (gettimeofday(&time,NULL)){
return 0;
}
return (double)time.tv_sec + (double)time.tv_usec * .000001;
}
void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes,
int frame_skip, char *prefix, char *out_filename, int http_stream_port, int dont_show, int ext_output)
{
//skip = frame_skip;
image **alphabet = load_alphabet();
int delay = frame_skip;
demo_names = names;
demo_alphabet = alphabet;
demo_classes = classes;
demo_thresh = thresh;
demo_ext_output = ext_output;
printf("Demon");
net = parse_network_cfg_custom(cfgfile, 1); // set batch=1
if(weightfile){
load_weights(&net, weightfile);
}
//set_batch_network(&net, 1);
fuse_conv_batchnorm(net);
calculate_binary_weights(net);
srand(2222222);
if(filename){
printf("video file: %sn", filename);
//#ifdef CV_VERSION_EPOCH // OpenCV 2.x
// cap = cvCaptureFromFile(filename);
//#else // OpenCV 3.x
cpp_video_capture = 1;
cap = get_capture_video_stream(filename);
//#endif
}else{
printf("Webcam index: %dn", cam_index);
//#ifdef CV_VERSION_EPOCH // OpenCV 2.x
// cap = cvCaptureFromCAM(cam_index);
//#else // OpenCV 3.x
cpp_video_capture = 1;
cap = get_capture_webcam(cam_index);
//#endif
}
if (!cap) {
#ifdef WIN32
printf("Check that you have copied file opencv_ffmpeg340_64.dll to the same directory where is darknet.exe n");
#endif
error("Couldn't connect to webcam.n");
}
layer l = net.layers[net.n-1];
int j;
avg = (float *) calloc(l.outputs, sizeof(float));
for(j = 0; j < FRAMES; ++j) predictions[j] = (float *) calloc(l.outputs, sizeof(float));
for(j = 0; j < FRAMES; ++j) images[j] = make_image(1,1,3);
//开辟存储空间
boxes = (box *)calloc(l.w*l.h*l.n, sizeof(box));
probs = (float **)calloc(l.w*l.h*l.n, sizeof(float *));
for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = (float *)calloc(l.classes, sizeof(float *));
if (l.classes != demo_classes) {
printf("Parameters don't match: in cfg-file classes=%d, in data-file classes=%d n", l.classes, demo_classes);
getchar();
exit(0);
}
flag_exit = 0;
//多线程
pthread_t fetch_thread;
pthread_t detect_thread;
fetch_in_thread(0);
det_img = in_img;
det_s = in_s;
fetch_in_thread(0);
detect_in_thread(0);
det_img = in_img;
det_s = in_s;
for(j = 0; j < FRAMES/2; ++j){
fetch_in_thread(0);
detect_in_thread(0);
det_img = in_img;
det_s = in_s;
}
int count = 0;
if(!prefix && !dont_show){ //不展示demo窗口
cvNamedWindow("Demo", CV_WINDOW_NORMAL);
cvMoveWindow("Demo", 0, 0);
cvResizeWindow("Demo", 1352, 1013);
}
CvVideoWriter* output_video_writer = NULL; // cv::VideoWriter output_video; 可添加检测视频的结果保存函数，不用添加，参数有路径就自动保存？？？？？
if (out_filename && !flag_exit)
{
CvSize size;
size.width = det_img->width, size.height = det_img->height;
int src_fps = 25;
src_fps = get_stream_fps(cap, cpp_video_capture);
//const char* output_name = "test_dnn_out.avi";
//output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('H', '2', '6', '4'), src_fps, size, 1);
output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('D', 'I', 'V', 'X'), src_fps, size, 1);
//output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('M', 'J', 'P', 'G'), src_fps, size, 1);
//output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('M', 'P', '4', 'V'), src_fps, size, 1);
//output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('M', 'P', '4', '2'), src_fps, size, 1);
//output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('X', 'V', 'I', 'D'), src_fps, size, 1);
//output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('W', 'M', 'V', '2'), src_fps, size, 1);
}
double before = get_wall_time();
while(1){ //进入循环，进入算法，展示结果
++count;
if(1){
if(pthread_create(&fetch_thread, 0, fetch_in_thread, 0)) error("Thread creation failed");
if(pthread_create(&detect_thread, 0, detect_in_thread, 0)) error("Thread creation failed");
if(!prefix){ //默认0进
if (!dont_show) {
show_image_cv_ipl(show_img, "Demo"); //demo窗口，展示结果视频
//save_cut_image();
printf("/*************demo.c 252***************");
int c = cvWaitKey(1);
if (c == 10) { //在demo窗口下按换行键Ctrl+Enter，暂停demo窗口的刷新，但是cmd后台仍然继续检测，再次按下有delay的显示（抽帧显示）第三次按下则还原实时显示。和在demo下长按enter一样的效果。demo下换行暂停后按enter会展示那一帧图片。到底是暂停还是delay展示？？？？？
if (frame_skip == 0) frame_skip = 60;
else if (frame_skip == 4) frame_skip = 0;
else if (frame_skip == 60) frame_skip = 4;
else frame_skip = 0;
}
else if (c == 27 || c == 1048603) // ESC - exit (OpenCV 2.x / 3.x)
{
flag_exit = 1;
}
}
}else{ //prefix有值就进。prefix为char真 类名？，dont_show为1，不展示demo，截图？？？默认为0不进
char buff[256];
sprintf(buff, "results//%s_%08d.jpg", prefix, count);
cvSaveImage(buff, show_img, 0); //—prefix 走此，不展示demo窗口，但没有存图
printf("**********demo.c 269 save prefix************");
//save_image(disp, buff);
}
// if you run it with param -http_port 8090 then open URL in your web-browser: http://localhost:8090
if (http_stream_port > 0 && show_img) {
//int port = 8090;
int port = http_stream_port;
int timeout = 200;
int jpeg_quality = 30; // 1 - 100
send_mjpeg(show_img, port, timeout, jpeg_quality); //与正常摄像头无关
}
// save video file 参数带有-out_filename就自动保存视频
if (output_video_writer && show_img) {
cvWriteFrame(output_video_writer, show_img);
printf("n cvWriteFrame n");
}
cvReleaseImage(&show_img);
pthread_join(fetch_thread, 0);
pthread_join(detect_thread, 0);
if (flag_exit == 1) break;
if(delay == 0){
show_img = det_img;
}
det_img = in_img;
det_s = in_s;
}else { //应该进不来
fetch_in_thread(0);
det_img = in_img;
det_s = in_s;
detect_in_thread(0);
show_img = det_img;
if (!dont_show) {
show_image_cv_ipl(show_img, "Demo"); //展示视频的结果窗口，show_img为处理后的图片
cvSaveImage("***********demo 307****", show_img, 0);
printf("/***************demo.c 307******************/");
cvWaitKey(1);
}
cvReleaseImage(&show_img);
}
--delay;
if(delay < 0){
delay = frame_skip;
double after = get_wall_time();
float curr = 1./(after - before);
fps = curr;
before = after;
}
}
printf("*****demo.c 321 ****** input video stream closed. n");
if (output_video_writer) {
cvReleaseVideoWriter(&output_video_writer);
printf("output_video_writer closed. n");
}
// free memory
cvReleaseImage(&show_img);
cvReleaseImage(&in_img);
free_image(in_s);
free(avg);
for (j = 0; j < FRAMES; ++j) free(predictions[j]);
for (j = 0; j < FRAMES; ++j) free_image(images[j]);
for (j = 0; j < l.w*l.h*l.n; ++j) free(probs[j]);
free(boxes);
free(probs);
free_ptrs(names, net.layers[net.n - 1].classes);
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);
}
#else //若没有opencv
void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes,
int frame_skip, char *prefix, char *out_filename, int http_stream_port, int dont_show, int ext_output)
{
fprintf(stderr, "Demo needs OpenCV for webcam images.n");
}
#endif
最后
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