From 1c05ebf522f0bb5776ba51a46d94aa101220fea1 Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Thu, 07 Jun 2018 00:39:30 +0000
Subject: [PATCH] Minor fix
---
src/detector.c | 815 ++++++++++++++++++++++++++++++++++------------------------
1 files changed, 477 insertions(+), 338 deletions(-)
diff --git a/src/detector.c b/src/detector.c
index f8515d4..0c0b14d 100644
--- a/src/detector.c
+++ b/src/detector.c
@@ -16,10 +16,10 @@
#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)
+#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)
+#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")
@@ -61,6 +61,11 @@
srand(time(0));
network net = nets[0];
+ if ((net.batch * net.subdivisions) == 1) {
+ printf("\n Error: You set incorrect value batch=1 for Training! You should set batch=64 subdivision=64 \n");
+ getchar();
+ }
+
int imgs = net.batch * net.subdivisions * ngpus;
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
data train, buffer;
@@ -76,20 +81,24 @@
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.paths = paths;
+ 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 = l.small_object;
+ args.small_object = net.small_object;
args.d = &buffer;
args.type = DETECTION_DATA;
- args.threads = 4;// 8;
+ args.threads = 16; // 64
args.angle = net.angle;
args.exposure = net.exposure;
@@ -97,6 +106,7 @@
args.hue = net.hue;
#ifdef OPENCV
+ args.threads = 3 * ngpus;
IplImage* img = NULL;
float max_img_loss = 5;
int number_of_lines = 100;
@@ -106,19 +116,30 @@
#endif //OPENCV
pthread_t load_thread = load_data(args);
- clock_t time;
+ 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("Resizing\n");
- int dim = (rand() % 12 + (init_w/32 - 5)) * 32; // +-160
- //int dim = (rand() % 10 + 10) * 32;
- //if (get_current_batch(net)+100 > net.max_batches) dim = 544;
+ //int dim = (rand() % 12 + (init_w/32 - 5)) * 32; // +-160
//int dim = (rand() % 4 + 16) * 32;
- printf("%d\n", dim);
- args.w = dim;
- args.h = dim;
+ //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;
@@ -126,11 +147,11 @@
load_thread = load_data(args);
for(i = 0; i < ngpus; ++i){
- resize_network(nets + i, dim, dim);
+ resize_network(nets + i, dim_w, dim_h);
}
net = nets[0];
}
- time=clock();
+ time=what_time_is_it_now();
pthread_join(load_thread, 0);
train = buffer;
load_thread = load_data(args);
@@ -152,9 +173,9 @@
save_image(im, "truth11");
*/
- printf("Loaded: %lf seconds\n", sec(clock()-time));
+ printf("Loaded: %lf seconds\n", (what_time_is_it_now()-time));
- time=clock();
+ time=what_time_is_it_now();
float loss = 0;
#ifdef GPU
if(ngpus == 1){
@@ -165,11 +186,11 @@
#else
loss = train_network(net, train);
#endif
- if (avg_loss < 0) avg_loss = loss;
+ 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("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs);
+ printf("\n %d: %f, %f avg loss, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), (what_time_is_it_now()-time), i*imgs);
#ifdef OPENCV
if(!dont_show)
@@ -177,7 +198,9 @@
#endif // OPENCV
//if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) {
- if (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
@@ -194,308 +217,313 @@
sprintf(buff, "%s/%s_final.weights", backup_directory, base);
save_weights(net, buff);
- //cvReleaseImage(&img);
- //cvDestroyAllWindows();
+#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, '_');
- return atoi(p+1);
+ 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, box *boxes, float **probs, int num_boxes, int classes, int w, int h)
+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 = boxes[i].x - boxes[i].w/2.;
- float xmax = boxes[i].x + boxes[i].w/2.;
- float ymin = boxes[i].y - boxes[i].h/2.;
- float ymax = boxes[i].y + boxes[i].h/2.;
+ 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;
+ 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;
+ float bx = xmin;
+ float by = ymin;
+ float bw = xmax - xmin;
+ float bh = ymax - ymin;
- for(j = 0; j < classes; ++j){
- if (probs[i][j]) fprintf(fp, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f},\n", image_id, coco_ids[j], bx, by, bw, bh, probs[i][j]);
- }
- }
+ 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, box *boxes, float **probs, int total, int classes, int w, int h)
+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 = boxes[i].x - boxes[i].w/2.;
- float xmax = boxes[i].x + boxes[i].w/2.;
- float ymin = boxes[i].y - boxes[i].h/2.;
- float ymax = boxes[i].y + boxes[i].h/2.;
+ 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 < 0) xmin = 0;
- if (ymin < 0) ymin = 0;
- if (xmax > w) xmax = w;
- if (ymax > h) ymax = h;
+ if (xmin < 1) xmin = 1;
+ if (ymin < 1) ymin = 1;
+ if (xmax > w) xmax = w;
+ if (ymax > h) ymax = h;
- for(j = 0; j < classes; ++j){
- if (probs[i][j]) fprintf(fps[j], "%s %f %f %f %f %f\n", id, probs[i][j],
- xmin, ymin, xmax, ymax);
- }
- }
+ for (j = 0; j < classes; ++j) {
+ if (dets[i].prob[j]) fprintf(fps[j], "%s %f %f %f %f %f\n", id, dets[i].prob[j],
+ xmin, ymin, xmax, ymax);
+ }
+ }
}
-void print_imagenet_detections(FILE *fp, int id, box *boxes, float **probs, int total, int classes, int w, int h)
+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 = boxes[i].x - boxes[i].w/2.;
- float xmax = boxes[i].x + boxes[i].w/2.;
- float ymin = boxes[i].y - boxes[i].h/2.;
- float ymax = boxes[i].y + boxes[i].h/2.;
+ 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;
+ 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_id = j;
- if (probs[i][class_id]) fprintf(fp, "%d %d %f %f %f %f %f\n", id, j+1, probs[i][class_id],
- xmin, ymin, xmax, ymax);
- }
- }
+ for (j = 0; j < classes; ++j) {
+ int class = j;
+ if (dets[i].prob[class]) fprintf(fp, "%d %d %f %f %f %f %f\n", id, j + 1, dets[i].prob[class],
+ xmin, ymin, xmax, ymax);
+ }
+ }
}
-void validate_detector(char *datacfg, char *cfgfile, char *weightfile)
+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);
+ 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);
- if(weightfile){
- load_weights(&net, weightfile);
- }
- set_batch_network(&net, 1);
- fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
- srand(time(0));
+ 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: %g\n", net.learning_rate, net.momentum, net.decay);
+ srand(time(0));
- char *base = "comp4_det_test_";
- list *plist = get_paths(valid_images);
- char **paths = (char **)list_to_array(plist);
+ list *plist = get_paths(valid_images);
+ char **paths = (char **)list_to_array(plist);
- layer l = net.layers[net.n-1];
- int classes = l.classes;
+ 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")){
- snprintf(buff, 1024, "%s/coco_results.json", prefix);
- fp = fopen(buff, "w");
- fprintf(fp, "[\n");
- coco = 1;
- } else if(0==strcmp(type, "imagenet")){
- snprintf(buff, 1024, "%s/imagenet-detection.txt", prefix);
- fp = fopen(buff, "w");
- imagenet = 1;
- classes = 200;
- } else {
- fps = calloc(classes, sizeof(FILE *));
- for(j = 0; j < classes; ++j){
- snprintf(buff, 1024, "%s/%s%s.txt", prefix, base, names[j]);
- fps[j] = fopen(buff, "w");
- }
- }
+ char buff[1024];
+ char *type = option_find_str(options, "eval", "voc");
+ FILE *fp = 0;
+ FILE **fps = 0;
+ int coco = 0;
+ int imagenet = 0;
+ if (0 == strcmp(type, "coco")) {
+ if (!outfile) outfile = "coco_results";
+ snprintf(buff, 1024, "%s/%s.json", prefix, outfile);
+ fp = fopen(buff, "w");
+ fprintf(fp, "[\n");
+ coco = 1;
+ }
+ else if (0 == strcmp(type, "imagenet")) {
+ if (!outfile) outfile = "imagenet-detection";
+ snprintf(buff, 1024, "%s/%s.txt", prefix, outfile);
+ fp = fopen(buff, "w");
+ imagenet = 1;
+ classes = 200;
+ }
+ else {
+ if (!outfile) outfile = "comp4_det_test_";
+ fps = calloc(classes, sizeof(FILE *));
+ for (j = 0; j < classes; ++j) {
+ snprintf(buff, 1024, "%s/%s%s.txt", prefix, outfile, names[j]);
+ fps[j] = fopen(buff, "w");
+ }
+ }
- box *boxes = calloc(l.w*l.h*l.n, sizeof(box));
- float **probs = calloc(l.w*l.h*l.n, sizeof(float *));
- for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(classes, sizeof(float *));
+ int m = plist->size;
+ int i = 0;
+ int t;
- int m = plist->size;
- int i=0;
- int t;
+ float thresh = .005;
+ float nms = .45;
- 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));
- int detection_count = 0;
+ load_args args = { 0 };
+ args.w = net.w;
+ args.h = net.h;
+ args.c = net.c;
+ args.type = IMAGE_DATA;
+ //args.type = LETTERBOX_DATA;
- int nthreads = 4;
- image *val = calloc(nthreads, sizeof(image));
- image *val_resized = calloc(nthreads, sizeof(image));
- image *buf = calloc(nthreads, sizeof(image));
- image *buf_resized = calloc(nthreads, sizeof(image));
- pthread_t *thr = calloc(nthreads, sizeof(pthread_t));
-
- load_args args = {0};
- args.w = net.w;
- args.h = net.h;
- args.type = IMAGE_DATA;
-
- for(t = 0; t < nthreads; ++t){
- args.path = paths[i+t];
- args.im = &buf[t];
- args.resized = &buf_resized[t];
- thr[t] = load_data_in_thread(args);
- }
- time_t start = time(0);
- for(i = nthreads; i < m+nthreads; i += nthreads){
- fprintf(stderr, "%d\n", i);
- for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
- pthread_join(thr[t], 0);
- val[t] = buf[t];
- val_resized[t] = buf_resized[t];
- }
- for(t = 0; t < nthreads && i+t < m; ++t){
- args.path = paths[i+t];
- args.im = &buf[t];
- args.resized = &buf_resized[t];
- thr[t] = load_data_in_thread(args);
- }
- for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
- char *path = paths[i+t-nthreads];
- char *id = basecfg(path);
- float *X = val_resized[t].data;
- network_predict(net, X);
- int w = val[t].w;
- int h = val[t].h;
- get_region_boxes(l, w, h, thresh, probs, boxes, 0, map);
- if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, classes, nms);
-
- int x, y;
- for (x = 0; x < (l.w*l.h*l.n); ++x) {
- for (y = 0; y < classes; ++y)
- {
- if (probs[x][y]) ++detection_count;
- }
+ for (t = 0; t < nthreads; ++t) {
+ args.path = paths[i + t];
+ args.im = &buf[t];
+ args.resized = &buf_resized[t];
+ thr[t] = load_data_in_thread(args);
+ }
+ time_t start = time(0);
+ for (i = nthreads; i < m + nthreads; i += nthreads) {
+ fprintf(stderr, "%d\n", i);
+ for (t = 0; t < nthreads && i + t - nthreads < m; ++t) {
+ pthread_join(thr[t], 0);
+ val[t] = buf[t];
+ val_resized[t] = buf_resized[t];
+ }
+ for (t = 0; t < nthreads && i + t < m; ++t) {
+ args.path = paths[i + t];
+ args.im = &buf[t];
+ args.resized = &buf_resized[t];
+ thr[t] = load_data_in_thread(args);
+ }
+ for (t = 0; t < nthreads && i + t - nthreads < m; ++t) {
+ char *path = paths[i + t - nthreads];
+ char *id = basecfg(path);
+ float *X = val_resized[t].data;
+ network_predict(net, X);
+ int w = val[t].w;
+ int h = val[t].h;
+ 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);
}
-
- if (coco){
- print_cocos(fp, path, boxes, probs, l.w*l.h*l.n, classes, w, h);
- } else if (imagenet){
- print_imagenet_detections(fp, i+t-nthreads+1, boxes, probs, l.w*l.h*l.n, classes, w, h);
- } else {
- print_detector_detections(fps, id, boxes, probs, l.w*l.h*l.n, classes, w, h);
- }
- free(id);
- free_image(val[t]);
- free_image(val_resized[t]);
- }
- }
- for(j = 0; j < classes; ++j){
- if(fps) fclose(fps[j]);
- }
- if(coco){
- fseek(fp, -2, SEEK_CUR);
- fprintf(fp, "\n]\n");
- fclose(fp);
- }
- printf("\n detection_count = %d \n", detection_count);
- fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
+ 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 Seconds\n", (double)time(0) - start);
}
void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile)
{
- network net = parse_network_cfg_custom(cfgfile, 1);
- if(weightfile){
- load_weights(&net, weightfile);
- }
- set_batch_network(&net, 1);
- fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
- srand(time(0));
+ 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);
+ list *plist = get_paths(valid_images);
+ char **paths = (char **)list_to_array(plist);
- layer l = net.layers[net.n-1];
- int classes = l.classes;
+ layer l = net.layers[net.n - 1];
- int j, k;
- box *boxes = calloc(l.w*l.h*l.n, sizeof(box));
- float **probs = calloc(l.w*l.h*l.n, sizeof(float *));
- for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(classes, sizeof(float *));
+ int j, k;
- int m = plist->size;
- int i=0;
+ int m = plist->size;
+ int i = 0;
- float thresh = .001;// .001; // .2;
- float iou_thresh = .5;
- float nms = .4;
+ float thresh = .001;
+ float iou_thresh = .5;
+ float nms = .4;
- int detection_count = 0, truth_count = 0;
+ int total = 0;
+ int correct = 0;
+ int proposals = 0;
+ float avg_iou = 0;
- 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);
- for(i = 0; i < m; ++i){
- char *path = paths[i];
- image orig = load_image_color(path, 0, 0);
- image sized = resize_image(orig, net.w, net.h);
- char *id = basecfg(path);
- network_predict(net, sized.data);
- get_region_boxes(l, 1, 1, thresh, probs, boxes, 1, 0);
- if (nms) do_nms(boxes, probs, l.w*l.h*l.n, 1, nms);
+ char labelpath[4096];
+ replace_image_to_label(path, labelpath);
- char labelpath[4096];
- find_replace(path, "images", "labels", labelpath);
- find_replace(labelpath, "JPEGImages", "labels", labelpath);
- find_replace(labelpath, ".jpg", ".txt", labelpath);
- find_replace(labelpath, ".JPEG", ".txt", labelpath);
- find_replace(labelpath, ".png", ".txt", labelpath);
-
- int num_labels = 0;
- box_label *truth = read_boxes(labelpath, &num_labels);
- truth_count += num_labels;
- for(k = 0; k < l.w*l.h*l.n; ++k){
- if(probs[k][0] > thresh){
- ++proposals;
- }
- }
+ 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 < l.w*l.h*l.n; ++k) {
- float iou = box_iou(boxes[k], t);
- if (probs[k][0] > thresh && iou > best_iou) {
+ 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, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total);
- free(id);
- free_image(orig);
- free_image(sized);
- }
- printf("\n truth_count = %d \n", truth_count);
+ avg_iou += best_iou;
+ if (best_iou > iou_thresh) {
+ ++correct;
+ }
+ }
+ //fprintf(stderr, " %s - %s - ", paths[i], labelpath);
+ fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals / (i + 1), avg_iou * 100 / total, 100.*correct / total);
+ free(id);
+ free_image(orig);
+ free_image(sized);
+ }
}
typedef struct {
@@ -528,13 +556,14 @@
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);
+ 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: %g\n", net.learning_rate, net.momentum, net.decay);
+ //set_batch_network(&net, 1);
+ fuse_conv_batchnorm(net);
srand(time(0));
list *plist = get_paths(valid_images);
@@ -550,10 +579,6 @@
layer l = net.layers[net.n - 1];
int classes = l.classes;
- box *boxes = calloc(l.w*l.h*l.n, sizeof(box));
- float **probs = calloc(l.w*l.h*l.n, sizeof(float *));
- for (j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(classes, sizeof(float *));
-
int m = plist->size;
int i = 0;
int t;
@@ -572,7 +597,9 @@
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;
@@ -611,15 +638,16 @@
char *id = basecfg(path);
float *X = val_resized[t].data;
network_predict(net, X);
- get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, map);
- if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, classes, nms);
+
+ int nboxes = 0;
+ int letterbox = (args.type == LETTERBOX_DATA);
+ float hier_thresh = 0;
+ detection *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];
- find_replace(path, "images", "labels", labelpath);
- find_replace(labelpath, "JPEGImages", "labels", labelpath);
- find_replace(labelpath, ".jpg", ".txt", labelpath);
- find_replace(labelpath, ".JPEG", ".txt", labelpath);
- find_replace(labelpath, ".png", ".txt", labelpath);
+ replace_image_to_label(path, labelpath);
int num_labels = 0;
box_label *truth = read_boxes(labelpath, &num_labels);
int i, j;
@@ -635,23 +663,22 @@
char *path_dif = paths_dif[image_index];
char labelpath_dif[4096];
- find_replace(path_dif, "images", "labels", labelpath_dif);
- find_replace(labelpath_dif, "JPEGImages", "labels", labelpath_dif);
- find_replace(labelpath_dif, ".jpg", ".txt", labelpath_dif);
- find_replace(labelpath_dif, ".JPEG", ".txt", labelpath_dif);
- find_replace(labelpath_dif, ".png", ".txt", labelpath_dif);
+ replace_image_to_label(path_dif, labelpath_dif);
+
truth_dif = read_boxes(labelpath_dif, &num_labels_dif);
}
- for (i = 0; i < (l.w*l.h*l.n); ++i) {
+ 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 = probs[i][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 = boxes[i];
+ 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;
@@ -664,8 +691,8 @@
{
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(boxes[i], t), prob, class_id, truth[j].id);
- float current_iou = box_iou(boxes[i], t);
+ // 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;
@@ -683,7 +710,7 @@
// 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(boxes[i], t);
+ float current_iou = box_iou(dets[i].bbox, t);
if (current_iou > iou_thresh && class_id == truth_dif[j].id) {
--detections_count;
break;
@@ -693,7 +720,13 @@
// calc avg IoU, true-positives, false-positives for required Threshold
if (prob > thresh_calc_avg_iou) {
- if (truth_index > -1) {
+ 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;
}
@@ -703,16 +736,27 @@
}
}
}
-
+
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, "%s\n", 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]);
}
}
- avg_iou = avg_iou / (tp_for_thresh + fp_for_thresh);
+ if((tp_for_thresh + fp_for_thresh) > 0)
+ avg_iou = avg_iou / (tp_for_thresh + fp_for_thresh);
// SORT(detections)
@@ -824,12 +868,32 @@
free(truth_classes_count);
fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
+ if (reinforcement_fd != NULL) fclose(reinforcement_fd);
}
#ifdef OPENCV
-void calc_anchors(char *datacfg, int num_of_clusters, int final_width, int final_height, int show)
+typedef struct {
+ float w, h;
+} anchors_t;
+
+int anchors_comparator(const void *pa, const void *pb)
{
- printf("\n num_of_clusters = %d, final_width = %d, final_height = %d \n", num_of_clusters, final_width, final_height);
+ 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));
@@ -847,11 +911,8 @@
for (i = 0; i < number_of_images; ++i) {
char *path = paths[i];
char labelpath[4096];
- find_replace(path, "images", "labels", labelpath);
- find_replace(labelpath, "JPEGImages", "labels", labelpath);
- find_replace(labelpath, ".jpg", ".txt", labelpath);
- find_replace(labelpath, ".JPEG", ".txt", labelpath);
- find_replace(labelpath, ".png", ".txt", labelpath);
+ replace_image_to_label(path, labelpath);
+
int num_labels = 0;
box_label *truth = read_boxes(labelpath, &num_labels);
//printf(" new path: %s \n", labelpath);
@@ -859,8 +920,8 @@
{
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 * final_width;
- rel_width_height_array[number_of_boxes * 2 - 1] = truth[j].h * final_height;
+ 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);
}
}
@@ -893,7 +954,10 @@
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)
@@ -918,7 +982,7 @@
float box_h = points->data.fl[i * 2 + 1];
//int cluster_idx = labels->data.i[i];
int cluster_idx = 0;
- float min_dist = 1000000;
+ 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];
@@ -950,9 +1014,12 @@
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);
+ printf("%s", buff);
fwrite(buff, sizeof(char), strlen(buff), fw);
- if (i + 1 < num_of_clusters) fwrite(", ", sizeof(char), 2, fw);;
+ if (i + 1 < num_of_clusters) {
+ fwrite(", ", sizeof(char), 2, fw);
+ printf(", ");
+ }
}
printf("\n");
fclose(fw);
@@ -964,15 +1031,15 @@
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 / final_width;
- pt2.y = centers->data.fl[j * 2 + 1] * img_size / final_height;
+ 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 / final_width;
- pt.y = points->data.fl[i * 2 + 1] * img_size / final_height;
+ 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;
@@ -992,33 +1059,42 @@
cvReleaseMat(&labels);
}
#else
-void calc_anchors(char *datacfg, int num_of_clusters, int final_width, int final_height, int show) {
+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, int dont_show)
+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");
- char **names = get_labels(name_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);
+ network net = parse_network_cfg_custom(cfgfile, 1); // set batch=1
if(weightfile){
load_weights(&net, weightfile);
}
- set_batch_network(&net, 1);
+ //set_batch_network(&net, 1);
+ fuse_conv_batchnorm(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, datacfg);
+ if(net.layers[net.n - 1].classes > names_size) getchar();
+ }
srand(2222222);
- clock_t time;
+ double time;
char buff[256];
char *input = buff;
int j;
- float nms=.4;
+ float nms=.45; // 0.4F
while(1){
if(filename){
strncpy(input, filename, 256);
- if (input[strlen(input) - 1] == 0x0d) input[strlen(input) - 1] = 0;
+ if(strlen(input) > 0)
+ if (input[strlen(input) - 1] == 0x0d) input[strlen(input) - 1] = 0;
} else {
printf("Enter Image Path: ");
fflush(stdout);
@@ -1026,30 +1102,64 @@
if(!input) return;
strtok(input, "\n");
}
- image im = load_image_color(input,0,0);
+ image im = load_image(input,0,0,net.c);
+ int letterbox = 0;
image sized = resize_image(im, net.w, net.h);
+ //image sized = letterbox_image(im, net.w, net.h); letterbox = 1;
layer l = net.layers[net.n-1];
- 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 *));
+ //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=clock();
+ time= what_time_is_it_now();
network_predict(net, X);
- printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
- get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, 0);
- if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, l.classes, nms);
- draw_detections(im, l.w*l.h*l.n, thresh, boxes, probs, names, alphabet, l.classes);
+ //network_predict_image(&net, im); letterbox = 1;
+ printf("%s: Predicted in %f seconds.\n", input, (what_time_is_it_now()-time));
+ //get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, 0);
+ // if (nms) do_nms_sort_v2(boxes, probs, l.w*l.h*l.n, l.classes, nms);
+ //draw_detections(im, l.w*l.h*l.n, thresh, boxes, probs, names, alphabet, l.classes);
+ int nboxes = 0;
+ detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letterbox);
+ if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
+ draw_detections_v3(im, dets, nboxes, thresh, names, alphabet, l.classes, ext_output);
save_image(im, "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 %2.4f\n", class_id, dets[i].bbox.x, dets[i].bbox.y, dets[i].bbox.w, dets[i].bbox.h);
+ fwrite(buff, sizeof(char), strlen(buff), fw);
+ }
+ }
+ fclose(fw);
+ }
+
+ free_detections(dets, nboxes);
free_image(im);
free_image(sized);
- free(boxes);
- free_ptrs((void **)probs, l.w*l.h*l.n);
+ //free(boxes);
+ //free_ptrs((void **)probs, l.w*l.h*l.n);
#ifdef OPENCV
if (!dont_show) {
cvWaitKey(0);
@@ -1058,6 +1168,23 @@
#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)
@@ -1066,13 +1193,19 @@
int show = find_arg(argc, argv, "-show");
int http_stream_port = find_int_arg(argc, argv, "-http_port", -1);
char *out_filename = find_char_arg(argc, argv, "-out_filename", 0);
+ 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", .24);
+ float thresh = find_float_arg(argc, argv, "-thresh", .25); // 0.24
+ float hier_thresh = find_float_arg(argc, argv, "-hier", .5);
int cam_index = find_int_arg(argc, argv, "-c", 0);
int frame_skip = find_int_arg(argc, argv, "-s", 0);
int num_of_clusters = find_int_arg(argc, argv, "-num_of_clusters", 5);
- int final_width = find_int_arg(argc, argv, "-final_width", 13);
- int final_heigh = find_int_arg(argc, argv, "-final_heigh", 13);
+ 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");
+ int save_labels = find_arg(argc, argv, "-save_labels");
if(argc < 4){
fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
return;
@@ -1106,22 +1239,28 @@
char *cfg = argv[4];
char *weights = (argc > 5) ? argv[5] : 0;
if(weights)
- if (weights[strlen(weights) - 1] == 0x0d) weights[strlen(weights) - 1] = 0;
+ 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, dont_show);
+ 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);
+ 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, final_width, final_heigh, show);
+ 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);
char *name_list = option_find_str(options, "names", "data/names.list");
char **names = get_labels(name_list);
if(filename)
- if (filename[strlen(filename) - 1] == 0x0d) filename[strlen(filename) - 1] = 0;
- demo(cfg, weights, thresh, cam_index, filename, names, classes, frame_skip, prefix, out_filename,
- http_stream_port, dont_show);
+ 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]);
}
--
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