From 5a47c46b39475fc3581b9819f488b977ea1beca3 Mon Sep 17 00:00:00 2001
From: Edmond Yoo <hj3yoo@uwaterloo.ca>
Date: Sun, 16 Sep 2018 03:11:04 +0000
Subject: [PATCH] Moving files from MTGCardDetector
---
src/detector.c | 1723 +++++++++++++++++++++++++++++++----------------------------
1 files changed, 902 insertions(+), 821 deletions(-)
diff --git a/src/detector.c b/src/detector.c
index e891cd7..a816c74 100644
--- a/src/detector.c
+++ b/src/detector.c
@@ -1,8 +1,3 @@
-#ifdef _DEBUG
-#include <stdlib.h>
-#include <crtdbg.h>
-#endif
-
#include "network.h"
#include "region_layer.h"
#include "cost_layer.h"
@@ -21,10 +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")
@@ -32,7 +27,7 @@
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
+#endif // OPENCV
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};
@@ -66,6 +61,15 @@
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: %g\n", net.learning_rate, net.momentum, net.decay);
data train, buffer;
@@ -79,14 +83,15 @@
//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);
+ 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;
@@ -94,10 +99,10 @@
args.flip = net.flip;
args.jitter = jitter;
args.num_boxes = l.max_boxes;
- args.small_object = net.small_object;
+ args.small_object = net.small_object;
args.d = &buffer;
args.type = DETECTION_DATA;
- args.threads = 16; // 64
+ args.threads = 16; // 64
args.angle = net.angle;
args.exposure = net.exposure;
@@ -105,28 +110,40 @@
args.hue = net.hue;
#ifdef OPENCV
- args.threads = 3;
- 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
+ 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){
+ if(l.random && count++%10 == 0){
printf("Resizing\n");
- int dim = (rand() % 12 + (init_w/32 - 5)) * 32; // +-160
- //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;
@@ -134,7 +151,7 @@
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];
}
@@ -173,28 +190,28 @@
#else
loss = train_network(net, train);
#endif
- if (avg_loss < 0 || avg_loss != avg_loss) avg_loss = loss; // if(-inf or nan)
+ 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, %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);
+ 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)
- draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches);
-#endif // 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;
+ //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);
+ 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);
- }
+ char buff[256];
+ sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
+ save_weights(net, buff);
+ }
free_data(train);
}
#ifdef GPU
@@ -204,848 +221,872 @@
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, '/');
- char *c = strrchr(filename, '_');
- if (c) p = c;
- 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, 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.;
+ 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 (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]);
- }
- }
+ 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;
+ 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;
+ 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 %f\n", id, dets[i].prob[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, 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.;
+ 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 = 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);
- }
- }
+ 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, 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); // 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));
+ 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));
- 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")) {
- 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");
- }
- }
+ 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;
+ 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 nthreads = 4;
+ image *val = calloc(nthreads, sizeof(image));
+ image *val_resized = calloc(nthreads, sizeof(image));
+ image *buf = calloc(nthreads, sizeof(image));
+ image *buf_resized = calloc(nthreads, sizeof(image));
+ pthread_t *thr = calloc(nthreads, sizeof(pthread_t));
- load_args args = { 0 };
- args.w = net.w;
- args.h = net.h;
- args.type = IMAGE_DATA;
- //args.type = LETTERBOX_DATA;
+ 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, "%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);
- }
- 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", time(0) - start);
+ 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);
+ }
+ 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); // set batch=1
- if (weightfile) {
- load_weights(&net, weightfile);
- }
- //set_batch_network(&net, 1);
- fuse_conv_batchnorm(net);
- 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("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];
+ layer l = net.layers[net.n - 1];
- int j, k;
+ int j, k;
- int m = plist->size;
- int i = 0;
+ int m = plist->size;
+ int i = 0;
- float thresh = .001;
- float iou_thresh = .5;
- float nms = .4;
+ float thresh = .001;
+ float iou_thresh = .5;
+ float nms = .4;
- 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_color(path, 0, 0);
- 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(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];
- find_replace(path, "images", "labels", labelpath);
- find_replace(labelpath, "JPEGImages", "labels", labelpath);
- find_replace(labelpath, ".jpg", ".txt", labelpath);
- find_replace(labelpath, ".png", ".txt", labelpath);
- find_replace(labelpath, ".bmp", ".txt", labelpath);
- find_replace(labelpath, ".JPG", ".txt", labelpath);
- find_replace(labelpath, ".JPEG", ".txt", labelpath);
+ 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 %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);
- }
+ 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 %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 {
- box b;
- float p;
- int class_id;
- int image_index;
- int truth_flag;
- int unique_truth_index;
+ 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;
+ 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);
+ 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);
- 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);
+ calculate_binary_weights(net);
+ srand(time(0));
- 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);
- 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.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, "%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) {
- 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;
- 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, ".png", ".txt", labelpath);
- find_replace(labelpath, ".bmp", ".txt", labelpath);
- find_replace(labelpath, ".JPG", ".txt", labelpath);
- find_replace(labelpath, ".JPEG", ".txt", labelpath);
- int num_labels = 0;
- box_label *truth = read_boxes(labelpath, &num_labels);
- 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];
- 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);
- 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;
-
- 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);
+ char **paths_dif = NULL;
+ if (difficult_valid_images) {
+ list *plist_dif = get_paths(difficult_valid_images);
+ paths_dif = (char **)list_to_array(plist_dif);
+ }
- int *truth_flags = calloc(unique_truth_count, sizeof(int));
+ layer l = net.layers[net.n - 1];
+ int classes = l.classes;
- int rank;
- for (rank = 0; rank < detections_count; ++rank) {
- if(rank % 100 == 0)
- printf(" rank = %d of ranks = %d \r", rank, detections_count);
+ int m = plist->size;
+ int i = 0;
+ int t;
- 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;
- }
- }
+ const float thresh = .005;
+ const float nms = .45;
+ const float iou_thresh = 0.5;
- 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
- }
+ 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));
- 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;
+ load_args args = { 0 };
+ args.w = net.w;
+ args.h = net.h;
+ args.c = net.c;
+ args.type = IMAGE_DATA;
+ //args.type = LETTERBOX_DATA;
- if ((tp + fp) > 0) pr[i][rank].precision = (double)tp / (double)(tp + fp);
- else pr[i][rank].precision = 0;
+ //const float thresh_calc_avg_iou = 0.24;
+ float avg_iou = 0;
+ int tp_for_thresh = 0;
+ int fp_for_thresh = 0;
- if ((tp + fn) > 0) pr[i][rank].recall = (double)tp / (double)(tp + fn);
- else pr[i][rank].recall = 0;
- }
- }
+ box_prob *detections = calloc(1, sizeof(box_prob));
+ int detections_count = 0;
+ int unique_truth_count = 0;
- free(truth_flags);
-
-
- double mean_average_precision = 0;
+ int *truth_classes_count = calloc(classes, sizeof(int));
- 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);
+ 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) {
+ 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);
- 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);
+ 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);
- 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);
+ 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]++;
+ }
- 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);
+ // 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, "%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]);
+ }
+ }
+
+ if((tp_for_thresh + fp_for_thresh) > 0)
+ avg_iou = avg_iou / (tp_for_thresh + fp_for_thresh);
- for (i = 0; i < classes; ++i) {
- free(pr[i]);
- }
- free(pr);
- free(detections);
- free(truth_classes_count);
+ // SORT(detections)
+ qsort(detections, detections_count, sizeof(box_prob), detections_comparator);
- fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
+ 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 Seconds\n", (double)(time(0) - start));
+ if (reinforcement_fd != NULL) fclose(reinforcement_fd);
}
#ifdef OPENCV
typedef struct {
- float w, h;
+ 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;
+ 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;
- }
+ 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));
+ //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);
+ 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 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];
- find_replace(path, "images", "labels", labelpath);
- find_replace(labelpath, "JPEGImages", "labels", labelpath);
- find_replace(labelpath, ".jpg", ".txt", labelpath);
- find_replace(labelpath, ".png", ".txt", labelpath);
- find_replace(labelpath, ".bmp", ".txt", labelpath);
- find_replace(labelpath, ".JPG", ".txt", labelpath);
- find_replace(labelpath, ".JPEG", ".txt", labelpath);
- int num_labels = 0;
- box_label *truth = read_boxes(labelpath, &num_labels);
- //printf(" new path: %s \n", labelpath);
- for (j = 0; j < num_labels; ++j)
- {
- 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");
+ int i, j;
+ for (i = 0; i < number_of_images; ++i) {
+ char *path = paths[i];
+ char labelpath[4096];
+ replace_image_to_label(path, labelpath);
- 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);
+ 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("\n\nWrong 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);
+ }
+ 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");
- 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));
- }
+ 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;
+ 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);
+ enum {
+ KMEANS_RANDOM_CENTERS = 0,
+ KMEANS_USE_INITIAL_LABELS = 1,
+ KMEANS_PP_CENTERS = 2
+ };
- // sort anchors
- qsort(centers->data.fl, num_of_clusters, 2*sizeof(float), anchors_comparator);
+ 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);
- //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)
+ // sort anchors
+ qsort(centers->data.fl, num_of_clusters, 2*sizeof(float), anchors_comparator);
- //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%)
+ //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)
- //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 };
+ //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%)
- // 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]);
+ //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 };
- 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) {
- printf(" 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);
+ // 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];
- char buff[1024];
- FILE* fw = fopen("anchors.txt", "wb");
- 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);
+ //for (i = 0; i < number_of_boxes; ++i)
+ // printf("%2.2f,%2.2f, ", points->data.fl[i * 2], points->data.fl[i * 2 + 1]);
- 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);
- }
+ 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;
+ }
- 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();
- }
+ 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);
- free(rel_width_height_array);
- cvReleaseMat(&points);
- cvReleaseMat(¢ers);
- cvReleaseMat(&labels);
+ char buff[1024];
+ FILE* fw = fopen("anchors.txt", "wb");
+ 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);
+
+ 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");
+ 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)
+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); // set batch=1
@@ -1053,18 +1094,24 @@
load_weights(&net, weightfile);
}
//set_batch_network(&net, 1);
- fuse_conv_batchnorm(net);
+ 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
+ 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;
+ if(strlen(input) > 0)
+ if (input[strlen(input) - 1] == 0x0d) input[strlen(input) - 1] = 0;
} else {
printf("Enter Image Path: ");
fflush(stdout);
@@ -1072,10 +1119,10 @@
if(!input) return;
strtok(input, "\n");
}
- image im = load_image_color(input,0,0);
- int letterbox = 0;
- //image sized = resize_image(im, net.w, net.h);
- image sized = letterbox_image(im, net.w, net.h); letterbox = 1;
+ 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));
@@ -1085,66 +1132,97 @@
float *X = sized.data;
time= what_time_is_it_now();
network_predict(net, X);
- //network_predict_image(&net, im); letterbox = 1;
+ //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);
- free_detections(dets, nboxes);
+ // 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");
- }
+ 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);
#ifdef OPENCV
- if (!dont_show) {
- cvWaitKey(0);
- cvDestroyAllWindows();
- }
+ if (!dont_show) {
+ cvWaitKey(0);
+ cvDestroyAllWindows();
+ }
#endif
if (filename) break;
}
- // free memory
- free_ptrs(names, net.layers[net.n - 1].classes);
- free_list(options);
+ // 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);
+ 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);
+ free_network(net);
}
void run_detector(int argc, char **argv)
{
- int dont_show = find_arg(argc, argv, "-dont_show");
- 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);
+ int dont_show = find_arg(argc, argv, "-dont_show");
+ 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", .25); // 0.24
- float hier_thresh = find_float_arg(argc, argv, "-hier", .5);
+ 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 width = find_int_arg(argc, argv, "-width", -1);
- int height = find_int_arg(argc, argv, "-height", -1);
+ int num_of_clusters = find_int_arg(argc, argv, "-num_of_clusters", 5);
+ 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;
@@ -1177,26 +1255,29 @@
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;
+ 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);
+ 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], "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);
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;
+ 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);
+ 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]);
+ else printf(" There isn't such command: %s", argv[2]);
}
--
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