From d28f7e6681ffe02a151b9dc89098d7fcef50b214 Mon Sep 17 00:00:00 2001
From: Alexey <AlexeyAB@users.noreply.github.com>
Date: Wed, 28 Mar 2018 20:51:14 +0000
Subject: [PATCH] Update Readme.md
---
src/detector.c | 643 +++++++++++++++++++++++++++++++++++++++++++++++++++++++---
1 files changed, 610 insertions(+), 33 deletions(-)
diff --git a/src/detector.c b/src/detector.c
index 367b3a3..77175b4 100644
--- a/src/detector.c
+++ b/src/detector.c
@@ -10,19 +10,28 @@
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#include "opencv2/core/core_c.h"
+//#include "opencv2/core/core.hpp"
#include "opencv2/core/version.hpp"
+#include "opencv2/imgproc/imgproc_c.h"
+
#ifndef CV_VERSION_EPOCH
#include "opencv2/videoio/videoio_c.h"
-#pragma comment(lib, "opencv_world320.lib")
+#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
-#pragma comment(lib, "opencv_core2413.lib")
-#pragma comment(lib, "opencv_imgproc2413.lib")
-#pragma comment(lib, "opencv_highgui2413.lib")
+#define OPENCV_VERSION CVAUX_STR(CV_VERSION_EPOCH)""CVAUX_STR(CV_VERSION_MAJOR)""CVAUX_STR(CV_VERSION_MINOR)
+#pragma comment(lib, "opencv_core" OPENCV_VERSION ".lib")
+#pragma comment(lib, "opencv_imgproc" OPENCV_VERSION ".lib")
+#pragma comment(lib, "opencv_highgui" OPENCV_VERSION ".lib")
#endif
-#endif
+
+IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size);
+void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches);
+#endif // OPENCV
+
static int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90};
-void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear)
+void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show)
{
list *options = read_data_cfg(datacfg);
char *train_images = option_find_str(options, "train", "data/train.list");
@@ -65,6 +74,11 @@
//int N = plist->size;
char **paths = (char **)list_to_array(plist);
+ int init_w = net.w;
+ int init_h = net.h;
+ int iter_save;
+ iter_save = get_current_batch(net);
+
load_args args = {0};
args.w = net.w;
args.h = net.h;
@@ -74,15 +88,25 @@
args.classes = classes;
args.jitter = jitter;
args.num_boxes = l.max_boxes;
+ args.small_object = l.small_object;
args.d = &buffer;
args.type = DETECTION_DATA;
- args.threads = 8;
+ args.threads = 64; // 8
args.angle = net.angle;
args.exposure = net.exposure;
args.saturation = net.saturation;
args.hue = net.hue;
+#ifdef OPENCV
+ 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);
clock_t time;
int count = 0;
@@ -90,8 +114,8 @@
while(get_current_batch(net) < net.max_batches){
if(l.random && count++%10 == 0){
printf("Resizing\n");
- int dim = (rand() % 10 + 10) * 32;
- if (get_current_batch(net)+100 > net.max_batches) dim = 544;
+ int dim = (rand() % 12 + (init_w/32 - 5)) * 32; // +-160
+ //if (get_current_batch(net)+100 > net.max_batches) dim = 544;
//int dim = (rand() % 4 + 16) * 32;
printf("%d\n", dim);
args.w = dim;
@@ -147,7 +171,16 @@
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);
- if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) {
+
+#ifdef OPENCV
+ if(!dont_show)
+ draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches);
+#endif // OPENCV
+
+ //if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) {
+ //if (i % 100 == 0) {
+ if(i >= (iter_save + 100)) {
+ iter_save = i;
#ifdef GPU
if (ngpus != 1) sync_nets(nets, ngpus, 0);
#endif
@@ -163,6 +196,9 @@
char buff[256];
sprintf(buff, "%s/%s_final.weights", backup_directory, base);
save_weights(net, buff);
+
+ //cvReleaseImage(&img);
+ //cvDestroyAllWindows();
}
@@ -234,8 +270,8 @@
if (ymax > h) ymax = h;
for(j = 0; j < classes; ++j){
- int class = j;
- if (probs[i][class]) fprintf(fp, "%d %d %f %f %f %f %f\n", id, j+1, probs[i][class],
+ 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);
}
}
@@ -253,7 +289,7 @@
int *map = 0;
if (mapf) map = read_map(mapf);
- network net = parse_network_cfg(cfgfile);
+ network net = parse_network_cfg_custom(cfgfile, 1);
if(weightfile){
load_weights(&net, weightfile);
}
@@ -304,6 +340,8 @@
float thresh = .005;
float nms = .45;
+ int detection_count = 0;
+
int nthreads = 4;
image *val = calloc(nthreads, sizeof(image));
image *val_resized = calloc(nthreads, sizeof(image));
@@ -345,6 +383,15 @@
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;
+ }
+ }
+
if (coco){
print_cocos(fp, path, boxes, probs, l.w*l.h*l.n, classes, w, h);
} else if (imagenet){
@@ -365,12 +412,13 @@
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));
}
void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile)
{
- network net = parse_network_cfg(cfgfile);
+ network net = parse_network_cfg_custom(cfgfile, 1);
if(weightfile){
load_weights(&net, weightfile);
}
@@ -394,10 +442,12 @@
int m = plist->size;
int i=0;
- float thresh = .2;// .001;
+ float thresh = .001;// .001; // .2;
float iou_thresh = .5;
float nms = .4;
+ int detection_count = 0, truth_count = 0;
+
int total = 0;
int correct = 0;
int proposals = 0;
@@ -421,6 +471,7 @@
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;
@@ -447,16 +498,516 @@
free_image(orig);
free_image(sized);
}
+ printf("\n truth_count = %d \n", truth_count);
}
-void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh)
+typedef struct {
+ box b;
+ float p;
+ int class_id;
+ int image_index;
+ int truth_flag;
+ int unique_truth_index;
+} box_prob;
+
+int detections_comparator(const void *pa, const void *pb)
+{
+ box_prob a = *(box_prob *)pa;
+ box_prob b = *(box_prob *)pb;
+ float diff = a.p - b.p;
+ if (diff < 0) return 1;
+ else if (diff > 0) return -1;
+ return 0;
+}
+
+void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou)
+{
+ int j;
+ list *options = read_data_cfg(datacfg);
+ char *valid_images = option_find_str(options, "valid", "data/train.txt");
+ char *difficult_valid_images = option_find_str(options, "difficult", NULL);
+ char *name_list = option_find_str(options, "names", "data/names.list");
+ char **names = get_labels(name_list);
+ char *mapf = option_find_str(options, "map", 0);
+ int *map = 0;
+ if (mapf) map = read_map(mapf);
+
+ 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));
+
+ 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;
+
+ 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;
+
+ 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;
+
+ //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);
+ 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);
+
+ 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);
+ 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);
+ }
+
+ for (i = 0; i < (l.w*l.h*l.n); ++i) {
+
+ int class_id;
+ for (class_id = 0; class_id < classes; ++class_id) {
+ float prob = probs[i][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].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(boxes[i], t), prob, class_id, truth[j].id);
+ float current_iou = box_iou(boxes[i], 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(boxes[i], 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) {
+ if (truth_index > -1) {
+ avg_iou += max_iou;
+ ++tp_for_thresh;
+ }
+ else
+ fp_for_thresh++;
+ }
+ }
+ }
+ }
+
+ unique_truth_count += num_labels;
+
+ free(id);
+ free_image(val[t]);
+ free_image(val_resized[t]);
+ }
+ }
+
+ avg_iou = avg_iou / (tp_for_thresh + fp_for_thresh);
+
+
+ // SORT(detections)
+ qsort(detections, detections_count, sizeof(box_prob), detections_comparator);
+
+ typedef struct {
+ double precision;
+ double recall;
+ int tp, fp, fn;
+ } pr_t;
+
+ // for PR-curve
+ pr_t **pr = calloc(classes, sizeof(pr_t*));
+ for (i = 0; i < classes; ++i) {
+ pr[i] = calloc(detections_count, sizeof(pr_t));
+ }
+ printf("detections_count = %d, unique_truth_count = %d \n", detections_count, unique_truth_count);
+
+
+ int *truth_flags = calloc(unique_truth_count, sizeof(int));
+
+ int rank;
+ for (rank = 0; rank < detections_count; ++rank) {
+ if(rank % 100 == 0)
+ printf(" rank = %d of ranks = %d \r", rank, detections_count);
+
+ if (rank > 0) {
+ int class_id;
+ for (class_id = 0; class_id < classes; ++class_id) {
+ pr[class_id][rank].tp = pr[class_id][rank - 1].tp;
+ pr[class_id][rank].fp = pr[class_id][rank - 1].fp;
+ }
+ }
+
+ box_prob d = detections[rank];
+ // if (detected && isn't detected before)
+ if (d.truth_flag == 1) {
+ if (truth_flags[d.unique_truth_index] == 0)
+ {
+ truth_flags[d.unique_truth_index] = 1;
+ pr[d.class_id][rank].tp++; // true-positive
+ }
+ }
+ else {
+ pr[d.class_id][rank].fp++; // false-positive
+ }
+
+ for (i = 0; i < classes; ++i)
+ {
+ const int tp = pr[i][rank].tp;
+ const int fp = pr[i][rank].fp;
+ const int fn = truth_classes_count[i] - tp; // false-negative = objects - true-positive
+ pr[i][rank].fn = fn;
+
+ if ((tp + fp) > 0) pr[i][rank].precision = (double)tp / (double)(tp + fp);
+ else pr[i][rank].precision = 0;
+
+ if ((tp + fn) > 0) pr[i][rank].recall = (double)tp / (double)(tp + fn);
+ else pr[i][rank].recall = 0;
+ }
+ }
+
+ free(truth_flags);
+
+
+ double mean_average_precision = 0;
+
+ for (i = 0; i < classes; ++i) {
+ double avg_precision = 0;
+ int point;
+ for (point = 0; point < 11; ++point) {
+ double cur_recall = point * 0.1;
+ double cur_precision = 0;
+ for (rank = 0; rank < detections_count; ++rank)
+ {
+ if (pr[i][rank].recall >= cur_recall) { // > or >=
+ if (pr[i][rank].precision > cur_precision) {
+ cur_precision = pr[i][rank].precision;
+ }
+ }
+ }
+ //printf("class_id = %d, point = %d, cur_recall = %.4f, cur_precision = %.4f \n", i, point, cur_recall, cur_precision);
+
+ avg_precision += cur_precision;
+ }
+ avg_precision = avg_precision / 11;
+ printf("class_id = %d, name = %s, \t ap = %2.2f %% \n", i, names[i], avg_precision*100);
+ mean_average_precision += avg_precision;
+ }
+
+ const float cur_precision = (float)tp_for_thresh / ((float)tp_for_thresh + (float)fp_for_thresh);
+ const float cur_recall = (float)tp_for_thresh / ((float)tp_for_thresh + (float)(unique_truth_count - tp_for_thresh));
+ const float f1_score = 2.F * cur_precision * cur_recall / (cur_precision + cur_recall);
+ printf(" for thresh = %1.2f, precision = %1.2f, recall = %1.2f, F1-score = %1.2f \n",
+ thresh_calc_avg_iou, cur_precision, cur_recall, f1_score);
+
+ printf(" for thresh = %0.2f, TP = %d, FP = %d, FN = %d, average IoU = %2.2f %% \n",
+ thresh_calc_avg_iou, tp_for_thresh, fp_for_thresh, unique_truth_count - tp_for_thresh, avg_iou * 100);
+
+ mean_average_precision = mean_average_precision / classes;
+ printf("\n mean average precision (mAP) = %f, or %2.2f %% \n", mean_average_precision, mean_average_precision*100);
+
+
+ for (i = 0; i < classes; ++i) {
+ free(pr[i]);
+ }
+ free(pr);
+ free(detections);
+ free(truth_classes_count);
+
+ fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
+}
+
+#ifdef OPENCV
+void calc_anchors(char *datacfg, int num_of_clusters, int final_width, int final_height, int show)
+{
+ printf("\n num_of_clusters = %d, final_width = %d, final_height = %d \n", num_of_clusters, final_width, final_height);
+
+ //float pointsdata[] = { 1,1, 2,2, 6,6, 5,5, 10,10 };
+ float *rel_width_height_array = calloc(1000, sizeof(float));
+
+ list *options = read_data_cfg(datacfg);
+ char *train_images = option_find_str(options, "train", "data/train.list");
+ list *plist = get_paths(train_images);
+ int number_of_images = plist->size;
+ char **paths = (char **)list_to_array(plist);
+
+ int number_of_boxes = 0;
+ printf(" read labels from %d images \n", number_of_images);
+
+ int i, j;
+ for (i = 0; i < number_of_images; ++i) {
+ char *path = paths[i];
+ char labelpath[4096];
+ 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);
+ //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 * final_width;
+ rel_width_height_array[number_of_boxes * 2 - 1] = truth[j].h * final_height;
+ printf("\r loaded \t image: %d \t box: %d", i+1, number_of_boxes);
+ }
+ }
+ printf("\n all loaded. \n");
+
+ CvMat* points = cvCreateMat(number_of_boxes, 2, CV_32FC1);
+ CvMat* centers = cvCreateMat(num_of_clusters, 2, CV_32FC1);
+ CvMat* labels = cvCreateMat(number_of_boxes, 1, CV_32SC1);
+
+ for (i = 0; i < number_of_boxes; ++i) {
+ points->data.fl[i * 2] = rel_width_height_array[i * 2];
+ points->data.fl[i * 2 + 1] = rel_width_height_array[i * 2 + 1];
+ //cvSet1D(points, i * 2, cvScalar(rel_width_height_array[i * 2], 0, 0, 0));
+ //cvSet1D(points, i * 2 + 1, cvScalar(rel_width_height_array[i * 2 + 1], 0, 0, 0));
+ }
+
+
+ const int attemps = 10;
+ double compactness;
+
+ enum {
+ KMEANS_RANDOM_CENTERS = 0,
+ KMEANS_USE_INITIAL_LABELS = 1,
+ KMEANS_PP_CENTERS = 2
+ };
+
+ printf("\n calculating k-means++ ...");
+ // Should be used: distance(box, centroid) = 1 - IoU(box, centroid)
+ cvKMeans2(points, num_of_clusters, labels,
+ cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 10000, 0), attemps,
+ 0, KMEANS_PP_CENTERS,
+ centers, &compactness);
+
+ //orig 2.0 anchors = 1.08,1.19, 3.42,4.41, 6.63,11.38, 9.42,5.11, 16.62,10.52
+ //float orig_anch[] = { 1.08,1.19, 3.42,4.41, 6.63,11.38, 9.42,5.11, 16.62,10.52 };
+ // worse than ours (even for 19x19 final size - for input size 608x608)
+
+ //orig anchors = 1.3221,1.73145, 3.19275,4.00944, 5.05587,8.09892, 9.47112,4.84053, 11.2364,10.0071
+ //float orig_anch[] = { 1.3221,1.73145, 3.19275,4.00944, 5.05587,8.09892, 9.47112,4.84053, 11.2364,10.0071 };
+ // orig (IoU=59.90%) better than ours (59.75%)
+
+ //gen_anchors.py = 1.19, 1.99, 2.79, 4.60, 4.53, 8.92, 8.06, 5.29, 10.32, 10.66
+ //float orig_anch[] = { 1.19, 1.99, 2.79, 4.60, 4.53, 8.92, 8.06, 5.29, 10.32, 10.66 };
+
+ // ours: anchors = 9.3813,6.0095, 3.3999,5.3505, 10.9476,11.1992, 5.0161,9.8314, 1.5003,2.1595
+ //float orig_anch[] = { 9.3813,6.0095, 3.3999,5.3505, 10.9476,11.1992, 5.0161,9.8314, 1.5003,2.1595 };
+ //for (i = 0; i < num_of_clusters * 2; ++i) centers->data.fl[i] = orig_anch[i];
+
+ //for (i = 0; i < number_of_boxes; ++i)
+ // printf("%2.2f,%2.2f, ", points->data.fl[i * 2], points->data.fl[i * 2 + 1]);
+
+ 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);
+
+ 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("\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 / final_width;
+ pt2.y = centers->data.fl[j * 2 + 1] * img_size / final_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;
+ 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 final_width, int final_height, int show) {
+ printf(" k-means++ can't be used without OpenCV, because there is used cvKMeans2 implementation \n");
+}
+#endif // OPENCV
+
+void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh, int dont_show)
{
list *options = read_data_cfg(datacfg);
char *name_list = option_find_str(options, "names", "data/names.list");
char **names = get_labels(name_list);
image **alphabet = load_alphabet();
- network net = parse_network_cfg(cfgfile);
+ network net = parse_network_cfg_custom(cfgfile, 1);
if(weightfile){
load_weights(&net, weightfile);
}
@@ -466,10 +1017,11 @@
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;
} else {
printf("Enter Image Path: ");
fflush(stdout);
@@ -478,30 +1030,41 @@
strtok(input, "\n");
}
image im = load_image_color(input,0,0);
+ int letter = 0;
image sized = resize_image(im, net.w, net.h);
+ //image sized = letterbox_image(im, net.w, net.h); letter = 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();
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);
+ //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);
+ int nboxes = 0;
+ detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letter);
+ if (nms) do_nms_sort_v3(dets, nboxes, l.classes, nms);
+ draw_detections_v3(im, dets, nboxes, thresh, names, alphabet, l.classes);
+ free_detections(dets, nboxes);
save_image(im, "predictions");
- show_image(im, "predictions");
+ if (!dont_show) {
+ show_image(im, "predictions");
+ }
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
- cvWaitKey(0);
- cvDestroyAllWindows();
+ if (!dont_show) {
+ cvWaitKey(0);
+ cvDestroyAllWindows();
+ }
#endif
if (filename) break;
}
@@ -509,11 +1072,18 @@
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 *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);
if(argc < 4){
fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
return;
@@ -546,16 +1116,23 @@
char *datacfg = argv[3];
char *cfg = argv[4];
char *weights = (argc > 5) ? argv[5] : 0;
+ if(weights)
+ 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);
- else if(0==strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear);
+ if(0==strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show);
+ 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], "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], "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);
- demo(cfg, weights, thresh, cam_index, filename, names, classes, frame_skip, prefix, out_filename);
+ if(filename)
+ 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);
}
}
--
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