mirror/darknet
1
0
Fork 0
mirror of https://github.com/pjreddie/darknet.git synced 2023-08-10 21:13:14 +03:00
Code Issues Projects Releases Wiki Activity

Show IoU, save anchors to file.Show anchors in the window if used -show

Browse Source
This commit is contained in:
AlexeyAB
2018-03-04 17:59:23 +03:00
parent f39160f6e8
commit 74f98da211
2 changed files with 98 additions and 11 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
build/darknet/x64/calc_anchors.cmd
View File

@ -4,5 +4,9 @@ rem # How to calculate Yolo v2 anchors using K-means++
darknet.exe detector calc_anchors data/voc.data -num_of_clusters 5 -final_width 13 -final_heigh 13
rem darknet.exe detector calc_anchors data/voc.data -num_of_clusters 5 -final_width 13 -final_heigh 13 -show
pause

105
src/detector.c
View File

@ -807,7 +807,7 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float
}
#ifdef OPENCV
void calc_anchors(char *datacfg, int num_of_clusters, int final_width, int final_height)
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);
@ -846,7 +846,6 @@ void calc_anchors(char *datacfg, int num_of_clusters, int final_width, int final
}
printf("\n all loaded. \n");
//int number_of_boxes = 10;
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);
@ -859,7 +858,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int final_width, int final
}
const int attemps = 1000;
const int attemps = 10;
double compactness;
enum {
@ -871,19 +870,102 @@ void calc_anchors(char *datacfg, int num_of_clusters, int final_width, int final
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, 1000, 0), attemps,
0, KMEANS_RANDOM_CENTERS,
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)
printf("\n");
printf("anchors = ");
for (i = 0; i < num_of_clusters; ++i) {
printf("%2.2f,%2.2f, ", centers->data.fl[i * 2], centers->data.fl[i * 2 + 1]);
}
//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 = 1000000;
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(&centers);
@ -961,6 +1043,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam
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);
@ -1010,7 +1093,7 @@ void run_detector(int argc, char **argv)
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);
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);