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

CVPR prep

Browse Source
This commit is contained in:
Joseph Redmon
2016-06-22 21:46:32 -07:00
parent e7072b8489
commit afb8b4f98b
98 changed files with 93 additions and 236 deletions
Download Patch File Download Diff File Expand all files Collapse all files

50
src/coco.c
View File

@ -6,11 +6,14 @@
#include "utils.h"
#include "parser.h"
#include "box.h"
#include "demo.h"
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#endif
void convert_detections(float *predictions, int classes, int num, int square, int side, int w, int h, float thresh, float **probs, box *boxes, int only_objectness);
char *coco_classes[] = {"person","bicycle","car","motorcycle","airplane","bus","train","truck","boat","traffic light","fire hydrant","stop sign","parking meter","bench","bird","cat","dog","horse","sheep","cow","elephant","bear","zebra","giraffe","backpack","umbrella","handbag","tie","suitcase","frisbee","skis","snowboard","sports ball","kite","baseball bat","baseball glove","skateboard","surfboard","tennis racket","bottle","wine glass","cup","fork","knife","spoon","bowl","banana","apple","sandwich","orange","broccoli","carrot","hot dog","pizza","donut","cake","chair","couch","potted plant","bed","dining table","toilet","tv","laptop","mouse","remote","keyboard","cell phone","microwave","oven","toaster","sink","refrigerator","book","clock","vase","scissors","teddy bear","hair drier","toothbrush"};
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};
@ -98,34 +101,6 @@ void train_coco(char *cfgfile, char *weightfile)
save_weights(net, buff);
}
void convert_coco_detections(float *predictions, int classes, int num, int square, int side, int w, int h, float thresh, float **probs, box *boxes, int only_objectness)
{
int i,j,n;
//int per_cell = 5*num+classes;
for (i = 0; i < side*side; ++i){
int row = i / side;
int col = i % side;
for(n = 0; n < num; ++n){
int index = i*num + n;
int p_index = side*side*classes + i*num + n;
float scale = predictions[p_index];
int box_index = side*side*(classes + num) + (i*num + n)*4;
boxes[index].x = (predictions[box_index + 0] + col) / side * w;
boxes[index].y = (predictions[box_index + 1] + row) / side * h;
boxes[index].w = pow(predictions[box_index + 2], (square?2:1)) * w;
boxes[index].h = pow(predictions[box_index + 3], (square?2:1)) * h;
for(j = 0; j < classes; ++j){
int class_index = i*classes;
float prob = scale*predictions[class_index+j];
probs[index][j] = (prob > thresh) ? prob : 0;
}
if(only_objectness){
probs[index][0] = scale;
}
}
}
}
void print_cocos(FILE *fp, int image_id, box *boxes, float **probs, int num_boxes, int classes, int w, int h)
{
int i, j;
@ -235,7 +210,7 @@ void validate_coco(char *cfgfile, char *weightfile)
float *predictions = network_predict(net, X);
int w = val[t].w;
int h = val[t].h;
convert_coco_detections(predictions, classes, l.n, square, side, w, h, thresh, probs, boxes, 0);
convert_detections(predictions, classes, l.n, square, side, w, h, thresh, probs, boxes, 0);
if (nms) do_nms_sort(boxes, probs, side*side*l.n, classes, iou_thresh);
print_cocos(fp, image_id, boxes, probs, side*side*l.n, classes, w, h);
free_image(val[t]);
@ -298,7 +273,7 @@ void validate_coco_recall(char *cfgfile, char *weightfile)
image sized = resize_image(orig, net.w, net.h);
char *id = basecfg(path);
float *predictions = network_predict(net, sized.data);
convert_coco_detections(predictions, classes, l.n, square, side, 1, 1, thresh, probs, boxes, 1);
convert_detections(predictions, classes, l.n, square, side, 1, 1, thresh, probs, boxes, 1);
if (nms) do_nms(boxes, probs, side*side*l.n, 1, nms_thresh);
char *labelpath = find_replace(path, "images", "labels");
@ -370,7 +345,7 @@ void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh)
time=clock();
float *predictions = network_predict(net, X);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
convert_coco_detections(predictions, l.classes, l.n, l.sqrt, l.side, 1, 1, thresh, probs, boxes, 0);
convert_detections(predictions, l.classes, l.n, l.sqrt, l.side, 1, 1, thresh, probs, boxes, 0);
if (nms) do_nms_sort(boxes, probs, l.side*l.side*l.n, l.classes, nms);
draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, coco_classes, coco_labels, 80);
show_image(im, "predictions");
@ -386,16 +361,6 @@ void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh)
}
}
void demo_coco(char *cfgfile, char *weightfile, float thresh, int cam_index, char *filename);
static void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, char* filename)
{
#if defined(OPENCV)
demo_coco(cfgfile, weightfile, thresh, cam_index, filename);
#else
fprintf(stderr, "Need to compile with OpenCV for demo.\n");
#endif
}
void run_coco(int argc, char **argv)
{
int i;
@ -406,7 +371,6 @@ void run_coco(int argc, char **argv)
}
float thresh = find_float_arg(argc, argv, "-thresh", .2);
int cam_index = find_int_arg(argc, argv, "-c", 0);
char *file = find_char_arg(argc, argv, "-file", 0);
if(argc < 4){
fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
@ -420,5 +384,5 @@ void run_coco(int argc, char **argv)
else if(0==strcmp(argv[2], "train")) train_coco(cfg, weights);
else if(0==strcmp(argv[2], "valid")) validate_coco(cfg, weights);
else if(0==strcmp(argv[2], "recall")) validate_coco_recall(cfg, weights);
else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, cam_index, file);
else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, cam_index, filename, coco_classes, coco_labels, 80);
}