From 351996d9f0390ef44412fa678bc7a073a94e23e5 Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Thu, 16 Mar 2017 18:49:36 +0000
Subject: [PATCH] Fixed memory leak in DLL, added load_image() & free_image(), added read_names_from_file()
---
src/detector.c | 402 ++++++++++++++++++++++++++++++++++++++-------------------
1 files changed, 267 insertions(+), 135 deletions(-)
diff --git a/src/detector.c b/src/detector.c
index 9498750..7535df8 100644
--- a/src/detector.c
+++ b/src/detector.c
@@ -1,32 +1,49 @@
#include "network.h"
-#include "detection_layer.h"
+#include "region_layer.h"
#include "cost_layer.h"
#include "utils.h"
#include "parser.h"
#include "box.h"
+#include "demo.h"
+#include "option_list.h"
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#endif
+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};
-static char *voc_names[] = {"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"};
-static image voc_labels[20];
-
-void train_detector(char *cfgfile, char *weightfile)
+void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear)
{
- char *train_images = "/data/voc/train.txt";
- char *backup_directory = "/home/pjreddie/backup/";
+ list *options = read_data_cfg(datacfg);
+ char *train_images = option_find_str(options, "train", "data/train.list");
+ char *backup_directory = option_find_str(options, "backup", "/backup/");
+
srand(time(0));
char *base = basecfg(cfgfile);
printf("%s\n", base);
float avg_loss = -1;
- network net = parse_network_cfg(cfgfile);
- if(weightfile){
- load_weights(&net, weightfile);
+ network *nets = calloc(ngpus, sizeof(network));
+
+ srand(time(0));
+ int seed = rand();
+ int i;
+ for(i = 0; i < ngpus; ++i){
+ srand(seed);
+#ifdef GPU
+ cuda_set_device(gpus[i]);
+#endif
+ nets[i] = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&nets[i], weightfile);
+ }
+ if(clear) *nets[i].seen = 0;
+ nets[i].learning_rate *= ngpus;
}
+ srand(time(0));
+ network net = nets[0];
+
+ int imgs = net.batch * net.subdivisions * ngpus;
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
- int imgs = net.batch*net.subdivisions;
- int i = *net.seen/imgs;
data train, buffer;
layer l = net.layers[net.n - 1];
@@ -49,93 +66,124 @@
args.num_boxes = l.max_boxes;
args.d = &buffer;
args.type = DETECTION_DATA;
+ args.threads = 8;
args.angle = net.angle;
args.exposure = net.exposure;
args.saturation = net.saturation;
args.hue = net.hue;
- pthread_t load_thread = load_data_in_thread(args);
+ pthread_t load_thread = load_data(args);
clock_t time;
+ int count = 0;
//while(i*imgs < N*120){
while(get_current_batch(net) < net.max_batches){
- i += 1;
+ 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() % 4 + 16) * 32;
+ printf("%d\n", dim);
+ args.w = dim;
+ args.h = dim;
+
+ pthread_join(load_thread, 0);
+ train = buffer;
+ free_data(train);
+ load_thread = load_data(args);
+
+ for(i = 0; i < ngpus; ++i){
+ resize_network(nets + i, dim, dim);
+ }
+ net = nets[0];
+ }
time=clock();
pthread_join(load_thread, 0);
train = buffer;
- load_thread = load_data_in_thread(args);
+ load_thread = load_data(args);
-/*
- int k;
- for(k = 0; k < l.max_boxes; ++k){
- box b = float_to_box(train.y.vals[10] + 1 + k*5);
- if(!b.x) break;
- printf("loaded: %f %f %f %f\n", b.x, b.y, b.w, b.h);
- }
- image im = float_to_image(448, 448, 3, train.X.vals[10]);
- int k;
- for(k = 0; k < l.max_boxes; ++k){
- box b = float_to_box(train.y.vals[10] + 1 + k*5);
- printf("%d %d %d %d\n", truth.x, truth.y, truth.w, truth.h);
- draw_bbox(im, b, 8, 1,0,0);
- }
- save_image(im, "truth11");
-*/
+ /*
+ int k;
+ for(k = 0; k < l.max_boxes; ++k){
+ box b = float_to_box(train.y.vals[10] + 1 + k*5);
+ if(!b.x) break;
+ printf("loaded: %f %f %f %f\n", b.x, b.y, b.w, b.h);
+ }
+ image im = float_to_image(448, 448, 3, train.X.vals[10]);
+ int k;
+ for(k = 0; k < l.max_boxes; ++k){
+ box b = float_to_box(train.y.vals[10] + 1 + k*5);
+ printf("%d %d %d %d\n", truth.x, truth.y, truth.w, truth.h);
+ draw_bbox(im, b, 8, 1,0,0);
+ }
+ save_image(im, "truth11");
+ */
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
- float loss = train_network(net, train);
+ float loss = 0;
+#ifdef GPU
+ if(ngpus == 1){
+ loss = train_network(net, train);
+ } else {
+ loss = train_networks(nets, ngpus, train, 4);
+ }
+#else
+ loss = train_network(net, train);
+#endif
if (avg_loss < 0) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
- printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs);
+ 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 GPU
+ 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);
}
free_data(train);
}
+#ifdef GPU
+ if(ngpus != 1) sync_nets(nets, ngpus, 0);
+#endif
char buff[256];
sprintf(buff, "%s/%s_final.weights", backup_directory, base);
save_weights(net, buff);
}
-static 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)
+
+static int get_coco_image_id(char *filename)
{
- 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 = index * (classes + 5) + 4;
- float scale = predictions[p_index];
- int box_index = index * (classes + 5);
- boxes[index].x = (predictions[box_index + 0] + col + .5) / side * w;
- boxes[index].y = (predictions[box_index + 1] + row + .5) / side * h;
- if(0){
- boxes[index].x = (logistic_activate(predictions[box_index + 0]) + col) / side * w;
- boxes[index].y = (logistic_activate(predictions[box_index + 1]) + row) / side * h;
- }
- boxes[index].w = pow(logistic_activate(predictions[box_index + 2]), (square?2:1)) * w;
- boxes[index].h = pow(logistic_activate(predictions[box_index + 3]), (square?2:1)) * h;
- if(1){
- boxes[index].x = ((col + .5)/side + predictions[box_index + 0] * .5) * w;
- boxes[index].y = ((row + .5)/side + predictions[box_index + 1] * .5) * h;
- boxes[index].w = (exp(predictions[box_index + 2]) * .5) * w;
- boxes[index].h = (exp(predictions[box_index + 3]) * .5) * h;
- }
- for(j = 0; j < classes; ++j){
- int class_index = index * (classes + 5) + 5;
- float prob = scale*predictions[class_index+j];
- probs[index][j] = (prob > thresh) ? prob : 0;
- }
- if(only_objectness){
- probs[index][0] = scale;
- }
+ char *p = strrchr(filename, '_');
+ return atoi(p+1);
+}
+
+static void print_cocos(FILE *fp, char *image_path, box *boxes, float **probs, int num_boxes, int classes, int w, int h)
+{
+ int i, j;
+ int image_id = get_coco_image_id(image_path);
+ for(i = 0; i < num_boxes; ++i){
+ float xmin = boxes[i].x - boxes[i].w/2.;
+ float xmax = boxes[i].x + boxes[i].w/2.;
+ float ymin = boxes[i].y - boxes[i].h/2.;
+ float ymax = boxes[i].y + boxes[i].h/2.;
+
+ 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;
+
+ for(j = 0; j < classes; ++j){
+ if (probs[i][j]) fprintf(fp, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f},\n", image_id, coco_ids[j], bx, by, bw, bh, probs[i][j]);
}
}
}
@@ -161,8 +209,40 @@
}
}
-void validate_detector(char *cfgfile, char *weightfile)
+void print_imagenet_detections(FILE *fp, int id, box *boxes, float **probs, int total, int classes, int w, int h)
{
+ int i, j;
+ for(i = 0; i < total; ++i){
+ float xmin = boxes[i].x - boxes[i].w/2.;
+ float xmax = boxes[i].x + boxes[i].w/2.;
+ float ymin = boxes[i].y - boxes[i].h/2.;
+ float ymax = boxes[i].y + boxes[i].h/2.;
+
+ 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 (probs[i][class]) fprintf(fp, "%d %d %f %f %f %f %f\n", id, j+1, probs[i][class],
+ xmin, ymin, xmax, ymax);
+ }
+ }
+}
+
+void validate_detector(char *datacfg, char *cfgfile, char *weightfile)
+{
+ 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(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
@@ -171,35 +251,50 @@
fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
srand(time(0));
- char *base = "results/comp4_det_test_";
- //list *plist = get_paths("data/voc.2007.test");
- list *plist = get_paths("/home/pjreddie/data/voc/2007_test.txt");
- //list *plist = get_paths("data/voc.2012.test");
+ char *base = "comp4_det_test_";
+ list *plist = get_paths(valid_images);
char **paths = (char **)list_to_array(plist);
layer l = net.layers[net.n-1];
int classes = l.classes;
- int side = l.w;
- int j;
- FILE **fps = calloc(classes, sizeof(FILE *));
- for(j = 0; j < classes; ++j){
- char buff[1024];
- snprintf(buff, 1024, "%s%s.txt", base, voc_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")){
+ snprintf(buff, 1024, "%s/coco_results.json", prefix);
+ fp = fopen(buff, "w");
+ fprintf(fp, "[\n");
+ coco = 1;
+ } else if(0==strcmp(type, "imagenet")){
+ snprintf(buff, 1024, "%s/imagenet-detection.txt", prefix);
+ fp = fopen(buff, "w");
+ imagenet = 1;
+ classes = 200;
+ } else {
+ fps = calloc(classes, sizeof(FILE *));
+ for(j = 0; j < classes; ++j){
+ snprintf(buff, 1024, "%s/%s%s.txt", prefix, base, names[j]);
+ fps[j] = fopen(buff, "w");
+ }
}
- box *boxes = calloc(side*side*l.n, sizeof(box));
- float **probs = calloc(side*side*l.n, sizeof(float *));
- for(j = 0; j < side*side*l.n; ++j) probs[j] = calloc(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(classes, sizeof(float *));
int m = plist->size;
int i=0;
int t;
- float thresh = .001;
- float nms = .5;
+ float thresh = .005;
+ float nms = .45;
- int nthreads = 2;
+ int nthreads = 4;
image *val = calloc(nthreads, sizeof(image));
image *val_resized = calloc(nthreads, sizeof(image));
image *buf = calloc(nthreads, sizeof(image));
@@ -235,19 +330,30 @@
char *path = paths[i+t-nthreads];
char *id = basecfg(path);
float *X = val_resized[t].data;
- float *predictions = network_predict(net, X);
+ network_predict(net, X);
int w = val[t].w;
int h = val[t].h;
- convert_detections(predictions, classes, l.n, 0, side, w, h, thresh, probs, boxes, 0);
- if (nms) do_nms_sort(boxes, probs, side*side*l.n, classes, nms);
- print_detector_detections(fps, id, boxes, probs, side*side*l.n, classes, w, 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);
+ if (coco){
+ print_cocos(fp, path, boxes, probs, l.w*l.h*l.n, classes, w, h);
+ } else if (imagenet){
+ print_imagenet_detections(fp, i+t-nthreads+1, boxes, probs, l.w*l.h*l.n, classes, w, h);
+ } else {
+ print_detector_detections(fps, id, boxes, probs, l.w*l.h*l.n, classes, w, h);
+ }
free(id);
free_image(val[t]);
free_image(val_resized[t]);
}
}
for(j = 0; j < classes; ++j){
- fclose(fps[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));
}
@@ -262,25 +368,16 @@
fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
srand(time(0));
- char *base = "results/comp4_det_test_";
list *plist = get_paths("data/voc.2007.test");
char **paths = (char **)list_to_array(plist);
layer l = net.layers[net.n-1];
int classes = l.classes;
- int square = l.sqrt;
- int side = l.side;
int j, k;
- FILE **fps = calloc(classes, sizeof(FILE *));
- for(j = 0; j < classes; ++j){
- char buff[1024];
- snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]);
- fps[j] = fopen(buff, "w");
- }
- box *boxes = calloc(side*side*l.n, sizeof(box));
- float **probs = calloc(side*side*l.n, sizeof(float *));
- for(j = 0; j < side*side*l.n; ++j) probs[j] = calloc(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(classes, sizeof(float *));
int m = plist->size;
int i=0;
@@ -299,18 +396,19 @@
image orig = load_image_color(path, 0, 0);
image sized = resize_image(orig, net.w, net.h);
char *id = basecfg(path);
- float *predictions = network_predict(net, sized.data);
- convert_detections(predictions, classes, l.n, square, l.w, 1, 1, thresh, probs, boxes, 1);
- if (nms) do_nms(boxes, probs, side*side*l.n, 1, nms);
+ network_predict(net, sized.data);
+ get_region_boxes(l, 1, 1, thresh, probs, boxes, 1, 0);
+ if (nms) do_nms(boxes, probs, l.w*l.h*l.n, 1, nms);
- char *labelpath = find_replace(path, "images", "labels");
- labelpath = find_replace(labelpath, "JPEGImages", "labels");
- labelpath = find_replace(labelpath, ".jpg", ".txt");
- labelpath = find_replace(labelpath, ".JPEG", ".txt");
+ 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);
int num_labels = 0;
box_label *truth = read_boxes(labelpath, &num_labels);
- for(k = 0; k < side*side*l.n; ++k){
+ for(k = 0; k < l.w*l.h*l.n; ++k){
if(probs[k][0] > thresh){
++proposals;
}
@@ -319,7 +417,7 @@
++total;
box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h};
float best_iou = 0;
- for(k = 0; k < side*side*l.n; ++k){
+ for(k = 0; k < l.w*l.h*l.n; ++k){
float iou = box_iou(boxes[k], t);
if(probs[k][0] > thresh && iou > best_iou){
best_iou = iou;
@@ -338,15 +436,17 @@
}
}
-void test_detector(char *cfgfile, char *weightfile, char *filename, float thresh)
+void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh)
{
+ 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);
if(weightfile){
load_weights(&net, weightfile);
}
- detection_layer l = net.layers[net.n-1];
- l.side = l.w;
set_batch_network(&net, 1);
srand(2222222);
clock_t time;
@@ -354,9 +454,6 @@
char *input = buff;
int j;
float nms=.4;
- box *boxes = calloc(l.side*l.side*l.n, sizeof(box));
- float **probs = calloc(l.side*l.side*l.n, sizeof(float *));
- for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float *));
while(1){
if(filename){
strncpy(input, filename, 256);
@@ -369,19 +466,26 @@
}
image im = load_image_color(input,0,0);
image sized = resize_image(im, net.w, net.h);
+ 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 *));
+
float *X = sized.data;
time=clock();
- float *predictions = network_predict(net, X);
+ network_predict(net, X);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
- convert_detections(predictions, l.classes, l.n, 0, l.w, 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, voc_names, voc_labels, 20);
- draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, voc_labels, 20);
+ 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);
save_image(im, "predictions");
show_image(im, "predictions");
free_image(im);
free_image(sized);
+ free(boxes);
+ free_ptrs((void **)probs, l.w*l.h*l.n);
#ifdef OPENCV
cvWaitKey(0);
cvDestroyAllWindows();
@@ -392,24 +496,52 @@
void run_detector(int argc, char **argv)
{
- int i;
- for(i = 0; i < 20; ++i){
- char buff[256];
- sprintf(buff, "data/labels/%s.png", voc_names[i]);
- voc_labels[i] = load_image_color(buff, 0, 0);
- }
-
- float thresh = find_float_arg(argc, argv, "-thresh", .2);
+ char *prefix = find_char_arg(argc, argv, "-prefix", 0);
+ float thresh = find_float_arg(argc, argv, "-thresh", .24);
+ int cam_index = find_int_arg(argc, argv, "-c", 0);
+ int frame_skip = find_int_arg(argc, argv, "-s", 0);
if(argc < 4){
fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
return;
}
+ char *gpu_list = find_char_arg(argc, argv, "-gpus", 0);
+ int *gpus = 0;
+ int gpu = 0;
+ int ngpus = 0;
+ if(gpu_list){
+ printf("%s\n", gpu_list);
+ int len = strlen(gpu_list);
+ ngpus = 1;
+ int i;
+ for(i = 0; i < len; ++i){
+ if (gpu_list[i] == ',') ++ngpus;
+ }
+ gpus = calloc(ngpus, sizeof(int));
+ for(i = 0; i < ngpus; ++i){
+ gpus[i] = atoi(gpu_list);
+ gpu_list = strchr(gpu_list, ',')+1;
+ }
+ } else {
+ gpu = gpu_index;
+ gpus = &gpu;
+ ngpus = 1;
+ }
- char *cfg = argv[3];
- char *weights = (argc > 4) ? argv[4] : 0;
- char *filename = (argc > 5) ? argv[5]: 0;
- if(0==strcmp(argv[2], "test")) test_detector(cfg, weights, filename, thresh);
- else if(0==strcmp(argv[2], "train")) train_detector(cfg, weights);
- else if(0==strcmp(argv[2], "valid")) validate_detector(cfg, weights);
+ int clear = find_arg(argc, argv, "-clear");
+
+ char *datacfg = argv[3];
+ char *cfg = argv[4];
+ char *weights = (argc > 5) ? argv[5] : 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);
+ else if(0==strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights);
else if(0==strcmp(argv[2], "recall")) validate_detector_recall(cfg, weights);
+ 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);
+ }
}
--
Gitblit v1.10.0