From 028696bf15efeca3acb3db8c42a96f7b9e0f55ff Mon Sep 17 00:00:00 2001
From: iovodov <b@ovdv.ru>
Date: Thu, 03 May 2018 13:33:46 +0000
Subject: [PATCH] Output improvements for detector results: When printing detector results, output was done in random order, obfuscating results for interpreting. Now: 1. Text output includes coordinates of rects in (left,right,top,bottom in pixels) along with label and score 2. Text output is sorted by rect lefts to simplify finding appropriate rects on image 3. If several class probs are > thresh for some detection, the most probable is written first and coordinates for others are not repeated 4. Rects are imprinted in image in order by their best class prob, so most probable rects are always on top and not overlayed by less probable ones 5. Most probable label for rect is always written first Also: 6. Message about low GPU memory include required amount
---
src/coco.c | 478 ++++++++++++++++++++---------------------------------------
1 files changed, 165 insertions(+), 313 deletions(-)
diff --git a/src/coco.c b/src/coco.c
index c016548..c95e30d 100644
--- a/src/coco.c
+++ b/src/coco.c
@@ -6,6 +6,7 @@
#include "utils.h"
#include "parser.h"
#include "box.h"
+#include "demo.h"
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
@@ -15,44 +16,14 @@
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 draw_coco(image im, float *pred, int side, char *label)
-{
- int classes = 1;
- int elems = 4+classes;
- int j;
- int r, c;
-
- for(r = 0; r < side; ++r){
- for(c = 0; c < side; ++c){
- j = (r*side + c) * elems;
- int class = max_index(pred+j, classes);
- if (pred[j+class] > 0.2){
- int width = pred[j+class]*5 + 1;
- printf("%f %s\n", pred[j+class], "object"); //coco_classes[class-1]);
- float red = get_color(0,class,classes);
- float green = get_color(1,class,classes);
- float blue = get_color(2,class,classes);
-
- j += classes;
-
- box predict = {pred[j+0], pred[j+1], pred[j+2], pred[j+3]};
- predict.x = (predict.x+c)/side;
- predict.y = (predict.y+r)/side;
-
- draw_bbox(im, predict, width, red, green, blue);
- }
- }
- }
- show_image(im, label);
-}
-
void train_coco(char *cfgfile, char *weightfile)
{
+ //char *train_images = "/home/pjreddie/data/voc/test/train.txt";
//char *train_images = "/home/pjreddie/data/coco/train.txt";
- char *train_images = "/home/pjreddie/data/voc/test/train.txt";
+ char *train_images = "data/coco.trainval.txt";
+ //char *train_images = "data/bags.train.list";
char *backup_directory = "/home/pjreddie/backup/";
srand(time(0));
- data_seed = time(0);
char *base = basecfg(cfgfile);
printf("%s\n", base);
float avg_loss = -1;
@@ -61,7 +32,7 @@
load_weights(&net, weightfile);
}
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
- int imgs = 128;
+ int imgs = net.batch*net.subdivisions;
int i = *net.seen/imgs;
data train, buffer;
@@ -70,9 +41,10 @@
int side = l.side;
int classes = l.classes;
+ float jitter = l.jitter;
list *plist = get_paths(train_images);
- int N = plist->size;
+ //int N = plist->size;
char **paths = (char **)list_to_array(plist);
load_args args = {0};
@@ -82,13 +54,20 @@
args.n = imgs;
args.m = plist->size;
args.classes = classes;
+ args.jitter = jitter;
args.num_boxes = side;
args.d = &buffer;
args.type = REGION_DATA;
+ 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);
clock_t time;
- while(i*imgs < N*120){
+ //while(i*imgs < N*120){
+ while(get_current_batch(net) < net.max_batches){
i += 1;
time=clock();
pthread_join(load_thread, 0);
@@ -97,25 +76,30 @@
printf("Loaded: %lf seconds\n", sec(clock()-time));
-/*
- image im = float_to_image(net.w, net.h, 3, train.X.vals[113]);
- image copy = copy_image(im);
- draw_coco(copy, train.y.vals[113], 7, "truth");
- cvWaitKey(0);
- free_image(copy);
- */
+ /*
+ image im = float_to_image(net.w, net.h, 3, train.X.vals[113]);
+ image copy = copy_image(im);
+ draw_coco(copy, train.y.vals[113], 7, "truth");
+ cvWaitKey(0);
+ free_image(copy);
+ */
time=clock();
float loss = train_network(net, train);
if (avg_loss < 0) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
- printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs);
- if(i%1000==0){
+ 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);
+ if(i%1000==0 || (i < 1000 && i%100 == 0)){
char buff[256];
sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
save_weights(net, buff);
}
+ if(i%100==0){
+ char buff[256];
+ sprintf(buff, "%s/%s.backup", backup_directory, base);
+ save_weights(net, buff);
+ }
free_data(train);
}
char buff[256];
@@ -123,59 +107,10 @@
save_weights(net, buff);
}
-void get_probs(float *predictions, int total, int classes, int inc, float **probs)
-{
- int i,j;
- for (i = 0; i < total; ++i){
- int index = i*inc;
- float scale = predictions[index];
- probs[i][0] = scale;
- for(j = 0; j < classes; ++j){
- probs[i][j] = scale*predictions[index+j+1];
- }
- }
-}
-void get_boxes(float *predictions, int n, int num_boxes, int per_box, box *boxes)
-{
- int i,j;
- for (i = 0; i < num_boxes*num_boxes; ++i){
- for(j = 0; j < n; ++j){
- int index = i*n+j;
- int offset = index*per_box;
- int row = i / num_boxes;
- int col = i % num_boxes;
- boxes[index].x = (predictions[offset + 0] + col) / num_boxes;
- boxes[index].y = (predictions[offset + 1] + row) / num_boxes;
- boxes[index].w = predictions[offset + 2];
- boxes[index].h = predictions[offset + 3];
- }
- }
-}
-
-void convert_cocos(float *predictions, int classes, int num_boxes, int num, int w, int h, float thresh, float **probs, box *boxes)
-{
- int i,j;
- int per_box = 4+classes;
- for (i = 0; i < num_boxes*num_boxes*num; ++i){
- int offset = i*per_box;
- for(j = 0; j < classes; ++j){
- float prob = predictions[offset+j];
- probs[i][j] = (prob > thresh) ? prob : 0;
- }
- int row = i / num_boxes;
- int col = i % num_boxes;
- offset += classes;
- boxes[i].x = (predictions[offset + 0] + col) / num_boxes;
- boxes[i].y = (predictions[offset + 1] + row) / num_boxes;
- boxes[i].w = predictions[offset + 2];
- boxes[i].h = predictions[offset + 3];
- }
-}
-
void print_cocos(FILE *fp, int image_id, box *boxes, float **probs, int num_boxes, int classes, int w, int h)
{
int i, j;
- for(i = 0; i < num_boxes*num_boxes; ++i){
+ 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.;
@@ -203,201 +138,6 @@
return atoi(p+1);
}
-void validate_recall(char *cfgfile, char *weightfile)
-{
- network net = parse_network_cfg(cfgfile);
- 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));
-
- char *val_images = "/home/pjreddie/data/voc/test/2007_test.txt";
- list *plist = get_paths(val_images);
- char **paths = (char **)list_to_array(plist);
-
- layer l = net.layers[net.n - 1];
-
- int num_boxes = l.side;
- int num = l.n;
- int classes = l.classes;
-
- int j;
-
- box *boxes = calloc(num_boxes*num_boxes*num, sizeof(box));
- float **probs = calloc(num_boxes*num_boxes*num, sizeof(float *));
- for(j = 0; j < num_boxes*num_boxes*num; ++j) probs[j] = calloc(classes+1, sizeof(float *));
-
- int N = plist->size;
- int i=0;
- int k;
-
- float iou_thresh = .5;
- float thresh = .1;
- int total = 0;
- int correct = 0;
- float avg_iou = 0;
- int nms = 1;
- int proposals = 0;
- int save = 1;
-
- for (i = 0; i < N; ++i) {
- char *path = paths[i];
- image orig = load_image_color(path, 0, 0);
- image resized = resize_image(orig, net.w, net.h);
-
- float *X = resized.data;
- float *predictions = network_predict(net, X);
- get_boxes(predictions+1+classes, num, num_boxes, 5+classes, boxes);
- get_probs(predictions, num*num_boxes*num_boxes, classes, 5+classes, probs);
- if (nms) do_nms(boxes, probs, num*num_boxes*num_boxes, (classes>0) ? classes : 1, iou_thresh);
-
- 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");
-
- int num_labels = 0;
- box_label *truth = read_boxes(labelpath, &num_labels);
- for(k = 0; k < num_boxes*num_boxes*num; ++k){
- if(probs[k][0] > thresh){
- ++proposals;
- if(save){
- char buff[256];
- sprintf(buff, "/data/extracted/nms_preds/%d", proposals);
- int dx = (boxes[k].x - boxes[k].w/2) * orig.w;
- int dy = (boxes[k].y - boxes[k].h/2) * orig.h;
- int w = boxes[k].w * orig.w;
- int h = boxes[k].h * orig.h;
- image cropped = crop_image(orig, dx, dy, w, h);
- image sized = resize_image(cropped, 224, 224);
-#ifdef OPENCV
- save_image_jpg(sized, buff);
-#endif
- free_image(sized);
- free_image(cropped);
- sprintf(buff, "/data/extracted/nms_pred_boxes/%d.txt", proposals);
- char *im_id = basecfg(path);
- FILE *fp = fopen(buff, "w");
- fprintf(fp, "%s %d %d %d %d\n", im_id, dx, dy, dx+w, dy+h);
- fclose(fp);
- free(im_id);
- }
- }
- }
- 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 < num_boxes*num_boxes*num; ++k){
- float iou = box_iou(boxes[k], t);
- if(probs[k][0] > thresh && iou > best_iou){
- best_iou = iou;
- }
- }
- avg_iou += best_iou;
- if(best_iou > iou_thresh){
- ++correct;
- }
- }
- free(truth);
- free_image(orig);
- free_image(resized);
- 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);
- }
-}
-
-void extract_boxes(char *cfgfile, char *weightfile)
-{
- network net = parse_network_cfg(cfgfile);
- 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));
-
- char *val_images = "/home/pjreddie/data/voc/test/train.txt";
- list *plist = get_paths(val_images);
- char **paths = (char **)list_to_array(plist);
-
- layer l = net.layers[net.n - 1];
-
- int num_boxes = l.side;
- int num = l.n;
- int classes = l.classes;
-
- int j;
-
- box *boxes = calloc(num_boxes*num_boxes*num, sizeof(box));
- float **probs = calloc(num_boxes*num_boxes*num, sizeof(float *));
- for(j = 0; j < num_boxes*num_boxes*num; ++j) probs[j] = calloc(classes+1, sizeof(float *));
-
- int N = plist->size;
- int i=0;
- int k;
-
- int count = 0;
- float iou_thresh = .3;
-
- for (i = 0; i < N; ++i) {
- fprintf(stderr, "%5d %5d\n", i, count);
- char *path = paths[i];
- image orig = load_image_color(path, 0, 0);
- image resized = resize_image(orig, net.w, net.h);
-
- float *X = resized.data;
- float *predictions = network_predict(net, X);
- get_boxes(predictions+1+classes, num, num_boxes, 5+classes, boxes);
- get_probs(predictions, num*num_boxes*num_boxes, classes, 5+classes, probs);
-
- 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");
-
- int num_labels = 0;
- box_label *truth = read_boxes(labelpath, &num_labels);
- FILE *label = stdin;
- for(k = 0; k < num_boxes*num_boxes*num; ++k){
- int overlaps = 0;
- for (j = 0; j < num_labels; ++j) {
- box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h};
- float iou = box_iou(boxes[k], t);
- if (iou > iou_thresh){
- if (!overlaps) {
- char buff[256];
- sprintf(buff, "/data/extracted/labels/%d.txt", count);
- label = fopen(buff, "w");
- overlaps = 1;
- }
- fprintf(label, "%d %f\n", truth[j].id, iou);
- }
- }
- if (overlaps) {
- char buff[256];
- sprintf(buff, "/data/extracted/imgs/%d", count++);
- int dx = (boxes[k].x - boxes[k].w/2) * orig.w;
- int dy = (boxes[k].y - boxes[k].h/2) * orig.h;
- int w = boxes[k].w * orig.w;
- int h = boxes[k].h * orig.h;
- image cropped = crop_image(orig, dx, dy, w, h);
- image sized = resize_image(cropped, 224, 224);
-#ifdef OPENCV
- save_image_jpg(sized, buff);
-#endif
- free_image(sized);
- free_image(cropped);
- fclose(label);
- }
- }
- free(truth);
- free_image(orig);
- free_image(resized);
- }
-}
-
void validate_coco(char *cfgfile, char *weightfile)
{
network net = parse_network_cfg(cfgfile);
@@ -408,13 +148,15 @@
fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
srand(time(0));
- char *base = "/home/pjreddie/backup/";
+ char *base = "results/";
list *plist = get_paths("data/coco_val_5k.list");
+ //list *plist = get_paths("/home/pjreddie/data/people-art/test.txt");
+ //list *plist = get_paths("/home/pjreddie/data/voc/test/2007_test.txt");
char **paths = (char **)list_to_array(plist);
- int num_boxes = 9;
- int num = 4;
- int classes = 1;
+ layer l = net.layers[net.n-1];
+ int classes = l.classes;
+ int side = l.side;
int j;
char buff[1024];
@@ -422,9 +164,9 @@
FILE *fp = fopen(buff, "w");
fprintf(fp, "[\n");
- box *boxes = calloc(num_boxes*num_boxes*num, sizeof(box));
- float **probs = calloc(num_boxes*num_boxes*num, sizeof(float *));
- for(j = 0; j < num_boxes*num_boxes*num; ++j) probs[j] = calloc(classes, sizeof(float *));
+ 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 *));
int m = plist->size;
int i=0;
@@ -434,17 +176,18 @@
int nms = 1;
float iou_thresh = .5;
- load_args args = {0};
- args.w = net.w;
- args.h = net.h;
- args.type = IMAGE_DATA;
-
int nthreads = 8;
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;
+
for(t = 0; t < nthreads; ++t){
args.path = paths[i+t];
args.im = &buf[t];
@@ -469,12 +212,12 @@
char *path = paths[i+t-nthreads];
int image_id = get_coco_image_id(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_cocos(predictions, classes, num_boxes, num, w, h, thresh, probs, boxes);
- if (nms) do_nms(boxes, probs, num_boxes, classes, iou_thresh);
- print_cocos(fp, image_id, boxes, probs, num_boxes, classes, w, h);
+ get_detection_boxes(l, w, h, thresh, probs, boxes, 0);
+ if (nms) do_nms_sort_v2(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]);
free_image(val_resized[t]);
}
@@ -482,21 +225,115 @@
fseek(fp, -2, SEEK_CUR);
fprintf(fp, "\n]\n");
fclose(fp);
+
fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
}
-void test_coco(char *cfgfile, char *weightfile, char *filename)
+void validate_coco_recall(char *cfgfile, char *weightfile)
{
-
network net = parse_network_cfg(cfgfile);
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));
+
+ char *base = "results/comp4_det_test_";
+ list *plist = get_paths("/home/pjreddie/data/voc/test/2007_test.txt");
+ char **paths = (char **)list_to_array(plist);
+
+ layer l = net.layers[net.n-1];
+ int classes = l.classes;
+ 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, coco_classes[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 *));
+
+ int m = plist->size;
+ int i=0;
+
+ float thresh = .001;
+ int nms = 0;
+ float iou_thresh = .5;
+ float nms_thresh = .5;
+
+ 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);
+ get_detection_boxes(l, 1, 1, thresh, probs, boxes, 1);
+ if (nms) do_nms(boxes, probs, side*side*l.n, 1, nms_thresh);
+
+ 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){
+ if(probs[k][0] > 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 < side*side*l.n; ++k){
+ float iou = box_iou(boxes[k], t);
+ if(probs[k][0] > thresh && iou > best_iou){
+ best_iou = iou;
+ }
+ }
+ avg_iou += best_iou;
+ if(best_iou > iou_thresh){
+ ++correct;
+ }
+ }
+
+ 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);
+ }
+}
+
+void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh)
+{
+ image **alphabet = load_alphabet();
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ detection_layer l = net.layers[net.n-1];
+ set_batch_network(&net, 1);
srand(2222222);
+ float nms = .4;
clock_t time;
char buff[256];
char *input = buff;
+ int j;
+ 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);
@@ -511,9 +348,13 @@
image sized = resize_image(im, net.w, net.h);
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));
- draw_coco(im, predictions, 7, "predictions");
+ get_detection_boxes(l, 1, 1, thresh, probs, boxes, 0);
+ if (nms) do_nms_sort_v2(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, alphabet, 80);
+ save_image(im, "prediction");
+ show_image(im, "predictions");
free_image(im);
free_image(sized);
#ifdef OPENCV
@@ -526,6 +367,15 @@
void run_coco(int argc, char **argv)
{
+ int dont_show = find_arg(argc, argv, "-dont_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", .2);
+ 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);
+
if(argc < 4){
fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
return;
@@ -534,8 +384,10 @@
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_coco(cfg, weights, filename);
+ if(0==strcmp(argv[2], "test")) test_coco(cfg, weights, filename, thresh);
else if(0==strcmp(argv[2], "train")) train_coco(cfg, weights);
- else if(0==strcmp(argv[2], "extract")) extract_boxes(cfg, weights);
- else if(0==strcmp(argv[2], "valid")) validate_recall(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, hier_thresh, cam_index, filename, coco_classes, 80, frame_skip,
+ prefix, out_filename, http_stream_port, dont_show);
}
--
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