From 170cebf8af215f8d7c20cbbe4e19a9356f235b60 Mon Sep 17 00:00:00 2001
From: Tino Hager <tino.hager@nager.at>
Date: Wed, 27 Jun 2018 21:57:54 +0000
Subject: [PATCH] repair tabs spaces
---
src/classifier.c | 496 +++++++++++++++++++++++++++++++-----------------------
1 files changed, 282 insertions(+), 214 deletions(-)
diff --git a/src/classifier.c b/src/classifier.c
index 208b7ed..b38f2fe 100644
--- a/src/classifier.c
+++ b/src/classifier.c
@@ -6,40 +6,28 @@
#include "assert.h"
#include "classifier.h"
#include "cuda.h"
+#ifdef WIN32
+#include <time.h>
+#include <winsock.h>
+#include "gettimeofday.h"
+#else
#include <sys/time.h>
+#endif
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
-image get_image_from_stream(CvCapture *cap);
+#include "opencv2/core/version.hpp"
+#ifndef CV_VERSION_EPOCH
+#include "opencv2/videoio/videoio_c.h"
#endif
+image get_image_from_stream(CvCapture *cap);
+image get_image_from_stream_cpp(CvCapture *cap);
+#include "http_stream.h"
-list *read_data_cfg(char *filename)
-{
- FILE *file = fopen(filename, "r");
- if(file == 0) file_error(filename);
- char *line;
- int nu = 0;
- list *options = make_list();
- while((line=fgetl(file)) != 0){
- ++ nu;
- strip(line);
- switch(line[0]){
- case '\0':
- case '#':
- case ';':
- free(line);
- break;
- default:
- if(!read_option(line, options)){
- fprintf(stderr, "Config file error line %d, could parse: %s\n", nu, line);
- free(line);
- }
- break;
- }
- }
- fclose(file);
- return options;
-}
+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
float *get_regression_values(char **labels, int n)
{
@@ -53,9 +41,8 @@
return v;
}
-void train_classifier_multi(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear)
+void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show)
{
-#ifdef GPU
int i;
float avg_loss = -1;
@@ -68,7 +55,9 @@
int seed = rand();
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);
@@ -99,10 +88,12 @@
load_args args = {0};
args.w = net.w;
args.h = net.h;
- args.threads = 16;
+ args.threads = 32;
+ args.hierarchy = net.hierarchy;
args.min = net.min_crop;
args.max = net.max_crop;
+ args.flip = net.flip;
args.angle = net.angle;
args.aspect = net.aspect;
args.exposure = net.exposure;
@@ -117,153 +108,77 @@
args.labels = labels;
args.type = CLASSIFICATION_DATA;
- data train;
- data buffer;
- pthread_t load_thread;
- args.d = &buffer;
- load_thread = load_data(args);
-
- int epoch = (*net.seen)/N;
- while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
- time=clock();
-
- pthread_join(load_thread, 0);
- train = buffer;
- load_thread = load_data(args);
-
- printf("Loaded: %lf seconds\n", sec(clock()-time));
- time=clock();
-
- float loss = train_networks(nets, ngpus, train, 4);
- if(avg_loss == -1) avg_loss = loss;
- avg_loss = avg_loss*.9 + loss*.1;
- printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen);
- free_data(train);
- if(*net.seen/N > epoch){
- epoch = *net.seen/N;
- char buff[256];
- sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
- save_weights(net, buff);
- }
- if(get_current_batch(net)%100 == 0){
- char buff[256];
- sprintf(buff, "%s/%s.backup",backup_directory,base);
- save_weights(net, buff);
- }
- }
- char buff[256];
- sprintf(buff, "%s/%s.weights", backup_directory, base);
- save_weights(net, buff);
-
- free_network(net);
- free_ptrs((void**)labels, classes);
- free_ptrs((void**)paths, plist->size);
- free_list(plist);
- free(base);
-#endif
-}
-
-
-void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear)
-{
- srand(time(0));
- float avg_loss = -1;
- char *base = basecfg(cfgfile);
- printf("%s\n", base);
- network net = parse_network_cfg(cfgfile);
- if(weightfile){
- load_weights(&net, weightfile);
- }
- if(clear) *net.seen = 0;
-
- int imgs = net.batch * net.subdivisions;
-
- printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
- list *options = read_data_cfg(datacfg);
-
- char *backup_directory = option_find_str(options, "backup", "/backup/");
- char *label_list = option_find_str(options, "labels", "data/labels.list");
- char *train_list = option_find_str(options, "train", "data/train.list");
- int classes = option_find_int(options, "classes", 2);
-
- char **labels = get_labels(label_list);
- list *plist = get_paths(train_list);
- char **paths = (char **)list_to_array(plist);
- printf("%d\n", plist->size);
- int N = plist->size;
- clock_t time;
-
- load_args args = {0};
- args.w = net.w;
- args.h = net.h;
- args.threads = 8;
-
- args.min = net.min_crop;
- args.max = net.max_crop;
- args.angle = net.angle;
- args.aspect = net.aspect;
- args.exposure = net.exposure;
- args.saturation = net.saturation;
- args.hue = net.hue;
- args.size = net.w;
-
- args.paths = paths;
- args.classes = classes;
- args.n = imgs;
- args.m = N;
- args.labels = labels;
- args.type = CLASSIFICATION_DATA;
-
- data train;
- data buffer;
- pthread_t load_thread;
- args.d = &buffer;
- load_thread = load_data(args);
-
- int epoch = (*net.seen)/N;
- while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
- time=clock();
-
- pthread_join(load_thread, 0);
- train = buffer;
- load_thread = load_data(args);
-
- printf("Loaded: %lf seconds\n", sec(clock()-time));
- time=clock();
-
#ifdef OPENCV
- if(0){
- int u;
- for(u = 0; u < imgs; ++u){
- image im = float_to_image(net.w, net.h, 3, train.X.vals[u]);
- show_image(im, "loaded");
- cvWaitKey(0);
- }
+ args.threads = 3;
+ 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
+
+ data train;
+ data buffer;
+ pthread_t load_thread;
+ args.d = &buffer;
+ load_thread = load_data(args);
+
+ int iter_save = get_current_batch(net);
+ while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
+ time=clock();
+
+ pthread_join(load_thread, 0);
+ train = buffer;
+ load_thread = load_data(args);
+
+ printf("Loaded: %lf seconds\n", sec(clock()-time));
+ time=clock();
+
+ 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
-
- float loss = train_network(net, train);
- free_data(train);
-
if(avg_loss == -1) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
+
+ i = get_current_batch(net);
+
printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen);
- if(*net.seen/N > epoch){
- epoch = *net.seen/N;
+#ifdef OPENCV
+ if(!dont_show)
+ draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches);
+#endif // OPENCV
+
+ if (i >= (iter_save + 100)) {
+ iter_save = i;
+#ifdef GPU
+ if (ngpus != 1) sync_nets(nets, ngpus, 0);
+#endif
char buff[256];
- sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
+ sprintf(buff, "%s/%s_%d.weights",backup_directory,base, i);
save_weights(net, buff);
}
- if(get_current_batch(net)%100 == 0){
- char buff[256];
- sprintf(buff, "%s/%s.backup",backup_directory,base);
- 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.weights", backup_directory, base);
+ sprintf(buff, "%s/%s_final.weights", backup_directory, base);
save_weights(net, buff);
+#ifdef OPENCV
+ cvReleaseImage(&img);
+ cvDestroyAllWindows();
+#endif
+
free_network(net);
free_ptrs((void**)labels, classes);
free_ptrs((void**)paths, plist->size);
@@ -271,6 +186,118 @@
free(base);
}
+
+/*
+ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear)
+ {
+ srand(time(0));
+ float avg_loss = -1;
+ char *base = basecfg(cfgfile);
+ printf("%s\n", base);
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ if(clear) *net.seen = 0;
+
+ int imgs = net.batch * net.subdivisions;
+
+ printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+ list *options = read_data_cfg(datacfg);
+
+ char *backup_directory = option_find_str(options, "backup", "/backup/");
+ char *label_list = option_find_str(options, "labels", "data/labels.list");
+ char *train_list = option_find_str(options, "train", "data/train.list");
+ int classes = option_find_int(options, "classes", 2);
+
+ char **labels = get_labels(label_list);
+ list *plist = get_paths(train_list);
+ char **paths = (char **)list_to_array(plist);
+ printf("%d\n", plist->size);
+ int N = plist->size;
+ clock_t time;
+
+ load_args args = {0};
+ args.w = net.w;
+ args.h = net.h;
+ args.threads = 8;
+
+ args.min = net.min_crop;
+ args.max = net.max_crop;
+ args.flip = net.flip;
+ args.angle = net.angle;
+ args.aspect = net.aspect;
+ args.exposure = net.exposure;
+ args.saturation = net.saturation;
+ args.hue = net.hue;
+ args.size = net.w;
+ args.hierarchy = net.hierarchy;
+
+ args.paths = paths;
+ args.classes = classes;
+ args.n = imgs;
+ args.m = N;
+ args.labels = labels;
+ args.type = CLASSIFICATION_DATA;
+
+ data train;
+ data buffer;
+ pthread_t load_thread;
+ args.d = &buffer;
+ load_thread = load_data(args);
+
+ int epoch = (*net.seen)/N;
+ while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
+ time=clock();
+
+ pthread_join(load_thread, 0);
+ train = buffer;
+ load_thread = load_data(args);
+
+ printf("Loaded: %lf seconds\n", sec(clock()-time));
+ time=clock();
+
+#ifdef OPENCV
+if(0){
+int u;
+for(u = 0; u < imgs; ++u){
+ image im = float_to_image(net.w, net.h, 3, train.X.vals[u]);
+ show_image(im, "loaded");
+ cvWaitKey(0);
+}
+}
+#endif
+
+float loss = train_network(net, train);
+free_data(train);
+
+if(avg_loss == -1) avg_loss = loss;
+avg_loss = avg_loss*.9 + loss*.1;
+printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen);
+if(*net.seen/N > epoch){
+ epoch = *net.seen/N;
+ char buff[256];
+ sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
+ save_weights(net, buff);
+}
+if(get_current_batch(net)%100 == 0){
+ char buff[256];
+ sprintf(buff, "%s/%s.backup",backup_directory,base);
+ save_weights(net, buff);
+}
+}
+char buff[256];
+sprintf(buff, "%s/%s.weights", backup_directory, base);
+save_weights(net, buff);
+
+free_network(net);
+free_ptrs((void**)labels, classes);
+free_ptrs((void**)paths, plist->size);
+free_list(plist);
+free(base);
+}
+*/
+
void validate_classifier_crop(char *datacfg, char *filename, char *weightfile)
{
int i = 0;
@@ -286,6 +313,7 @@
char *valid_list = option_find_str(options, "valid", "data/train.list");
int classes = option_find_int(options, "classes", 2);
int topk = option_find_int(options, "top", 1);
+ if (topk > classes) topk = classes;
char **labels = get_labels(label_list);
list *plist = get_paths(valid_list);
@@ -354,6 +382,7 @@
char *valid_list = option_find_str(options, "valid", "data/train.list");
int classes = option_find_int(options, "classes", 2);
int topk = option_find_int(options, "top", 1);
+ if (topk > classes) topk = classes;
char **labels = get_labels(label_list);
list *plist = get_paths(valid_list);
@@ -367,11 +396,11 @@
int *indexes = calloc(topk, sizeof(int));
for(i = 0; i < m; ++i){
- int class = -1;
+ int class_id = -1;
char *path = paths[i];
for(j = 0; j < classes; ++j){
if(strstr(path, labels[j])){
- class = j;
+ class_id = j;
break;
}
}
@@ -394,15 +423,16 @@
float *pred = calloc(classes, sizeof(float));
for(j = 0; j < 10; ++j){
float *p = network_predict(net, images[j].data);
+ if(net.hierarchy) hierarchy_predictions(p, net.outputs, net.hierarchy, 1);
axpy_cpu(classes, 1, p, 1, pred, 1);
free_image(images[j]);
}
free_image(im);
top_k(pred, classes, topk, indexes);
free(pred);
- if(indexes[0] == class) avg_acc += 1;
+ if(indexes[0] == class_id) avg_acc += 1;
for(j = 0; j < topk; ++j){
- if(indexes[j] == class) avg_topk += 1;
+ if(indexes[j] == class_id) avg_topk += 1;
}
printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
@@ -425,6 +455,7 @@
char *valid_list = option_find_str(options, "valid", "data/train.list");
int classes = option_find_int(options, "classes", 2);
int topk = option_find_int(options, "top", 1);
+ if (topk > classes) topk = classes;
char **labels = get_labels(label_list);
list *plist = get_paths(valid_list);
@@ -439,11 +470,11 @@
int size = net.w;
for(i = 0; i < m; ++i){
- int class = -1;
+ int class_id = -1;
char *path = paths[i];
for(j = 0; j < classes; ++j){
if(strstr(path, labels[j])){
- class = j;
+ class_id = j;
break;
}
}
@@ -454,14 +485,15 @@
//show_image(crop, "cropped");
//cvWaitKey(0);
float *pred = network_predict(net, resized.data);
+ if(net.hierarchy) hierarchy_predictions(pred, net.outputs, net.hierarchy, 1);
free_image(im);
free_image(resized);
top_k(pred, classes, topk, indexes);
- if(indexes[0] == class) avg_acc += 1;
+ if(indexes[0] == class_id) avg_acc += 1;
for(j = 0; j < topk; ++j){
- if(indexes[j] == class) avg_topk += 1;
+ if(indexes[j] == class_id) avg_topk += 1;
}
printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
@@ -482,9 +514,12 @@
list *options = read_data_cfg(datacfg);
char *label_list = option_find_str(options, "labels", "data/labels.list");
+ char *leaf_list = option_find_str(options, "leaves", 0);
+ if(leaf_list) change_leaves(net.hierarchy, leaf_list);
char *valid_list = option_find_str(options, "valid", "data/train.list");
int classes = option_find_int(options, "classes", 2);
int topk = option_find_int(options, "top", 1);
+ if (topk > classes) topk = classes;
char **labels = get_labels(label_list);
list *plist = get_paths(valid_list);
@@ -498,11 +533,11 @@
int *indexes = calloc(topk, sizeof(int));
for(i = 0; i < m; ++i){
- int class = -1;
+ int class_id = -1;
char *path = paths[i];
for(j = 0; j < classes; ++j){
if(strstr(path, labels[j])){
- class = j;
+ class_id = j;
break;
}
}
@@ -513,15 +548,16 @@
//show_image(crop, "cropped");
//cvWaitKey(0);
float *pred = network_predict(net, crop.data);
+ if(net.hierarchy) hierarchy_predictions(pred, net.outputs, net.hierarchy, 1);
if(resized.data != im.data) free_image(resized);
free_image(im);
free_image(crop);
top_k(pred, classes, topk, indexes);
- if(indexes[0] == class) avg_acc += 1;
+ if(indexes[0] == class_id) avg_acc += 1;
for(j = 0; j < topk; ++j){
- if(indexes[j] == class) avg_topk += 1;
+ if(indexes[j] == class_id) avg_topk += 1;
}
printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
@@ -544,6 +580,7 @@
char *valid_list = option_find_str(options, "valid", "data/train.list");
int classes = option_find_int(options, "classes", 2);
int topk = option_find_int(options, "top", 1);
+ if (topk > classes) topk = classes;
char **labels = get_labels(label_list);
list *plist = get_paths(valid_list);
@@ -559,11 +596,11 @@
int *indexes = calloc(topk, sizeof(int));
for(i = 0; i < m; ++i){
- int class = -1;
+ int class_id = -1;
char *path = paths[i];
for(j = 0; j < classes; ++j){
if(strstr(path, labels[j])){
- class = j;
+ class_id = j;
break;
}
}
@@ -573,6 +610,7 @@
image r = resize_min(im, scales[j]);
resize_network(&net, r.w, r.h);
float *p = network_predict(net, r.data);
+ if(net.hierarchy) hierarchy_predictions(p, net.outputs, net.hierarchy, 1);
axpy_cpu(classes, 1, p, 1, pred, 1);
flip_image(r);
p = network_predict(net, r.data);
@@ -582,9 +620,9 @@
free_image(im);
top_k(pred, classes, topk, indexes);
free(pred);
- if(indexes[0] == class) avg_acc += 1;
+ if(indexes[0] == class_id) avg_acc += 1;
for(j = 0; j < topk; ++j){
- if(indexes[j] == class) avg_topk += 1;
+ if(indexes[j] == class_id) avg_topk += 1;
}
printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
@@ -593,7 +631,7 @@
void try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int layer_num)
{
- network net = parse_network_cfg(cfgfile);
+ network net = parse_network_cfg_custom(cfgfile, 1);
if(weightfile){
load_weights(&net, weightfile);
}
@@ -604,7 +642,9 @@
char *name_list = option_find_str(options, "names", 0);
if(!name_list) name_list = option_find_str(options, "labels", "data/labels.list");
+ int classes = option_find_int(options, "classes", 2);
int top = option_find_int(options, "top", 1);
+ if (top > classes) top = classes;
int i = 0;
char **names = get_labels(name_list);
@@ -672,10 +712,9 @@
}
}
-
-void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename)
+void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top)
{
- network net = parse_network_cfg(cfgfile);
+ network net = parse_network_cfg_custom(cfgfile, 1);
if(weightfile){
load_weights(&net, weightfile);
}
@@ -686,7 +725,9 @@
char *name_list = option_find_str(options, "names", 0);
if(!name_list) name_list = option_find_str(options, "labels", "data/labels.list");
- int top = option_find_int(options, "top", 1);
+ int classes = option_find_int(options, "classes", 2);
+ if (top == 0) top = option_find_int(options, "top", 1);
+ if (top > classes) top = classes;
int i = 0;
char **names = get_labels(name_list);
@@ -706,18 +747,21 @@
strtok(input, "\n");
}
image im = load_image_color(input, 0, 0);
- image r = resize_min(im, size);
- resize_network(&net, r.w, r.h);
+ image r = letterbox_image(im, net.w, net.h);
+ //image r = resize_min(im, size);
+ //resize_network(&net, r.w, r.h);
printf("%d %d\n", r.w, r.h);
float *X = r.data;
time=clock();
float *predictions = network_predict(net, X);
- top_predictions(net, top, indexes);
+ if(net.hierarchy) hierarchy_predictions(predictions, net.outputs, net.hierarchy, 0);
+ top_k(predictions, net.outputs, top, indexes);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
for(i = 0; i < top; ++i){
int index = indexes[i];
- printf("%s: %f\n", names[index], predictions[index]);
+ if(net.hierarchy) printf("%d, %s: %f, parent: %s \n",index, names[index], predictions[index], (net.hierarchy->parent[index] >= 0) ? names[net.hierarchy->parent[index]] : "Root");
+ else printf("%s: %f\n",names[index], predictions[index]);
}
if(r.data != im.data) free_image(r);
free_image(im);
@@ -855,13 +899,18 @@
srand(2222222);
CvCapture * cap;
- if(filename){
- cap = cvCaptureFromFile(filename);
- }else{
- cap = cvCaptureFromCAM(cam_index);
+ if (filename) {
+ //cap = cvCaptureFromFile(filename);
+ cap = get_capture_video_stream(filename);
+ }
+ else {
+ //cap = cvCaptureFromCAM(cam_index);
+ cap = get_capture_webcam(cam_index);
}
+ int classes = option_find_int(options, "classes", 2);
int top = option_find_int(options, "top", 1);
+ if (top > classes) top = classes;
char *name_list = option_find_str(options, "names", 0);
char **names = get_labels(name_list);
@@ -881,7 +930,8 @@
struct timeval tval_before, tval_after, tval_result;
gettimeofday(&tval_before, NULL);
- image in = get_image_from_stream(cap);
+ //image in = get_image_from_stream(cap);
+ image in = get_image_from_stream_cpp(cap);
if(!in.data) break;
image in_s = resize_image(in, net.w, net.h);
@@ -899,15 +949,15 @@
float curr_threat = 0;
if(1){
curr_threat = predictions[0] * 0 +
- predictions[1] * .6 +
- predictions[2];
+ predictions[1] * .6 +
+ predictions[2];
} else {
curr_threat = predictions[218] +
- predictions[539] +
- predictions[540] +
- predictions[368] +
- predictions[369] +
- predictions[370];
+ predictions[539] +
+ predictions[540] +
+ predictions[368] +
+ predictions[369] +
+ predictions[370];
}
threat = roll * curr_threat + (1-roll) * threat;
@@ -987,13 +1037,18 @@
srand(2222222);
CvCapture * cap;
- if(filename){
- cap = cvCaptureFromFile(filename);
- }else{
- cap = cvCaptureFromCAM(cam_index);
+ if (filename) {
+ //cap = cvCaptureFromFile(filename);
+ cap = get_capture_video_stream(filename);
+ }
+ else {
+ //cap = cvCaptureFromCAM(cam_index);
+ cap = get_capture_webcam(cam_index);
}
+ int classes = option_find_int(options, "classes", 2);
int top = option_find_int(options, "top", 1);
+ if (top > classes) top = classes;
char *name_list = option_find_str(options, "names", 0);
char **names = get_labels(name_list);
@@ -1010,7 +1065,8 @@
struct timeval tval_before, tval_after, tval_result;
gettimeofday(&tval_before, NULL);
- image in = get_image_from_stream(cap);
+ //image in = get_image_from_stream(cap);
+ image in = get_image_from_stream_cpp(cap);
image in_s = resize_image(in, net.w, net.h);
show_image(in, "Threat Detection");
@@ -1054,7 +1110,7 @@
{
#ifdef OPENCV
printf("Classifier Demo\n");
- network net = parse_network_cfg(cfgfile);
+ network net = parse_network_cfg_custom(cfgfile, 1);
if(weightfile){
load_weights(&net, weightfile);
}
@@ -1065,12 +1121,16 @@
CvCapture * cap;
if(filename){
- cap = cvCaptureFromFile(filename);
+ //cap = cvCaptureFromFile(filename);
+ cap = get_capture_video_stream(filename);
}else{
- cap = cvCaptureFromCAM(cam_index);
+ //cap = cvCaptureFromCAM(cam_index);
+ cap = get_capture_webcam(cam_index);
}
+ int classes = option_find_int(options, "classes", 2);
int top = option_find_int(options, "top", 1);
+ if (top > classes) top = classes;
char *name_list = option_find_str(options, "names", 0);
char **names = get_labels(name_list);
@@ -1087,11 +1147,13 @@
struct timeval tval_before, tval_after, tval_result;
gettimeofday(&tval_before, NULL);
- image in = get_image_from_stream(cap);
+ //image in = get_image_from_stream(cap);
+ image in = get_image_from_stream_cpp(cap);
image in_s = resize_image(in, net.w, net.h);
show_image(in, "Classifier");
float *predictions = network_predict(net, in_s.data);
+ if(net.hierarchy) hierarchy_predictions(predictions, net.outputs, net.hierarchy, 1);
top_predictions(net, top, indexes);
printf("\033[2J");
@@ -1126,6 +1188,7 @@
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);
@@ -1140,9 +1203,15 @@
gpus[i] = atoi(gpu_list);
gpu_list = strchr(gpu_list, ',')+1;
}
+ } else {
+ gpu = gpu_index;
+ gpus = &gpu;
+ ngpus = 1;
}
+ int dont_show = find_arg(argc, argv, "-dont_show");
int cam_index = find_int_arg(argc, argv, "-c", 0);
+ int top = find_int_arg(argc, argv, "-t", 0);
int clear = find_arg(argc, argv, "-clear");
char *data = argv[3];
char *cfg = argv[4];
@@ -1150,10 +1219,9 @@
char *filename = (argc > 6) ? argv[6]: 0;
char *layer_s = (argc > 7) ? argv[7]: 0;
int layer = layer_s ? atoi(layer_s) : -1;
- if(0==strcmp(argv[2], "predict")) predict_classifier(data, cfg, weights, filename);
+ if(0==strcmp(argv[2], "predict")) predict_classifier(data, cfg, weights, filename, top);
else if(0==strcmp(argv[2], "try")) try_classifier(data, cfg, weights, filename, atoi(layer_s));
- else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights, clear);
- else if(0==strcmp(argv[2], "trainm")) train_classifier_multi(data, cfg, weights, gpus, ngpus, clear);
+ else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights, gpus, ngpus, clear, dont_show);
else if(0==strcmp(argv[2], "demo")) demo_classifier(data, cfg, weights, cam_index, filename);
else if(0==strcmp(argv[2], "gun")) gun_classifier(data, cfg, weights, cam_index, filename);
else if(0==strcmp(argv[2], "threat")) threat_classifier(data, cfg, weights, cam_index, filename);
--
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