From 8a504c737d2bc3b9b37cb79cb50fbf7eecda07df Mon Sep 17 00:00:00 2001
From: Tino Hager <tino.hager@nager.at>
Date: Wed, 27 Jun 2018 21:56:47 +0000
Subject: [PATCH] repair tabs spaces
---
src/classifier.c | 587 ++++++++++++++++++++++++++++++++++++++++++---------------
1 files changed, 429 insertions(+), 158 deletions(-)
diff --git a/src/classifier.c b/src/classifier.c
index e59f7ae..b38f2fe 100644
--- a/src/classifier.c
+++ b/src/classifier.c
@@ -5,39 +5,29 @@
#include "blas.h"
#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"
+#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)
{
@@ -51,25 +41,36 @@
return v;
}
-void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear)
+void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show)
{
- int nthreads = 8;
int i;
- data_seed = time(0);
- 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;
- printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
- int imgs = net.batch*net.subdivisions/nthreads;
- assert(net.batch*net.subdivisions % nthreads == 0);
+ printf("%d\n", ngpus);
+ network *nets = calloc(ngpus, sizeof(network));
+ srand(time(0));
+ 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);
+ }
+ 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);
list *options = read_data_cfg(datacfg);
char *backup_directory = option_find_str(options, "backup", "/backup/");
@@ -84,17 +85,17 @@
int N = plist->size;
clock_t time;
- pthread_t *load_threads = calloc(nthreads, sizeof(pthread_t));
- data *trains = calloc(nthreads, sizeof(data));
- data *buffers = calloc(nthreads, sizeof(data));
-
load_args args = {0};
args.w = net.w;
args.h = net.h;
+ 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;
args.saturation = net.saturation;
args.hue = net.hue;
@@ -107,70 +108,76 @@
args.labels = labels;
args.type = CLASSIFICATION_DATA;
- for(i = 0; i < nthreads; ++i){
- args.d = buffers + i;
- load_threads[i] = load_data_in_thread(args);
- }
+#ifdef OPENCV
+ 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
- int epoch = (*net.seen)/N;
+ 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();
- for(i = 0; i < nthreads; ++i){
- pthread_join(load_threads[i], 0);
- trains[i] = buffers[i];
- }
- data train = concat_datas(trains, nthreads);
- for(i = 0; i < nthreads; ++i){
- args.d = buffers + i;
- load_threads[i] = load_data_in_thread(args);
- }
+ 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);
- }
+ float loss = 0;
+#ifdef GPU
+ if(ngpus == 1){
+ loss = train_network(net, train);
+ } else {
+ loss = train_networks(nets, ngpus, train, 4);
}
- #endif
-
- float loss = train_network(net, train);
+#else
+ loss = train_network(net, train);
+#endif
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);
+#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, i);
+ save_weights(net, buff);
+ }
free_data(train);
- for(i = 0; i < nthreads; ++i){
- free_data(trains[i]);
- }
- 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);
- }
}
+#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);
- for(i = 0; i < nthreads; ++i){
- pthread_join(load_threads[i], 0);
- free_data(buffers[i]);
- }
- free(buffers);
- free(trains);
- free(load_threads);
+#ifdef OPENCV
+ cvReleaseImage(&img);
+ cvDestroyAllWindows();
+#endif
free_network(net);
free_ptrs((void**)labels, classes);
@@ -179,6 +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;
@@ -194,6 +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);
@@ -262,6 +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);
@@ -275,11 +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;
}
}
@@ -302,15 +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));
@@ -333,6 +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);
@@ -347,11 +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;
}
}
@@ -362,14 +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));
@@ -390,9 +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);
@@ -406,11 +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;
}
}
@@ -421,15 +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));
@@ -452,6 +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);
@@ -467,11 +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;
}
}
@@ -481,6 +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);
@@ -490,9 +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));
@@ -501,7 +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);
}
@@ -512,7 +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);
@@ -545,29 +677,29 @@
float *X = im.data;
time=clock();
float *predictions = network_predict(net, X);
-
+
layer l = net.layers[layer_num];
for(i = 0; i < l.c; ++i){
- if(l.rolling_mean) printf("%f %f %f\n", l.rolling_mean[i], l.rolling_variance[i], l.scales[i]);
+ if(l.rolling_mean) printf("%f %f %f\n", l.rolling_mean[i], l.rolling_variance[i], l.scales[i]);
}
- #ifdef GPU
+#ifdef GPU
cuda_pull_array(l.output_gpu, l.output, l.outputs);
- #endif
+#endif
for(i = 0; i < l.outputs; ++i){
printf("%f\n", l.output[i]);
}
/*
-
- printf("\n\nWeights\n");
- for(i = 0; i < l.n*l.size*l.size*l.c; ++i){
- printf("%f\n", l.filters[i]);
- }
- printf("\n\nBiases\n");
- for(i = 0; i < l.n; ++i){
- printf("%f\n", l.biases[i]);
- }
- */
+ printf("\n\nWeights\n");
+ for(i = 0; i < l.n*l.size*l.size*l.c; ++i){
+ printf("%f\n", l.filters[i]);
+ }
+
+ printf("\n\nBiases\n");
+ for(i = 0; i < l.n; ++i){
+ printf("%f\n", l.biases[i]);
+ }
+ */
top_predictions(net, top, indexes);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
@@ -580,10 +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);
}
@@ -594,7 +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);
@@ -614,18 +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);
@@ -763,13 +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);
@@ -789,22 +930,35 @@
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);
- image out = in;
- int x1 = out.w / 20;
- int y1 = out.h / 20;
- int x2 = 2*x1;
- int y2 = out.h - out.h/20;
+ image out = in;
+ int x1 = out.w / 20;
+ int y1 = out.h / 20;
+ int x2 = 2*x1;
+ int y2 = out.h - out.h/20;
- int border = .01*out.h;
- int h = y2 - y1 - 2*border;
- int w = x2 - x1 - 2*border;
+ int border = .01*out.h;
+ int h = y2 - y1 - 2*border;
+ int w = x2 - x1 - 2*border;
float *predictions = network_predict(net, in_s.data);
- float curr_threat = predictions[0] * 0 + predictions[1] * .6 + predictions[2];
+ float curr_threat = 0;
+ if(1){
+ curr_threat = predictions[0] * 0 +
+ predictions[1] * .6 +
+ predictions[2];
+ } else {
+ curr_threat = predictions[218] +
+ predictions[539] +
+ predictions[540] +
+ predictions[368] +
+ predictions[369] +
+ predictions[370];
+ }
threat = roll * curr_threat + (1-roll) * threat;
draw_box_width(out, x2 + border, y1 + .02*h, x2 + .5 * w, y1 + .02*h + border, border, 0,0,0);
@@ -820,11 +974,11 @@
y1 + .02*h + 3*border, .5*border, 0,0,0);
draw_box_width(out, x2 + border, y1 + .42*h, x2 + .5 * w, y1 + .42*h + border, border, 0,0,0);
if(threat > .57) {
- draw_box_width(out, x2 + .5 * w + border,
- y1 + .42*h - 2*border,
- x2 + .5 * w + 6*border,
- y1 + .42*h + 3*border, 3*border, 1,1,0);
- }
+ draw_box_width(out, x2 + .5 * w + border,
+ y1 + .42*h - 2*border,
+ x2 + .5 * w + 6*border,
+ y1 + .42*h + 3*border, 3*border, 1,1,0);
+ }
draw_box_width(out, x2 + .5 * w + border,
y1 + .42*h - 2*border,
x2 + .5 * w + 6*border,
@@ -840,7 +994,7 @@
top_predictions(net, top, indexes);
char buff[256];
sprintf(buff, "/home/pjreddie/tmp/threat_%06d", count);
- save_image(out, buff);
+ //save_image(out, buff);
printf("\033[2J");
printf("\033[1;1H");
@@ -851,7 +1005,7 @@
printf("%.1f%%: %s\n", predictions[index]*100, names[index]);
}
- if(0){
+ if(1){
show_image(out, "Threat");
cvWaitKey(10);
}
@@ -867,9 +1021,11 @@
}
-void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename)
+void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename)
{
#ifdef OPENCV
+ int bad_cats[] = {218, 539, 540, 1213, 1501, 1742, 1911, 2415, 4348, 19223, 368, 369, 370, 1133, 1200, 1306, 2122, 2301, 2537, 2823, 3179, 3596, 3639, 4489, 5107, 5140, 5289, 6240, 6631, 6762, 7048, 7171, 7969, 7984, 7989, 8824, 8927, 9915, 10270, 10448, 13401, 15205, 18358, 18894, 18895, 19249, 19697};
+
printf("Classifier Demo\n");
network net = parse_network_cfg(cfgfile);
if(weightfile){
@@ -881,13 +1037,100 @@
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);
+
+ int *indexes = calloc(top, sizeof(int));
+
+ if(!cap) error("Couldn't connect to webcam.\n");
+ cvNamedWindow("Threat Detection", CV_WINDOW_NORMAL);
+ cvResizeWindow("Threat Detection", 512, 512);
+ float fps = 0;
+ int i;
+
+ while(1){
+ 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_cpp(cap);
+ image in_s = resize_image(in, net.w, net.h);
+ show_image(in, "Threat Detection");
+
+ float *predictions = network_predict(net, in_s.data);
+ top_predictions(net, top, indexes);
+
+ printf("\033[2J");
+ printf("\033[1;1H");
+
+ int threat = 0;
+ for(i = 0; i < sizeof(bad_cats)/sizeof(bad_cats[0]); ++i){
+ int index = bad_cats[i];
+ if(predictions[index] > .01){
+ printf("Threat Detected!\n");
+ threat = 1;
+ break;
+ }
+ }
+ if(!threat) printf("Scanning...\n");
+ for(i = 0; i < sizeof(bad_cats)/sizeof(bad_cats[0]); ++i){
+ int index = bad_cats[i];
+ if(predictions[index] > .01){
+ printf("%s\n", names[index]);
+ }
+ }
+
+ free_image(in_s);
+ free_image(in);
+
+ cvWaitKey(10);
+
+ gettimeofday(&tval_after, NULL);
+ timersub(&tval_after, &tval_before, &tval_result);
+ float curr = 1000000.f/((long int)tval_result.tv_usec);
+ fps = .9*fps + .1*curr;
+ }
+#endif
+}
+
+void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename)
+{
+#ifdef OPENCV
+ printf("Classifier Demo\n");
+ network net = parse_network_cfg_custom(cfgfile, 1);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ set_batch_network(&net, 1);
+ list *options = read_data_cfg(datacfg);
+
+ srand(2222222);
+ CvCapture * cap;
+
+ 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);
@@ -904,11 +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");
@@ -941,7 +1186,32 @@
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;
+ }
+
+ 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];
@@ -949,10 +1219,11 @@
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], "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);
else if(0==strcmp(argv[2], "test")) test_classifier(data, cfg, weights, layer);
else if(0==strcmp(argv[2], "label")) label_classifier(data, cfg, weights);
--
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