From 8f1b4e0962857d402f9d017fcbf387ef0eceb7c4 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Thu, 01 Sep 2016 23:48:41 +0000
Subject: [PATCH] updates and things
---
src/classifier.c | 686 +++++++++++++++++++++++++++++++++++++++++++++++++++++++-
1 files changed, 664 insertions(+), 22 deletions(-)
diff --git a/src/classifier.c b/src/classifier.c
index ddd88b1..e59f7ae 100644
--- a/src/classifier.c
+++ b/src/classifier.c
@@ -2,6 +2,10 @@
#include "utils.h"
#include "parser.h"
#include "option_list.h"
+#include "blas.h"
+#include "assert.h"
+#include "classifier.h"
+#include <sys/time.h>
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
@@ -35,8 +39,23 @@
return options;
}
-void train_classifier(char *datacfg, char *cfgfile, char *weightfile)
+float *get_regression_values(char **labels, int n)
{
+ float *v = calloc(n, sizeof(float));
+ int i;
+ for(i = 0; i < n; ++i){
+ char *p = strchr(labels[i], ' ');
+ *p = 0;
+ v[i] = atof(p+1);
+ }
+ return v;
+}
+
+void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear)
+{
+ int nthreads = 8;
+ int i;
+
data_seed = time(0);
srand(time(0));
float avg_loss = -1;
@@ -46,8 +65,10 @@
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 = 1024;
+ int imgs = net.batch*net.subdivisions/nthreads;
+ assert(net.batch*net.subdivisions % nthreads == 0);
list *options = read_data_cfg(datacfg);
@@ -62,43 +83,78 @@
printf("%d\n", plist->size);
int N = plist->size;
clock_t time;
- pthread_t load_thread;
- data train;
- data buffer;
+
+ 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.min = net.min_crop;
+ args.max = net.max_crop;
+ args.angle = net.angle;
+ 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.d = &buffer;
args.type = CLASSIFICATION_DATA;
- load_thread = load_data_in_thread(args);
+ for(i = 0; i < nthreads; ++i){
+ args.d = buffers + i;
+ load_threads[i] = load_data_in_thread(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;
+ for(i = 0; i < nthreads; ++i){
+ pthread_join(load_threads[i], 0);
+ trains[i] = buffers[i];
+ }
+ data train = concat_datas(trains, nthreads);
- load_thread = load_data_in_thread(args);
+ for(i = 0; i < nthreads; ++i){
+ args.d = buffers + i;
+ load_threads[i] = load_data_in_thread(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);
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);
+ 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(*net.seen%1000 == 0){
+ if(get_current_batch(net)%100 == 0){
char buff[256];
sprintf(buff, "%s/%s.backup",backup_directory,base);
save_weights(net, buff);
@@ -108,8 +164,14 @@
sprintf(buff, "%s/%s.weights", backup_directory, base);
save_weights(net, buff);
- pthread_join(load_thread, 0);
- free_data(buffer);
+ for(i = 0; i < nthreads; ++i){
+ pthread_join(load_threads[i], 0);
+ free_data(buffers[i]);
+ }
+ free(buffers);
+ free(trains);
+ free(load_threads);
+
free_network(net);
free_ptrs((void**)labels, classes);
free_ptrs((void**)paths, plist->size);
@@ -117,7 +179,7 @@
free(base);
}
-void validate_classifier(char *datacfg, char *filename, char *weightfile)
+void validate_classifier_crop(char *datacfg, char *filename, char *weightfile)
{
int i = 0;
network net = parse_network_cfg(filename);
@@ -151,13 +213,14 @@
load_args args = {0};
args.w = net.w;
args.h = net.h;
+
args.paths = paths;
args.classes = classes;
args.n = num;
args.m = 0;
args.labels = labels;
args.d = &buffer;
- args.type = CLASSIFICATION_DATA;
+ args.type = OLD_CLASSIFICATION_DATA;
pthread_t load_thread = load_data_in_thread(args);
for(i = 1; i <= splits; ++i){
@@ -183,7 +246,260 @@
}
}
-void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename)
+void validate_classifier_10(char *datacfg, char *filename, char *weightfile)
+{
+ int i, j;
+ network net = parse_network_cfg(filename);
+ set_batch_network(&net, 1);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ srand(time(0));
+
+ list *options = read_data_cfg(datacfg);
+
+ char *label_list = option_find_str(options, "labels", "data/labels.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);
+
+ char **labels = get_labels(label_list);
+ list *plist = get_paths(valid_list);
+
+ char **paths = (char **)list_to_array(plist);
+ int m = plist->size;
+ free_list(plist);
+
+ float avg_acc = 0;
+ float avg_topk = 0;
+ int *indexes = calloc(topk, sizeof(int));
+
+ for(i = 0; i < m; ++i){
+ int class = -1;
+ char *path = paths[i];
+ for(j = 0; j < classes; ++j){
+ if(strstr(path, labels[j])){
+ class = j;
+ break;
+ }
+ }
+ int w = net.w;
+ int h = net.h;
+ int shift = 32;
+ image im = load_image_color(paths[i], w+shift, h+shift);
+ image images[10];
+ images[0] = crop_image(im, -shift, -shift, w, h);
+ images[1] = crop_image(im, shift, -shift, w, h);
+ images[2] = crop_image(im, 0, 0, w, h);
+ images[3] = crop_image(im, -shift, shift, w, h);
+ images[4] = crop_image(im, shift, shift, w, h);
+ flip_image(im);
+ images[5] = crop_image(im, -shift, -shift, w, h);
+ images[6] = crop_image(im, shift, -shift, w, h);
+ images[7] = crop_image(im, 0, 0, w, h);
+ images[8] = crop_image(im, -shift, shift, w, h);
+ images[9] = crop_image(im, shift, shift, w, h);
+ float *pred = calloc(classes, sizeof(float));
+ for(j = 0; j < 10; ++j){
+ float *p = network_predict(net, images[j].data);
+ 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;
+ for(j = 0; j < topk; ++j){
+ if(indexes[j] == class) avg_topk += 1;
+ }
+
+ printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
+ }
+}
+
+void validate_classifier_full(char *datacfg, char *filename, char *weightfile)
+{
+ int i, j;
+ network net = parse_network_cfg(filename);
+ set_batch_network(&net, 1);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ srand(time(0));
+
+ list *options = read_data_cfg(datacfg);
+
+ char *label_list = option_find_str(options, "labels", "data/labels.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);
+
+ char **labels = get_labels(label_list);
+ list *plist = get_paths(valid_list);
+
+ char **paths = (char **)list_to_array(plist);
+ int m = plist->size;
+ free_list(plist);
+
+ float avg_acc = 0;
+ float avg_topk = 0;
+ int *indexes = calloc(topk, sizeof(int));
+
+ int size = net.w;
+ for(i = 0; i < m; ++i){
+ int class = -1;
+ char *path = paths[i];
+ for(j = 0; j < classes; ++j){
+ if(strstr(path, labels[j])){
+ class = j;
+ break;
+ }
+ }
+ image im = load_image_color(paths[i], 0, 0);
+ image resized = resize_min(im, size);
+ resize_network(&net, resized.w, resized.h);
+ //show_image(im, "orig");
+ //show_image(crop, "cropped");
+ //cvWaitKey(0);
+ float *pred = network_predict(net, resized.data);
+
+ free_image(im);
+ free_image(resized);
+ top_k(pred, classes, topk, indexes);
+
+ if(indexes[0] == class) avg_acc += 1;
+ for(j = 0; j < topk; ++j){
+ if(indexes[j] == class) avg_topk += 1;
+ }
+
+ printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
+ }
+}
+
+
+void validate_classifier_single(char *datacfg, char *filename, char *weightfile)
+{
+ int i, j;
+ network net = parse_network_cfg(filename);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ set_batch_network(&net, 1);
+ srand(time(0));
+
+ list *options = read_data_cfg(datacfg);
+
+ char *label_list = option_find_str(options, "labels", "data/labels.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);
+
+ char **labels = get_labels(label_list);
+ list *plist = get_paths(valid_list);
+
+ char **paths = (char **)list_to_array(plist);
+ int m = plist->size;
+ free_list(plist);
+
+ float avg_acc = 0;
+ float avg_topk = 0;
+ int *indexes = calloc(topk, sizeof(int));
+
+ for(i = 0; i < m; ++i){
+ int class = -1;
+ char *path = paths[i];
+ for(j = 0; j < classes; ++j){
+ if(strstr(path, labels[j])){
+ class = j;
+ break;
+ }
+ }
+ image im = load_image_color(paths[i], 0, 0);
+ image resized = resize_min(im, net.w);
+ image crop = crop_image(resized, (resized.w - net.w)/2, (resized.h - net.h)/2, net.w, net.h);
+ //show_image(im, "orig");
+ //show_image(crop, "cropped");
+ //cvWaitKey(0);
+ float *pred = network_predict(net, crop.data);
+
+ 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;
+ for(j = 0; j < topk; ++j){
+ if(indexes[j] == class) avg_topk += 1;
+ }
+
+ printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
+ }
+}
+
+void validate_classifier_multi(char *datacfg, char *filename, char *weightfile)
+{
+ int i, j;
+ network net = parse_network_cfg(filename);
+ set_batch_network(&net, 1);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ srand(time(0));
+
+ list *options = read_data_cfg(datacfg);
+
+ char *label_list = option_find_str(options, "labels", "data/labels.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);
+
+ char **labels = get_labels(label_list);
+ list *plist = get_paths(valid_list);
+ int scales[] = {224, 288, 320, 352, 384};
+ int nscales = sizeof(scales)/sizeof(scales[0]);
+
+ char **paths = (char **)list_to_array(plist);
+ int m = plist->size;
+ free_list(plist);
+
+ float avg_acc = 0;
+ float avg_topk = 0;
+ int *indexes = calloc(topk, sizeof(int));
+
+ for(i = 0; i < m; ++i){
+ int class = -1;
+ char *path = paths[i];
+ for(j = 0; j < classes; ++j){
+ if(strstr(path, labels[j])){
+ class = j;
+ break;
+ }
+ }
+ float *pred = calloc(classes, sizeof(float));
+ image im = load_image_color(paths[i], 0, 0);
+ for(j = 0; j < nscales; ++j){
+ image r = resize_min(im, scales[j]);
+ resize_network(&net, r.w, r.h);
+ float *p = network_predict(net, r.data);
+ axpy_cpu(classes, 1, p, 1, pred, 1);
+ flip_image(r);
+ p = network_predict(net, r.data);
+ axpy_cpu(classes, 1, p, 1, pred, 1);
+ if(r.data != im.data) free_image(r);
+ }
+ free_image(im);
+ top_k(pred, classes, topk, indexes);
+ free(pred);
+ if(indexes[0] == class) avg_acc += 1;
+ for(j = 0; j < topk; ++j){
+ if(indexes[j] == class) avg_topk += 1;
+ }
+
+ printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
+ }
+}
+
+void try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int layer_num)
{
network net = parse_network_cfg(cfgfile);
if(weightfile){
@@ -214,10 +530,45 @@
if(!input) return;
strtok(input, "\n");
}
- image im = load_image_color(input, net.w, net.h);
+ image orig = load_image_color(input, 0, 0);
+ image r = resize_min(orig, 256);
+ image im = crop_image(r, (r.w - 224 - 1)/2 + 1, (r.h - 224 - 1)/2 + 1, 224, 224);
+ float mean[] = {0.48263312050943, 0.45230225481413, 0.40099074308742};
+ float std[] = {0.22590347483426, 0.22120921437787, 0.22103996251583};
+ float var[3];
+ var[0] = std[0]*std[0];
+ var[1] = std[1]*std[1];
+ var[2] = std[2]*std[2];
+
+ normalize_cpu(im.data, mean, var, 1, 3, im.w*im.h);
+
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]);
+ }
+ #ifdef GPU
+ cuda_pull_array(l.output_gpu, l.output, l.outputs);
+ #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]);
+ }
+ */
+
top_predictions(net, top, indexes);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
for(i = 0; i < top; ++i){
@@ -229,6 +580,99 @@
}
}
+
+void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename)
+{
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ set_batch_network(&net, 1);
+ srand(2222222);
+
+ list *options = read_data_cfg(datacfg);
+
+ 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 i = 0;
+ char **names = get_labels(name_list);
+ clock_t time;
+ int *indexes = calloc(top, sizeof(int));
+ char buff[256];
+ char *input = buff;
+ int size = net.w;
+ while(1){
+ if(filename){
+ strncpy(input, filename, 256);
+ }else{
+ printf("Enter Image Path: ");
+ fflush(stdout);
+ input = fgets(input, 256, stdin);
+ if(!input) return;
+ strtok(input, "\n");
+ }
+ image im = load_image_color(input, 0, 0);
+ 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);
+ 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(r.data != im.data) free_image(r);
+ free_image(im);
+ if (filename) break;
+ }
+}
+
+
+void label_classifier(char *datacfg, char *filename, char *weightfile)
+{
+ int i;
+ network net = parse_network_cfg(filename);
+ set_batch_network(&net, 1);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ srand(time(0));
+
+ list *options = read_data_cfg(datacfg);
+
+ char *label_list = option_find_str(options, "names", "data/labels.list");
+ char *test_list = option_find_str(options, "test", "data/train.list");
+ int classes = option_find_int(options, "classes", 2);
+
+ char **labels = get_labels(label_list);
+ list *plist = get_paths(test_list);
+
+ char **paths = (char **)list_to_array(plist);
+ int m = plist->size;
+ free_list(plist);
+
+ for(i = 0; i < m; ++i){
+ image im = load_image_color(paths[i], 0, 0);
+ image resized = resize_min(im, net.w);
+ image crop = crop_image(resized, (resized.w - net.w)/2, (resized.h - net.h)/2, net.w, net.h);
+ float *pred = network_predict(net, crop.data);
+
+ if(resized.data != im.data) free_image(resized);
+ free_image(im);
+ free_image(crop);
+ int ind = max_index(pred, classes);
+
+ printf("%s\n", labels[ind]);
+ }
+}
+
+
void test_classifier(char *datacfg, char *cfgfile, char *weightfile, int target_layer)
{
int curr = 0;
@@ -262,7 +706,7 @@
args.m = 0;
args.labels = 0;
args.d = &buffer;
- args.type = CLASSIFICATION_DATA;
+ args.type = OLD_CLASSIFICATION_DATA;
pthread_t load_thread = load_data_in_thread(args);
for(curr = net.batch; curr < m; curr += net.batch){
@@ -280,7 +724,7 @@
time=clock();
matrix pred = network_predict_data(net, val);
-
+
int i, j;
if (target_layer >= 0){
//layer l = net.layers[target_layer];
@@ -296,12 +740,200 @@
free_matrix(pred);
- fprintf(stderr, "%lf seconds, %d images\n", sec(clock()-time), val.X.rows);
+ fprintf(stderr, "%lf seconds, %d images, %d total\n", sec(clock()-time), val.X.rows, curr);
free_data(val);
}
}
+void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename)
+{
+#ifdef OPENCV
+ float threat = 0;
+ float roll = .2;
+
+ printf("Classifier Demo\n");
+ network net = parse_network_cfg(cfgfile);
+ 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);
+ }else{
+ cap = cvCaptureFromCAM(cam_index);
+ }
+
+ int top = option_find_int(options, "top", 1);
+
+ 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", CV_WINDOW_NORMAL);
+ //cvResizeWindow("Threat", 512, 512);
+ float fps = 0;
+ int i;
+
+ int count = 0;
+
+ while(1){
+ ++count;
+ struct timeval tval_before, tval_after, tval_result;
+ gettimeofday(&tval_before, NULL);
+
+ image in = get_image_from_stream(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;
+
+ 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];
+ 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);
+ if(threat > .97) {
+ draw_box_width(out, x2 + .5 * w + border,
+ y1 + .02*h - 2*border,
+ x2 + .5 * w + 6*border,
+ y1 + .02*h + 3*border, 3*border, 1,0,0);
+ }
+ draw_box_width(out, x2 + .5 * w + border,
+ y1 + .02*h - 2*border,
+ x2 + .5 * w + 6*border,
+ 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, .5*border, 0,0,0);
+
+ draw_box_width(out, x1, y1, x2, y2, border, 0,0,0);
+ for(i = 0; i < threat * h ; ++i){
+ float ratio = (float) i / h;
+ float r = (ratio < .5) ? (2*(ratio)) : 1;
+ float g = (ratio < .5) ? 1 : 1 - 2*(ratio - .5);
+ draw_box_width(out, x1 + border, y2 - border - i, x2 - border, y2 - border - i, 1, r, g, 0);
+ }
+ top_predictions(net, top, indexes);
+ char buff[256];
+ sprintf(buff, "/home/pjreddie/tmp/threat_%06d", count);
+ save_image(out, buff);
+
+ printf("\033[2J");
+ printf("\033[1;1H");
+ printf("\nFPS:%.0f\n",fps);
+
+ for(i = 0; i < top; ++i){
+ int index = indexes[i];
+ printf("%.1f%%: %s\n", predictions[index]*100, names[index]);
+ }
+
+ if(0){
+ show_image(out, "Threat");
+ cvWaitKey(10);
+ }
+ free_image(in_s);
+ free_image(in);
+
+ 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(cfgfile);
+ 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);
+ }else{
+ cap = cvCaptureFromCAM(cam_index);
+ }
+
+ int top = option_find_int(options, "top", 1);
+
+ 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("Classifier", CV_WINDOW_NORMAL);
+ cvResizeWindow("Classifier", 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_s = resize_image(in, net.w, net.h);
+ show_image(in, "Classifier");
+
+ float *predictions = network_predict(net, in_s.data);
+ top_predictions(net, top, indexes);
+
+ printf("\033[2J");
+ printf("\033[1;1H");
+ printf("\nFPS:%.0f\n",fps);
+
+ for(i = 0; i < top; ++i){
+ int index = indexes[i];
+ printf("%.1f%%: %s\n", predictions[index]*100, 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 run_classifier(int argc, char **argv)
{
if(argc < 4){
@@ -309,6 +941,8 @@
return;
}
+ int cam_index = find_int_arg(argc, argv, "-c", 0);
+ int clear = find_arg(argc, argv, "-clear");
char *data = argv[3];
char *cfg = argv[4];
char *weights = (argc > 5) ? argv[5] : 0;
@@ -316,9 +950,17 @@
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);
- else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights);
+ 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], "demo")) demo_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], "valid")) validate_classifier(data, cfg, weights);
+ else if(0==strcmp(argv[2], "label")) label_classifier(data, cfg, weights);
+ else if(0==strcmp(argv[2], "valid")) validate_classifier_single(data, cfg, weights);
+ else if(0==strcmp(argv[2], "validmulti")) validate_classifier_multi(data, cfg, weights);
+ else if(0==strcmp(argv[2], "valid10")) validate_classifier_10(data, cfg, weights);
+ else if(0==strcmp(argv[2], "validcrop")) validate_classifier_crop(data, cfg, weights);
+ else if(0==strcmp(argv[2], "validfull")) validate_classifier_full(data, cfg, weights);
}
--
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