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darknet/src/classifier.c

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#include "network.h"
#include "utils.h"
#include "parser.h"
#include "option_list.h"
#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"
IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size, int dont_show);
void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches,
float precision, int draw_precision, char *accuracy_name, int dont_show, int mjpeg_port);
#endif
float validate_classifier_single(char *datacfg, char *filename, char *weightfile, network *existing_net, int topk_custom);
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 *gpus, int ngpus, int clear, int dont_show, int mjpeg_port, int calc_topk)
{
int i;
float avg_loss = -1;
char *base = basecfg(cfgfile);
printf("%s\n", base);
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/");
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 train_images_num = plist->size;
clock_t time;
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;
args.size = net.w;
args.paths = paths;
args.classes = classes;
args.n = imgs;
args.m = train_images_num;
args.labels = labels;
args.type = CLASSIFICATION_DATA;
#ifdef OPENCV
//args.threads = 3;
IplImage* img = NULL;
float max_img_loss = 10;
int number_of_lines = 100;
int img_size = 1000;
img = draw_train_chart(max_img_loss, net.max_batches, number_of_lines, img_size, dont_show);
#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);
int iter_save_last = get_current_batch(net);
int iter_topk = get_current_batch(net);
float topk = 0;
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
if(avg_loss == -1) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
i = get_current_batch(net);
int calc_topk_for_each = iter_topk + 4 * train_images_num / (net.batch * net.subdivisions); // calculate TOPk for each 4 Epochs
calc_topk_for_each = fmax(calc_topk_for_each, net.burn_in);
calc_topk_for_each = fmax(calc_topk_for_each, 1000);
if (i % 10 == 0) {
if (calc_topk) {
fprintf(stderr, "\n (next TOP5 calculation at %d iterations) ", calc_topk_for_each);
if (topk > 0) fprintf(stderr, " Last accuracy TOP5 = %2.2f %% \n", topk * 100);
}
if (net.cudnn_half) {
if (i < net.burn_in * 3) fprintf(stderr, " Tensor Cores are disabled until the first %d iterations are reached.\n", 3 * net.burn_in);
else fprintf(stderr, " Tensor Cores are used.\n");
}
}
int draw_precision = 0;
if (calc_topk && (i >= calc_topk_for_each || i == net.max_batches)) {
iter_topk = i;
topk = validate_classifier_single(datacfg, cfgfile, weightfile, &net, 5); // calc TOP5
printf("\n accuracy TOP5 = %f \n", topk);
draw_precision = 1;
}
printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/ train_images_num, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen);
#ifdef OPENCV
draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches, topk, draw_precision, "top5", dont_show, mjpeg_port);
#endif // OPENCV
if (i >= (iter_save + 1000)) {
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);
}
if (i >= (iter_save_last + 100)) {
iter_save_last = i;
#ifdef GPU
if (ngpus != 1) sync_nets(nets, ngpus, 0);
#endif
char buff[256];
sprintf(buff, "%s/%s_last.weights", backup_directory, base, i);
save_weights(net, buff);
}
free_data(train);
}
#ifdef GPU
if (ngpus != 1) sync_nets(nets, ngpus, 0);
#endif
char buff[256];
sprintf(buff, "%s/%s_final.weights", backup_directory, base);
save_weights(net, buff);
#ifdef OPENCV
cvReleaseImage(&img);
cvDestroyAllWindows();
#endif
free_network(net);
free_ptrs((void**)labels, classes);
free_ptrs((void**)paths, plist->size);
free_list(plist);
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;
network net = parse_network_cfg(filename);
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);
if (topk > classes) topk = classes;
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);
clock_t time;
float avg_acc = 0;
float avg_topk = 0;
int splits = m/1000;
int num = (i+1)*m/splits - i*m/splits;
data val, buffer;
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 = OLD_CLASSIFICATION_DATA;
pthread_t load_thread = load_data_in_thread(args);
for(i = 1; i <= splits; ++i){
time=clock();
pthread_join(load_thread, 0);
val = buffer;
num = (i+1)*m/splits - i*m/splits;
char **part = paths+(i*m/splits);
if(i != splits){
args.paths = part;
load_thread = load_data_in_thread(args);
}
printf("Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time));
time=clock();
float *acc = network_accuracies(net, val, topk);
avg_acc += acc[0];
avg_topk += acc[1];
printf("%d: top 1: %f, top %d: %f, %lf seconds, %d images\n", i, avg_acc/i, topk, avg_topk/i, sec(clock()-time), val.X.rows);
free_data(val);
}
}
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);
if (topk > classes) topk = classes;
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_id = -1;
char *path = paths[i];
for(j = 0; j < classes; ++j){
if(strstr(path, labels[j])){
class_id = 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);
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_id) avg_acc += 1;
for(j = 0; j < topk; ++j){
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));
}
}
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);
if (topk > classes) topk = classes;
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_id = -1;
char *path = paths[i];
for(j = 0; j < classes; ++j){
if(strstr(path, labels[j])){
class_id = 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);
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_id) avg_acc += 1;
for(j = 0; j < topk; ++j){
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));
}
}
float validate_classifier_single(char *datacfg, char *filename, char *weightfile, network *existing_net, int topk_custom)
{
int i, j;
network net;
int old_batch = -1;
if (existing_net) {
net = *existing_net; // for validation during training
old_batch = net.batch;
set_batch_network(&net, 1);
}
else {
net = parse_network_cfg_custom(filename, 1, 0);
if (weightfile) {
load_weights(&net, weightfile);
}
//set_batch_network(&net, 1);
fuse_conv_batchnorm(net);
calculate_binary_weights(net);
}
srand(time(0));
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_custom > 0) topk = topk_custom; // for validation during training
if (topk > classes) topk = classes;
printf(" TOP calculation...\n");
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_id = -1;
char *path = paths[i];
for(j = 0; j < classes; ++j){
if(strstr(path, labels[j])){
class_id = 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(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_id) avg_acc += 1;
for(j = 0; j < topk; ++j){
if(indexes[j] == class_id) avg_topk += 1;
}
if (existing_net) printf("\r");
else printf("\n");
printf("%d: top 1: %f, top %d: %f", i, avg_acc/(i+1), topk, avg_topk/(i+1));
}
if (existing_net) {
set_batch_network(&net, old_batch);
}
float topk_result = avg_topk / i;
return topk_result;
}
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);
if (topk > classes) topk = classes;
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_id = -1;
char *path = paths[i];
for(j = 0; j < classes; ++j){
if(strstr(path, labels[j])){
class_id = 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);
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);
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_id) avg_acc += 1;
for(j = 0; j < topk; ++j){
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));
}
}
void try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int layer_num)
{
network net = parse_network_cfg_custom(cfgfile, 1, 0);
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 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);
clock_t time;
int *indexes = calloc(top, sizeof(int));
char buff[256];
char *input = buff;
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 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){
int index = indexes[i];
printf("%s: %f\n", names[index], predictions[index]);
}
free_image(im);
if (filename) break;
}
}
void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top)
{
network net = parse_network_cfg_custom(cfgfile, 1, 0);
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 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);
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 = 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);
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];
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);
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;
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
srand(time(0));
list *options = read_data_cfg(datacfg);
char *test_list = option_find_str(options, "test", "data/test.list");
int classes = option_find_int(options, "classes", 2);
list *plist = get_paths(test_list);
char **paths = (char **)list_to_array(plist);
int m = plist->size;
free_list(plist);
clock_t time;
data val, buffer;
load_args args = {0};
args.w = net.w;
args.h = net.h;
args.paths = paths;
args.classes = classes;
args.n = net.batch;
args.m = 0;
args.labels = 0;
args.d = &buffer;
args.type = OLD_CLASSIFICATION_DATA;
pthread_t load_thread = load_data_in_thread(args);
for(curr = net.batch; curr < m; curr += net.batch){
time=clock();
pthread_join(load_thread, 0);
val = buffer;
if(curr < m){
args.paths = paths + curr;
if (curr + net.batch > m) args.n = m - curr;
load_thread = load_data_in_thread(args);
}
fprintf(stderr, "Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time));
time=clock();
matrix pred = network_predict_data(net, val);
int i, j;
if (target_layer >= 0){
//layer l = net.layers[target_layer];
}
for(i = 0; i < pred.rows; ++i){
printf("%s", paths[curr-net.batch+i]);
for(j = 0; j < pred.cols; ++j){
printf("\t%g", pred.vals[i][j]);
}
printf("\n");
}
free_matrix(pred);
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);
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", 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);
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;
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 = 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);
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(1){
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 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){
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);
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, 0);
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);
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 = 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");
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){
fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
return;
}
int mjpeg_port = find_int_arg(argc, argv, "-mjpeg_port", -1);
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 calc_topk = find_arg(argc, argv, "-topk");
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];
char *weights = (argc > 5) ? argv[5] : 0;
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, 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, gpus, ngpus, clear, dont_show, mjpeg_port, calc_topk);
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);
else if(0==strcmp(argv[2], "valid")) validate_classifier_single(data, cfg, weights, NULL, -1);
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);
}