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#include "darknet.h"
void extract_voxel(char *lfile, char *rfile, char *prefix)
{
#ifdef OPENCV
int w = 1920;
int h = 1080;
int shift = 0;
int count = 0;
CvCapture *lcap = cvCaptureFromFile(lfile);
CvCapture *rcap = cvCaptureFromFile(rfile);
while(1){
image l = get_image_from_stream(lcap);
image r = get_image_from_stream(rcap);
if(!l.w || !r.w) break;
if(count%100 == 0) {
shift = best_3d_shift_r(l, r, -l.h/100, l.h/100);
printf("%d\n", shift);
}
image ls = crop_image(l, (l.w - w)/2, (l.h - h)/2, w, h);
image rs = crop_image(r, 105 + (r.w - w)/2, (r.h - h)/2 + shift, w, h);
char buff[256];
sprintf(buff, "%s_%05d_l", prefix, count);
save_image(ls, buff);
sprintf(buff, "%s_%05d_r", prefix, count);
save_image(rs, buff);
free_image(l);
free_image(r);
free_image(ls);
free_image(rs);
++count;
}
#else
printf("need OpenCV for extraction\n");
#endif
}
void train_voxel(char *cfgfile, char *weightfile)
{
char *train_images = "/data/imagenet/imagenet1k.train.list";
char *backup_directory = "/home/pjreddie/backup/";
srand(time(0));
char *base = basecfg(cfgfile);
printf("%s\n", base);
float avg_loss = -1;
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = net.batch*net.subdivisions;
int i = *net.seen/imgs;
data train, buffer;
list *plist = get_paths(train_images);
//int N = plist->size;
char **paths = (char **)list_to_array(plist);
load_args args = {0};
args.w = net.w;
args.h = net.h;
args.scale = 4;
args.paths = paths;
args.n = imgs;
args.m = plist->size;
args.d = &buffer;
args.type = SUPER_DATA;
pthread_t load_thread = load_data_in_thread(args);
clock_t time;
//while(i*imgs < N*120){
while(get_current_batch(net) < net.max_batches){
i += 1;
time=clock();
pthread_join(load_thread, 0);
train = buffer;
load_thread = load_data_in_thread(args);
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
float loss = train_network(net, train);
if (avg_loss < 0) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs);
if(i%1000==0){
char buff[256];
sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
save_weights(net, buff);
}
if(i%100==0){
char buff[256];
sprintf(buff, "%s/%s.backup", backup_directory, base);
save_weights(net, buff);
}
free_data(train);
}
char buff[256];
sprintf(buff, "%s/%s_final.weights", backup_directory, base);
save_weights(net, buff);
}
void test_voxel(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);
clock_t time;
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 im = load_image_color(input, 0, 0);
resize_network(&net, im.w, im.h);
printf("%d %d\n", im.w, im.h);
float *X = im.data;
time=clock();
network_predict(net, X);
image out = get_network_image(net);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
save_image(out, "out");
free_image(im);
if (filename) break;
}
}
void run_voxel(int argc, char **argv)
{
if(argc < 4){
fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
return;
}
char *cfg = argv[3];
char *weights = (argc > 4) ? argv[4] : 0;
char *filename = (argc > 5) ? argv[5] : 0;
if(0==strcmp(argv[2], "train")) train_voxel(cfg, weights);
else if(0==strcmp(argv[2], "test")) test_voxel(cfg, weights, filename);
else if(0==strcmp(argv[2], "extract")) extract_voxel(argv[3], argv[4], argv[5]);
/*
else if(0==strcmp(argv[2], "valid")) validate_voxel(cfg, weights);
*/
}
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