void run_char_rnn(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 *filename = find_char_arg(argc, argv, "-file", "data/shakespeare.txt");
char *seed = find_char_arg(argc, argv, "-seed", "\n\n");
int len = find_int_arg(argc, argv, "-len", 1000);
real_t temp = find_real_t_arg(argc, argv, "-temp", .7);
int rseed = find_int_arg(argc, argv, "-srand", time(0));
int clear = find_arg(argc, argv, "-clear");
int tokenized = find_arg(argc, argv, "-tokenized");
char *tokens = find_char_arg(argc, argv, "-tokens", 0);
char *cfg = argv[3];
char *weights = (argc > 4) ? argv[4] : 0;
if (0 == strcmp(argv[2], "train"))
train_char_rnn(cfg, weights, filename, clear, tokenized);
else if (0 == strcmp(argv[2], "valid"))
valid_char_rnn(cfg, weights, seed);
else if (0 == strcmp(argv[2], "validtactic"))
valid_tactic_rnn(cfg, weights, seed);
else if (0 == strcmp(argv[2], "vec"))
vec_char_rnn(cfg, weights, seed);
else if (0 == strcmp(argv[2], "generate"))
test_char_rnn(cfg, weights, len, seed, temp, rseed, tokens);
else if (0 == strcmp(argv[2], "generatetactic"))
test_tactic_rnn(cfg, weights, len, temp, rseed, tokens);
}
void run_yolo(int argc, char **argv)
{
int dont_show = find_arg(argc, argv, "-dont_show");
int http_stream_port = find_int_arg(argc, argv, "-http_port", -1);
char *out_filename = find_char_arg(argc, argv, "-out_filename", 0);
char *prefix = find_char_arg(argc, argv, "-prefix", 0);
float thresh = find_float_arg(argc, argv, "-thresh", .2);
float hier_thresh = find_float_arg(argc, argv, "-hier", .5);
int cam_index = find_int_arg(argc, argv, "-c", 0);
int frame_skip = find_int_arg(argc, argv, "-s", 0);
int ext_output = find_arg(argc, argv, "-ext_output");
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], "test")) test_yolo(cfg, weights, filename, thresh);
else if(0==strcmp(argv[2], "train")) train_yolo(cfg, weights);
else if(0==strcmp(argv[2], "valid")) validate_yolo(cfg, weights);
else if(0==strcmp(argv[2], "recall")) validate_yolo_recall(cfg, weights);
else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, hier_thresh, cam_index, filename, voc_names, 20, frame_skip,
prefix, out_filename, http_stream_port, dont_show, ext_output);
}
void run_detector(int argc, char **argv)
{
char *prefix = find_char_arg(argc, argv, "-prefix", 0);
float thresh = find_float_arg(argc, argv, "-thresh", .24);
int cam_index = find_int_arg(argc, argv, "-c", 0);
int frame_skip = find_int_arg(argc, argv, "-s", 0);
if(argc < 4){
fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
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 clear = find_arg(argc, argv, "-clear");
char *datacfg = argv[3];
char *cfg = argv[4];
char *weights = (argc > 5) ? argv[5] : 0;
char *filename = (argc > 6) ? argv[6]: 0;
if(0==strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh);
else if(0==strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear);
else if(0==strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights);
else if(0==strcmp(argv[2], "recall")) validate_detector_recall(cfg, weights);
else if(0==strcmp(argv[2], "demo")) {
list *options = read_data_cfg(datacfg);
int classes = option_find_int(options, "classes", 20);
char *name_list = option_find_str(options, "names", "data/names.list");
char **names = get_labels(name_list);
demo(cfg, weights, thresh, cam_index, filename, names, classes, frame_skip, prefix);
}
}
void run_coco(int argc, char **argv)
{
int i;
for(i = 0; i < 80; ++i){
char buff[256];
sprintf(buff, "data/labels/%s.png", coco_classes[i]);
coco_labels[i] = load_image_color(buff, 0, 0);
}
float thresh = find_float_arg(argc, argv, "-thresh", .2);
int cam_index = find_int_arg(argc, argv, "-c", 0);
char *file = find_char_arg(argc, argv, "-file", 0);
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], "test")) test_coco(cfg, weights, filename, thresh);
else if(0==strcmp(argv[2], "train")) train_coco(cfg, weights);
else if(0==strcmp(argv[2], "valid")) validate_coco(cfg, weights);
else if(0==strcmp(argv[2], "recall")) validate_coco_recall(cfg, weights);
else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, cam_index, file);
}
void run_lsd(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 clear = find_arg(argc, argv, "-clear");
int display = find_arg(argc, argv, "-display");
int batches = find_int_arg(argc, argv, "-b", 0);
char *file = find_char_arg(argc, argv, "-file", "/home/kunle12/data/imagenet/imagenet1k.train.list");
char *cfg = argv[3];
char *weights = (argc > 4) ? argv[4] : 0;
char *filename = (argc > 5) ? argv[5] : 0;
char *acfg = argv[5];
char *aweights = (argc > 6) ? argv[6] : 0;
//if(0==strcmp(argv[2], "train")) train_lsd(cfg, weights, clear);
//else if(0==strcmp(argv[2], "train2")) train_lsd2(cfg, weights, acfg, aweights, clear);
//else if(0==strcmp(argv[2], "traincolor")) train_colorizer(cfg, weights, acfg, aweights, clear);
//else if(0==strcmp(argv[2], "train3")) train_lsd3(argv[3], argv[4], argv[5], argv[6], argv[7], argv[8], clear);
if(0==strcmp(argv[2], "traingan")) train_dcgan(cfg, weights, acfg, aweights, clear, display, file, batches);
else if(0==strcmp(argv[2], "trainprog")) train_prog(cfg, weights, acfg, aweights, clear, display, file, batches);
else if(0==strcmp(argv[2], "traincolor")) train_colorizer(cfg, weights, acfg, aweights, clear, display);
else if(0==strcmp(argv[2], "gan")) test_dcgan(cfg, weights);
else if(0==strcmp(argv[2], "inter")) inter_dcgan(cfg, weights);
else if(0==strcmp(argv[2], "test")) test_lsd(cfg, weights, filename, 0);
else if(0==strcmp(argv[2], "color")) test_lsd(cfg, weights, filename, 1);
/*
else if(0==strcmp(argv[2], "valid")) validate_lsd(cfg, weights);
*/
}
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;
}
char *gpu_list = find_char_arg(argc, argv, "-gpus", 0);
int ngpus;
int *gpus = read_intlist(gpu_list, &ngpus, gpu_index);
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);
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);
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);
}
void run_char_rnn(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 *filename = find_char_arg(argc, argv, "-file", "data/shakespeare.txt");
char *seed = find_char_arg(argc, argv, "-seed", "\n");
int len = find_int_arg(argc, argv, "-len", 100);
float temp = find_float_arg(argc, argv, "-temp", 1);
int rseed = find_int_arg(argc, argv, "-srand", time(0));
char *cfg = argv[3];
char *weights = (argc > 4) ? argv[4] : 0;
if(0==strcmp(argv[2], "train")) train_char_rnn(cfg, weights, filename);
else if(0==strcmp(argv[2], "test")) test_char_rnn(cfg, weights, len, seed, temp, rseed);
}
void run_regressor(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 *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 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;
char *filename = (argc > 6) ? argv[6] : 0;
if (0 == strcmp(argv[2], "test"))
predict_regressor(data, cfg, weights);
else if (0 == strcmp(argv[2], "train"))
train_regressor(data, cfg, weights, gpus, ngpus, clear);
else if (0 == strcmp(argv[2], "demo"))
demo_regressor(data, cfg, weights, cam_index, filename);
}
void run_yolo(int argc, char **argv)
{
char *prefix = find_char_arg(argc, argv, "-prefix", 0);
float thresh = find_float_arg(argc, argv, "-thresh", .2);
int cam_index = find_int_arg(argc, argv, "-c", 0);
int frame_skip = find_int_arg(argc, argv, "-s", 0);
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], "test")) test_yolo(cfg, weights, filename, thresh);
else if(0==strcmp(argv[2], "train")) train_yolo(cfg, weights);
else if(0==strcmp(argv[2], "valid")) validate_yolo(cfg, weights);
else if(0==strcmp(argv[2], "recall")) validate_yolo_recall(cfg, weights);
else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, cam_index, filename, voc_names, 20, frame_skip, prefix, .5);
}
void run_nightmare(int argc, char **argv)
{
srand(0);
if(argc < 4){
fprintf(stderr, "usage: %s %s [cfg] [weights] [image] [layer] [options! (optional)]\n", argv[0], argv[1]);
return;
}
char *cfg = argv[2];
char *weights = argv[3];
char *input = argv[4];
int max_layer = atoi(argv[5]);
int range = find_int_arg(argc, argv, "-range", 1);
int norm = find_int_arg(argc, argv, "-norm", 1);
int rounds = find_int_arg(argc, argv, "-rounds", 1);
int iters = find_int_arg(argc, argv, "-iters", 10);
int octaves = find_int_arg(argc, argv, "-octaves", 4);
float zoom = find_float_arg(argc, argv, "-zoom", 1.);
float rate = find_float_arg(argc, argv, "-rate", .04);
float thresh = find_float_arg(argc, argv, "-thresh", 1.);
float rotate = find_float_arg(argc, argv, "-rotate", 0);
float momentum = find_float_arg(argc, argv, "-momentum", .9);
float lambda = find_float_arg(argc, argv, "-lambda", .01);
char *prefix = find_char_arg(argc, argv, "-prefix", 0);
int reconstruct = find_arg(argc, argv, "-reconstruct");
int smooth_size = find_int_arg(argc, argv, "-smooth", 1);
network net = parse_network_cfg(cfg);
load_weights(&net, weights);
char *cfgbase = basecfg(cfg);
char *imbase = basecfg(input);
set_batch_network(&net, 1);
image im = load_image_color(input, 0, 0);
if(0){
float scale = 1;
if(im.w > 512 || im.h > 512){
if(im.w > im.h) scale = 512.0/im.w;
else scale = 512.0/im.h;
}
image resized = resize_image(im, scale*im.w, scale*im.h);
free_image(im);
im = resized;
}
float *features = 0;
image update;
if (reconstruct){
resize_network(&net, im.w, im.h);
int zz = 0;
network_predict(net, im.data);
image out_im = get_network_image(net);
image crop = crop_image(out_im, zz, zz, out_im.w-2*zz, out_im.h-2*zz);
//flip_image(crop);
image f_im = resize_image(crop, out_im.w, out_im.h);
free_image(crop);
printf("%d features\n", out_im.w*out_im.h*out_im.c);
im = resize_image(im, im.w, im.h);
f_im = resize_image(f_im, f_im.w, f_im.h);
features = f_im.data;
int i;
for(i = 0; i < 14*14*512; ++i){
features[i] += rand_uniform(-.19, .19);
}
free_image(im);
im = make_random_image(im.w, im.h, im.c);
update = make_image(im.w, im.h, im.c);
}
int e;
int n;
for(e = 0; e < rounds; ++e){
fprintf(stderr, "Iteration: ");
fflush(stderr);
for(n = 0; n < iters; ++n){
fprintf(stderr, "%d, ", n);
fflush(stderr);
if(reconstruct){
reconstruct_picture(net, features, im, update, rate, momentum, lambda, smooth_size, 1);
//if ((n+1)%30 == 0) rate *= .5;
show_image(im, "reconstruction");
#ifdef OPENCV
cvWaitKey(10);
#endif
}else{
int layer = max_layer + rand()%range - range/2;
int octave = rand()%octaves;
optimize_picture(&net, im, layer, 1/pow(1.33333333, octave), rate, thresh, norm);
}
}
fprintf(stderr, "done\n");
if(0){
image g = grayscale_image(im);
free_image(im);
im = g;
}
char buff[256];
if (prefix){
sprintf(buff, "%s/%s_%s_%d_%06d",prefix, imbase, cfgbase, max_layer, e);
}else{
sprintf(buff, "%s_%s_%d_%06d",imbase, cfgbase, max_layer, e);
}
printf("%d %s\n", e, buff);
save_image(im, buff);
//show_image(im, buff);
//cvWaitKey(0);
if(rotate){
image rot = rotate_image(im, rotate);
free_image(im);
im = rot;
}
image crop = crop_image(im, im.w * (1. - zoom)/2., im.h * (1.-zoom)/2., im.w*zoom, im.h*zoom);
image resized = resize_image(crop, im.w, im.h);
free_image(im);
free_image(crop);
im = resized;
}
}
int main(int argc, char **argv)
{
//test_resize("data/bad.jpg");
//test_box();
//test_convolutional_layer();
if(argc < 2){
fprintf(stderr, "usage: %s <function>\n", argv[0]);
return 0;
}
gpu_index = find_int_arg(argc, argv, "-i", 0);
if(find_arg(argc, argv, "-nogpu")) {
gpu_index = -1;
}
#ifndef GPU
gpu_index = -1;
#else
if(gpu_index >= 0){
cuda_set_device(gpu_index);
}
#endif
if (0 == strcmp(argv[1], "average")){
average(argc, argv);
} else if (0 == strcmp(argv[1], "yolo")){
run_yolo(argc, argv);
} else if (0 == strcmp(argv[1], "super")){
run_super(argc, argv);
} else if (0 == strcmp(argv[1], "lsd")){
run_lsd(argc, argv);
} else if (0 == strcmp(argv[1], "detector")){
run_detector(argc, argv);
} else if (0 == strcmp(argv[1], "detect")){
float thresh = find_float_arg(argc, argv, "-thresh", .5);
char *filename = (argc > 4) ? argv[4]: 0;
char *outfile = find_char_arg(argc, argv, "-out", 0);
int fullscreen = find_arg(argc, argv, "-fullscreen");
test_detector("cfg/coco.data", argv[2], argv[3], filename, thresh, .5, outfile, fullscreen);
} else if (0 == strcmp(argv[1], "cifar")){
run_cifar(argc, argv);
} else if (0 == strcmp(argv[1], "go")){
run_go(argc, argv);
} else if (0 == strcmp(argv[1], "rnn")){
run_char_rnn(argc, argv);
} else if (0 == strcmp(argv[1], "coco")){
run_coco(argc, argv);
} else if (0 == strcmp(argv[1], "classify")){
predict_classifier("cfg/imagenet1k.data", argv[2], argv[3], argv[4], 5);
} else if (0 == strcmp(argv[1], "classifier")){
run_classifier(argc, argv);
} else if (0 == strcmp(argv[1], "regressor")){
run_regressor(argc, argv);
} else if (0 == strcmp(argv[1], "isegmenter")){
run_isegmenter(argc, argv);
} else if (0 == strcmp(argv[1], "segmenter")){
run_segmenter(argc, argv);
} else if (0 == strcmp(argv[1], "art")){
run_art(argc, argv);
} else if (0 == strcmp(argv[1], "tag")){
run_tag(argc, argv);
} else if (0 == strcmp(argv[1], "3d")){
composite_3d(argv[2], argv[3], argv[4], (argc > 5) ? atof(argv[5]) : 0);
} else if (0 == strcmp(argv[1], "test")){
test_resize(argv[2]);
} else if (0 == strcmp(argv[1], "nightmare")){
run_nightmare(argc, argv);
} else if (0 == strcmp(argv[1], "rgbgr")){
rgbgr_net(argv[2], argv[3], argv[4]);
} else if (0 == strcmp(argv[1], "reset")){
reset_normalize_net(argv[2], argv[3], argv[4]);
} else if (0 == strcmp(argv[1], "denormalize")){
denormalize_net(argv[2], argv[3], argv[4]);
} else if (0 == strcmp(argv[1], "statistics")){
statistics_net(argv[2], argv[3]);
} else if (0 == strcmp(argv[1], "normalize")){
normalize_net(argv[2], argv[3], argv[4]);
} else if (0 == strcmp(argv[1], "rescale")){
rescale_net(argv[2], argv[3], argv[4]);
} else if (0 == strcmp(argv[1], "ops")){
operations(argv[2]);
} else if (0 == strcmp(argv[1], "speed")){
speed(argv[2], (argc > 3 && argv[3]) ? atoi(argv[3]) : 0);
} else if (0 == strcmp(argv[1], "oneoff")){
oneoff(argv[2], argv[3], argv[4]);
} else if (0 == strcmp(argv[1], "oneoff2")){
oneoff2(argv[2], argv[3], argv[4], atoi(argv[5]));
} else if (0 == strcmp(argv[1], "print")){
print_weights(argv[2], argv[3], atoi(argv[4]));
} else if (0 == strcmp(argv[1], "partial")){
partial(argv[2], argv[3], argv[4], atoi(argv[5]));
} else if (0 == strcmp(argv[1], "average")){
average(argc, argv);
} else if (0 == strcmp(argv[1], "visualize")){
visualize(argv[2], (argc > 3) ? argv[3] : 0);
} else if (0 == strcmp(argv[1], "mkimg")){
mkimg(argv[2], argv[3], atoi(argv[4]), atoi(argv[5]), atoi(argv[6]), argv[7]);
} else if (0 == strcmp(argv[1], "imtest")){
test_resize(argv[2]);
} else {
fprintf(stderr, "Not an option: %s\n", argv[1]);
}
return 0;
}
void run_nightmare(int argc, char **argv)
{
srand(0);
if(argc < 4){
fprintf(stderr, "usage: %s %s [cfg] [weights] [image] [layer] [options! (optional)]\n", argv[0], argv[1]);
return;
}
char *cfg = argv[2];
char *weights = argv[3];
char *input = argv[4];
int max_layer = atoi(argv[5]);
int range = find_int_arg(argc, argv, "-range", 1);
int rounds = find_int_arg(argc, argv, "-rounds", 1);
int iters = find_int_arg(argc, argv, "-iters", 10);
int octaves = find_int_arg(argc, argv, "-octaves", 4);
float zoom = find_float_arg(argc, argv, "-zoom", 1.);
float rate = find_float_arg(argc, argv, "-rate", .04);
float thresh = find_float_arg(argc, argv, "-thresh", 1.);
float rotate = find_float_arg(argc, argv, "-rotate", 0);
char *prefix = find_char_arg(argc, argv, "-prefix", 0);
network net = parse_network_cfg(cfg);
load_weights(&net, weights);
char *cfgbase = basecfg(cfg);
char *imbase = basecfg(input);
set_batch_network(&net, 1);
image im = load_image_color(input, 0, 0);
if(0){
float scale = 1;
if(im.w > 512 || im.h > 512){
if(im.w > im.h) scale = 512.0/im.w;
else scale = 512.0/im.h;
}
image resized = resize_image(im, scale*im.w, scale*im.h);
free_image(im);
im = resized;
}
int e;
int n;
for(e = 0; e < rounds; ++e){
fprintf(stderr, "Iteration: ");
fflush(stderr);
for(n = 0; n < iters; ++n){
fprintf(stderr, "%d, ", n);
fflush(stderr);
int layer = max_layer + rand()%range - range/2;
int octave = rand()%octaves;
optimize_picture(&net, im, layer, 1/pow(1.33333333, octave), rate, thresh);
}
fprintf(stderr, "done\n");
if(0){
image g = grayscale_image(im);
free_image(im);
im = g;
}
char buff[256];
if (prefix){
sprintf(buff, "%s/%s_%s_%d_%06d",prefix, imbase, cfgbase, max_layer, e);
}else{
sprintf(buff, "%s_%s_%d_%06d",imbase, cfgbase, max_layer, e);
}
printf("%d %s\n", e, buff);
save_image(im, buff);
//show_image(im, buff);
//cvWaitKey(0);
if(rotate){
image rot = rotate_image(im, rotate);
free_image(im);
im = rot;
}
image crop = crop_image(im, im.w * (1. - zoom)/2., im.h * (1.-zoom)/2., im.w*zoom, im.h*zoom);
image resized = resize_image(crop, im.w, im.h);
free_image(im);
free_image(crop);
im = resized;
}
}