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);
if(net.hierarchy) hierarchy_predictions(pred, net.outputs, net.hierarchy, 1, 1);
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 get_region_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness, int *map)
{
int i;
float *const predictions = l.output;
#pragma omp parallel for
for (i = 0; i < l.w*l.h; ++i){
int j, n;
int row = i / l.w;
int col = i % l.w;
for(n = 0; n < l.n; ++n){
int index = i*l.n + n;
int p_index = index * (l.classes + 5) + 4;
float scale = predictions[p_index];
if(l.classfix == -1 && scale < .5) scale = 0;
int box_index = index * (l.classes + 5);
boxes[index] = get_region_box(predictions, l.biases, n, box_index, col, row, l.w, l.h);
boxes[index].x *= w;
boxes[index].y *= h;
boxes[index].w *= w;
boxes[index].h *= h;
int class_index = index * (l.classes + 5) + 5;
if(l.softmax_tree){
hierarchy_predictions(predictions + class_index, l.classes, l.softmax_tree, 0);
int found = 0;
if(map){
for(j = 0; j < 200; ++j){
float prob = scale*predictions[class_index+map[j]];
probs[index][j] = (prob > thresh) ? prob : 0;
}
} else {
for(j = l.classes - 1; j >= 0; --j){
if(!found && predictions[class_index + j] > .5){
found = 1;
} else {
predictions[class_index + j] = 0;
}
float prob = predictions[class_index+j];
probs[index][j] = (scale > thresh) ? prob : 0;
}
}
} else {
for(j = 0; j < l.classes; ++j){
float prob = scale*predictions[class_index+j];
probs[index][j] = (prob > thresh) ? prob : 0;
}
}
if(only_objectness){
probs[index][0] = scale;
}
}
}
}
void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top)
{
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");
if(top == 0) 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;
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);
//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, 1, 1);
top_k(predictions, net.outputs, top, indexes);
fprintf(stderr, "%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]);
printf("%5.2f%%: %s\n", predictions[index]*100, names[index]);
}
if(r.data != im.data) free_image(r);
free_image(im);
if (filename) break;
}
}
void get_region_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, float tree_thresh, int relative, detection *dets)
{
int i, j, n, z;
float *predictions = l.output;
if (l.batch == 2) {
float *flip = l.output + l.outputs;
for (j = 0; j < l.h; ++j) {
for (i = 0; i < l.w / 2; ++i) {
for (n = 0; n < l.n; ++n) {
for (z = 0; z < l.classes + l.coords + 1; ++z) {
int i1 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + i;
int i2 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + (l.w - i - 1);
float swap = flip[i1];
flip[i1] = flip[i2];
flip[i2] = swap;
if (z == 0) {
flip[i1] = -flip[i1];
flip[i2] = -flip[i2];
}
}
}
}
}
for (i = 0; i < l.outputs; ++i) {
l.output[i] = (l.output[i] + flip[i]) / 2.;
}
}
for (i = 0; i < l.w*l.h; ++i) {
int row = i / l.w;
int col = i % l.w;
for (n = 0; n < l.n; ++n) {
int index = n*l.w*l.h + i;
for (j = 0; j < l.classes; ++j) {
dets[index].prob[j] = 0;
}
int obj_index = entry_index(l, 0, n*l.w*l.h + i, l.coords);
int box_index = entry_index(l, 0, n*l.w*l.h + i, 0);
int mask_index = entry_index(l, 0, n*l.w*l.h + i, 4);
float scale = l.background ? 1 : predictions[obj_index];
dets[index].bbox = get_region_box(predictions, l.biases, n, box_index, col, row, l.w, l.h);// , l.w*l.h);
dets[index].objectness = scale > thresh ? scale : 0;
if (dets[index].mask) {
for (j = 0; j < l.coords - 4; ++j) {
dets[index].mask[j] = l.output[mask_index + j*l.w*l.h];
}
}
int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + !l.background);
if (l.softmax_tree) {
hierarchy_predictions(predictions + class_index, l.classes, l.softmax_tree, 0);// , l.w*l.h);
if (map) {
for (j = 0; j < 200; ++j) {
int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + 1 + map[j]);
float prob = scale*predictions[class_index];
dets[index].prob[j] = (prob > thresh) ? prob : 0;
}
}
else {
int j = hierarchy_top_prediction(predictions + class_index, l.softmax_tree, tree_thresh, l.w*l.h);
dets[index].prob[j] = (scale > thresh) ? scale : 0;
}
}
else {
if (dets[index].objectness) {
for (j = 0; j < l.classes; ++j) {
int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + 1 + j);
float prob = scale*predictions[class_index];
dets[index].prob[j] = (prob > thresh) ? prob : 0;
}
}
}
}
}
correct_region_boxes(dets, l.w*l.h*l.n, w, h, netw, neth, relative);
}
void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename)
{
#ifdef OPENCV
printf("Classifier Demo\n");
network *net = load_network(cfgfile, weightfile, 0);
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);
if(net->hierarchy) hierarchy_predictions(predictions, net->outputs, net->hierarchy, 1, 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 validate_classifier_multi(char *datacfg, char *cfg, char *weights)
{
int i, j;
network *net = load_network(cfg, weights, 0);
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);
//int scales[] = {224, 288, 320, 352, 384};
int scales[] = {224, 256, 288, 320};
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_max(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 , 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) 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_10(char *datacfg, char *filename, char *weightfile)
{
int i, j;
network *net = load_network(filename, weightfile, 0);
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;
}
}
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, 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;
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));
}
}
