void test_box()
{
test_dintersect();
test_dunion();
box a = {0, 0, 1, 1};
box dxa= {0+.00001, 0, 1, 1};
box dya= {0, 0+.00001, 1, 1};
box dwa= {0, 0, 1+.00001, 1};
box dha= {0, 0, 1, 1+.00001};
box b = {.5, 0, .2, .2};
float iou = box_iou(a,b);
iou = (1-iou)*(1-iou);
printf("%f\n", iou);
dbox d = diou(a, b);
printf("%f %f %f %f\n", d.dx, d.dy, d.dw, d.dh);
float xiou = box_iou(dxa, b);
float yiou = box_iou(dya, b);
float wiou = box_iou(dwa, b);
float hiou = box_iou(dha, b);
xiou = ((1-xiou)*(1-xiou) - iou)/(.00001);
yiou = ((1-yiou)*(1-yiou) - iou)/(.00001);
wiou = ((1-wiou)*(1-wiou) - iou)/(.00001);
hiou = ((1-hiou)*(1-hiou) - iou)/(.00001);
printf("manual %f %f %f %f\n", xiou, yiou, wiou, hiou);
}
void do_nms_sort(detection *dets, int total, int classes, float thresh)
{
int i, j, k;
k = total-1;
for(i = 0; i <= k; ++i){
if(dets[i].objectness == 0){
detection swap = dets[i];
dets[i] = dets[k];
dets[k] = swap;
--k;
--i;
}
}
total = k+1;
for(k = 0; k < classes; ++k){
for(i = 0; i < total; ++i){
dets[i].sort_class = k;
}
qsort(dets, total, sizeof(detection), nms_comparator);
for(i = 0; i < total; ++i){
if(dets[i].prob[k] == 0) continue;
box a = dets[i].bbox;
for(j = i+1; j < total; ++j){
box b = dets[j].bbox;
if (box_iou(a, b) > thresh){
dets[j].prob[k] = 0;
}
}
}
}
}
float delta_region_box(box truth, float *x, float *biases, int n, int index, int i, int j, int w, int h, float *delta, float scale, int stride)
{
box pred = get_region_box(x, biases, n, index, i, j, w, h, stride);
float iou = box_iou(pred, truth);
float tx = (truth.x*w - i);
float ty = (truth.y*h - j);
float tw = log(truth.w*w / biases[2*n]);
float th = log(truth.h*h / biases[2*n + 1]);
delta[index + 0*stride] = scale * (tx - x[index + 0*stride]);
delta[index + 1*stride] = scale * (ty - x[index + 1*stride]);
delta[index + 2*stride] = scale * (tw - x[index + 2*stride]);
delta[index + 3*stride] = scale * (th - x[index + 3*stride]);
return iou;
}
void do_nms(box *boxes, float **probs, int total, int classes, float thresh)
{
int i, j, k;
for(i = 0; i < total; ++i){
int any = 0;
for(k = 0; k < classes; ++k) any = any || (probs[i][k] > 0);
if(!any) {
continue;
}
for(j = i+1; j < total; ++j){
if (box_iou(boxes[i], boxes[j]) > thresh){
for(k = 0; k < classes; ++k){
if (probs[i][k] < probs[j][k]) probs[i][k] = 0;
else probs[j][k] = 0;
}
}
}
}
}
float delta_region_box(box truth, float *x, float *biases, int n, int index, int i, int j, int w, int h, float *delta, float scale)
{
box pred = get_region_box(x, biases, n, index, i, j, w, h);
float iou = box_iou(pred, truth);
float tx = (truth.x*w - i);
float ty = (truth.y*h - j);
float tw = log(truth.w / biases[2*n]);
float th = log(truth.h / biases[2*n + 1]);
if(DOABS){
tw = log(truth.w*w / biases[2*n]);
th = log(truth.h*h / biases[2*n + 1]);
}
delta[index + 0] = scale * (tx - logistic_activate(x[index + 0])) * logistic_gradient(logistic_activate(x[index + 0]));
delta[index + 1] = scale * (ty - logistic_activate(x[index + 1])) * logistic_gradient(logistic_activate(x[index + 1]));
delta[index + 2] = scale * (tw - x[index + 2]);
delta[index + 3] = scale * (th - x[index + 3]);
return iou;
}
void validate_detector_recall(char *cfgfile, char *weightfile)
{
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
set_batch_network(&net, 1);
fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
srand(time(0));
list *plist = get_paths("data/voc.2007.test");
char **paths = (char **)list_to_array(plist);
layer l = net.layers[net.n-1];
int classes = l.classes;
int j, k;
box *boxes = calloc(l.w*l.h*l.n, sizeof(box));
float **probs = calloc(l.w*l.h*l.n, sizeof(float *));
for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(classes, sizeof(float *));
int m = plist->size;
int i=0;
float thresh = .001;
float iou_thresh = .5;
float nms = .4;
int total = 0;
int correct = 0;
int proposals = 0;
float avg_iou = 0;
for(i = 0; i < m; ++i){
char *path = paths[i];
image orig = load_image_color(path, 0, 0);
image sized = resize_image(orig, net.w, net.h);
char *id = basecfg(path);
network_predict(net, sized.data);
get_region_boxes(l, 1, 1, thresh, probs, boxes, 1, 0, .5);
if (nms) do_nms(boxes, probs, l.w*l.h*l.n, 1, nms);
char labelpath[4096];
find_replace(path, "images", "labels", labelpath);
find_replace(labelpath, "JPEGImages", "labels", labelpath);
find_replace(labelpath, ".jpg", ".txt", labelpath);
find_replace(labelpath, ".JPEG", ".txt", labelpath);
int num_labels = 0;
box_label *truth = read_boxes(labelpath, &num_labels);
for(k = 0; k < l.w*l.h*l.n; ++k){
if(probs[k][0] > thresh){
++proposals;
}
}
for (j = 0; j < num_labels; ++j) {
++total;
box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h};
float best_iou = 0;
for(k = 0; k < l.w*l.h*l.n; ++k){
float iou = box_iou(boxes[k], t);
if(probs[k][0] > thresh && iou > best_iou){
best_iou = iou;
}
}
avg_iou += best_iou;
if(best_iou > iou_thresh){
++correct;
}
}
fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total);
free(id);
free_image(orig);
free_image(sized);
}
}
void validate_yolo_classify(char *datacfg, char *cfgfile, char *weightfile)
{
list *options = read_data_cfg(datacfg);
//char *train_list = option_find_str(options, "train", "data/train_list.txt");
//char *test_list = option_find_str(options, "test", "data/test_list.txt");
char *valid_list = option_find_str(options, "valid", "data/valid_list.txt");
//char *backup_directory = option_find_str(options, "backup", "/backup/");
//char *label_list = option_find_str(options, "labels", "data/labels_list.txt");
//int classes = option_find_int(options, "classes", 2);
//char **labels = get_labels(label_list);
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
set_batch_network(&net, 1);
fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
srand(time(0));
char *base = "results/comp4_det_test_";
//list *plist = get_paths("data/voc.2007.test");
list *plist = get_paths(valid_list);
char **paths = (char **)list_to_array(plist);
layer l = net.layers[net.n-1];
int classes = l.classes;
int square = l.sqrt;
int side = l.side;
int j, k;
FILE **fps = calloc(classes, sizeof(FILE *));
for(j = 0; j < classes; ++j){
char buff[1024];
snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]);
fps[j] = fopen(buff, "w");
}
box *boxes = calloc(side*side*l.n, sizeof(box));
float **probs = calloc(side*side*l.n, sizeof(float *));
for(j = 0; j < side*side*l.n; ++j) probs[j] = calloc(classes, sizeof(float *));
int m = plist->size;
int i=0;
//float thresh = .001;
float thresh = .2;
float iou_thresh = .5;
//float nms = 0;
float nms = 0.5;
int total = 0;
int correct = 0;
int class_correct = 0;
int proposals = 0;
float avg_iou = 0;
for(i = 0; i < m; ++i){
char *path = paths[i];
image orig = load_image_color(path, 0, 0);
image sized = resize_image(orig, net.w, net.h);
char *id = basecfg(path);
float *predictions = network_predict(net, sized.data);
convert_yolo_detections(predictions, classes, l.n, square, side, 1, 1, thresh, probs, boxes, 0);
//if (nms) do_nms(boxes, probs, side*side*l.n, 1, nms);
if (nms) do_nms(boxes, probs, side*side*l.n, classes, nms);
char *labelpath = find_replace(path, "images", "labels");
labelpath = find_replace(labelpath, "JPEGImages", "labels");
labelpath = find_replace(labelpath, ".jpg", ".txt");
labelpath = find_replace(labelpath, ".JPEG", ".txt");
labelpath = find_replace(labelpath, ".bmp", ".txt");
labelpath = find_replace(labelpath, ".dib", ".txt");
labelpath = find_replace(labelpath, ".jpe", ".txt");
labelpath = find_replace(labelpath, ".jp2", ".txt");
labelpath = find_replace(labelpath, ".png", ".txt");
labelpath = find_replace(labelpath, ".pbm", ".txt");
labelpath = find_replace(labelpath, ".pgm", ".txt");
labelpath = find_replace(labelpath, ".ppm", ".txt");
labelpath = find_replace(labelpath, ".sr", ".txt");
labelpath = find_replace(labelpath, ".ras", ".txt");
labelpath = find_replace(labelpath, ".tiff", ".txt");
labelpath = find_replace(labelpath, ".tif", ".txt");
int num_labels = 0;
box_label *truth = read_boxes(labelpath, &num_labels);
for(k = 0; k < side*side*l.n; ++k){
int class = max_index(probs[k], classes);
float prob = probs[k][class];
//fprintf(stderr, "path=%s\t\tk=%d\tprob=%f\tclass=%d\n", path, k, prob, class);
if(prob > thresh){
++proposals;
}
}
for (j = 0; j < num_labels; ++j) {
++total;
box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h};
float best_iou = 0;
int pre_class = -1;
for(k = 0; k < side*side*l.n; ++k){
float iou = box_iou(boxes[k], t);
int class = max_index(probs[k], classes);
float prob = probs[k][class];
//fprintf(stderr, "path=%s\t\tk=%d\tprob=%f\tclass=%d\n", path, k, prob, class);
if(prob > thresh && iou > best_iou){
best_iou = iou;
pre_class = class;
}
}
avg_iou += best_iou;
if(best_iou > iou_thresh){
++correct;
}
if(pre_class == truth[j].id){
++class_correct;
}
//fprintf(stderr, "true_class=%d\tpre_class=%d\n", truth[j].id, pre_class);
}
fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\t\tClassify:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total, 100.*class_correct/total);
free(id);
free_image(orig);
free_image(sized);
}
void forward_detection_layer(const detection_layer l, network_state state)
{
int in_i = 0;
int out_i = 0;
int locations = get_detection_layer_locations(l);
int i,j;
for(i = 0; i < l.batch*locations; ++i){
int mask = (!state.truth || state.truth[out_i + (l.background || l.objectness) + l.classes + 2]);
float scale = 1;
if(l.joint) scale = state.input[in_i++];
else if(l.objectness){
l.output[out_i++] = 1-state.input[in_i++];
scale = mask;
}
else if(l.background) l.output[out_i++] = scale*state.input[in_i++];
for(j = 0; j < l.classes; ++j){
l.output[out_i++] = scale*state.input[in_i++];
}
if(l.objectness){
}else if(l.background){
softmax_array(l.output + out_i - l.classes-l.background, l.classes+l.background, l.output + out_i - l.classes-l.background);
activate_array(state.input+in_i, l.coords, LOGISTIC);
}
for(j = 0; j < l.coords; ++j){
l.output[out_i++] = mask*state.input[in_i++];
}
}
float avg_iou = 0;
int count = 0;
if(l.does_cost && state.train){
*(l.cost) = 0;
int size = get_detection_layer_output_size(l) * l.batch;
memset(l.delta, 0, size * sizeof(float));
for (i = 0; i < l.batch*locations; ++i) {
int classes = l.objectness+l.classes;
int offset = i*(classes+l.coords);
for (j = offset; j < offset+classes; ++j) {
*(l.cost) += pow(state.truth[j] - l.output[j], 2);
l.delta[j] = state.truth[j] - l.output[j];
}
box truth;
truth.x = state.truth[j+0]/7;
truth.y = state.truth[j+1]/7;
truth.w = pow(state.truth[j+2], 2);
truth.h = pow(state.truth[j+3], 2);
box out;
out.x = l.output[j+0]/7;
out.y = l.output[j+1]/7;
out.w = pow(l.output[j+2], 2);
out.h = pow(l.output[j+3], 2);
if(!(truth.w*truth.h)) continue;
float iou = box_iou(out, truth);
avg_iou += iou;
++count;
dbox delta = diou(out, truth);
l.delta[j+0] = 10 * delta.dx/7;
l.delta[j+1] = 10 * delta.dy/7;
l.delta[j+2] = 10 * delta.dw * 2 * sqrt(out.w);
l.delta[j+3] = 10 * delta.dh * 2 * sqrt(out.h);
*(l.cost) += pow((1-iou), 2);
l.delta[j+0] = 4 * (state.truth[j+0] - l.output[j+0]);
l.delta[j+1] = 4 * (state.truth[j+1] - l.output[j+1]);
l.delta[j+2] = 4 * (state.truth[j+2] - l.output[j+2]);
l.delta[j+3] = 4 * (state.truth[j+3] - l.output[j+3]);
if(l.rescore){
for (j = offset; j < offset+classes; ++j) {
if(state.truth[j]) state.truth[j] = iou;
l.delta[j] = state.truth[j] - l.output[j];
}
}
}
printf("Avg IOU: %f\n", avg_iou/count);
}
}
void forward_region_layer(const region_layer l, network_state state)
{
int i,j,b,t,n;
int size = l.coords + l.classes + 1;
memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float));
#ifndef GPU
flatten(l.output, l.w*l.h, size*l.n, l.batch, 1);
#endif
for (b = 0; b < l.batch; ++b){
for(i = 0; i < l.h*l.w*l.n; ++i){
int index = size*i + b*l.outputs;
l.output[index + 4] = logistic_activate(l.output[index + 4]);
}
}
#ifndef GPU
if (l.softmax_tree){
for (b = 0; b < l.batch; ++b){
for(i = 0; i < l.h*l.w*l.n; ++i){
int index = size*i + b*l.outputs;
softmax_tree(l.output + index + 5, 1, 0, 1, l.softmax_tree, l.output + index + 5);
}
}
} else if (l.softmax){
for (b = 0; b < l.batch; ++b){
for(i = 0; i < l.h*l.w*l.n; ++i){
int index = size*i + b*l.outputs;
softmax(l.output + index + 5, l.classes, 1, l.output + index + 5, 1);
}
}
}
#endif
if(!state.train) return;
memset(l.delta, 0, l.outputs * l.batch * sizeof(float));
float avg_iou = 0;
float recall = 0;
float avg_cat = 0;
float avg_obj = 0;
float avg_anyobj = 0;
int count = 0;
int class_count = 0;
*(l.cost) = 0;
for (b = 0; b < l.batch; ++b) {
if(l.softmax_tree){
int onlyclass_id = 0;
for(t = 0; t < l.max_boxes; ++t){
box truth = float_to_box(state.truth + t*5 + b*l.truths);
if(!truth.x) break; // continue;
int class_id = state.truth[t*5 + b*l.truths + 4];
float maxp = 0;
int maxi = 0;
if(truth.x > 100000 && truth.y > 100000){
for(n = 0; n < l.n*l.w*l.h; ++n){
int index = size*n + b*l.outputs + 5;
float scale = l.output[index-1];
float p = scale*get_hierarchy_probability(l.output + index, l.softmax_tree, class_id);
if(p > maxp){
maxp = p;
maxi = n;
}
}
int index = size*maxi + b*l.outputs + 5;
delta_region_class(l.output, l.delta, index, class_id, l.classes, l.softmax_tree, l.class_scale, &avg_cat, l.focal_loss);
++class_count;
onlyclass_id = 1;
break;
}
}
if(onlyclass_id) continue;
}
for (j = 0; j < l.h; ++j) {
for (i = 0; i < l.w; ++i) {
for (n = 0; n < l.n; ++n) {
int index = size*(j*l.w*l.n + i*l.n + n) + b*l.outputs;
box pred = get_region_box(l.output, l.biases, n, index, i, j, l.w, l.h);
float best_iou = 0;
int best_class_id = -1;
for(t = 0; t < l.max_boxes; ++t){
box truth = float_to_box(state.truth + t*5 + b*l.truths);
int class_id = state.truth[t * 5 + b*l.truths + 4];
if (class_id >= l.classes) continue; // if label contains class_id more than number of classes in the cfg-file
if(!truth.x) break; // continue;
float iou = box_iou(pred, truth);
if (iou > best_iou) {
best_class_id = state.truth[t*5 + b*l.truths + 4];
best_iou = iou;
}
}
avg_anyobj += l.output[index + 4];
l.delta[index + 4] = l.noobject_scale * ((0 - l.output[index + 4]) * logistic_gradient(l.output[index + 4]));
if(l.classfix == -1) l.delta[index + 4] = l.noobject_scale * ((best_iou - l.output[index + 4]) * logistic_gradient(l.output[index + 4]));
else{
if (best_iou > l.thresh) {
l.delta[index + 4] = 0;
if(l.classfix > 0){
delta_region_class(l.output, l.delta, index + 5, best_class_id, l.classes, l.softmax_tree, l.class_scale*(l.classfix == 2 ? l.output[index + 4] : 1), &avg_cat, l.focal_loss);
++class_count;
}
}
}
if(*(state.net.seen) < 12800){
box truth = {0};
truth.x = (i + .5)/l.w;
truth.y = (j + .5)/l.h;
truth.w = l.biases[2*n];
truth.h = l.biases[2*n+1];
if(DOABS){
truth.w = l.biases[2*n]/l.w;
truth.h = l.biases[2*n+1]/l.h;
}
delta_region_box(truth, l.output, l.biases, n, index, i, j, l.w, l.h, l.delta, .01);
}
}
}
}
for(t = 0; t < l.max_boxes; ++t){
box truth = float_to_box(state.truth + t*5 + b*l.truths);
int class_id = state.truth[t * 5 + b*l.truths + 4];
if (class_id >= l.classes) {
printf(" Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes-1);
getchar();
continue; // if label contains class_id more than number of classes in the cfg-file
}
if(!truth.x) break; // continue;
float best_iou = 0;
int best_index = 0;
int best_n = 0;
i = (truth.x * l.w);
j = (truth.y * l.h);
//printf("%d %f %d %f\n", i, truth.x*l.w, j, truth.y*l.h);
box truth_shift = truth;
truth_shift.x = 0;
truth_shift.y = 0;
//printf("index %d %d\n",i, j);
for(n = 0; n < l.n; ++n){
int index = size*(j*l.w*l.n + i*l.n + n) + b*l.outputs;
box pred = get_region_box(l.output, l.biases, n, index, i, j, l.w, l.h);
if(l.bias_match){
pred.w = l.biases[2*n];
pred.h = l.biases[2*n+1];
if(DOABS){
pred.w = l.biases[2*n]/l.w;
pred.h = l.biases[2*n+1]/l.h;
}
}
//printf("pred: (%f, %f) %f x %f\n", pred.x, pred.y, pred.w, pred.h);
pred.x = 0;
pred.y = 0;
float iou = box_iou(pred, truth_shift);
if (iou > best_iou){
best_index = index;
best_iou = iou;
best_n = n;
}
}
//printf("%d %f (%f, %f) %f x %f\n", best_n, best_iou, truth.x, truth.y, truth.w, truth.h);
float iou = delta_region_box(truth, l.output, l.biases, best_n, best_index, i, j, l.w, l.h, l.delta, l.coord_scale);
if(iou > .5) recall += 1;
avg_iou += iou;
//l.delta[best_index + 4] = iou - l.output[best_index + 4];
avg_obj += l.output[best_index + 4];
l.delta[best_index + 4] = l.object_scale * (1 - l.output[best_index + 4]) * logistic_gradient(l.output[best_index + 4]);
if (l.rescore) {
l.delta[best_index + 4] = l.object_scale * (iou - l.output[best_index + 4]) * logistic_gradient(l.output[best_index + 4]);
}
if (l.map) class_id = l.map[class_id];
delta_region_class(l.output, l.delta, best_index + 5, class_id, l.classes, l.softmax_tree, l.class_scale, &avg_cat, l.focal_loss);
++count;
++class_count;
}
}
//printf("\n");
#ifndef GPU
flatten(l.delta, l.w*l.h, size*l.n, l.batch, 0);
#endif
*(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2);
printf("Region Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, Avg Recall: %f, count: %d\n", avg_iou/count, avg_cat/class_count, avg_obj/count, avg_anyobj/(l.w*l.h*l.n*l.batch), recall/count, count);
}
void forward_detection_layer(const detection_layer l, network_state state)
{
int locations = l.side*l.side;
int i,j;
memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float));
int b;
if (l.softmax){
for(b = 0; b < l.batch; ++b){
int index = b*l.inputs;
for (i = 0; i < locations; ++i) {
int offset = i*l.classes;
softmax_array(l.output + index + offset, l.classes, 1,
l.output + index + offset);
}
}
}
if(state.train){
float avg_iou = 0;
float avg_cat = 0;
float avg_allcat = 0;
float avg_obj = 0;
float avg_anyobj = 0;
int count = 0;
*(l.cost) = 0;
int size = l.inputs * l.batch;
memset(l.delta, 0, size * sizeof(float));
for (b = 0; b < l.batch; ++b){
int index = b*l.inputs;
for (i = 0; i < locations; ++i) {
int truth_index = (b*locations + i)*(1+l.coords+l.classes);
int is_obj = state.truth[truth_index];
for (j = 0; j < l.n; ++j) {
int p_index = index + locations*l.classes + i*l.n + j;
l.delta[p_index] = l.noobject_scale*(0 - l.output[p_index]);
*(l.cost) += l.noobject_scale*pow(l.output[p_index], 2);
avg_anyobj += l.output[p_index];
}
int best_index = -1;
float best_iou = 0;
float best_rmse = 20;
if (!is_obj){
continue;
}
int class_index = index + i*l.classes;
for(j = 0; j < l.classes; ++j) {
l.delta[class_index+j] = l.class_scale * (state.truth[truth_index+1+j] - l.output[class_index+j]);
*(l.cost) += l.class_scale * pow(state.truth[truth_index+1+j] - l.output[class_index+j], 2);
if(state.truth[truth_index + 1 + j]) avg_cat += l.output[class_index+j];
avg_allcat += l.output[class_index+j];
}
box truth = float_to_box(state.truth + truth_index + 1 + l.classes);
truth.x /= l.side;
truth.y /= l.side;
for(j = 0; j < l.n; ++j){
int box_index = index + locations*(l.classes + l.n) + (i*l.n + j) * l.coords;
box out = float_to_box(l.output + box_index);
out.x /= l.side;
out.y /= l.side;
if (l.sqrt){
out.w = out.w*out.w;
out.h = out.h*out.h;
}
float iou = box_iou(out, truth);
//iou = 0;
float rmse = box_rmse(out, truth);
if(best_iou > 0 || iou > 0){
if(iou > best_iou){
best_iou = iou;
best_index = j;
}
}else{
if(rmse < best_rmse){
best_rmse = rmse;
best_index = j;
}
}
}
if(l.forced){
if(truth.w*truth.h < .1){
best_index = 1;
}else{
best_index = 0;
}
}
if(l.random && *(state.net.seen) < 64000){
best_index = rand()%l.n;
}
int box_index = index + locations*(l.classes + l.n) + (i*l.n + best_index) * l.coords;
int tbox_index = truth_index + 1 + l.classes;
box out = float_to_box(l.output + box_index);
out.x /= l.side;
out.y /= l.side;
if (l.sqrt) {
out.w = out.w*out.w;
out.h = out.h*out.h;
}
float iou = box_iou(out, truth);
//printf("%d,", best_index);
int p_index = index + locations*l.classes + i*l.n + best_index;
*(l.cost) -= l.noobject_scale * pow(l.output[p_index], 2);
*(l.cost) += l.object_scale * pow(1-l.output[p_index], 2);
avg_obj += l.output[p_index];
l.delta[p_index] = l.object_scale * (1.-l.output[p_index]);
if(l.rescore){
l.delta[p_index] = l.object_scale * (iou - l.output[p_index]);
}
l.delta[box_index+0] = l.coord_scale*(state.truth[tbox_index + 0] - l.output[box_index + 0]);
l.delta[box_index+1] = l.coord_scale*(state.truth[tbox_index + 1] - l.output[box_index + 1]);
l.delta[box_index+2] = l.coord_scale*(state.truth[tbox_index + 2] - l.output[box_index + 2]);
l.delta[box_index+3] = l.coord_scale*(state.truth[tbox_index + 3] - l.output[box_index + 3]);
if(l.sqrt){
l.delta[box_index+2] = l.coord_scale*(sqrt(state.truth[tbox_index + 2]) - l.output[box_index + 2]);
l.delta[box_index+3] = l.coord_scale*(sqrt(state.truth[tbox_index + 3]) - l.output[box_index + 3]);
}
*(l.cost) += pow(1-iou, 2);
avg_iou += iou;
++count;
}
}
if(0){
float *costs = calloc(l.batch*locations*l.n, sizeof(float));
for (b = 0; b < l.batch; ++b) {
int index = b*l.inputs;
for (i = 0; i < locations; ++i) {
for (j = 0; j < l.n; ++j) {
int p_index = index + locations*l.classes + i*l.n + j;
costs[b*locations*l.n + i*l.n + j] = l.delta[p_index]*l.delta[p_index];
}
}
}
int indexes[100];
top_k(costs, l.batch*locations*l.n, 100, indexes);
float cutoff = costs[indexes[99]];
for (b = 0; b < l.batch; ++b) {
int index = b*l.inputs;
for (i = 0; i < locations; ++i) {
for (j = 0; j < l.n; ++j) {
int p_index = index + locations*l.classes + i*l.n + j;
if (l.delta[p_index]*l.delta[p_index] < cutoff) l.delta[p_index] = 0;
}
}
}
free(costs);
}
*(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2);
if ( l.b_debug )
{
printf("Detection Avg IOU: %f, Pos Cat: %f, All Cat: %f, Pos Obj: %f, Any Obj: %f, count: %d\n", avg_iou/count, avg_cat/count, avg_allcat/(count*l.classes), avg_obj/count, avg_anyobj/(l.batch*locations*l.n), count);
}
}
}
void validate_coco_recall(char *cfgfile, char *weightfile)
{
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
set_batch_network(&net, 1);
fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
srand(time(0));
char *base = "results/comp4_det_test_";
list *plist = get_paths("/home/pjreddie/data/voc/test/2007_test.txt");
char **paths = (char **)list_to_array(plist);
layer l = net.layers[net.n-1];
int classes = l.classes;
int square = l.sqrt;
int side = l.side;
int j, k;
FILE **fps = calloc(classes, sizeof(FILE *));
for(j = 0; j < classes; ++j){
char buff[1024];
_snprintf(buff, 1024, "%s%s.txt", base, coco_classes[j]);
fps[j] = fopen(buff, "w");
}
box *boxes = calloc(side*side*l.n, sizeof(box));
float **probs = calloc(side*side*l.n, sizeof(float *));
for(j = 0; j < side*side*l.n; ++j) probs[j] = calloc(classes, sizeof(float *));
int m = plist->size;
int i=0;
float thresh = .001;
int nms = 0;
float iou_thresh = .5;
float nms_thresh = .5;
int total = 0;
int correct = 0;
int proposals = 0;
float avg_iou = 0;
for(i = 0; i < m; ++i){
char *path = paths[i];
image orig = load_image_color(path, 0, 0);
image sized = resize_image(orig, net.w, net.h);
char *id = basecfg(path);
float *predictions = network_predict(net, sized.data);
convert_detections(predictions, classes, l.n, square, side, 1, 1, thresh, probs, boxes, 1);
if (nms) do_nms(boxes, probs, side*side*l.n, 1, nms_thresh);
char *labelpath = find_replace(path, "images", "labels");
labelpath = find_replace(labelpath, "JPEGImages", "labels");
labelpath = find_replace(labelpath, ".jpg", ".txt");
labelpath = find_replace(labelpath, ".JPEG", ".txt");
int num_labels = 0;
box_label *truth = read_boxes(labelpath, &num_labels);
for(k = 0; k < side*side*l.n; ++k){
if(probs[k][0] > thresh){
++proposals;
}
}
for (j = 0; j < num_labels; ++j) {
++total;
box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h};
float best_iou = 0;
for(k = 0; k < side*side*l.n; ++k){
float iou = box_iou(boxes[k], t);
if(probs[k][0] > thresh && iou > best_iou){
best_iou = iou;
}
}
avg_iou += best_iou;
if(best_iou > iou_thresh){
++correct;
}
}
fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total);
free(id);
free_image(orig);
free_image(sized);
}
}
void validate_yolo_recall(char *cfg, char *weights)
{
network *net = load_network(cfg, weights, 0);
set_batch_network(net, 1);
fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
srand(time(0));
char *base = "results/comp4_det_test_";
list *plist = get_paths("data/voc.2007.test");
char **paths = (char **)list_to_array(plist);
layer l = net->layers[net->n-1];
int classes = l.classes;
int side = l.side;
int j, k;
FILE **fps = calloc(classes, sizeof(FILE *));
for(j = 0; j < classes; ++j){
char buff[1024];
snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]);
fps[j] = fopen(buff, "w");
}
int m = plist->size;
int i=0;
float thresh = .001;
float iou_thresh = .5;
float nms = 0;
int total = 0;
int correct = 0;
int proposals = 0;
float avg_iou = 0;
for(i = 0; i < m; ++i){
char *path = paths[i];
image orig = load_image_color(path, 0, 0);
image sized = resize_image(orig, net->w, net->h);
char *id = basecfg(path);
network_predict(net, sized.data);
int nboxes = 0;
detection *dets = get_network_boxes(net, orig.w, orig.h, thresh, 0, 0, 1, &nboxes);
if (nms) do_nms_obj(dets, side*side*l.n, 1, nms);
char labelpath[4096];
find_replace(path, "images", "labels", labelpath);
find_replace(labelpath, "JPEGImages", "labels", labelpath);
find_replace(labelpath, ".jpg", ".txt", labelpath);
find_replace(labelpath, ".JPEG", ".txt", labelpath);
int num_labels = 0;
box_label *truth = read_boxes(labelpath, &num_labels);
for(k = 0; k < side*side*l.n; ++k){
if(dets[k].objectness > thresh){
++proposals;
}
}
for (j = 0; j < num_labels; ++j) {
++total;
box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h};
float best_iou = 0;
for(k = 0; k < side*side*l.n; ++k){
float iou = box_iou(dets[k].bbox, t);
if(dets[k].objectness > thresh && iou > best_iou){
best_iou = iou;
}
}
avg_iou += best_iou;
if(best_iou > iou_thresh){
++correct;
}
}
fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total);
free_detections(dets, nboxes);
free(id);
free_image(orig);
free_image(sized);
}
free_network( net );
}
void extract_boxes(char *cfgfile, char *weightfile)
{
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
set_batch_network(&net, 1);
fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
srand(time(0));
char *val_images = "/home/pjreddie/data/voc/test/train.txt";
list *plist = get_paths(val_images);
char **paths = (char **)list_to_array(plist);
layer l = net.layers[net.n - 1];
int num_boxes = l.side;
int num = l.n;
int classes = l.classes;
int j;
box *boxes = calloc(num_boxes*num_boxes*num, sizeof(box));
float **probs = calloc(num_boxes*num_boxes*num, sizeof(float *));
for(j = 0; j < num_boxes*num_boxes*num; ++j) probs[j] = calloc(classes+1, sizeof(float *));
int N = plist->size;
int i=0;
int k;
int count = 0;
float iou_thresh = .3;
for (i = 0; i < N; ++i) {
fprintf(stderr, "%5d %5d\n", i, count);
char *path = paths[i];
image orig = load_image_color(path, 0, 0);
image resized = resize_image(orig, net.w, net.h);
float *X = resized.data;
float *predictions = network_predict(net, X);
get_boxes(predictions+1+classes, num, num_boxes, 5+classes, boxes);
get_probs(predictions, num*num_boxes*num_boxes, classes, 5+classes, probs);
char *labelpath = find_replace(path, "images", "labels");
labelpath = find_replace(labelpath, "JPEGImages", "labels");
labelpath = find_replace(labelpath, ".jpg", ".txt");
labelpath = find_replace(labelpath, ".JPEG", ".txt");
int num_labels = 0;
box_label *truth = read_boxes(labelpath, &num_labels);
FILE *label = stdin;
for(k = 0; k < num_boxes*num_boxes*num; ++k){
int overlaps = 0;
for (j = 0; j < num_labels; ++j) {
box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h};
float iou = box_iou(boxes[k], t);
if (iou > iou_thresh){
if (!overlaps) {
char buff[256];
sprintf(buff, "/data/extracted/labels/%d.txt", count);
label = fopen(buff, "w");
overlaps = 1;
}
fprintf(label, "%d %f\n", truth[j].id, iou);
}
}
if (overlaps) {
char buff[256];
sprintf(buff, "/data/extracted/imgs/%d", count++);
int dx = (boxes[k].x - boxes[k].w/2) * orig.w;
int dy = (boxes[k].y - boxes[k].h/2) * orig.h;
int w = boxes[k].w * orig.w;
int h = boxes[k].h * orig.h;
image cropped = crop_image(orig, dx, dy, w, h);
image sized = resize_image(cropped, 224, 224);
#ifdef OPENCV
save_image_jpg(sized, buff);
#endif
free_image(sized);
free_image(cropped);
fclose(label);
}
}
free(truth);
free_image(orig);
free_image(resized);
}
}
void validate_recall(char *cfgfile, char *weightfile)
{
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
set_batch_network(&net, 1);
fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
srand(time(0));
char *val_images = "/home/pjreddie/data/voc/test/2007_test.txt";
list *plist = get_paths(val_images);
char **paths = (char **)list_to_array(plist);
layer l = net.layers[net.n - 1];
int num_boxes = l.side;
int num = l.n;
int classes = l.classes;
int j;
box *boxes = calloc(num_boxes*num_boxes*num, sizeof(box));
float **probs = calloc(num_boxes*num_boxes*num, sizeof(float *));
for(j = 0; j < num_boxes*num_boxes*num; ++j) probs[j] = calloc(classes+1, sizeof(float *));
int N = plist->size;
int i=0;
int k;
float iou_thresh = .5;
float thresh = .1;
int total = 0;
int correct = 0;
float avg_iou = 0;
int nms = 1;
int proposals = 0;
int save = 1;
for (i = 0; i < N; ++i) {
char *path = paths[i];
image orig = load_image_color(path, 0, 0);
image resized = resize_image(orig, net.w, net.h);
float *X = resized.data;
float *predictions = network_predict(net, X);
get_boxes(predictions+1+classes, num, num_boxes, 5+classes, boxes);
get_probs(predictions, num*num_boxes*num_boxes, classes, 5+classes, probs);
if (nms) do_nms(boxes, probs, num*num_boxes*num_boxes, (classes>0) ? classes : 1, iou_thresh);
char *labelpath = find_replace(path, "images", "labels");
labelpath = find_replace(labelpath, "JPEGImages", "labels");
labelpath = find_replace(labelpath, ".jpg", ".txt");
labelpath = find_replace(labelpath, ".JPEG", ".txt");
int num_labels = 0;
box_label *truth = read_boxes(labelpath, &num_labels);
for(k = 0; k < num_boxes*num_boxes*num; ++k){
if(probs[k][0] > thresh){
++proposals;
if(save){
char buff[256];
sprintf(buff, "/data/extracted/nms_preds/%d", proposals);
int dx = (boxes[k].x - boxes[k].w/2) * orig.w;
int dy = (boxes[k].y - boxes[k].h/2) * orig.h;
int w = boxes[k].w * orig.w;
int h = boxes[k].h * orig.h;
image cropped = crop_image(orig, dx, dy, w, h);
image sized = resize_image(cropped, 224, 224);
#ifdef OPENCV
save_image_jpg(sized, buff);
#endif
free_image(sized);
free_image(cropped);
sprintf(buff, "/data/extracted/nms_pred_boxes/%d.txt", proposals);
char *im_id = basecfg(path);
FILE *fp = fopen(buff, "w");
fprintf(fp, "%s %d %d %d %d\n", im_id, dx, dy, dx+w, dy+h);
fclose(fp);
free(im_id);
}
}
}
for (j = 0; j < num_labels; ++j) {
++total;
box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h};
float best_iou = 0;
for(k = 0; k < num_boxes*num_boxes*num; ++k){
float iou = box_iou(boxes[k], t);
if(probs[k][0] > thresh && iou > best_iou){
best_iou = iou;
}
}
avg_iou += best_iou;
if(best_iou > iou_thresh){
++correct;
}
}
free(truth);
free_image(orig);
free_image(resized);
fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total);
}
}
void forward_region_layer(const region_layer l, network_state state)
{
int locations = l.side*l.side;
int i,j;
for(i = 0; i < l.batch*locations; ++i){
for(j = 0; j < l.n; ++j){
int in_index = i*l.n*l.coords + j*l.coords;
int out_index = i*l.n*5 + j*5;
float prob = state.input[in_index+0];
float x = state.input[in_index+1];
float y = state.input[in_index+2];
float w = state.input[in_index+3];
float h = state.input[in_index+4];
/*
float min_w = state.input[in_index+5];
float max_w = state.input[in_index+6];
float min_h = state.input[in_index+7];
float max_h = state.input[in_index+8];
*/
l.output[out_index+0] = prob;
l.output[out_index+1] = x;
l.output[out_index+2] = y;
l.output[out_index+3] = w;
l.output[out_index+4] = h;
}
}
if(state.train){
float avg_iou = 0;
int count = 0;
*(l.cost) = 0;
int size = l.inputs * l.batch;
memset(l.delta, 0, size * sizeof(float));
for (i = 0; i < l.batch*locations; ++i) {
for(j = 0; j < l.n; ++j){
int in_index = i*l.n*l.coords + j*l.coords;
l.delta[in_index+0] = .1*(0-state.input[in_index+0]);
}
int truth_index = i*5;
int best_index = -1;
float best_iou = 0;
float best_rmse = 4;
int bg = !state.truth[truth_index];
if(bg) continue;
box truth = {state.truth[truth_index+1], state.truth[truth_index+2], state.truth[truth_index+3], state.truth[truth_index+4]};
truth.x /= l.side;
truth.y /= l.side;
for(j = 0; j < l.n; ++j){
int out_index = i*l.n*5 + j*5;
box out = {l.output[out_index+1], l.output[out_index+2], l.output[out_index+3], l.output[out_index+4]};
//printf("\n%f %f %f %f %f\n", l.output[out_index+0], out.x, out.y, out.w, out.h);
out.x /= l.side;
out.y /= l.side;
float iou = box_iou(out, truth);
float rmse = box_rmse(out, truth);
if(best_iou > 0 || iou > 0){
if(iou > best_iou){
best_iou = iou;
best_index = j;
}
}else{
if(rmse < best_rmse){
best_rmse = rmse;
best_index = j;
}
}
}
printf("%d", best_index);
//int out_index = i*l.n*5 + best_index*5;
//box out = {l.output[out_index+1], l.output[out_index+2], l.output[out_index+3], l.output[out_index+4]};
int in_index = i*l.n*l.coords + best_index*l.coords;
l.delta[in_index+0] = (1-state.input[in_index+0]);
l.delta[in_index+1] = state.truth[truth_index+1] - state.input[in_index+1];
l.delta[in_index+2] = state.truth[truth_index+2] - state.input[in_index+2];
l.delta[in_index+3] = state.truth[truth_index+3] - state.input[in_index+3];
l.delta[in_index+4] = state.truth[truth_index+4] - state.input[in_index+4];
/*
l.delta[in_index+5] = 0 - state.input[in_index+5];
l.delta[in_index+6] = 1 - state.input[in_index+6];
l.delta[in_index+7] = 0 - state.input[in_index+7];
l.delta[in_index+8] = 1 - state.input[in_index+8];
*/
/*
float x = state.input[in_index+1];
float y = state.input[in_index+2];
float w = state.input[in_index+3];
float h = state.input[in_index+4];
float min_w = state.input[in_index+5];
float max_w = state.input[in_index+6];
float min_h = state.input[in_index+7];
float max_h = state.input[in_index+8];
*/
avg_iou += best_iou;
++count;
}
printf("\nAvg IOU: %f %d\n", avg_iou/count, count);
}
}
void validate_yolo_recall(char *cfgfile, char *weightfile, char *val_images, char *out_dir, float th, int log, int draw)
{
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
set_batch_network(&net, 1);
fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
srand(time(0));
//create output directory if it does not exist
struct stat st= {0};
if(stat(out_dir,&st)==-1){
fprintf(stderr,"Creating output directory\n");
mkdir(out_dir,0700);
}
char *base = out_dir;
list *plist = get_paths(val_images);
char **paths = (char **)list_to_array(plist);
layer l = net.layers[net.n-1];
int classes = l.classes;
int square = l.sqrt;
//int side = l.side;
int rows = l.rows;
int cols = l.cols;
int j, k;
FILE **fps = calloc(classes, sizeof(FILE *));
for(j = 0; j < classes; ++j){
char buff[1024];
snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]);
fps[j] = fopen(buff, "w");
}
box *boxes = calloc(rows*cols*l.n, sizeof(box));
float **probs = calloc(rows*cols*l.n, sizeof(float *));
for(j = 0; j < rows*cols*l.n; ++j) probs[j] = calloc(classes, sizeof(float *));
int m = plist->size;
int i=0;
float thresh = th;
float iou_thresh[11] = {0.0,0.05,0.1,0.15,0.20,0.25,0.30,0.35,0.40,0.45,0.5};
float nms = 0.1;
int total = 0;
int correct[11] = {0,0,0,0,0,0,0,0,0,0,0};
int proposals = 0;
float avg_iou = 0;
Vector id_found;
Vector id_invalid;
initArray(&id_found,5);
initArray(&id_invalid,5);
for(i = 0; i < m; ++i){
char * image_path = strtok(paths[i]," ");
char * label_path = strtok(NULL," ");
image orig = load_image(image_path, 0, 0,net.c);
image color;
if(draw)
color = load_image(image_path, 0, 0, 3);
image sized = resize_image(orig, net.w, net.h);
//char *id = basecfg(path);
float *predictions = network_predict(net, sized.data);
convert_detections(predictions, classes, l.n, square, rows, cols, 1, 1, thresh, probs, boxes, 0);
if (nms) do_nms(boxes, probs, rows*cols*l.n, 1, nms);
int num_labels = 0;
box_label *truth = read_boxes(label_path, &num_labels);
int old_p = proposals;
for(k = 0; k < rows*cols*l.n; ++k){
if(probs[k][0] > thresh){
++proposals;
}
}
if(old_p!=proposals){
if(log){
char filename[256];
sprintf(filename, "%s/%d.txt", base,i);
printf("log in file %s\n",filename);
FILE * out = fopen(filename, "w");
fprintf(out,"W\tH\tX\tY\n");
for(k=0; k<rows*cols*l.n; ++k){
if(probs[k][0] > thresh){
fprintf(out, "%f\t%f\t%f\t%f\n",boxes[k].w,boxes[k].h,boxes[k].x,boxes[k].y);
}
}
fclose(out);
}
if(draw){
draw_detections(color, l.rows*l.cols*l.n, thresh, boxes, probs, voc_names, voc_labels, CLASSNUM);
show_image(color, "predictions");
#ifdef OPENCV
cvWaitKey(0);
//cvDestroyAllWindows();
#endif
}
}
for (j = 0; j < num_labels; ++j) {
++total;
while(id_found.used <= truth[j].id){
insertArray(&id_invalid,0);
insertArray(&id_found,0);
}
if(truth[j].classe > CLASSNUM-1)
id_invalid.array[truth[j].id]=1;
box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h};
float best_iou = 0;
for(k = 0; k < rows*cols*l.n; ++k){
float iou = box_iou(boxes[k], t);
//find overlapping prediction
if(iou > best_iou){
//find the predicted class
float best_score = thresh;
int best_class_index = -1;
for(int c=0; c<CLASSNUM; c++){
if(probs[k][c]>best_score){
best_score = probs[k][c];
best_class_index = c;
}
}
//check if it's good or not
if(best_class_index == truth[j].classe)
best_iou = iou;
}
}
avg_iou += best_iou;
for(int k=0; k<11; k++){
if(best_iou > iou_thresh[k]){
id_found.array[truth[j].id]=1;
++correct[k];
}
}
}
if(i%10==0){
printf("\033[2J");
printf("\033[1;1H");
printf("#img\tPred\tTP\ttot\tRPs/Img\tAvg-IOU\tRecall\tPrecision\n");
printf("%5d\t%5d\t%5d\t%5d\t%.2f\t%.2f%%\t%.2f%%\t%.2f%%\n", i, proposals, correct[10], total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct[10]/total, 100.*correct[10]/proposals);
printf("IOU_th\tTP\tFP\tRecall\tPrecision\n");
for(int k=0; k<11; k++){
printf("%.2f%%\t%5d\t%5d\t%.2f%%\t%.2f%%\t\n", iou_thresh[k], correct[k], proposals-correct[k], 100.*correct[k]/total, 100.*correct[k]/proposals);
}
int found=0;
int invalid = 0;
for(int i=0; i<id_found.used; i++){
if(id_invalid.array[i]!=1)
found+=id_found.array[i];
invalid+=id_invalid.array[i];
}
printf("Founded: %d/%d\t%d\n", found, id_found.used-invalid,invalid);
}
//free(id);
free_image(orig);
free_image(sized);
}
for(j = 0; j < classes; ++j){
fprintf(fps[j],"IOU_th;TP;FP;Recall;Precision\n");
for(int k=0; k<11; k++){
fprintf(fps[j],"%.2f%%;%5d;%5d;%.2f%%;%.2f%%;\n", iou_thresh[k], correct[k], proposals-correct[k], 100.*correct[k]/total, 100.*correct[k]/proposals);
}
fprintf(fps[j], "\n\nFounded;Total;\n");
int found=0;
int invalid = 0;
for(int i=0; i<id_found.used; i++){
if(id_invalid.array[i]!=1)
found+=id_found.array[i];
invalid+=id_invalid.array[i];
}
fprintf(fps[j], "%d;%d;\n", found, id_found.used-invalid);
fclose(fps[j]);
}
freeArray(&id_found);
}
