void *detect_in_thread_coco(void *ptr)
{
float nms = .4;
layer_t l = net.layers[net.n-1];
float *X = det_s.data;
float *prediction = network_predict(net, X);
memcpy(predictions[demo_index], prediction, l.outputs*sizeof(float));
mean_arrays(predictions, FRAMES, l.outputs, avg);
free_image(det_s);
convert_coco_detections(avg, l.classes, l.n, l.sqrt, l.side, 1, 1, demo_thresh, probs, boxes, 0);
if (nms > 0) do_nms(boxes, probs, l.side*l.side*l.n, l.classes, nms);
printf("\033[2J");
printf("\033[1;1H");
printf("\nFPS:%.0f\n",fps);
printf("Objects:\n\n");
images[demo_index] = det;
det = images[(demo_index + FRAMES/2 + 1)%FRAMES];
demo_index = (demo_index + 1)%FRAMES;
draw_detections(det, l.side*l.side*l.n, demo_thresh, boxes, probs, coco_classes, coco_labels, 80);
return 0;
}
void *detect_in_thread(void *ptr)
{
float nms = .4;
layer l = net.layers[net.n-1];
float *X = det_s.data;
float *prediction = network_predict(net, X);
memcpy(predictions[demo_index], prediction, l.outputs*sizeof(float));
mean_arrays(predictions, FRAMES, l.outputs, avg);
l.output = avg;
free_image(det_s);
if(l.type == DETECTION){
get_detection_boxes(l, 1, 1, demo_thresh, probs, boxes, 0);
} else if (l.type == REGION){
get_region_boxes(l, 1, 1, demo_thresh, probs, boxes, 0, 0, demo_hier_thresh);
} else {
error("Last layer must produce detections\n");
}
if (nms > 0) do_nms(boxes, probs, l.w*l.h*l.n, l.classes, nms);
printf("\033[2J");
printf("\033[1;1H");
printf("\nFPS:%.1f\n",fps);
printf("Objects:\n\n");
images[demo_index] = det;
det = images[(demo_index + FRAMES/2 + 1)%FRAMES];
demo_index = (demo_index + 1)%FRAMES;
draw_detections(det, l.w*l.h*l.n, demo_thresh, boxes, probs, demo_names, demo_alphabet, demo_classes);
return 0;
}
void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh)
{
int show_flag = 1;
list *options = read_data_cfg(datacfg);
char *name_list = option_find_str(options, "names", "data/names.list");
char **names = get_labels(name_list);
image **alphabet = load_alphabet();
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;
int j;
float nms=.4;
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 sized = resize_image(im, net.w, net.h);
layer l = net.layers[net.n-1];
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(l.classes + 1, sizeof(float *));
float *X = sized.data;
time=clock();
network_predict(net, X);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, 0, hier_thresh);
if (l.softmax_tree && nms) do_nms_obj(boxes, probs, l.w*l.h*l.n, l.classes, nms);
else if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, l.classes, nms);
draw_detections(im, l.w*l.h*l.n, thresh, boxes, probs, names, alphabet, l.classes, show_flag);
save_image(im, "predictions");
show_image(im, "predictions");
free_image(im);
free_image(sized);
free(boxes);
free_ptrs((void **)probs, l.w*l.h*l.n);
#ifdef OPENCV
cvWaitKey(0);
cvDestroyAllWindows();
#endif
if (filename) break;
}
}
void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh)
{
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
detection_layer l = net.layers[net.n-1];
set_batch_network(&net, 1);
srand(2222222);
clock_t time;
char buff[256];
char *input = buff;
int j;
float nms=.5;
box *boxes = calloc(l.side*l.side*l.n, sizeof(box));
float **probs = calloc(l.side*l.side*l.n, sizeof(float *));
for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float *));
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 sized = resize_image(im, net.w, net.h);
float *X = sized.data;
time=clock();
float *predictions = network_predict(net, X);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
convert_yolo_detections(predictions, l.classes, l.n, l.sqrt, l.side, 1, 1, thresh, probs, boxes, 0);
if (nms) do_nms_sort(boxes, probs, l.side*l.side*l.n, l.classes, nms);
//draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, voc_labels, 20);
draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, 0, 20);
show_image(im, "predictions");
save_image(im, "predictions");
show_image(sized, "resized");
free_image(im);
free_image(sized);
#ifdef OPENCV
cvWaitKey(0);
cvDestroyAllWindows();
#endif
if (filename) break;
}
}
void *detect_in_thread(void *ptr)
{
float nms = .4;
layer_t l = net.layers[net.n-1];
float *X = det_s.data;
float *predictions = network_predict(net, X);
free_image(det_s);
convert_yolo_detections(predictions, l.classes, l.n, l.sqrt, l.side, 1, 1, demo_thresh, probs, boxes, 0);
if (nms > 0) do_nms(boxes, probs, l.side*l.side*l.n, l.classes, nms);
printf("\033[2J");
printf("\033[1;1H");
printf("\nFPS:%.0f\n",fps);
printf("Objects:\n\n");
draw_detections(det, l.side*l.side*l.n, demo_thresh, boxes, probs, voc_names, voc_labels, 20);
return 0;
}
void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh)
{
image **alphabet = load_alphabet();
network *net = load_network(cfgfile, weightfile, 0);
layer l = net->layers[net->n-1];
set_batch_network(net, 1);
srand(2222222);
float nms = .4;
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);
image sized = resize_image(im, net->w, net->h);
float *X = sized.data;
time=clock();
network_predict(net, X);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
int nboxes = 0;
detection *dets = get_network_boxes(net, 1, 1, thresh, 0, 0, 0, &nboxes);
if (nms) do_nms_sort(dets, l.side*l.side*l.n, l.classes, nms);
draw_detections(im, dets, l.side*l.side*l.n, thresh, coco_classes, alphabet, 80);
save_image(im, "prediction");
show_image(im, "predictions");
free_detections(dets, nboxes);
free_image(im);
free_image(sized);
#ifdef OPENCV
cvWaitKey(0);
cvDestroyAllWindows();
#endif
if (filename) break;
}
}
Mat Darknet::predict(const Mat &ori, float thresh)
{
Mat img;
cv::resize(ori, img, cv::Size(priv->net.w, priv->net.h));
image im = toDarkImage(img);
float nms = 0.5;
/* NOTE: network_predict doesn't allocate any data */
float *predictions = network_predict(priv->net, im.data);
const detection_layer l = priv->l;
convert_yolo_detections(predictions, l.classes, l.n, l.sqrt, l.side, 1, 1, thresh, priv->probs, priv->boxes, 0);
if (nms)
do_nms_sort(priv->boxes, priv->probs, l.side*l.side*l.n, l.classes, nms);
draw_detections(im, l.side*l.side*l.n, thresh, priv->boxes, priv->probs, voc_names, 0, 20);
Mat ret = toCVImage(im);
free_image(im);
return ret;
}
void Darknet::predict(const QString &filename, float thresh)
{
const Mat ori = OpenCV::loadImage(getAbs(filename), -1);
Mat img;
cv::resize(ori, img, cv::Size(priv->net.w, priv->net.h));
image im = toDarkImage(img);
float nms = 0.5;
/* NOTE: network_predict doesn't allocate any data */
float *predictions = network_predict(priv->net, im.data);
const detection_layer l = priv->l;
convert_yolo_detections(predictions, l.classes, l.n, l.sqrt, l.side, 1, 1, thresh, priv->probs, priv->boxes, 0);
if (nms)
do_nms_sort(priv->boxes, priv->probs, l.side*l.side*l.n, l.classes, nms);
draw_detections(im, l.side*l.side*l.n, thresh, priv->boxes, priv->probs, voc_names, 0, 20);
save_image(im, (char *)qPrintable(getAbs(QString(filename).replace(".jpg", "_pred.jpg"))));
//ffDebug() << priv->net.n << priv->net.outputs << priv->l.classes;
}
static void yolo_image2(const char *cfg, const char *weights, const char *filename, float thresh)
{
int i;
for (i = 0; i < 20; ++i){
char buff[256];
sprintf(buff, "/home/amenmd/myfs/source-codes/oss/darknet/data/labels/%s.png", voc_names[i]);
voc_labels[i] = load_image_color(buff, 0, 0);
}
network net = parse_network_cfg((char *)cfg);
load_weights(&net, (char *)weights);
detection_layer l = net.layers[net.n-1];
set_batch_network(&net, 1);
int j;
float nms=.5;
box *boxes = (box *)calloc(l.side*l.side*l.n, sizeof(box));
float **probs = (float **)calloc(l.side*l.side*l.n, sizeof(float *));
for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = (float *)calloc(l.classes, sizeof(float *));
image im = load_image_color((char *)filename, 0, 0);
image sized = resize_image(im, net.w, net.h);
float *X = sized.data;
float *predictions = network_predict(net, X);
convert_yolo_detections(predictions, l.classes, l.n, l.sqrt, l.side, 1, 1, thresh, probs, boxes, 0);
if (nms) do_nms_sort(boxes, probs, l.side*l.side*l.n, l.classes, nms);
//draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, voc_labels, 20);
draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, 0, 20);
show_image(im, "predictions");
save_image(im, "predictions");
show_image(sized, "resized");
free_image(im);
free_image(sized);
}
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);
}
