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darknet
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Output improvements for detector results:

Browse Source 
When printing detector results, output was done in random order, obfuscating results for interpreting. Now:
1. Text output includes coordinates of rects in (left,right,top,bottom in pixels) along with label and score
2. Text output is sorted by rect lefts to simplify finding appropriate rects on image
3. If several class probs are > thresh for some detection, the most probable is written first and coordinates for others are not repeated
4. Rects are imprinted in image in order by their best class prob, so most probable rects are always on top and not overlayed by less probable ones
5. Most probable label for rect is always written first
Also:
6. Message about low GPU memory include required amount

Upd:
* struct detection changes reverted to keep it unchanged
* cmd line key "-ext_output" is added to manage extended output (rect coords in detector test mode)

# Conflicts:
#	src/detector.c
pull/741/head
IlyaOvodov 7 years ago
parent 028696bf15
commit 1b5afb4583
5 changed files with 58 additions and 39 deletions
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  1. 10
      src/box.h
  2. 5
      src/darknet.c
  3. 10
      src/detector.c
  4. 70
      src/image.c
  5. 2
      src/image.h

10
src/box.h
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@ -30,10 +30,14 @@ typedef struct detection {
float *mask;
float objectness;
int sort_class;
} detection;
typedef struct detection_with_class {
detection det;
// The most probable class id: the best class index in this->prob.
// Is filled temporary when processing results, otherwise not initialized
int best_class;
} detection;
} detection_with_class;
box float_to_box(float *f);
float box_iou(box a, box b);
@ -46,4 +50,8 @@ YOLODLL_API void do_nms_obj(detection *dets, int total, int classes, float thres
box decode_box(box b, box anchor);
box encode_box(box b, box anchor);
// Creates array of detections with prob > thresh and fills best_class for them
// Return number of selected detections in *selected_detections_num
detection_with_class* get_actual_detections(detection *dets, int dets_num, float thresh, int* selected_detections_num);
#endif

5
src/darknet.c
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@ -13,7 +13,7 @@
#endif
extern void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top);
extern void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh);
extern void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, int ext_output);
extern void run_voxel(int argc, char **argv);
extern void run_yolo(int argc, char **argv);
extern void run_detector(int argc, char **argv);
@ -382,8 +382,9 @@ int main(int argc, char **argv)
run_detector(argc, argv);
} else if (0 == strcmp(argv[1], "detect")){
float thresh = find_float_arg(argc, argv, "-thresh", .24);
int ext_output = find_arg(argc, argv, "-ext_output");
char *filename = (argc > 4) ? argv[4]: 0;
test_detector("cfg/coco.data", argv[2], argv[3], filename, thresh);
test_detector("cfg/coco.data", argv[2], argv[3], filename, thresh, ext_output);
} else if (0 == strcmp(argv[1], "cifar")){
run_cifar(argc, argv);
} else if (0 == strcmp(argv[1], "go")){

10
src/detector.c
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@ -1036,7 +1036,8 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int
}
#endif // OPENCV
void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh, int dont_show)
void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh,
float hier_thresh, int dont_show, int ext_output)
{
list *options = read_data_cfg(datacfg);
char *name_list = option_find_str(options, "names", "data/names.list");
@ -1088,7 +1089,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam
int nboxes = 0;
detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letterbox);
if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
draw_detections_v3(im, dets, nboxes, thresh, names, alphabet, l.classes);
draw_detections_v3(im, dets, nboxes, thresh, names, alphabet, l.classes, ext_output);
free_detections(dets, nboxes);
save_image(im, "predictions");
if (!dont_show) {
@ -1124,6 +1125,9 @@ void run_detector(int argc, char **argv)
int num_of_clusters = find_int_arg(argc, argv, "-num_of_clusters", 5);
int width = find_int_arg(argc, argv, "-width", -1);
int height = find_int_arg(argc, argv, "-height", -1);
// extended output in test mode (output of rect bound coords)
// and for recall mode (extended output table-like format with results for best_class fit)
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;
@ -1160,7 +1164,7 @@ void run_detector(int argc, char **argv)
if(strlen(weights) > 0)
if (weights[strlen(weights) - 1] == 0x0d) weights[strlen(weights) - 1] = 0;
char *filename = (argc > 6) ? argv[6]: 0;
if(0==strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show);
if(0==strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show, ext_output);
else if(0==strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show);
else if(0==strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile);
else if(0==strcmp(argv[2], "recall")) validate_detector_recall(datacfg, cfg, weights);

70
src/image.c
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@ -232,21 +232,22 @@ image **load_alphabet()
// Creates array of detections with prob > thresh and fills best_class for them
detection** get_actual_detections(detection *dets, int dets_num, int classes, float thresh, int* selected_detections_num)
detection_with_class* get_actual_detections(detection *dets, int dets_num, float thresh, int* selected_detections_num)
{
int selected_num = 0;
detection** result_arr = calloc(dets_num, sizeof(detection*));
detection_with_class* result_arr = calloc(dets_num, sizeof(detection_with_class));
for (int i = 0; i < dets_num; ++i) {
dets[i].best_class = -1;
for (int j = 0; j < classes; ++j) {
if (dets[i].prob[j] > thresh) {
if (dets[i].best_class < 0 || dets[i].prob[dets[i].best_class] < dets[i].prob[j]) {
dets[i].best_class = j;
}
int best_class = -1;
float best_class_prob = thresh;
for (int j = 0; j < dets[i].classes; ++j) {
if (dets[i].prob[j] > best_class_prob ) {
best_class = j;
best_class_prob = dets[i].prob[j];
}
}
if (dets[i].best_class >= 0) {
result_arr[selected_num] = &(dets[i]);
if (best_class >= 0) {
result_arr[selected_num].det = dets[i];
result_arr[selected_num].best_class = best_class;
++selected_num;
}
}
@ -256,36 +257,41 @@ detection** get_actual_detections(detection *dets, int dets_num, int classes, fl
}
// compare to sort detection** by bbox.x
int compare_by_lefts(const void *a, const void *b) {
const float delta = ((*(detection**)a)->bbox.x - (*(detection**)a)->bbox.w / 2) - ((*(detection**)b)->bbox.x - (*(detection**)b)->bbox.w / 2);
int compare_by_lefts(const void *a_ptr, const void *b_ptr) {
const detection_with_class* a = (detection_with_class*)a_ptr;
const detection_with_class* b = (detection_with_class*)b_ptr;
const float delta = (a->det.bbox.x - a->det.bbox.w/2) - (b->det.bbox.x - b->det.bbox.w/2);
return delta < 0 ? -1 : delta > 0 ? 1 : 0;
}
// compare to sort detection** by best_class probability
int compare_by_probs(const void *a_ptr, const void *b_ptr) {
const detection* a = *(detection**)a_ptr;
const detection* b = *(detection**)b_ptr;
float delta = a->prob[a->best_class] - b->prob[b->best_class];
const detection_with_class* a = (detection_with_class*)a_ptr;
const detection_with_class* b = (detection_with_class*)b_ptr;
float delta = a->det.prob[a->best_class] - b->det.prob[b->best_class];
return delta < 0 ? -1 : delta > 0 ? 1 : 0;
}
void draw_detections_v3(image im, detection *dets, int num, float thresh, char **names, image **alphabet, int classes)
void draw_detections_v3(image im, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output)
{
int selected_detections_num;
detection** selected_detections = get_actual_detections(dets, num, classes, thresh, &selected_detections_num);
detection_with_class* selected_detections = get_actual_detections(dets, num, thresh, &selected_detections_num);
// text output
qsort(selected_detections, selected_detections_num, sizeof(*selected_detections), compare_by_lefts);
for (int i = 0; i < selected_detections_num; ++i) {
const int best_class = selected_detections[i]->best_class;
printf("%s: %.0f%%\t(left: %.0f\ttop: %.0f\tw: %0.f\th: %0.f)\n", names[best_class],
selected_detections[i]->prob[best_class] * 100,
(selected_detections[i]->bbox.x - selected_detections[i]->bbox.w / 2)*im.w,
(selected_detections[i]->bbox.y - selected_detections[i]->bbox.h / 2)*im.h,
selected_detections[i]->bbox.w*im.w, selected_detections[i]->bbox.h*im.h);
const int best_class = selected_detections[i].best_class;
printf("%s: %.0f%%", names[best_class], selected_detections[i].det.prob[best_class] * 100);
if (ext_output)
printf("\t(left: %.0f\ttop: %.0f\tw: %0.f\th: %0.f)\n",
(selected_detections[i].det.bbox.x - selected_detections[i].det.bbox.w / 2)*im.w,
(selected_detections[i].det.bbox.y - selected_detections[i].det.bbox.h / 2)*im.h,
selected_detections[i].det.bbox.w*im.w, selected_detections[i].det.bbox.h*im.h);
else
printf("\n");
for (int j = 0; j < classes; ++j) {
if (selected_detections[i]->prob[j] > thresh && j != best_class) {
printf("%s: %.0f%%\n", names[j], selected_detections[i]->prob[j] * 100);
if (selected_detections[i].det.prob[j] > thresh && j != best_class) {
printf("%s: %.0f%%\n", names[j], selected_detections[i].det.prob[j] * 100);
}
}
}
@ -304,8 +310,8 @@ void draw_detections_v3(image im, detection *dets, int num, float thresh, char *
}
*/
//printf("%d %s: %.0f%%\n", i, names[dets[i].best_class], prob*100);
int offset = selected_detections[i]->best_class * 123457 % classes;
//printf("%d %s: %.0f%%\n", i, names[selected_detections[i].best_class], prob*100);
int offset = selected_detections[i].best_class * 123457 % classes;
float red = get_color(2, offset, classes);
float green = get_color(1, offset, classes);
float blue = get_color(0, offset, classes);
@ -316,7 +322,7 @@ void draw_detections_v3(image im, detection *dets, int num, float thresh, char *
rgb[0] = red;
rgb[1] = green;
rgb[2] = blue;
box b = selected_detections[i]->bbox;
box b = selected_detections[i].det.bbox;
//printf("%f %f %f %f\n", b.x, b.y, b.w, b.h);
int left = (b.x - b.w / 2.)*im.w;
@ -338,9 +344,9 @@ void draw_detections_v3(image im, detection *dets, int num, float thresh, char *
draw_box_width(im, left, top, right, bot, width, red, green, blue);
if (alphabet) {
char labelstr[4096] = { 0 };
strcat(labelstr, names[selected_detections[i]->best_class]);
strcat(labelstr, names[selected_detections[i].best_class]);
for (int j = 0; j < classes; ++j) {
if (selected_detections[i]->prob[j] > thresh && j != selected_detections[i]->best_class) {
if (selected_detections[i].det.prob[j] > thresh && j != selected_detections[i].best_class) {
strcat(labelstr, ", ");
strcat(labelstr, names[j]);
}
@ -349,8 +355,8 @@ void draw_detections_v3(image im, detection *dets, int num, float thresh, char *
draw_label(im, top + width, left, label, rgb);
free_image(label);
}
if (selected_detections[i]->mask) {
image mask = float_to_image(14, 14, 1, selected_detections[i]->mask);
if (selected_detections[i].det.mask) {
image mask = float_to_image(14, 14, 1, selected_detections[i].det.mask);
image resized_mask = resize_image(mask, b.w*im.w, b.h*im.h);
image tmask = threshold_image(resized_mask, .5);
embed_image(tmask, im, left, top);

2
src/image.h
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@ -23,7 +23,7 @@ void draw_bbox(image a, box bbox, int w, float r, float g, float b);
void draw_label(image a, int r, int c, image label, const float *rgb);
void write_label(image a, int r, int c, image *characters, char *string, float *rgb);
void draw_detections(image im, int num, float thresh, box *boxes, float **probs, char **names, image **labels, int classes);
void draw_detections_v3(image im, detection *dets, int num, float thresh, char **names, image **alphabet, int classes);
void draw_detections_v3(image im, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output);
image image_distance(image a, image b);
void scale_image(image m, float s);
image crop_image(image im, int dx, int dy, int w, int h);

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