Calculation of PR curve

[deepkshikha]

How to calculate PR curve for testing folder. I have added some code in detector.c to calculate the value but it's not working

void validate_detector_PRcurve(char *datacfg, char *cfgfile, char *weightfile)
{
list *options = read_data_cfg(datacfg);
char *valid_images = option_find_str(options, "valid", "data/voc.2007.test");

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("/home/deepshikha/Desktop/easy-yolo/text1.txt");
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+1, sizeof(float *));

int m = plist->size;
int i=0;

float iou_thresh = .5;
float nms = .4;

for(float thresh = 0; thresh < 1; thresh = thresh + 0.01){	
int total = 0;
    int TP = 0, FP = 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, sized.w, sized.h, net.w, net.h, thresh, probs, boxes, 1, 0, .5, 1);
	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, ".png", ".txt", 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){
		++TP;
	    }
	}
	FP = proposals - TP;

	free(id);
	free_image(orig);
	free_image(sized);
}
fprintf(stderr, "Thresh:%.4f\tRecall:%.2f%%\tPrecision:%.2f%%\n", thresh, 100.*TP/total, 100.*TP/(TP+FP));
}

}