#include <stdio.h>
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#include "network.h"
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#include "detection_layer.h"
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#include "cost_layer.h"
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#include "utils.h"
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#include "parser.h"
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#include "box.h"
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#ifdef OPENCV
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#include "opencv2/highgui/highgui_c.h"
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#endif
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char *coco_classes[] = {"person","bicycle","car","motorcycle","airplane","bus","train","truck","boat","traffic light","fire hydrant","stop sign","parking meter","bench","bird","cat","dog","horse","sheep","cow","elephant","bear","zebra","giraffe","backpack","umbrella","handbag","tie","suitcase","frisbee","skis","snowboard","sports ball","kite","baseball bat","baseball glove","skateboard","surfboard","tennis racket","bottle","wine glass","cup","fork","knife","spoon","bowl","banana","apple","sandwich","orange","broccoli","carrot","hot dog","pizza","donut","cake","chair","couch","potted plant","bed","dining table","toilet","tv","laptop","mouse","remote","keyboard","cell phone","microwave","oven","toaster","sink","refrigerator","book","clock","vase","scissors","teddy bear","hair drier","toothbrush"};
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int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90};
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void draw_coco(image im, float *pred, int side, char *label)
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{
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int classes = 1;
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int elems = 4+classes;
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int j;
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int r, c;
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for(r = 0; r < side; ++r){
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for(c = 0; c < side; ++c){
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j = (r*side + c) * elems;
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int class = max_index(pred+j, classes);
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if (pred[j+class] > 0.2){
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int width = pred[j+class]*5 + 1;
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printf("%f %s\n", pred[j+class], "object"); //coco_classes[class-1]);
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float red = get_color(0,class,classes);
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float green = get_color(1,class,classes);
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float blue = get_color(2,class,classes);
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j += classes;
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box predict = {pred[j+0], pred[j+1], pred[j+2], pred[j+3]};
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predict.x = (predict.x+c)/side;
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predict.y = (predict.y+r)/side;
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draw_bbox(im, predict, width, red, green, blue);
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}
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}
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}
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show_image(im, label);
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}
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void train_coco(char *cfgfile, char *weightfile)
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{
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//char *train_images = "/home/pjreddie/data/coco/train.txt";
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char *train_images = "/home/pjreddie/data/voc/test/train.txt";
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char *backup_directory = "/home/pjreddie/backup/";
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srand(time(0));
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data_seed = time(0);
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char *base = basecfg(cfgfile);
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printf("%s\n", base);
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float avg_loss = -1;
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network net = parse_network_cfg(cfgfile);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
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int imgs = 128;
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int i = *net.seen/imgs;
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data train, buffer;
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layer l = net.layers[net.n - 1];
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int side = l.side;
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int classes = l.classes;
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list *plist = get_paths(train_images);
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int N = plist->size;
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char **paths = (char **)list_to_array(plist);
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load_args args = {0};
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args.w = net.w;
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args.h = net.h;
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args.paths = paths;
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args.n = imgs;
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args.m = plist->size;
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args.classes = classes;
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args.num_boxes = side;
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args.d = &buffer;
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args.type = REGION_DATA;
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pthread_t load_thread = load_data_in_thread(args);
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clock_t time;
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while(i*imgs < N*120){
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i += 1;
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time=clock();
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pthread_join(load_thread, 0);
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train = buffer;
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load_thread = load_data_in_thread(args);
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printf("Loaded: %lf seconds\n", sec(clock()-time));
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/*
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image im = float_to_image(net.w, net.h, 3, train.X.vals[113]);
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image copy = copy_image(im);
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draw_coco(copy, train.y.vals[113], 7, "truth");
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cvWaitKey(0);
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free_image(copy);
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*/
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time=clock();
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float loss = train_network(net, train);
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if (avg_loss < 0) avg_loss = loss;
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avg_loss = avg_loss*.9 + loss*.1;
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printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs);
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if((i-1)*imgs <= N && i*imgs > N){
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fprintf(stderr, "First stage done\n");
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net.learning_rate *= 10;
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char buff[256];
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sprintf(buff, "%s/%s_first_stage.weights", backup_directory, base);
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save_weights(net, buff);
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}
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if((i-1)*imgs <= 80*N && i*imgs > N*80){
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fprintf(stderr, "Second stage done.\n");
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char buff[256];
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sprintf(buff, "%s/%s_second_stage.weights", backup_directory, base);
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save_weights(net, buff);
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}
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if(i%1000==0){
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char buff[256];
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sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
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save_weights(net, buff);
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}
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free_data(train);
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}
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char buff[256];
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sprintf(buff, "%s/%s_final.weights", backup_directory, base);
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save_weights(net, buff);
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}
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void get_probs(float *predictions, int total, int classes, int inc, float **probs)
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{
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int i,j;
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for (i = 0; i < total; ++i){
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int index = i*inc;
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float scale = predictions[index];
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probs[i][0] = scale;
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for(j = 0; j < classes; ++j){
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probs[i][j] = scale*predictions[index+j+1];
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}
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}
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}
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void get_boxes(float *predictions, int n, int num_boxes, int per_box, box *boxes)
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{
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int i,j;
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for (i = 0; i < num_boxes*num_boxes; ++i){
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for(j = 0; j < n; ++j){
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int index = i*n+j;
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int offset = index*per_box;
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int row = i / num_boxes;
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int col = i % num_boxes;
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boxes[index].x = (predictions[offset + 0] + col) / num_boxes;
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boxes[index].y = (predictions[offset + 1] + row) / num_boxes;
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boxes[index].w = predictions[offset + 2];
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boxes[index].h = predictions[offset + 3];
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}
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}
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}
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void convert_cocos(float *predictions, int classes, int num_boxes, int num, int w, int h, float thresh, float **probs, box *boxes)
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{
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int i,j;
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int per_box = 4+classes;
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for (i = 0; i < num_boxes*num_boxes*num; ++i){
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int offset = i*per_box;
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for(j = 0; j < classes; ++j){
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float prob = predictions[offset+j];
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probs[i][j] = (prob > thresh) ? prob : 0;
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}
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int row = i / num_boxes;
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int col = i % num_boxes;
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offset += classes;
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boxes[i].x = (predictions[offset + 0] + col) / num_boxes;
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boxes[i].y = (predictions[offset + 1] + row) / num_boxes;
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boxes[i].w = predictions[offset + 2];
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boxes[i].h = predictions[offset + 3];
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}
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}
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void print_cocos(FILE *fp, int image_id, box *boxes, float **probs, int num_boxes, int classes, int w, int h)
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{
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int i, j;
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for(i = 0; i < num_boxes*num_boxes; ++i){
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float xmin = boxes[i].x - boxes[i].w/2.;
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float xmax = boxes[i].x + boxes[i].w/2.;
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float ymin = boxes[i].y - boxes[i].h/2.;
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float ymax = boxes[i].y + boxes[i].h/2.;
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if (xmin < 0) xmin = 0;
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if (ymin < 0) ymin = 0;
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if (xmax > w) xmax = w;
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if (ymax > h) ymax = h;
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float bx = xmin;
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float by = ymin;
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float bw = xmax - xmin;
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float bh = ymax - ymin;
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for(j = 0; j < classes; ++j){
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if (probs[i][j]) fprintf(fp, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f},\n", image_id, coco_ids[j], bx, by, bw, bh, probs[i][j]);
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}
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}
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}
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int get_coco_image_id(char *filename)
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{
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char *p = strrchr(filename, '_');
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return atoi(p+1);
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}
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void validate_recall(char *cfgfile, char *weightfile)
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{
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network net = parse_network_cfg(cfgfile);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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set_batch_network(&net, 1);
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fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
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srand(time(0));
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char *val_images = "/home/pjreddie/data/voc/test/2007_test.txt";
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list *plist = get_paths(val_images);
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char **paths = (char **)list_to_array(plist);
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layer l = net.layers[net.n - 1];
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int num_boxes = l.side;
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int num = l.n;
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int classes = l.classes;
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int j;
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box *boxes = calloc(num_boxes*num_boxes*num, sizeof(box));
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float **probs = calloc(num_boxes*num_boxes*num, sizeof(float *));
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for(j = 0; j < num_boxes*num_boxes*num; ++j) probs[j] = calloc(classes+1, sizeof(float *));
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int N = plist->size;
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int i=0;
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int k;
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float iou_thresh = .5;
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float thresh = .1;
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int total = 0;
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int correct = 0;
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float avg_iou = 0;
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int nms = 1;
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int proposals = 0;
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int save = 1;
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for (i = 0; i < N; ++i) {
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char *path = paths[i];
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image orig = load_image_color(path, 0, 0);
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image resized = resize_image(orig, net.w, net.h);
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float *X = resized.data;
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float *predictions = network_predict(net, X);
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get_boxes(predictions+1+classes, num, num_boxes, 5+classes, boxes);
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get_probs(predictions, num*num_boxes*num_boxes, classes, 5+classes, probs);
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if (nms) do_nms(boxes, probs, num*num_boxes*num_boxes, (classes>0) ? classes : 1, iou_thresh);
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char *labelpath = find_replace(path, "images", "labels");
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labelpath = find_replace(labelpath, "JPEGImages", "labels");
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labelpath = find_replace(labelpath, ".jpg", ".txt");
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labelpath = find_replace(labelpath, ".JPEG", ".txt");
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int num_labels = 0;
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box_label *truth = read_boxes(labelpath, &num_labels);
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for(k = 0; k < num_boxes*num_boxes*num; ++k){
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if(probs[k][0] > thresh){
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++proposals;
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if(save){
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char buff[256];
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sprintf(buff, "/data/extracted/nms_preds/%d", proposals);
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int dx = (boxes[k].x - boxes[k].w/2) * orig.w;
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int dy = (boxes[k].y - boxes[k].h/2) * orig.h;
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int w = boxes[k].w * orig.w;
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int h = boxes[k].h * orig.h;
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image cropped = crop_image(orig, dx, dy, w, h);
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image sized = resize_image(cropped, 224, 224);
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#ifdef OPENCV
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save_image_jpg(sized, buff);
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#endif
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free_image(sized);
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free_image(cropped);
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sprintf(buff, "/data/extracted/nms_pred_boxes/%d.txt", proposals);
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char *im_id = basecfg(path);
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FILE *fp = fopen(buff, "w");
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fprintf(fp, "%s %d %d %d %d\n", im_id, dx, dy, dx+w, dy+h);
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fclose(fp);
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free(im_id);
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}
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}
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}
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for (j = 0; j < num_labels; ++j) {
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++total;
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box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h};
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float best_iou = 0;
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for(k = 0; k < num_boxes*num_boxes*num; ++k){
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float iou = box_iou(boxes[k], t);
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if(probs[k][0] > thresh && iou > best_iou){
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best_iou = iou;
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}
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}
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avg_iou += best_iou;
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if(best_iou > iou_thresh){
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++correct;
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}
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}
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free(truth);
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free_image(orig);
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free_image(resized);
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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);
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}
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}
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void extract_boxes(char *cfgfile, char *weightfile)
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{
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network net = parse_network_cfg(cfgfile);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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set_batch_network(&net, 1);
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fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
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srand(time(0));
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char *val_images = "/home/pjreddie/data/voc/test/train.txt";
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list *plist = get_paths(val_images);
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char **paths = (char **)list_to_array(plist);
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layer l = net.layers[net.n - 1];
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int num_boxes = l.side;
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int num = l.n;
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int classes = l.classes;
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int j;
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box *boxes = calloc(num_boxes*num_boxes*num, sizeof(box));
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float **probs = calloc(num_boxes*num_boxes*num, sizeof(float *));
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for(j = 0; j < num_boxes*num_boxes*num; ++j) probs[j] = calloc(classes+1, sizeof(float *));
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int N = plist->size;
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int i=0;
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int k;
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int count = 0;
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float iou_thresh = .3;
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for (i = 0; i < N; ++i) {
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fprintf(stderr, "%5d %5d\n", i, count);
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char *path = paths[i];
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image orig = load_image_color(path, 0, 0);
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image resized = resize_image(orig, net.w, net.h);
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float *X = resized.data;
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float *predictions = network_predict(net, X);
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get_boxes(predictions+1+classes, num, num_boxes, 5+classes, boxes);
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get_probs(predictions, num*num_boxes*num_boxes, classes, 5+classes, probs);
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char *labelpath = find_replace(path, "images", "labels");
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labelpath = find_replace(labelpath, "JPEGImages", "labels");
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labelpath = find_replace(labelpath, ".jpg", ".txt");
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labelpath = find_replace(labelpath, ".JPEG", ".txt");
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int num_labels = 0;
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box_label *truth = read_boxes(labelpath, &num_labels);
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FILE *label = stdin;
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for(k = 0; k < num_boxes*num_boxes*num; ++k){
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int overlaps = 0;
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for (j = 0; j < num_labels; ++j) {
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box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h};
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float iou = box_iou(boxes[k], t);
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if (iou > iou_thresh){
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if (!overlaps) {
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char buff[256];
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sprintf(buff, "/data/extracted/labels/%d.txt", count);
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label = fopen(buff, "w");
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overlaps = 1;
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}
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fprintf(label, "%d %f\n", truth[j].id, iou);
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}
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}
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if (overlaps) {
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char buff[256];
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sprintf(buff, "/data/extracted/imgs/%d", count++);
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int dx = (boxes[k].x - boxes[k].w/2) * orig.w;
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int dy = (boxes[k].y - boxes[k].h/2) * orig.h;
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int w = boxes[k].w * orig.w;
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int h = boxes[k].h * orig.h;
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image cropped = crop_image(orig, dx, dy, w, h);
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image sized = resize_image(cropped, 224, 224);
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#ifdef OPENCV
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save_image_jpg(sized, buff);
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#endif
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free_image(sized);
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free_image(cropped);
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fclose(label);
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}
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}
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free(truth);
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free_image(orig);
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free_image(resized);
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}
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}
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void validate_coco(char *cfgfile, char *weightfile)
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{
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network net = parse_network_cfg(cfgfile);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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set_batch_network(&net, 1);
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fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
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srand(time(0));
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char *base = "/home/pjreddie/backup/";
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list *plist = get_paths("data/coco_val_5k.list");
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char **paths = (char **)list_to_array(plist);
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int num_boxes = 9;
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int num = 4;
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int classes = 1;
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int j;
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char buff[1024];
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snprintf(buff, 1024, "%s/coco_results.json", base);
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FILE *fp = fopen(buff, "w");
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fprintf(fp, "[\n");
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box *boxes = calloc(num_boxes*num_boxes*num, sizeof(box));
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float **probs = calloc(num_boxes*num_boxes*num, sizeof(float *));
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for(j = 0; j < num_boxes*num_boxes*num; ++j) probs[j] = calloc(classes, sizeof(float *));
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int m = plist->size;
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int i=0;
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int t;
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float thresh = .01;
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int nms = 1;
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float iou_thresh = .5;
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load_args args = {0};
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args.w = net.w;
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args.h = net.h;
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args.type = IMAGE_DATA;
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int nthreads = 8;
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image *val = calloc(nthreads, sizeof(image));
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image *val_resized = calloc(nthreads, sizeof(image));
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image *buf = calloc(nthreads, sizeof(image));
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image *buf_resized = calloc(nthreads, sizeof(image));
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pthread_t *thr = calloc(nthreads, sizeof(pthread_t));
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for(t = 0; t < nthreads; ++t){
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args.path = paths[i+t];
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args.im = &buf[t];
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args.resized = &buf_resized[t];
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thr[t] = load_data_in_thread(args);
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}
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time_t start = time(0);
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for(i = nthreads; i < m+nthreads; i += nthreads){
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fprintf(stderr, "%d\n", i);
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for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
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pthread_join(thr[t], 0);
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val[t] = buf[t];
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val_resized[t] = buf_resized[t];
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}
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for(t = 0; t < nthreads && i+t < m; ++t){
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args.path = paths[i+t];
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args.im = &buf[t];
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args.resized = &buf_resized[t];
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thr[t] = load_data_in_thread(args);
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}
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for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
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char *path = paths[i+t-nthreads];
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int image_id = get_coco_image_id(path);
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float *X = val_resized[t].data;
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float *predictions = network_predict(net, X);
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int w = val[t].w;
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int h = val[t].h;
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convert_cocos(predictions, classes, num_boxes, num, w, h, thresh, probs, boxes);
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if (nms) do_nms(boxes, probs, num_boxes, classes, iou_thresh);
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print_cocos(fp, image_id, boxes, probs, num_boxes, classes, w, h);
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free_image(val[t]);
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free_image(val_resized[t]);
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}
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}
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fseek(fp, -2, SEEK_CUR);
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fprintf(fp, "\n]\n");
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fclose(fp);
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fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
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}
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void test_coco(char *cfgfile, char *weightfile, char *filename)
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{
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network net = parse_network_cfg(cfgfile);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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set_batch_network(&net, 1);
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srand(2222222);
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clock_t time;
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char input[256];
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while(1){
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if(filename){
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strncpy(input, filename, 256);
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} else {
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printf("Enter Image Path: ");
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fflush(stdout);
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fgets(input, 256, stdin);
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strtok(input, "\n");
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}
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image im = load_image_color(input,0,0);
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image sized = resize_image(im, net.w, net.h);
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float *X = sized.data;
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time=clock();
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float *predictions = network_predict(net, X);
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printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
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draw_coco(im, predictions, 7, "predictions");
|
free_image(im);
|
free_image(sized);
|
#ifdef OPENCV
|
cvWaitKey(0);
|
cvDestroyAllWindows();
|
#endif
|
if (filename) break;
|
}
|
}
|
|
void run_coco(int argc, char **argv)
|
{
|
if(argc < 4){
|
fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
|
return;
|
}
|
|
char *cfg = argv[3];
|
char *weights = (argc > 4) ? argv[4] : 0;
|
char *filename = (argc > 5) ? argv[5]: 0;
|
if(0==strcmp(argv[2], "test")) test_coco(cfg, weights, filename);
|
else if(0==strcmp(argv[2], "train")) train_coco(cfg, weights);
|
else if(0==strcmp(argv[2], "extract")) extract_boxes(cfg, weights);
|
else if(0==strcmp(argv[2], "valid")) validate_recall(cfg, weights);
|
}
|
