Extracting features from VOC with temp filters

Joseph Redmon

2014-02-15 228d3663f871d0e4bdee468572eb80141cb4fe3f

 src/tests.c

@@ -366,20 +366,21 @@

void train_VOC()
{
    network net = parse_network_cfg("cfg/voc_backup_ramp_80.cfg");
    network net = parse_network_cfg("cfg/voc_backup_sig_20.cfg");
    srand(2222222);
    int i = 0;
    int i = 20;
    char *labels[] = {"aeroplane","bicycle","bird","boat","bottle","bus","car","cat","chair","cow","diningtable","dog","horse","motorbike","person","pottedplant","sheep","sofa","train","tvmonitor"};
    float lr = .00001;
    float momentum = .9;
    float decay = 0.01;
    while(i++ < 1000 || 1){
        visualize_network(net);
        cvWaitKey(100);
        data train = load_data_image_pathfile_random("images/VOC2012/train_paths.txt", 1000, labels, 20, 300, 400);

        image im = float_to_image(300, 400, 3,train.X.vals[0]);
        show_image(im, "input");
        visualize_network(net);
        cvWaitKey(100);

        normalize_data_rows(train);
        clock_t start = clock(), end;
        float loss = train_network_sgd(net, train, 1000, lr, momentum, decay);
@@ -388,13 +389,61 @@
        free_data(train);
        if(i%10==0){
            char buff[256];
            sprintf(buff, "cfg/voc_backup_ramp_%d.cfg", i);
            sprintf(buff, "cfg/voc_backup_sig_%d.cfg", i);
            save_network(net, buff);
        }
        //lr *= .99;
    }
}

void features_VOC()
{
    int i,j;
    network net = parse_network_cfg("cfg/voc_features.cfg");
    char *path_file = "images/VOC2012/all_paths.txt";
    char *out_dir = "voc_features/";
    list *paths = get_paths(path_file);
    node *n = paths->front;
    while(n){
        char *path = (char *)n->val;
        char buff[1024];
        sprintf(buff, "%s%s.txt",out_dir, path);
        FILE *fp = fopen(buff, "w");
        if(fp == 0) file_error(buff);

        IplImage* src = 0;
        if( (src = cvLoadImage(path,-1)) == 0 )
        {
            printf("Cannot load file image %s\n", path);
            exit(0);
        }

        for(i = 0; i < 10; ++i){
            int w = 1024 - 90*i; //PICKED WITH CAREFUL CROSS-VALIDATION!!!!
            int h = (int)((double)w/src->width * src->height);
            IplImage *sized = cvCreateImage(cvSize(w,h), src->depth, src->nChannels);
            cvResize(src, sized, CV_INTER_LINEAR);
            image im = ipl_to_image(sized);
            reset_network_size(net, im.h, im.w, im.c);
            forward_network(net, im.data);
            free_image(im);
            image out = get_network_image_layer(net, 5);
            fprintf(fp, "%d, %d, %d\n",out.c, out.h, out.w);
            for(j = 0; j < out.c*out.h*out.w; ++j){
                if(j != 0)fprintf(fp, ",");
                fprintf(fp, "%g", out.data[j]);
            }
            fprintf(fp, "\n");
            out.c = 1;
            show_image(out, "output");
            cvWaitKey(10);
            cvReleaseImage(&sized);
        }
        fclose(fp);
        n = n->next;
    }
}

int main()
{
    //feenableexcept(FE_DIVBYZERO | FE_INVALID | FE_OVERFLOW);
@@ -406,7 +455,8 @@
    //test_ensemble();
    //test_nist();
    //test_full();
    train_VOC();
    //train_VOC();
    features_VOC();
    //test_random_preprocess();
    //test_random_classify();
    //test_parser();