Example #1
0
/*! Write a binary grayscale pgm. */
int tc_write_image(tc_image *img, const char *filename)
{

    int r, b, c;
    FILE * file;
    if ((file = fopen(filename, "wb")) == NULL)
    {
        tc_write_log("tc_write_image: Can't open file for writing.\r\n");
        return ERR;
    }
    if (!strstr(filename, ".pgm") &&
            !strstr(filename, ".PGM") &&
            !strstr(filename, ".PPM") &&
            !strstr(filename, ".ppm"))
    {
        tc_write_log("tc_write_image: Don't recognize image type.");
        fclose(file);
        return ERR;
    }

    switch(img->chans)
    {
    case 1:
        fprintf(file, "P5\n");
        fprintf(file, "%d %d\n", img->cols, img->rows);
        fprintf(file, "255\n");
        break;
    case 3:
        fprintf(file, "P6\n");
        fprintf(file, "%d %d\n", img->cols, img->rows);
        fprintf(file, "255\n");
        break;
    default:
        tc_write_log("tc_write_image: writing special pgm format");
        /* our convention for channels > 3 */
        fprintf(file, "H%i\n",img->chans);
        fprintf(file, "%d %d\n", img->cols, img->rows);
        fprintf(file, "255\n");
        break;
    }

    for (r=0; r < img->rows; r++)
    {
        for (c=0; c < img->cols; c++)
        {
            for (b=0; b < img->chans; b++)
            {
                putc(pixel_to_uchar(tc_get(img, r, c, b)), file);
            }
        }
    }
    fclose(file);
    return OK;
}
Example #2
0
/*! Allocate a new image and fill it with a cropped portion of
 * the data in copy of src data. */
int tc_crop_image(tc_image **dst, tc_image *src, const int top,
                  const int left, const int height, const int width)
{
    int b,c,r;
    pixel_t x;

    if (src == NULL || dst == NULL)
    {
        tc_write_log("crop_image: NULL image.\r\n");
        return ERR;
    }
    if (src->rows < 1 || src->cols < 1 || src->chans < 1)
    {
        tc_write_log("clone_image: Bad dimensions.\r\n");
        return ERR;
    }
    if ((top < 0) || (top >= src->rows) ||
            (left < 0) || (left >= src->cols)  ||
            (height <= 0) || ((top+height) > src->rows) ||
            (width <= 0) || ((left+width) > src->cols))
    {
        tc_write_log("crop_image: Bad subwindow dimensions.\r\n");
        return ERR;
    }
    if (tc_alloc_image(dst, height, width, src->chans) == ERR)
    {
        tc_write_log("crop_image: Out of memory.\r\n");
        return ERR;
    }
    for (b=0; b<(*dst)->chans; b++)
    {
        for (r=0; r<(*dst)->rows; r++)
        {
            for (c=0; c<(*dst)->cols; c++)
            {
                x = tc_get(src, top+r, left+c, b);
                tc_set((*dst), r, c, b, x);
            }
        }
    }
    return OK;
}
Example #3
0
cell_t *parse_word(seg_t w, cell_t *module, unsigned int n, cell_t *entry) {
  cell_t *c;
  cell_t *data = NULL;
  csize_t in = 0, out = 1;
  if(w.s[0] == '?' && w.n == 1) {
    c = param(T_ANY, entry);
  } else if(in = 1, out = 1, match_param_word("ap", w, &in, &out)) {
    c = func(OP_ap, ++in, ++out);
  } else if(in = 1, out = 1, match_param_word("comp", w, &in, &out)) {
    in += 2;
    c = func(OP_compose, in, ++out);
  } else if(in = 1, out = 1, match_param_word("external", w, &in, &out)) {
    c = func(OP_external, ++in, out);
  } else {
    cell_t *e = lookup_word(w);
    if(!e) e = module_lookup_compiled(w, &module);
    if(e) {
      in = e->entry.in;
      out = e->entry.out;
      if(FLAG(*e, entry, PRIMITIVE)) {
        if(e->op == OP_placeholder) {
          c = func(OP_placeholder, n + 1, 1);
          int x = trace_alloc(entry, n + 2);
          in = n;
          out = 1;
          data = var_create(T_LIST, tc_get(entry, x), 0, 0);
        } else {
          c = func(e->op, e->entry.in, e->entry.out);
        }
      } else {
        c = func(OP_exec, e->entry.in + 1, e->entry.out);
        data = e;
      }
    } else {
      return NULL;
    }
  }
  if(in) c->expr.arg[0] = (cell_t *)(intptr_t)(in - 1);
  TRAVERSE(c, out) {
    *p = dep(c);
  }
Example #4
0
int tc_random_dataset(tc_dataset **d,
                      char **image_filenames,
                      char **label_filenames,
                      tc_colormap *label_colormap,
                      int nimages,
                      int ndata,
                      int sampling_method,
                      long int seed)
{
    srand(seed);
    tc_datum *datum;
    int i, j, current_label=1;

    if (d == NULL || image_filenames == NULL)
    {
        return ERR;
    }

    /* read images */
    tc_dataset *dataset;
    dataset = (tc_dataset *) malloc(sizeof(tc_dataset));
    if (dataset == NULL)
    {
        tc_write_log("Out of memory in random_dataset\n");
        *d = NULL;
        return ERR;
    }
    tc_init_dataset(dataset);

    dataset->nimages = nimages;

    /* read images */
    for (i=0; i<nimages; i++)
    {
        if (label_filenames == NULL)
        {
            tc_free_dataset(dataset);
            *d = NULL;
            return ERR;
        }

        tc_read_image(&(dataset->images[i]), image_filenames[i]);
        tc_read_image(&(dataset->labels[i]), label_filenames[i]);

        /* Change the values of the image if we're relabeling pixels
             * as classes (this reallocates labels[i] to have one channel) */
        if (label_colormap != NULL)
        {
            if (tc_label_image(&(dataset->labels[i]), label_colormap,
                               &(dataset->classes[i])) == ERR)
            {
                tc_free_dataset(dataset);
                *d = NULL;
                return ERR;
            }
            fprintf(stderr,"image %d contains:\n",i);
            for (j=0; j<label_colormap->nclasses; j++)
            {
                if (dataset->classes[i][j]>0)
                {
                    fprintf(stderr, "%d instances of class %d\n",
                            dataset->classes[i][j],j);
                }
            }
        }

        if (dataset->labels[i]->chans > 1)
        {
            tc_write_log("Use '--colorlabels' with multi-channel labels\n");
            tc_free_dataset(dataset);
            *d = NULL;
            return ERR;
        }
    }



    /* set up datasets */
    dataset->ndata = ndata;
    dataset->data = (tc_datum *) malloc(sizeof(tc_datum) * ndata);

    srand((uint64_t) NULL);
    for (i=0; i<ndata; i++)
    {
        datum = &(dataset->data[i]);
        int r, c, image, label = 0;

        while (!label)
        {
            /* look for a classified pixel */
            image = rand() % nimages;
            if (sampling_method == TC_BALANCED_SAMPLING &&
                    dataset->classes[image][current_label]==0)
            {
                /* image does not contain this class, skip if balancing */
                continue;
            }

            r = rand() % dataset->images[image]->rows;
            c = rand() % dataset->images[image]->cols;
            label = tc_get(dataset->labels[image], r, c, 0);

            if (label >= MAX_N_CLASSES ||
                    (sampling_method == TC_BALANCED_SAMPLING &&
                     label != current_label) )
            {
                //fprintf(stderr, "Label image value %i > %d, ignoring.\n",
                //         label, MAX_N_CLASSES);
                label = 0;
            }
        }

        datum->image = image;
        datum->r = r;
        datum->c = c;
        datum->label = label;
        dataset->represented[label]++;

        if (dataset->represented[label]==1)
        {
            fprintf(stderr, "new class %i at (%i,%i).\n",
                    label, r, c);
        }
        if ((datum->label+1) > (dataset->nclasses))
        {
            dataset->nclasses = (datum->label + 1);
        }

        if (i==ndata-1)
        {
            datum->next = NULL;
        }
        else
        {
            datum->next = &(dataset->data[i+1]);
        }

        if (sampling_method == TC_BALANCED_SAMPLING)
        {
            /* update current class \in [1,nclasses] */
            current_label = ((current_label+1) % (label_colormap->nclasses));
            if (current_label==0) current_label++; /* skip unlabeled */
        }
    }

    fprintf(stderr, "%i classes in dataset.\n", dataset->nclasses);
    for (i=1; i<dataset->nclasses; i++)
    {
        fprintf(stderr, "class %d (%d total samples)\n",i,dataset->represented[i]);
    }

    *d = dataset;

    return OK;
}