Crop extraction from tiled TIFF image file directory without whole page loading using tifffile

get_crop.py

from tifffile import TiffFile
import numpy as np


def get_crop(page, i0, j0, h, w):
    """Extract a crop from a TIFF image file directory (IFD).
    
    Only the tiles englobing the crop area are loaded and not the whole page.
    This is usefull for large Whole slide images that can't fit int RAM.

    Parameters
    ----------
    page : TiffPage
        TIFF image file directory (IFD) from which the crop must be extracted.
    i0, j0: int
        Coordinates of the top left corner of the desired crop.
    h: int
        Desired crop height.
    w: int
        Desired crop width.

    Returns
    -------
    out : ndarray of shape (imagedepth, h, w, sampleperpixel)
        Extracted crop.

    """

    if not page.is_tiled:
        raise ValueError("Input page must be tiled.")

    im_width = page.imagewidth
    im_height = page.imagelength

    if h < 1 or w < 1:
        raise ValueError("h and w must be strictly positive.")

    if i0 < 0 or j0 < 0 or i0 + h >= im_height or j0 + w >= im_width:
        raise ValueError("Requested crop area is out of image bounds.")

    tile_width, tile_height = page.tilewidth, page.tilelength
    i1, j1 = i0 + h, j0 + w

    tile_i0, tile_j0 = i0 // tile_height, j0 // tile_width
    tile_i1, tile_j1 = np.ceil([i1 / tile_height, j1 / tile_width]).astype(int)

    tile_per_line = int(np.ceil(im_width / tile_width))

    out = np.empty((page.imagedepth,
                    (tile_i1 - tile_i0) * tile_height,
                    (tile_j1 - tile_j0) * tile_width,
                    page.samplesperpixel), dtype=page.dtype)

    fh = page.parent.filehandle

    jpegtables = page.tags.get('JPEGTables', None)
    if jpegtables is not None:
        jpegtables = jpegtables.value

    for i in range(tile_i0, tile_i1):
        for j in range(tile_j0, tile_j1):
            index = int(i * tile_per_line + j)

            offset = page.dataoffsets[index]
            bytecount = page.databytecounts[index]

            fh.seek(offset)
            data = fh.read(bytecount)
            tile, indices, shape = page.decode(data, index, jpegtables,
                                               jpegheader=page.jpegheader)

            im_i = (i - tile_i0) * tile_height
            im_j = (j - tile_j0) * tile_width
            out[:, im_i: im_i + tile_height, im_j: im_j + tile_width, :] = tile

    im_i0 = i0 - tile_i0 * tile_height
    im_j0 = j0 - tile_j0 * tile_width

    return out[:, im_i0: im_i0 + h, im_j0: im_j0 + w, :]

Hi,
Bcoz of this one,

Do you know how can I resample this kind of image?
I have an .NDPI image whose image size is around 186496 × 36608. I want to downsample this image as in the range of ((186496, 36608), (93248, 18304), (46624, 9152), (23312, 4576), (11656, 2288), (5828, 1144), (2914, 572), (1457, 286), (728, 143)). Is it possible?

Hi,
Bcoz of this one,

Do you know how can I resample this kind of image?
I have an .NDPI image whose image size is around 186496 × 36608. I want to downsample this image as in the range of ((186496, 36608), (93248, 18304), (46624, 9152), (23312, 4576), (11656, 2288), (5828, 1144), (2914, 572), (1457, 286), (728, 143)). Is it possible?

This is a task like clipping raster geotiff by polygon, maybe other python library (install by conda is recommended) like GDAL, osgeo, fiona have a neat function to do this job, rather than using this basic package.

Hi,

FYI I tried using the method on a NDPI image, and it almost worked out of the box but I got an error on the page.decode in line 69 (Jpeg8Error), because the data didn't contain the JPEG headers.

Changing it to:

tile, indices, shape = page.decode(page.jpegheader+data, index, jpegtables)

and it works well.

In case anyone gets the same problem !

Excellent! Thank you @adfoucart 🙏. Another option is to use the jpegheader argument:

tile, indices, shape = page.decode(data, index, jpegtables, jpegheader=page.jpegheader)

I will add this modification to the gist 😉