User guide

Install

Install stitch2d with pip:

pip install stitch2d

Or install from the GitHub repository using git and pip:

git clone https://github.com/adamancer/stitch2d
cd stitch2d
pip install .

Quick start

The following code can be used to create and save a mosaic:

from stitch2d import create_mosaic


mosaic = create_mosaic("/path/to/tiles")

try:
    mosaic.load_params()
except FileNotFoundError:
    mosaic.downsample(0.6)
    mosaic.align()
    mosaic.reset_tiles()
    mosaic.save_params()

mosaic.smooth_seams()
mosaic.save("mosaic.jpg")

A simple stitching workflow is also available from the command line. To create a smoothed mosaic and save it as a JPEG, run:

stitch2d path/to/tiles --smooth -output mosaic.jpg

For more information about using this command, including available parameters, run:

stitch2d --help

Overview

stitch2d includes two classes that can be used to create mosaics from a list of tiles:

Mosaic, which incorporates no information about how the tiles in the mosaic are arranged
StructuredMosaic, which arranges the tiles into a grid based on parameters supplied by the user

You can also use create_mosaic(), as above, which accepts the same arguments as StructuredMosaic. This function returns a StructuredMosaic if grid parameters are provided or can be inferred from the filenames of the tiles or a Mosaic if not.

Mosaic

Since Mosaic doesn’t know anything about the tile structure, it can be slow, especially for large grids where lots of tiles need to be compared. It’s almost always faster to use StructuredMosaic where possible.

Initialize a Mosaic by pointing it to the directory where the tiles of interest live:

from stitch2d import Mosaic

mosaic = Mosaic("/path/to/tiles")

Mosaic also includes a class attribute, num_cores, to specify how many cores it should use when aligning and stitching a mosaic. By default, it uses one core. Modify this value with:

Mosaic.num_cores = 2

Even when using multiple cores, detecting and extracting features can be time consuming. One way to speed up the process is to reduce the resolution of the tiles being analyzed:

mosaic.downsample(0.6)  # downsamples all tiles larger than 0.6 mp

Alternatively you can resize the tiles without the size check:

mosaic.resize(0.6)      # resizes all tiles to 0.6 mp

You can then align the smaller tiles:

mosaic.align()

In either case, you can restore the full-size images prior to stitching the mosaic together:

mosaic.reset_tiles()

Sometimes brightness and contrast can vary significantly between adjacent tiles, producing a checkerboard effect when the mosaic is stitched together. This can be mitigated in many cases using smooth_seams(), which aligns brightness/contrast between neighboring tiles by comparing areas of overlap:

mosaic.smooth_seams()

Once the tiles have been positioned, the mosaic can be viewed:

mosaic.show()

Or saved to a file:

mosaic.save("mosaic.tif")

Or returned as a numpy array if you need more control over the final mosaic:

arr = mosaic.stitch()

The default backend, opencv, orders color channels as BGR. You may want to reorder the color channels before working with the image in a different program. To get an RGB image from a BGR image, use:

arr = arr[...,::-1].copy()

New in 1.1: Or specify the desired channel order when stitching:

arr = mosaic.stitch("RGB")

Once the tiles are positioned, their locations are stored in the params attribute, which can be saved as JSON:

mosaic.save_params("params.json")

Those parameters can then be loaded into a new mosaic if needed:

mosaic.load_params("params.json")

StructuredMosaic

StructuredMosaic allows the user to specify how the tiles in the mosaic should be arranged. For tilesets of known structure, it is generally faster but otherwise works the same as Mosaic. Initialize a structured mosaic with:

from stitch2d import StructuredMosaic

mosaic = StructuredMosaic(
    "/path/to/tiles",
    dim=15,                  # number of tiles in primary axis
    origin="upper left",     # position of first tile
    direction="horizontal",  # primary axis (i.e., the direction to traverse first)
    pattern="snake"          # snake or raster
  )

For large tilesets where adequate-but-imperfect tile placement is acceptable, StructuredMosaic can use its knowledge of the tile grid to quickly build a mosaic based on the positions of only a handful of tiles:

# Stop aligning once 5 tiles have been successfully placed
mosaic.align(limit=5)

# Build the rest of the mosaic based on the positioned tiles. If from_placed
# is True, missing tiles are appended to the already positioned tiles. If
# False, a new mosaic is calculated from scratch.
mosaic.build_out(from_placed=True)

The build_out() method can also be used to ensure that all tiles (including those that could not be placed using feature matching) appear in the final mosaic. The primary disadvantage of this method is that the placement of those tiles is less precise.

Beyond 8-bit images

New in 1.2: The Tile class now includes a prep_imdata() method that can be used to tweak the image data being used to align the mosaic. When using the default OpenCVTile class, this method creates an 8-bit copy of the image data to use for feature detection and matching while retaining the original data to use when building the mosaic.

The default behavior of prep_imdata() is simplistic. To customize it, use a subclass. For example, the default method scales the intensities of the original data based on the maximum intensity found in the array. For images with a small number of extremely bright pixels, this can yield unusably dim images. A better approach may be to use np.percentile():

import numpy as np

class MyTile(OpenCVTile):

    def prep_imdata(self):
        imdata = self.imdata - self.imdata.min()
        return  np.uint8(255 * imdata / np.percentile(imdata, 99))

mosaic = create_mosaic("path/to/tiles", tile_class=MyTile)

Similar tools

The opencv package includes powerful tools for stitching 2D and 3D images). Much of that functionality has been ported to Python as the stitching package, which streamlines the opencv API and includes a useful tutorial. I didn’t have any luck getting it to work consistently with microscope tilesets, but it includes advanced features missing from this package (lens corrections, affine transformations beyond simple translation, etc.) and can be configured to work with 2D images. It’s definitely worth a look for tilesets more complex than the simple case handled here.

Fiji also includes a 2D/3D stitching tool.