This tool is useful for auto aligning, cropping, and scaling HR and LR images for training image based neural networks. It is a CLI that takes pairs of high and low resolution images that are misaligned, misscaled, cropped out, and rotated, and outputs new, properly aligned images for use in training neural networks.
For best performance, it is highly recommended that you install torch with cuda from https://pytorch.org/get-started/locally/ ImgAlign is installed through python pip using the command: pip install ImgAlign
Accepts file inputs or directories for the HR and LR inputs. Open a command prompt window in a folder containing folders named HR and LR which contain the HR and LR images with matching names, or use -l and -g to specify folders and images. Use the options -s (or --scale) to set the scaling multiple. Output images are saved in Output folder and are scaled properly. I recommend starting with a command like "ImgAlign -s 2 -ai -j" to use optical flow to improve an affine alignment.
Example:
ImgAlign -s 2 -j -ai
Example 2 with some settings enabled with default vaules:
ImgAlign -s 2 -m 0 -g HR\ -l LR\ -c -i -1 -j -ai
usage: ImgAlign.exe [-h] -s SCALE [-m MODE] [-l LR] [-g HR] [-o OUTPUT] [-c] [-t THRESHOLD] [-j] [-r] [-f] [-w] [-ai]
[-q QUALITY] [-aq] [-u] [-a] [-O] [-i COLOR] [-n THREADS] [-e]
optional arguments:
-h, --help show this help message and exit
-s SCALE, --scale SCALE
Positive integer value. How many times bigger you want the HR resolution to be from the LR
resolution.
-m MODE, --mode MODE Options: 0 or 1. Mode 0 manipulates the HR images while remaining true to the LR images aside
from cropping. Mode 1 manipulates the LR images and remains true to the HR images aside from
cropping. In almost every case, you will want to use mode 0 so as not to alter the
degradations on the LR images.
-l LR, --lr LR LR File or folder directory. Use this to specify your low resolution image file or folder of
images. By default, ImgAlign will use images in the LR folder in the current working
directory.
-g HR, --hr HR HR File or folder directory. Use this to specify your high resolution image file or folder of
images. By default, ImgAlign will use images in the HR folder in the current working
directory.
-o OUTPUT, --output OUTPUT
Output folder directory. Defaults to current terminal directory. Use this to specify where
your Output folder will be saved.
-c, --autocrop Disabled by default. If enabled, this auto crops black boarders around HR and LR images.
Manually cropping images before running through ImgAlign will usually yield more consistent
results so that dark frames aren't overcropped
-t THRESHOLD, --threshold THRESHOLD
Integer 0-255, default 50. Luminance threshold for autocropping. Higher values cause more
agressive cropping.
-j, --affine Basic affine alignment. Used as default if no other option is specified
-r, --rotate Disabled by default. If enabled, this allows rotations when aligning images.
-f, --full Disabled by default. If enabled, this allows full homography mapping of the image, correcting
rotations, translations, and perspecive warping.
-w, --warp Disabled by default. Match images using Thin Plate Splines, allowing full image warping.
Because of the nature of TPS warping, this option requires that manual or semiautomatic points
are used.
-ai, --ai Disabled by default. This option allows use of RAFT optical flow to align images. This can be
used in conjunction with any of the aligning methods, affine, rotation, homography, or warping
to improve alignment, or by itself. This method can occasionally cause artifacts in the output
depending on the type of low resolution images being used, this can usually be fixed by
lowering the quality parameter to 2 or 1.
-q QUALITY, --quality QUALITY
Integer 1-3, Default 3. Quality of the AI alignment. This also functions as a maximum quality
used when auto quality is enabled. Higher numbers are more aggressive and ususally improves
alignment, but can cause AI artifacts on some sources. Lower numbers might impact alignment,
but causes fewer AI artifacts, uses less VRAM, runs a little faster, and is more suitable for
multithreading.
-aq, --autoquality Enabled by default. Using this option disables the auto quality step down to try to fix AI
artifacts.
-u, --manual Disabled by default. Manual mode. If enabled, this opens windows for working pairs of images
to be aligned. Double click pairs of matching points on each image in sequence, and close the
windows when finished.
-a, --semiauto Disabled by default. Semiautomatic mode. Automatically finds matching points, but loads them
into a viewer window to manually delete or add more.
-O, --overlay Enabled by default. After saving aligned images, this option will create a separate 50:50
merge of the aligned images in the Overlay folder. Useful for quickly checking through image
sets for poorly aligned outputs.
-i COLOR, --color COLOR
Default disabled. After alignment, option -1 changes the colors of the HR image to match those
of the LR image. Option 1 changes the color of the LR images to match the HR images. This can
occasionally cause miscolored regions in the altered images, so examine the results carefully.
-n THREADS, --threads THREADS
Default 1. Number of threads to use for automatic matching. Large images require a lot of RAM,
so start small to test first.
-e, --score Disabled by default. Calculate an alignment score for each processed pair of images. These
scores should be taken with a grain of salt, they are mainly to give a general idea of how
well aligned things are.
Manual Keys:
Double click left: Select point.
Click and Drag left: Pan image.
Scroll Wheel: Zoom in and out.
Click Scroll Wheel: Delete matching pairs of points.
Double Click right: Reset image view.
Spacebar: Toggle edge detection view
u: Undo last point selection.
w: Close all windows to progress.
p: Preview alignment. Overlays images using current alignment points.
Examples
Misaligned image pairs: Image pairs
ImgAlign -s 2 -ai -j -c
ImgAlign -s 1 -ai -j -c
ImgAlign -s 4 -ai -f -c
4x With full homography and Raft
ImgAlign -s 2 -ai -r -q 1
2x with rotations and Raft quality 2
I used lines of code from this site to get started with basic alignment: https://learnopencv.com/feature-based-image-alignment-using-opencv-c-python/
Autocrop code: https://stackoverflow.com/questions/13538748/crop-black-edges-with-opencv
Algorithm to find the largest rectangle contained in a rotated rectangle: https://stackoverflow.com/questions/16702966/rotate-image-and-crop-out-black-borders
RAFT optical flow architecture and model: https://github.com/princeton-vl/RAFT
Largest rectangle in histogram: https://www.interviewbit.com/blog/largest-rectangle-in-histogram/