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https://github.com/sbrunner/deskew
Library used to deskew a scanned document
https://github.com/sbrunner/deskew
deskew deskewing scanning
Last synced: 12 days ago
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Library used to deskew a scanned document
- Host: GitHub
- URL: https://github.com/sbrunner/deskew
- Owner: sbrunner
- License: mit
- Created: 2019-04-18T11:45:27.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2024-10-03T13:32:06.000Z (about 1 month ago)
- Last Synced: 2024-10-14T19:52:09.586Z (28 days ago)
- Topics: deskew, deskewing, scanning
- Language: Python
- Size: 21.1 MB
- Stars: 413
- Watchers: 4
- Forks: 46
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Deskew
Note: Skew is measured in degrees. Deskewing is a process whereby skew is removed by rotating an image by the same amount as its skew but in the opposite direction. This results in a horizontally and vertically aligned image where the text runs across the page rather than at an angle.
The return angle is between -45 and 45 degrees to don't arbitrary change the image orientation.
By using the library you can set the argument `angle_pm_90` to `True` to have an angle between -90 and 90 degrees.
## Skew detection and correction in images containing text
| Image with skew | Image after deskew |
| ---------------------------------------------------- | ------------------------------------------------------------------ |
|  |  |
## Installation
You can install deskew directly from pypi directly using the following comment
```bash
python3 -m pip install deskew
```
Or to upgrade to newer version
```bash
python3 -m pip install -U deskew
```
## Cli usage
Get the skew angle:
```bash
deskew input.png
```
Deskew an image:
```bash
deskew --output output.png input.png
```
## Lib usage
With scikit-image:
```python
import numpy as np
from skimage import io
from skimage.color import rgb2gray
from skimage.transform import rotate
from deskew import determine_skew
image = io.imread('input.png')
grayscale = rgb2gray(image)
angle = determine_skew(grayscale)
rotated = rotate(image, angle, resize=True) * 255
io.imsave('output.png', rotated.astype(np.uint8))
```
With OpenCV:
```python
import math
from typing import Tuple, Union
import cv2
import numpy as np
from deskew import determine_skew
def rotate(
image: np.ndarray, angle: float, background: Union[int, Tuple[int, int, int]]
) -> np.ndarray:
old_width, old_height = image.shape[:2]
angle_radian = math.radians(angle)
width = abs(np.sin(angle_radian) * old_height) + abs(np.cos(angle_radian) * old_width)
height = abs(np.sin(angle_radian) * old_width) + abs(np.cos(angle_radian) * old_height)
image_center = tuple(np.array(image.shape[1::-1]) / 2)
rot_mat = cv2.getRotationMatrix2D(image_center, angle, 1.0)
rot_mat[1, 2] += (width - old_width) / 2
rot_mat[0, 2] += (height - old_height) / 2
return cv2.warpAffine(image, rot_mat, (int(round(height)), int(round(width))), borderValue=background)
image = cv2.imread('input.png')
grayscale = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
angle = determine_skew(grayscale)
rotated = rotate(image, angle, (0, 0, 0))
cv2.imwrite('output.png', rotated)
```
## Debug images
If you get wrong skew angle you can generate debug images, that can help you to tune the skewing detection.
If you install deskew with `pip install deskew[debug_images]` you can get some debug images used for
the skew detection with the function `determine_skew_debug_images`.
To start the investigation you should first increase the `num_peaks` (default `20`) and use
the `determine_skew_debug_images` function.
Then you can try to tune the following arguments `num_peaks`, `angle_pm_90`, `min_angle`, `max_angle`,
`min_deviation` and eventually `sigma`.
Inspired by Alyn: https://github.com/kakul/Alyn
## Contributing
Install the pre-commit hooks:
```bash
pip install pre-commit
pre-commit install --allow-missing-config
```