Deskew

https://pypi.org/project/deskew/
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Deskew is a Python library for detecting and correcting image skew in documents containing text,...
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### Install pre-commit hooks

Source: https://pypi.org/project/deskew

Commands to install pre-commit hooks for development.

```bash
pip install pre-commit
pre-commit install --allow-missing-config
```

--------------------------------

### Install deskew

Source: https://pypi.org/project/deskew

Commands to install or upgrade the deskew package using pip.

```bash
python3 -m pip install deskew
```

```bash
python3 -m pip install -U deskew
```

--------------------------------

### Use deskew CLI

Source: https://pypi.org/project/deskew

Command-line interface examples for detecting skew angle and deskewing an image.

```bash
deskew input.png
```

```bash
deskew --output output.png input.png
```

--------------------------------

### Deskew with scikit-image

Source: https://pypi.org/project/deskew

Example of using deskew with the scikit-image library to process and rotate an 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))
```

--------------------------------

### Deskew with OpenCV

Source: https://pypi.org/project/deskew

Example of using deskew with OpenCV, including a custom rotation function to handle image dimensions.

```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)
```
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