# image_array_and_histogram
Utilities to convert images to NumPy arrays, compute grayscale histograms, and
reconstruct images from arrays.
Version 1.1.0 introduces PEP-8 function names and fixes the historical axis
ordering bug. Arrays are now always shaped `(height, width)`. The old camelCase
names are still available but deprecated.
## Installation
```sh
$ pip install image-array-and-histogram
```
## Functions (current API)
- `get_image_array(image, ensure_grayscale=True)` – Return a 2D uint8 NumPy array (height, width) from a PIL image. Converts to grayscale by default.
- `get_hist(image_or_array, as_density=False)` – Return a 256-length list of counts (or probabilities if `as_density=True`). Accepts either a PIL image or a NumPy/list array.
- `array_to_image(arr, width=None, height=None)` – Build a grayscale PIL image from a 1D or 2D array.
Deprecated aliases (will emit `DeprecationWarning`): `getImageArray`, `getHist`, `getImageFromArray`.
## Quick Start
```python
from PIL import Image
import numpy as np
from image_array_and_histogram import get_image_array, get_hist, array_to_image
# Load image and get array
img = Image.open('photo.jpg')
arr = get_image_array(img) # shape (H, W)
# Compute histogram
hist = get_hist(arr) # list of 256 counts
# Normalize histogram
hist_density = get_hist(arr, as_density=True)
# Create an image from a NumPy array
gradient = np.linspace(0, 255, 256, dtype=np.uint8).reshape(16, 16)
gradient_img = array_to_image(gradient)
gradient_img.save('gradient.png')
```
## Notes
- If you pass a color image to `get_image_array` or `get_hist`, it will be converted to grayscale (mode 'L').
- Histogram computation is vectorized with NumPy (`numpy.bincount`) for speed.
- For legacy behavior (<=1.0.x) the array shape used `(width, height)`. Adjust any downstream code if it relied on that ordering.
## Testing
After cloning the repository:
```sh
pip install -e .[dev]
pytest -q
```
## License
MIT
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"description": "# image_array_and_histogram\n\nUtilities to convert images to NumPy arrays, compute grayscale histograms, and\nreconstruct images from arrays.\n\nVersion 1.1.0 introduces PEP-8 function names and fixes the historical axis\nordering bug. Arrays are now always shaped `(height, width)`. The old camelCase\nnames are still available but deprecated.\n\n## Installation\n\n```sh\n$ pip install image-array-and-histogram\n```\n\n## Functions (current API)\n\n- `get_image_array(image, ensure_grayscale=True)` \u2013 Return a 2D uint8 NumPy array (height, width) from a PIL image. Converts to grayscale by default.\n- `get_hist(image_or_array, as_density=False)` \u2013 Return a 256-length list of counts (or probabilities if `as_density=True`). Accepts either a PIL image or a NumPy/list array.\n- `array_to_image(arr, width=None, height=None)` \u2013 Build a grayscale PIL image from a 1D or 2D array.\n\nDeprecated aliases (will emit `DeprecationWarning`): `getImageArray`, `getHist`, `getImageFromArray`.\n\n## Quick Start\n\n```python\nfrom PIL import Image\nimport numpy as np\nfrom image_array_and_histogram import get_image_array, get_hist, array_to_image\n\n# Load image and get array\nimg = Image.open('photo.jpg')\narr = get_image_array(img) # shape (H, W)\n\n# Compute histogram\nhist = get_hist(arr) # list of 256 counts\n\n# Normalize histogram\nhist_density = get_hist(arr, as_density=True)\n\n# Create an image from a NumPy array\ngradient = np.linspace(0, 255, 256, dtype=np.uint8).reshape(16, 16)\ngradient_img = array_to_image(gradient)\ngradient_img.save('gradient.png')\n```\n\n## Notes\n\n- If you pass a color image to `get_image_array` or `get_hist`, it will be converted to grayscale (mode 'L').\n- Histogram computation is vectorized with NumPy (`numpy.bincount`) for speed.\n- For legacy behavior (<=1.0.x) the array shape used `(width, height)`. Adjust any downstream code if it relied on that ordering.\n\n## Testing\n\nAfter cloning the repository:\n\n```sh\npip install -e .[dev]\npytest -q\n```\n\n## License\n\nMIT\n",
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