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
$ 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 ifas_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
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_arrayorget_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:
pip install -e .[dev]
pytest -q
License
MIT
1 maintainer
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