imgmatrix-analysis

ImageAnalysis

A Python package for image analysis with matrix operations.

Installation

pip install imageanalysis

For development:

pip install imageanalysis[dev]

Quick Start

from imageanalysis import ImageMatrix, histogram, mean_intensity, rotate

# Load an image
img = ImageMatrix.from_file("photo.jpg")

# Basic properties
print(f"Size: {img.width}x{img.height}")
print(f"Channels: {img.channels}")

# Convert to grayscale
gray = img.to_grayscale()

# Apply transformations
rotated = rotate(img, 45)

# Analyze the image
mean = mean_intensity(img)
hist, bins = histogram(img)

# Save the result
rotated.save("rotated.jpg")

Features

Core Operations

from imageanalysis import ImageMatrix

# Create images
black = ImageMatrix.zeros(100, 100, 3)
white = ImageMatrix.ones(100, 100, 3)

# Load from file
img = ImageMatrix.from_file("image.png")

# Create from numpy array
import numpy as np
data = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
img = ImageMatrix.from_array(data)

# Image arithmetic
brightened = img + 50
darkened = img - 30
scaled = img * 0.5

# Normalize to [0, 1] range
normalized = img.normalize()

# Convert to grayscale
gray = img.to_grayscale()

Transformations

from imageanalysis import rotate, flip_horizontal, flip_vertical, resize, crop

# Rotate by angle
rotated = rotate(img, 45)

# Flip
h_flipped = flip_horizontal(img)
v_flipped = flip_vertical(img)

# Resize
resized = resize(img, (200, 200))
resized = resize(img, (200, 200), resample='lanczos')

# Crop (left, top, right, bottom)
cropped = crop(img, (10, 10, 100, 100))

Analysis

from imageanalysis import histogram, mean_intensity, std_intensity, contrast, entropy
from imageanalysis.analysis import statistics, pixel_distribution

# Histogram
hist, bins = histogram(img)
hist_red, _ = histogram(img, channel=0)

# Basic statistics
mean = mean_intensity(img)
std = std_intensity(img)
c = contrast(img)
e = entropy(img)

# Comprehensive statistics
stats = statistics(img)
# Returns: mean, std, min, max, median, variance, skewness, kurtosis, entropy, contrast

# Percentile distribution
dist = pixel_distribution(img, percentiles=(10, 25, 50, 75, 90))

API Reference

ImageMatrix

The core class for handling images as matrices.

Method	Description
from_file(path)	Load image from file
from_array(array)	Create from numpy array
zeros(h, w, c)	Create black image
ones(h, w, c)	Create white image
save(path)	Save image to file
to_grayscale()	Convert to grayscale
normalize()	Normalize to [0, 1]
copy()	Deep copy

Transform Functions

Function	Description
rotate(img, angle)	Rotate image
flip_horizontal(img)	Flip left-right
flip_vertical(img)	Flip up-down
resize(img, size)	Resize image
crop(img, box)	Crop image

Analysis Functions

Function	Description
histogram(img)	Calculate histogram
mean_intensity(img)	Mean pixel value
std_intensity(img)	Std deviation
contrast(img)	Michelson contrast
entropy(img)	Shannon entropy
statistics(img)	All statistics
pixel_distribution(img)	Percentiles

Building & Uploading to PyPI

Build the package

pip install build
python -m build

Upload to TestPyPI (recommended first)

pip install twine
twine upload --repository testpypi dist/*

Upload to PyPI

twine upload dist/*

Development

Setup

git clone https://github.com/yourusername/imageanalysis.git
cd imageanalysis
pip install -e ".[dev]"

Run tests

pytest
pytest --cov=imageanalysis

Format code

black imageanalysis tests
flake8 imageanalysis tests

License

MIT License