polars-cv 0.6.0

A Polars plugin for vision/array operations

polars-cv

ℹ️ Note: This is a largely AI developed project and still in its early stages. Use at your own discretion.

A Polars plugin for high-performance vision and array operations.

Features

• Modular Pipelines: Define image processing pipelines and apply them to DataFrame columns.
• Expression Arguments: Use Polars expressions for dynamic, per-row parameters.
• Zero-Copy Performance: Efficient memory management with stride-aware operations.
• Multi-Domain: Seamlessly move between images, geometry (contours), and numeric results.

Installation

pip install polars-cv

Quick Start

import polars as pl
from polars_cv import Pipeline

# Define a pipeline and apply it to a column
pipe = Pipeline().source("image_bytes").resize(height=224, width=224).grayscale()

df = pl.DataFrame({"image": [img1_bytes, img2_bytes]})
result = df.with_columns(
    processed=pl.col("image").cv.pipe(pipe).sink("numpy")
)

Dynamic Pipelines

Use Polars expressions for per-row parameter values:

pipe = (
    Pipeline()
    .source("image_bytes")
    .resize(height=pl.col("target_h"), width=pl.col("target_w"))
    .crop(top=pl.col("crop_y"), left=pl.col("crop_x"), height=100, width=100)
)

df = pl.DataFrame({
    "image": [img1_bytes, img2_bytes],
    "target_h": [224, 256],
    "target_w": [224, 256],
    "crop_x": [10, 20],
    "crop_y": [5, 15],
})

result = df.with_columns(
    processed=pl.col("image").cv.pipe(pipe).sink("numpy")
)

Operations

• Image: resize, grayscale, blur, threshold, crop, rotate, pad, flip.
• Compute: normalize, scale, clamp, relu, cast.
• Geometry: extract_contours, rasterize, area, perimeter, centroid, bounding_box.
• Analysis: histogram, perceptual_hash, extract_shape.
• Reductions: reduce_sum, reduce_mean, reduce_std, reduce_max, reduce_min.

For full details, see the Documentation