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Perceptual Hashing

polars-cv provides perceptual hashing for finding visually similar images. Unlike cryptographic hashes, perceptual hashes produce similar fingerprints for similar images.

Basic Usage

from polars_cv import Pipeline
import polars as pl

# Create hash pipeline
hash_pipe = Pipeline().source("image_bytes").perceptual_hash()

df = pl.DataFrame({"image": [image_bytes]})
result = df.with_columns(
    hash=pl.col("image").cv.pipe(hash_pipe).sink("list")
)

Algorithms

Algorithm Speed Robustness Best For
perceptual Medium High Most use cases (default)
average Fastest Lower Quick approximate matching
difference Fast Medium General purpose
blockhash Medium High Crop-resistant matching 
Pipeline().source("image_bytes").perceptual_hash(algorithm="average")

Comparing Hashes

Use hamming_distance() and hash_similarity() for batch comparison:

from polars_cv import hamming_distance, hash_similarity

# Compare hashes from two pipelines
result = df.with_columns(
    similarity=hash_similarity(
        pl.col("image_a").cv.pipe(hash_pipe),
        pl.col("image_b").cv.pipe(hash_pipe)
    )
)

Next Steps