pymatting 1.1.15: estimate_alpha_knn

Clean-room public code sample — generation instructions

Write a brand-new, minimal, self-contained code sample in this clean-room directory.
Do not copy, paraphrase, or reference any existing project source. Work only from this spec.

A csx.json manifest scaffold already exists. Do not recreate it from memory. Preserve its case.goal, packages and symbols; fill its empty case.contract with exact assertions and correct its environment, commands and verifierAdapter for the files you generate.

Goal: verify pymatting.estimate_alpha_knn in pkg:pypi/pymatting@1.1.15
Kind: HOW

Use EXACTLY these public packages and versions:
  - pkg:pypi/pymatting@1.1.15
Demonstrate these symbols/APIs:
  - pymatting.estimate_alpha_knn

Rules:
  - One focused purpose; the smallest project that proves the goal.
  - Include a contract test (test/contract.*) that runs OFFLINE and exits 0 exactly when the goal behavior works.
  - Pin every dependency with a lockfile so resolution is reproducible.
  - No secrets, credentials, or tokens. No real URLs (only example.com or localhost). No absolute paths.
  - No personal names, emails, company names, or project identifiers of any kind.
  - No binaries and no generated output (node_modules, dist, target, venv, .git, .env).
  - Keep it under 200 files and 256KB packed.

{"case":{"caseId":"case:sha256:b445e665952e0b9c0449c76e2a7221c6188224c861654df769a2b14d6547a496","contract":["assert estimate_alpha_knn computes alpha matte bounded in [0, 1] matching trimap constraints","assert estimate_alpha_knn supports custom laplacian_kwargs and cg_kwargs options","assert estimate_alpha_knn raises ValueError when image and trimap dimensions do not match","assert estimate_alpha_knn raises ValueError when trimap is missing foreground values","assert estimate_alpha_knn raises ValueError when trimap is missing background values"],"goal":"verify pymatting.estimate_alpha_knn in pkg:pypi/pymatting@1.1.15","kind":"HOW","packages":["pkg:pypi/pymatting@1.1.15"],"schemaVersion":1,"symbols":["pymatting.estimate_alpha_knn"]},"contractCommand":["python3","test/contract.py"],"environment":{"arch":"x64","distro":"ubuntu","ecosystem":"pypi","libc":"glibc","libcVersion":"2.39","os":"linux","osVersionBucket":"24","packageManager":"pip","schemaVersion":1},"license":"MIT-0","packages":["pkg:pypi/pymatting@1.1.15"],"schemaVersion":1,"subject":"pkg:pypi/pymatting@1.1.15","symbols":["pymatting.estimate_alpha_knn"],"verifierAdapter":"python@1"}

[project]
name = "verify-pymatting-estimate-alpha-knn"
version = "0.1.0"
dependencies = [
    "pymatting==1.1.15",
]

pymatting==1.1.15
numpy
scipy
pillow

{
  "schemaVersion": 1,
  "goal": "verify pymatting.estimate_alpha_knn in pkg:pypi/pymatting@1.1.15",
  "kind": "HOW",
  "packages": [
    "pkg:pypi/pymatting@1.1.15"
  ],
  "symbols": [
    "pymatting.estimate_alpha_knn"
  ]
}

import numpy as np
from pymatting import estimate_alpha_knn


def test_estimate_alpha_knn_basic():
    h, w = 12, 12
    image = np.zeros((h, w, 3), dtype=np.float64)
    trimap = np.full((h, w), 0.5, dtype=np.float64)

    trimap[:, :4] = 0.0
    trimap[:, 8:] = 1.0

    image[:, :4] = [0.1, 0.1, 0.1]
    image[:, 8:] = [0.9, 0.9, 0.9]
    image[:, 4:8] = [0.5, 0.5, 0.5]

    alpha = estimate_alpha_knn(image, trimap)
    assert alpha.shape == (h, w)
    assert np.all(alpha >= -1e-5) and np.all(alpha <= 1.0 + 1e-5)
    assert np.all(alpha[:, :4] < 0.1)
    assert np.all(alpha[:, 8:] > 0.9)


def test_estimate_alpha_knn_custom_options():
    h, w = 10, 10
    image = np.zeros((h, w, 3), dtype=np.float64)
    trimap = np.full((h, w), 0.5, dtype=np.float64)

    trimap[:, :3] = 0.0
    trimap[:, 7:] = 1.0

    image[:, :3] = [0.2, 0.2, 0.2]
    image[:, 7:] = [0.8, 0.8, 0.8]
    image[:, 3:7] = [0.5, 0.5, 0.5]

    alpha = estimate_alpha_knn(
        image,
        trimap,
        laplacian_kwargs={"n_neighbors": [5, 5]},
        cg_kwargs={"maxiter": 500, "rtol": 1e-5},
    )
    assert alpha.shape == (h, w)
    assert np.all(alpha >= -1e-5) and np.all(alpha <= 1.0 + 1e-5)
    assert np.all(alpha[:, :3] < 0.1)
    assert np.all(alpha[:, 7:] > 0.9)


def test_estimate_alpha_knn_dimension_mismatch():
    h, w = 10, 10
    image = np.zeros((h, w, 3), dtype=np.float64)
    bad_trimap = np.full((h + 2, w), 0.5, dtype=np.float64)
    bad_trimap[:, :3] = 0.0
    bad_trimap[:, 7:] = 1.0

    try:
        estimate_alpha_knn(image, bad_trimap)
        assert False, "estimate_alpha_knn should have raised ValueError for mismatched dimensions"
    except ValueError as exc:
        assert isinstance(exc, ValueError)


def test_estimate_alpha_knn_missing_foreground():
    h, w = 8, 8
    image = np.zeros((h, w, 3), dtype=np.float64)
    no_fg_trimap = np.zeros((h, w), dtype=np.float64)

    try:
        estimate_alpha_knn(image, no_fg_trimap)
        assert False, "estimate_alpha_knn should have raised ValueError when foreground is missing"
    except ValueError as exc:
        assert isinstance(exc, ValueError)


def test_estimate_alpha_knn_missing_background():
    h, w = 8, 8
    image = np.zeros((h, w, 3), dtype=np.float64)
    no_bg_trimap = np.ones((h, w), dtype=np.float64)

    try:
        estimate_alpha_knn(image, no_bg_trimap)
        assert False, "estimate_alpha_knn should have raised ValueError when background is missing"
    except ValueError as exc:
        assert isinstance(exc, ValueError)


def main():
    test_estimate_alpha_knn_basic()
    test_estimate_alpha_knn_custom_options()
    test_estimate_alpha_knn_dimension_mismatch()
    test_estimate_alpha_knn_missing_foreground()
    test_estimate_alpha_knn_missing_background()
    print("All contract assertions passed.")


if __name__ == "__main__":
    main()