pymatting 1.1.15: estimate_foreground_ml

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_foreground_ml 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_foreground_ml

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:f4ff436552c1e4111f232cf2509e950e6bed45a41c1c50e660e07aac41a7d984","contract":["assert estimate_foreground_ml estimates foreground RGB image given input image and alpha matte","assert estimate_foreground_ml returns both foreground and background arrays when return_background is True","assert estimate_foreground_ml preserves input color values when alpha matte is fully opaque","assert estimate_foreground_ml computes bounded foreground values on continuous gradient inputs"],"goal":"verify pymatting.estimate_foreground_ml in pkg:pypi/pymatting@1.1.15","kind":"HOW","packages":["pkg:pypi/pymatting@1.1.15"],"schemaVersion":1,"symbols":["pymatting.estimate_foreground_ml"]},"contractCommand":["python3","test/contract.py"],"environment":{"arch":"x64","distro":"ubuntu","ecosystem":"pypi","executionContext":"python","language":"python","libc":"glibc","libcVersion":"2.39","moduleSystem":"standard","os":"linux","osVersionBucket":"24","packageManager":"pip","packageManagerVersion":"24.0","runtime":"python","runtimeVersion":"3.12","schemaVersion":1},"license":"MIT-0","packages":["pkg:pypi/pymatting@1.1.15"],"schemaVersion":1,"subject":"pkg:pypi/pymatting@1.1.15","symbols":["pymatting.estimate_foreground_ml"],"verifierAdapter":"python@1"}

[project]
name = "verify-pymatting-estimate-foreground-ml"
version = "0.1.0"
dependencies = [
    "pymatting==1.1.15",
]

pymatting==1.1.15
numpy
scipy
pillow
numba

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

import numpy as np
from pymatting import estimate_foreground_ml


def test_estimate_foreground_basic():
    h, w = 16, 16
    image = np.zeros((h, w, 3), dtype=np.float64)
    alpha = np.zeros((h, w), dtype=np.float64)

    image[:, :8] = [0.2, 0.3, 0.4]
    image[:, 8:] = [0.8, 0.7, 0.6]
    alpha[:, 8:] = 1.0

    fg = estimate_foreground_ml(image, alpha)
    assert fg.shape == (h, w, 3)
    assert np.all(fg >= -1e-5) and np.all(fg <= 1.0 + 1e-5)


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

    image[:, :6] = [0.1, 0.2, 0.3]
    image[:, 6:] = [0.9, 0.8, 0.7]
    alpha[:, :6] = 0.0
    alpha[:, 6:] = 1.0

    fg, bg = estimate_foreground_ml(image, alpha, return_background=True)
    assert fg.shape == (h, w, 3)
    assert bg.shape == (h, w, 3)
    assert np.all(fg >= -1e-5) and np.all(fg <= 1.0 + 1e-5)
    assert np.all(bg >= -1e-5) and np.all(bg <= 1.0 + 1e-5)


def test_estimate_foreground_pure_regions():
    h, w = 8, 8
    image = np.full((h, w, 3), 0.75, dtype=np.float64)
    alpha_fg = np.ones((h, w), dtype=np.float64)

    fg = estimate_foreground_ml(image, alpha_fg)
    assert fg.shape == (h, w, 3)
    assert np.allclose(fg, image, atol=1e-3)


def test_estimate_foreground_smooth_gradient():
    h, w = 10, 10
    image = np.linspace(0.0, 1.0, h * w * 3, dtype=np.float64).reshape((h, w, 3))
    alpha = np.linspace(0.0, 1.0, h * w, dtype=np.float64).reshape((h, w))

    fg = estimate_foreground_ml(image, alpha)
    assert fg.shape == (h, w, 3)
    assert np.all(fg >= -1e-5) and np.all(fg <= 1.0 + 1e-5)


def main():
    test_estimate_foreground_basic()
    test_estimate_foreground_and_background()
    test_estimate_foreground_pure_regions()
    test_estimate_foreground_smooth_gradient()
    print("All contract assertions passed.")


if __name__ == "__main__":
    main()