CodeSampleX

Exemple

pymatting 1.1.15: estimate_alpha_sm

Échantillon vérifié pour pypi pymatting 1.1.15: estimate_alpha_sm. Le contrat s'est exécuté sur python 3.12 · linux debian/x64 · docker et a réussi.

sha256:e45d18b58f274982681166e701b056ed16f5da700573e9af777cd32fb43b5df4

Ce réseau offre une seule chose : un échantillon qui compile. Il l'a exécuté dans un bac à sable et conservé le reçu signé. Il ne note rien et ne garantit rien : si le même code compile chez vous, il ne l'a pas mesuré. Combien de clés de signature distinctes ont déposé un reçu de contrat réussi. Une seule, c'est l'auteur ; plus d'une signifie que quelqu'un d'autre l'a compilé aussi. Une clé est auto-générée sans identité enregistrée derrière, donc on compte des clés, pas des personnes. MIT-0

Preuves d'exécution

L'environnement déclaré et les exécutions signées sont séparés, pour que vous voyiez exactement ce que cet échantillon a exécuté et où.

Base de preuve
Contrat signé réussi
Reçus de vérification
1
Clés de signature qui l’ont compilé
1
Environnement déclaré python 3.12 linux 24 · ubuntu · glibc 2.39 x64 python 3.12 python pip

Environnements des exécutions de vérification

Environnement Contrat Étapes Exécution
python 3.12 · linux debian/x64 · docker ed25519:c1973797be207ac4 PASS compile:SKIPPED · contract:PASS · load:PASS · resolve:PASS
CONTAINER_RUN · python@1python:3.12-slim@sha256:09f7da3bc104…		 2026-09-02

Cas

HOW
Objectif
verify pymatting.estimate_alpha_sm in pkg:pypi/pymatting@1.1.15
Paquets
Symboles
  • pymatting.estimate_alpha_sm
Environnement
python 3.12
Créé
2026-09-02T09:25:46Z

Contrat

  1. pymatting.estimate_alpha_sm(image, trimap) returns estimated alpha matte of shape (h, w) and float32 dtype with values bounded in [0.0, 1.0]
  2. pymatting.estimate_alpha_sm preserves definite foreground (1.0) and definite background (0.0) regions from trimap in estimated alpha matte
  3. pymatting.estimate_alpha_sm(image, trimap, return_foreground_background=True) returns a tuple of (alpha, foreground, background) numpy ndarrays
  4. pymatting.estimate_alpha_sm accepts custom sample gathering parameters and local smoothing radii
  5. pymatting.estimate_alpha_sm asserts that image and trimap have matching dimensions and float dtypes, raising AssertionError on dimension or type mismatch

Fichiers

  • PROMPT.md
  • csx.json
  • pyproject.toml
  • requirements.txt
  • spec.json
  • src/__init__.py
  • src/matting_pipeline.py
  • test/__init__.py
  • test/contract.py
  • uv.lock

Télécharger l’artefact source (tar.gz)

Code source

PROMPT.md
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_sm 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_sm
Constraints:
  - executionContext: node
Required runtime conditions:
  - ecosystem: npm
  - language: javascript
  - moduleSystem: cjs
  - packageManager: npm@10.9.8
  - runtime: node@22.23.2

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.
csx.json
{"case":{"caseId":"case:sha256:bacbbb5c74283127879e1d9fd7be2962d3ed43a5d4c2bbd4d1290bb83d986d12","contract":["pymatting.estimate_alpha_sm(image, trimap) returns estimated alpha matte of shape (h, w) and float32 dtype with values bounded in [0.0, 1.0]","pymatting.estimate_alpha_sm preserves definite foreground (1.0) and definite background (0.0) regions from trimap in estimated alpha matte","pymatting.estimate_alpha_sm(image, trimap, return_foreground_background=True) returns a tuple of (alpha, foreground, background) numpy ndarrays","pymatting.estimate_alpha_sm accepts custom sample gathering parameters and local smoothing radii","pymatting.estimate_alpha_sm asserts that image and trimap have matching dimensions and float dtypes, raising AssertionError on dimension or type mismatch"],"goal":"verify pymatting.estimate_alpha_sm in pkg:pypi/pymatting@1.1.15","kind":"HOW","packages":["pkg:pypi/pymatting@1.1.15"],"schemaVersion":1,"symbols":["pymatting.estimate_alpha_sm"]},"contractCommand":["python","test/contract.py"],"environment":{"arch":"x64","distro":"ubuntu","ecosystem":"pypi","language":"python","libc":"glibc","libcVersion":"2.39","os":"linux","osVersionBucket":"24","packageManager":"pip","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_alpha_sm"],"verifierAdapter":"python@1"}
pyproject.toml
[project]
name = "sample-pymatting-estimate-alpha-sm"
version = "0.1.0"
description = "Clean-room verification sample for pymatting.estimate_alpha_sm in pkg:pypi/pymatting@1.1.15"
readme = "PROMPT.md"
requires-python = ">=3.11"
dependencies = [
    "pymatting==1.1.15",
]
requirements.txt
pymatting==1.1.15
spec.json
{
  "schemaVersion": 1,
  "goal": "verify pymatting.estimate_alpha_sm in pkg:pypi/pymatting@1.1.15",
  "kind": "HOW",
  "packages": [
    "pkg:pypi/pymatting@1.1.15"
  ],
  "symbols": [
    "pymatting.estimate_alpha_sm"
  ]
}
src/__init__.py
"""PyMatting estimate_alpha_sm verification package."""

from src.matting_pipeline import (
    estimate_alpha_with_shared_matting,
    estimate_alpha_and_layers,
)

__all__ = [
    "estimate_alpha_with_shared_matting",
    "estimate_alpha_and_layers",
]
src/matting_pipeline.py
"""Helper module demonstrating shared matting alpha estimation with PyMatting."""

from typing import Tuple
import numpy as np
import pymatting


def estimate_alpha_with_shared_matting(
    image: np.ndarray,
    trimap: np.ndarray,
    expansion_radius: int = 10,
    expansion_threshold: float = 0.02,
    gathering_angles: int = 4,
    refinement_radius: int = 5,
    smoothing_radius: int = 5,
) -> np.ndarray:
    """Estimate alpha matte using Shared Matting algorithm.

    Args:
        image: RGB image ndarray of shape (h, w, 3) with float32 or float64 dtype.
        trimap: Trimap ndarray of shape (h, w) with float32 or float64 dtype.
        expansion_radius: Trimap expansion radius.
        expansion_threshold: Pixel similarity threshold for trimap expansion.
        gathering_angles: Number of directions for sample gathering.
        refinement_radius: Radius for sample refinement.
        smoothing_radius: Radius for local smoothing.

    Returns:
        Estimated alpha matte ndarray of shape (h, w) with float32 dtype.
    """
    return pymatting.estimate_alpha_sm(
        image,
        trimap,
        return_foreground_background=False,
        trimap_expansion_radius=expansion_radius,
        trimap_expansion_threshold=expansion_threshold,
        sample_gathering_angles=gathering_angles,
        sample_refinement_radius=refinement_radius,
        local_smoothing_radius1=smoothing_radius,
        local_smoothing_radius2=smoothing_radius,
        local_smoothing_radius3=smoothing_radius,
    )


def estimate_alpha_and_layers(
    image: np.ndarray,
    trimap: np.ndarray,
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
    """Estimate alpha matte, foreground layer, and background layer using Shared Matting.

    Args:
        image: RGB image ndarray of shape (h, w, 3) with float32 or float64 dtype.
        trimap: Trimap ndarray of shape (h, w) with float32 or float64 dtype.

    Returns:
        Tuple containing (alpha, foreground, background) ndarrays.
    """
    return pymatting.estimate_alpha_sm(
        image,
        trimap,
        return_foreground_background=True,
    )
test/__init__.py
"""Contract test package."""
test/contract.py
"""Contract verification test for pymatting.estimate_alpha_sm."""

import os
import sys
import numpy as np

# Ensure repository root is on sys.path
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

import pymatting
from src.matting_pipeline import (
    estimate_alpha_with_shared_matting,
    estimate_alpha_and_layers,
)


def create_synthetic_matting_data(h: int = 16, w: int = 16):
    """Create a minimal synthetic RGB image and trimap for matting tests."""
    image = np.zeros((h, w, 3), dtype=np.float64)
    image[:, : w // 2] = 1.0  # White foreground on left
    image[:, w // 2 :] = 0.0  # Black background on right

    trimap = np.full((h, w), 0.5, dtype=np.float64)  # Unknown middle
    trimap[:, : w // 4] = 1.0  # Definite foreground
    trimap[:, 3 * (w // 4) :] = 0.0  # Definite background

    return image, trimap


def test_estimate_alpha_sm_default_output():
    """Verify pymatting.estimate_alpha_sm(image, trimap) returns estimated alpha matte of shape (h, w) and float32 dtype with values bounded in [0.0, 1.0]."""
    image, trimap = create_synthetic_matting_data(16, 16)
    alpha = pymatting.estimate_alpha_sm(image, trimap)

    assert isinstance(alpha, np.ndarray), f"Expected np.ndarray, got {type(alpha)}"
    assert alpha.shape == (16, 16), f"Expected shape (16, 16), got {alpha.shape}"
    assert alpha.dtype == np.float32, f"Expected float32 dtype, got {alpha.dtype}"
    assert np.all(alpha >= 0.0) and np.all(alpha <= 1.0), "Alpha values must be bounded in [0.0, 1.0]"


def test_estimate_alpha_sm_preserves_trimap_regions():
    """Verify pymatting.estimate_alpha_sm preserves definite foreground (1.0) and definite background (0.0) regions from trimap in estimated alpha matte."""
    image, trimap = create_synthetic_matting_data(16, 16)
    alpha = pymatting.estimate_alpha_sm(image, trimap)

    # Definite foreground (trimap == 1.0) should be close to 1.0
    fg_region = alpha[:, :4]
    assert np.all(fg_region >= 0.95), f"Foreground alpha should be >= 0.95, got min {fg_region.min()}"

    # Definite background (trimap == 0.0) should be close to 0.0
    bg_region = alpha[:, 12:]
    assert np.all(bg_region <= 0.05), f"Background alpha should be <= 0.05, got max {bg_region.max()}"


def test_estimate_alpha_sm_return_foreground_background():
    """Verify pymatting.estimate_alpha_sm(image, trimap, return_foreground_background=True) returns a tuple of (alpha, foreground, background) numpy ndarrays."""
    image, trimap = create_synthetic_matting_data(16, 16)
    result = pymatting.estimate_alpha_sm(image, trimap, return_foreground_background=True)

    assert isinstance(result, tuple) and len(result) == 3, f"Expected 3-tuple, got {type(result)}"
    alpha, foreground, background = result

    assert isinstance(alpha, np.ndarray) and alpha.shape == (16, 16)
    assert isinstance(foreground, np.ndarray) and foreground.shape == (16, 16, 3)
    assert isinstance(background, np.ndarray) and background.shape == (16, 16, 3)

    # Compare with helper function from src
    alpha_helper, fg_helper, bg_helper = estimate_alpha_and_layers(image, trimap)
    assert np.allclose(alpha, alpha_helper, atol=1e-5)
    assert np.allclose(foreground, fg_helper, atol=1e-5)
    assert np.allclose(background, bg_helper, atol=1e-5)


def test_estimate_alpha_sm_custom_parameters():
    """Verify pymatting.estimate_alpha_sm accepts custom sample gathering parameters and local smoothing radii."""
    image, trimap = create_synthetic_matting_data(16, 16)
    alpha = estimate_alpha_with_shared_matting(
        image,
        trimap,
        expansion_radius=2,
        expansion_threshold=0.05,
        gathering_angles=4,
        refinement_radius=2,
        smoothing_radius=2,
    )

    assert isinstance(alpha, np.ndarray)
    assert alpha.shape == (16, 16)
    assert alpha.dtype == np.float32
    assert np.all(alpha >= 0.0) and np.all(alpha <= 1.0)


def test_estimate_alpha_sm_assertion_on_invalid_inputs():
    """Verify pymatting.estimate_alpha_sm asserts that image and trimap have matching dimensions and float dtypes, raising AssertionError on dimension or type mismatch."""
    image, trimap = create_synthetic_matting_data(16, 16)

    # Mismatched spatial shape
    bad_trimap_shape = np.full((16, 8), 0.5, dtype=np.float64)
    try:
        pymatting.estimate_alpha_sm(image, bad_trimap_shape)
        assert False, "Expected AssertionError for mismatched shape"
    except AssertionError:
        pass

    # Invalid image dtype (uint8)
    bad_image_dtype = (image * 255).astype(np.uint8)
    try:
        pymatting.estimate_alpha_sm(bad_image_dtype, trimap)
        assert False, "Expected AssertionError for non-float image dtype"
    except AssertionError:
        pass

    # Invalid trimap dtype (int32)
    bad_trimap_dtype = np.ones((16, 16), dtype=np.int32)
    try:
        pymatting.estimate_alpha_sm(image, bad_trimap_dtype)
        assert False, "Expected AssertionError for non-float trimap dtype"
    except AssertionError:
        pass

    # Non-RGB image (missing color dimension)
    bad_image_2d = np.zeros((16, 16), dtype=np.float64)
    try:
        pymatting.estimate_alpha_sm(bad_image_2d, trimap)
        assert False, "Expected AssertionError for 2D image without 3 channels"
    except AssertionError:
        pass


def main():
    test_estimate_alpha_sm_default_output()
    test_estimate_alpha_sm_preserves_trimap_regions()
    test_estimate_alpha_sm_return_foreground_background()
    test_estimate_alpha_sm_custom_parameters()
    test_estimate_alpha_sm_assertion_on_invalid_inputs()
    print("All contract tests passed successfully.")


if __name__ == "__main__":
    main()
uv.lock (début seulement)
version = 1
revision = 3
requires-python = ">=3.11"
resolution-markers = [
    "python_full_version >= '3.12'",
    "python_full_version < '3.12'",
]

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