CodeSampleX

Ejemplo

pymatting 1.1.15: estimate_foreground_cf

Muestra verificada para pypi pymatting 1.1.15: estimate_foreground_cf. El contrato se ejecutó en python 3.12 · linux debian/x64 · docker y pasó.

sha256:425887d726769feed6e8ec078198437a3b0b1fb29fb97add655f4d347e95c747

Esta red ofrece una sola cosa: una muestra que compila. La ejecutó en un sandbox y guardó el recibo firmado. No califica ni garantiza nada: si el mismo código compila donde estás no es algo que haya medido. Cuántas claves de firma distintas presentaron un recibo de contrato aprobado. Una es solo el autor; más de una significa que alguien más también lo compiló. Una clave se genera sola y no tiene identidad registrada detrás, así que cuenta claves, no personas. MIT-0

Evidencia de ejecución

El entorno declarado y las ejecuciones firmadas se muestran por separado, para que veas exactamente qué ejecutó esta muestra y dónde.

Base de evidencia
Contrato firmado aprobado
Recibos de verificación
1
Claves de firma que lo compilaron
1
Entorno declarado python 3.12 linux 24 · ubuntu · glibc 2.39 x64 python 3.12 python pip 24

Entornos de las ejecuciones de verificación

Entorno Contrato Etapas Ejecución
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

Caso

HOW
Objetivo
verify pymatting.estimate_foreground_cf in pkg:pypi/pymatting@1.1.15
Paquetes
Símbolos
  • pymatting.estimate_foreground_cf
Entorno
python 3.12
Creado
2026-09-02T12:22:31Z

Contrato

  1. assert estimate_foreground_cf returns float64 ndarray foreground matching input image shape and bounded in [0, 1]
  2. assert estimate_foreground_cf with return_background=True returns tuple of (foreground, background) ndarrays
  3. assert estimate_foreground_cf accepts custom regularization and rtol parameters and initial guesses
  4. assert estimate_foreground_cf raises AssertionError when alpha spatial dimensions do not match image

Archivos

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

Descargar el artefacto de código fuente (tar.gz)

Código fuente

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_foreground_cf 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_cf
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:sha256:3674565baedeb3e46aeda78c9af7f1a9b29852057523648b14363bacc3afd78a","constraints":{"executionContext":"python"},"contract":["assert estimate_foreground_cf returns float64 ndarray foreground matching input image shape and bounded in [0, 1]","assert estimate_foreground_cf with return_background=True returns tuple of (foreground, background) ndarrays","assert estimate_foreground_cf accepts custom regularization and rtol parameters and initial guesses","assert estimate_foreground_cf raises AssertionError when alpha spatial dimensions do not match image"],"goal":"verify pymatting.estimate_foreground_cf in pkg:pypi/pymatting@1.1.15","kind":"HOW","packages":["pkg:pypi/pymatting@1.1.15"],"schemaVersion":1,"symbols":["pymatting.estimate_foreground_cf"]},"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_cf"],"verifierAdapter":"python@1"}
pyproject.toml
[project]
name = "verify-pymatting"
version = "0.1.0"
dependencies = [
    "pymatting==1.1.15",
    "numpy",
    "scipy",
    "numba",
    "pillow",
]
requirements.txt
pymatting==1.1.15
numpy
scipy
numba
pillow
sample.py
"""Demonstration of foreground estimation using pymatting.estimate_foreground_cf."""

from typing import Optional, Tuple, Union
import numpy as np
from pymatting import estimate_foreground_cf


def extract_foreground(
    image: np.ndarray,
    alpha: np.ndarray,
    regularization: float = 1e-5,
    rtol: float = 1e-5,
    return_background: bool = False,
    foreground_guess: Optional[np.ndarray] = None,
    background_guess: Optional[np.ndarray] = None,
) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]:
    """Estimate the foreground (and optionally background) from an image and alpha matte."""
    return estimate_foreground_cf(
        image,
        alpha,
        regularization=regularization,
        rtol=rtol,
        return_background=return_background,
        foreground_guess=foreground_guess,
        background_guess=background_guess,
    )
spec.json
{
  "schemaVersion": 1,
  "goal": "verify pymatting.estimate_foreground_cf in pkg:pypi/pymatting@1.1.15",
  "kind": "HOW",
  "packages": [
    "pkg:pypi/pymatting@1.1.15"
  ],
  "symbols": [
    "pymatting.estimate_foreground_cf"
  ],
  "constraints": {
    "executionContext": "node"
  },
  "runtimeConditions": {
    "ecosystem": "npm",
    "language": "javascript",
    "moduleSystem": "cjs",
    "packageManager": "npm@10.9.8",
    "runtime": "node@22.23.2"
  }
}
test/contract.py
import os
import sys
import numpy as np

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

from pymatting import estimate_foreground_cf
from sample import extract_foreground


def test_estimate_foreground_basic():
    """assert estimate_foreground_cf returns float64 ndarray foreground matching input image shape and bounded in [0, 1]"""
    h, w = 8, 8
    image = np.zeros((h, w, 3), dtype=np.float64)
    image[:, :4] = [0.2, 0.3, 0.4]
    image[:, 4:] = [0.8, 0.7, 0.6]

    alpha = np.zeros((h, w), dtype=np.float64)
    alpha[:, 4:] = 1.0

    fg = estimate_foreground_cf(image, alpha)
    assert fg.shape == (h, w, 3)
    assert fg.dtype == np.float64
    assert np.all(fg >= -1e-5) and np.all(fg <= 1.0 + 1e-5)

    # Check via helper wrapper
    fg_helper = extract_foreground(image, alpha)
    assert fg_helper.shape == (h, w, 3)
    assert np.allclose(fg, fg_helper)


def test_estimate_foreground_return_background():
    """assert estimate_foreground_cf with return_background=True returns tuple of (foreground, background) ndarrays"""
    h, w = 8, 8
    image = np.zeros((h, w, 3), dtype=np.float64)
    image[:, :4] = [0.1, 0.2, 0.3]
    image[:, 4:] = [0.9, 0.8, 0.7]

    alpha = np.zeros((h, w), dtype=np.float64)
    alpha[:, 4:] = 1.0

    fg, bg = estimate_foreground_cf(image, alpha, return_background=True)
    assert isinstance(fg, np.ndarray)
    assert isinstance(bg, np.ndarray)
    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)

    # Check via helper wrapper
    fg_h, bg_h = extract_foreground(image, alpha, return_background=True)
    assert np.allclose(fg, fg_h)
    assert np.allclose(bg, bg_h)


def test_estimate_foreground_custom_parameters_and_guesses():
    """assert estimate_foreground_cf accepts custom regularization and rtol parameters and initial guesses"""
    h, w = 6, 6
    image = np.zeros((h, w, 3), dtype=np.float64)
    image[:, :3] = [0.1, 0.2, 0.3]
    image[:, 3:] = [0.7, 0.8, 0.9]

    alpha = np.zeros((h, w), dtype=np.float64)
    alpha[:, 3:] = 1.0

    fg_guess = np.ones((h, w, 3), dtype=np.float64) * 0.5
    bg_guess = np.ones((h, w, 3), dtype=np.float64) * 0.5

    fg, bg = estimate_foreground_cf(
        image,
        alpha,
        regularization=1e-4,
        rtol=1e-4,
        return_background=True,
        foreground_guess=fg_guess,
        background_guess=bg_guess,
    )
    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_mismatched_dimensions_raises():
    """assert estimate_foreground_cf raises AssertionError when alpha spatial dimensions do not match image"""
    h, w = 8, 8
    image = np.zeros((h, w, 3), dtype=np.float64)
    bad_alpha = np.zeros((h + 2, w), dtype=np.float64)

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


def main():
    test_estimate_foreground_basic()
    test_estimate_foreground_return_background()
    test_estimate_foreground_custom_parameters_and_guesses()
    test_estimate_foreground_mismatched_dimensions_raises()
    print("All contract assertions passed.")


if __name__ == "__main__":
    main()
uv.lock (solo la primera parte)
version = 1
revision = 3
requires-python = ">=3.12"

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Seeder de origen

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