HYUNJUNGLEE
b9342f6726
S-CANVAS (Saman Corp.) — DXF + DEM + AI 기반 3D 조감도 생성 엔진. ~24k LOC Python (scanvas_maker.py 7072 LOC GUI + 구조물 파서/빌더 다수). 이 커밋은 7-iter cleanup이 적용된 상태로 import: - F821 8 + B023 6: 비동기 lambda + except/loop 변수 캡처 NameError (Py3.13에서 reproduce 확인된 진짜 버그) - RUF012 4 + RUF013 1: ClassVar / implicit Optional 명시화 - F811/B905/B904/F401/F841/W293/F541/UP/SIM/RUF/PLR 700+ cleanup/modernization 신규 파일: - ruff.toml: target=py313, Korean unicode/저자 스타일/도메인 복잡도 무력화 - requirements-py313.txt: pyproj>=3.7, scipy>=1.14, numpy>=2.0.2 (Py3.13 wheel) - .gitignore: gcp-key.json, 캐시, 백업, 생성 이미지 제외 검증: ruff 0 errors, py_compile 0 errors, import 33/33 OK on Py3.13.13. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
98 lines
3.5 KiB
Python
98 lines
3.5 KiB
Python
"""품질 검증기 - 생성된 이미지가 기준을 충족하는지 자동 검사한다."""
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from __future__ import annotations
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from dataclasses import dataclass
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from pathlib import Path
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import numpy as np
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try:
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import cv2
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from PIL import Image
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except ImportError as e:
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raise ImportError("opencv-python, Pillow이 필요합니다: pip install opencv-python Pillow") from e
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@dataclass
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class ValidationResult:
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passed: bool
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score: float # 0.0 ~ 1.0 종합 품질 점수
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resolution_ok: bool
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sharpness_ok: bool
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color_diversity_ok: bool
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messages: list[str]
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@property
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def summary(self) -> str:
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status = "PASS" if self.passed else "FAIL"
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return f"[{status}] score={self.score:.2f} | " + " | ".join(self.messages)
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class QualityValidator:
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"""이미지 품질을 자동으로 검증한다."""
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def __init__(
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self,
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min_resolution: int = 2048,
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sharpness_threshold: float = 100.0,
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color_diversity_threshold: float = 0.15,
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):
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self.min_resolution = min_resolution
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self.sharpness_threshold = sharpness_threshold
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self.color_diversity_threshold = color_diversity_threshold
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def validate(self, image_path: Path) -> ValidationResult:
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if not image_path.exists():
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return ValidationResult(
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passed=False, score=0.0,
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resolution_ok=False, sharpness_ok=False, color_diversity_ok=False,
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messages=["파일이 존재하지 않음"],
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)
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img_pil = Image.open(image_path).convert("RGB")
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img_cv = cv2.cvtColor(np.array(img_pil), cv2.COLOR_RGB2BGR)
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resolution_ok, res_msg = self._check_resolution(img_pil)
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sharpness_ok, sharp_score, sharp_msg = self._check_sharpness(img_cv)
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color_ok, color_msg = self._check_color_diversity(img_cv)
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scores = [
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1.0 if resolution_ok else 0.0,
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min(sharp_score / (self.sharpness_threshold * 3), 1.0),
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1.0 if color_ok else 0.3,
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]
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overall_score = float(np.mean(scores))
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passed = resolution_ok and sharpness_ok and color_ok
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return ValidationResult(
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passed=passed,
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score=overall_score,
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resolution_ok=resolution_ok,
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sharpness_ok=sharpness_ok,
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color_diversity_ok=color_ok,
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messages=[res_msg, sharp_msg, color_msg],
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)
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def _check_resolution(self, img: Image.Image):
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w, h = img.size
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ok = w >= self.min_resolution and h >= self.min_resolution
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msg = f"해상도={w}x{h} {'OK' if ok else f'(최소 {self.min_resolution} 필요)'}"
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return ok, msg
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def _check_sharpness(self, img_cv):
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gray = cv2.cvtColor(img_cv, cv2.COLOR_BGR2GRAY)
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lap_var = cv2.Laplacian(gray, cv2.CV_64F).var()
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ok = lap_var >= self.sharpness_threshold
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msg = f"선명도={lap_var:.1f} {'OK' if ok else f'(임계값 {self.sharpness_threshold})'}"
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return ok, float(lap_var), msg
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def _check_color_diversity(self, img_cv):
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"""색상 다양성 검사 - 단색 평면 출력을 탐지한다."""
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hsv = cv2.cvtColor(img_cv, cv2.COLOR_BGR2HSV)
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s_channel = hsv[:, :, 1].astype(np.float32) / 255.0
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mean_saturation = float(s_channel.mean())
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ok = mean_saturation >= self.color_diversity_threshold
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msg = f"색상다양성={mean_saturation:.3f} {'OK' if ok else f'(임계값 {self.color_diversity_threshold})'}"
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return ok, msg
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