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harness/quality_validator.py

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+"""품질 검증기 - 생성된 이미지가 기준을 충족하는지 자동 검사한다."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from pathlib import Path
+from typing import List
+
+import numpy as np
+
+try:
+    import cv2
+    from PIL import Image
+except ImportError:
+    raise ImportError("opencv-python, Pillow이 필요합니다: pip install opencv-python Pillow")
+
+
+@dataclass
+class ValidationResult:
+    passed: bool
+    score: float            # 0.0 ~ 1.0 종합 품질 점수
+    resolution_ok: bool
+    sharpness_ok: bool
+    color_diversity_ok: bool
+    messages: List[str]
+
+    @property
+    def summary(self) -> str:
+        status = "PASS" if self.passed else "FAIL"
+        return f"[{status}] score={self.score:.2f} | " + " | ".join(self.messages)
+
+
+class QualityValidator:
+    """이미지 품질을 자동으로 검증한다."""
+
+    def __init__(
+        self,
+        min_resolution: int = 2048,
+        sharpness_threshold: float = 100.0,
+        color_diversity_threshold: float = 0.15,
+    ):
+        self.min_resolution = min_resolution
+        self.sharpness_threshold = sharpness_threshold
+        self.color_diversity_threshold = color_diversity_threshold
+
+    def validate(self, image_path: Path) -> ValidationResult:
+        if not image_path.exists():
+            return ValidationResult(
+                passed=False, score=0.0,
+                resolution_ok=False, sharpness_ok=False, color_diversity_ok=False,
+                messages=["파일이 존재하지 않음"],
+            )
+
+        img_pil = Image.open(image_path).convert("RGB")
+        img_cv = cv2.cvtColor(np.array(img_pil), cv2.COLOR_RGB2BGR)
+
+        resolution_ok, res_msg = self._check_resolution(img_pil)
+        sharpness_ok, sharp_score, sharp_msg = self._check_sharpness(img_cv)
+        color_ok, color_msg = self._check_color_diversity(img_cv)
+
+        scores = [
+            1.0 if resolution_ok else 0.0,
+            min(sharp_score / (self.sharpness_threshold * 3), 1.0),
+            1.0 if color_ok else 0.3,
+        ]
+        overall_score = float(np.mean(scores))
+        passed = resolution_ok and sharpness_ok and color_ok
+
+        return ValidationResult(
+            passed=passed,
+            score=overall_score,
+            resolution_ok=resolution_ok,
+            sharpness_ok=sharpness_ok,
+            color_diversity_ok=color_ok,
+            messages=[res_msg, sharp_msg, color_msg],
+        )
+
+    def _check_resolution(self, img: Image.Image):
+        w, h = img.size
+        ok = w >= self.min_resolution and h >= self.min_resolution
+        msg = f"해상도={w}x{h} {'OK' if ok else f'(최소 {self.min_resolution} 필요)'}"
+        return ok, msg
+
+    def _check_sharpness(self, img_cv):
+        gray = cv2.cvtColor(img_cv, cv2.COLOR_BGR2GRAY)
+        lap_var = cv2.Laplacian(gray, cv2.CV_64F).var()
+        ok = lap_var >= self.sharpness_threshold
+        msg = f"선명도={lap_var:.1f} {'OK' if ok else f'(임계값 {self.sharpness_threshold})'}"
+        return ok, float(lap_var), msg
+
+    def _check_color_diversity(self, img_cv):
+        """색상 다양성 검사 - 단색 평면 출력을 탐지한다."""
+        hsv = cv2.cvtColor(img_cv, cv2.COLOR_BGR2HSV)
+        s_channel = hsv[:, :, 1].astype(np.float32) / 255.0
+        mean_saturation = float(s_channel.mean())
+        ok = mean_saturation >= self.color_diversity_threshold
+        msg = f"색상다양성={mean_saturation:.3f} {'OK' if ok else f'(임계값 {self.color_diversity_threshold})'}"
+        return ok, msg