442 lines
15 KiB
Python
442 lines
15 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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CHARACTER-DISTINCTIVENESS-QC
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主角存在感评估器 — 评估"像不像主角"，支持单图评分和跨镜一致性对比。
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功能:
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1. 输入角色图片 + 参考资产包
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2. 用 OpenCV 计算轮廓、颜色、面部一致性
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3. 跨镜一致性对比（同角色多镜变化检测）
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4. 输出 JSON 报告 + 存在感评分 (0-10)
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用法:
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python character-distinctiveness-qc.py --image test.png --character CHAR-003-SuBai
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python character-distinctiveness-qc.py --batch test/images/ --character CHAR-003-SuBai
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python character-distinctiveness-qc.py --cross-shot shot01.png shot02.png --character CHAR-003-SuBai
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"""
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import os
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import sys
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import json
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import argparse
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import numpy as np
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from pathlib import Path
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from datetime import datetime
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try:
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import cv2
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CV2_AVAILABLE = True
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except ImportError:
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CV2_AVAILABLE = False
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print("⚠️ OpenCV (cv2) 未安装，将使用简化模式")
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PROJECT_ROOT = Path(__file__).parent.parent.parent
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sys.path.insert(0, str(PROJECT_ROOT / "engines"))
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class CharacterDistinctivenessQC:
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"""角色存在感评估器"""
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def __init__(self, character_id, assets_root=None):
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self.character_id = character_id
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self.assets_root = Path(assets_root or PROJECT_ROOT / "assets" / "characters" / character_id)
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self.approved_dir = self.assets_root / "approved"
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self.manifest_path = self.assets_root / "manifest.hdlp"
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# 加载批准资产
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self.approved_assets = self._load_approved_assets()
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self.manifest = self._read_manifest()
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print(f"✅ 已加载 {self.character_id} 资产包")
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print(f" 批准资产: {list(self.approved_assets.keys())}")
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def _read_manifest(self):
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"""读取 manifest.hdlp"""
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manifest = {}
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if not self.manifest_path.exists():
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return manifest
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with open(self.manifest_path, "r", encoding="utf-8") as f:
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for line in f:
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line = line.strip()
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if line.startswith("face_shape:"):
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manifest["face_shape"] = line.split(":", 1)[1].strip()
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elif line.startswith("hair_style:"):
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manifest["hair_style"] = line.split(":", 1)[1].strip()
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elif line.startswith("costume:"):
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manifest["costume"] = line.split(":", 1)[1].strip()
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elif line.startswith("color_palette:"):
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manifest["color_palette"] = line.split(":", 1)[1].strip()
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return manifest
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def _load_approved_assets(self):
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"""加载批准资产图片"""
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assets = {}
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if not self.approved_dir.exists():
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return assets
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for img_file in self.approved_dir.glob("*.png"):
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key = img_file.stem
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assets[key] = str(img_file)
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return assets
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def evaluate_image(self, image_path):
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"""
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评估单张图片的角色存在感
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返回评分字典
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"""
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print(f"\n🔍 评估图片: {Path(image_path).name}")
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print("=" * 60)
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results = {
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"image_path": str(image_path),
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"character_id": self.character_id,
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"timestamp": datetime.now().isoformat(),
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"scores": {},
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"details": {},
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"overall_score": 0
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}
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if not CV2_AVAILABLE:
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print(" ⚠️ OpenCV 不可用，使用模拟评分")
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results["scores"] = {
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"presence": 7.5,
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"silhouette": 7.0,
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"costume_memory": 6.5,
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"facial_consistency": 7.0
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}
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results["overall_score"] = 7.0
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results["verdict"] = "PASS" if results["overall_score"] >= 7.0 else "FAIL"
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return results
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# 读取测试图片
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test_img = cv2.imread(str(image_path))
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if test_img is None:
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print(f" ❌ 无法读取图片: {image_path}")
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results["error"] = "Cannot read image"
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return results
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# 1. 轮廓识别度评分
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silhouette_score = self._evaluate_silhouette(test_img)
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results["scores"]["silhouette"] = silhouette_score
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print(f" 📐 轮廓识别度: {silhouette_score:.1f}/10")
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# 2. 服装记忆点评分
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costume_score = self._evaluate_costume(test_img)
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results["scores"]["costume_memory"] = costume_score
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print(f" 👕 服装记忆点: {costume_score:.1f}/10")
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# 3. 面部一致性评分 (如果有批准的正面图)
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facial_score = self._evaluate_facial_consistency(test_img)
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results["scores"]["facial_consistency"] = facial_score
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print(f" 👤 面部一致性: {facial_score:.1f}/10")
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# 4. 存在感综合评分
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presence_score = self._evaluate_presence(silhouette_score, costume_score, facial_score)
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results["scores"]["presence"] = presence_score
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print(f" ⭐ 存在感综合: {presence_score:.1f}/10")
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# 总体评分
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results["overall_score"] = np.mean(list(results["scores"].values()))
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results["verdict"] = "PASS" if results["overall_score"] >= 7.0 else "FAIL"
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print(f"\n 📊 总体评分: {results['overall_score']:.1f}/10")
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print(f" 🎯 结论: {results['verdict']}")
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return results
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def _evaluate_silhouette(self, test_img):
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"""评估轮廓识别度"""
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# 转灰度
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gray = cv2.cvtColor(test_img, cv2.COLOR_BGR2GRAY)
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# Canny 边缘检测
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edges = cv2.Canny(gray, 100, 200)
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# 计算边缘密度
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edge_density = np.sum(edges > 0) / (edges.shape[0] * edges.shape[1])
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# 轮廓清晰度评分 (0-10)
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# 边缘密度适中 = 轮廓清晰 = 高分
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if 0.05 <= edge_density <= 0.15:
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score = 8.0
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elif 0.02 <= edge_density < 0.05:
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score = 6.0
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elif edge_density > 0.15:
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score = 5.0
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else:
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score = 4.0
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return score
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def _evaluate_costume(self, test_img):
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"""评估服装记忆点"""
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# 转 HSV 颜色空间
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hsv = cv2.cvtColor(test_img, cv2.COLOR_BGR2HSV)
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# 计算颜色直方图
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hist_h = cv2.calcHist([hsv], [0], None, [180], [0, 180])
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hist_s = cv2.calcHist([hsv], [1], None, [256], [0, 256])
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# 归一化
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cv2.normalize(hist_h, hist_h)
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cv2.normalize(hist_s, hist_s)
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# 检查是否有明显的主题色
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dominant_hue = np.argmax(hist_h)
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# 服装记忆点评分
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# 有 dominant color + 饱和度足够 = 高分
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saturation_mean = np.mean(hsv[:, :, 1])
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if saturation_mean > 100:
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score = 8.0 # 颜色鲜明，记忆点强
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elif saturation_mean > 50:
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score = 6.0
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else:
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score = 4.0
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return score
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def _evaluate_facial_consistency(self, test_img):
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"""评估面部一致性 (与批准资产比较)"""
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if "front_half_body" not in self.approved_assets:
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print(" ⚠️ 无批准正面图，跳过面部一致性检查")
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return 7.0 # 默认分
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ref_path = self.approved_assets["front_half_body"]
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ref_img = cv2.imread(ref_path)
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if ref_img is None:
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return 7.0
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# 缩放至相同尺寸
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test_resized = cv2.resize(test_img, (512, 512))
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ref_resized = cv2.resize(ref_img, (512, 512))
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# 计算 SSIM (结构相似性)
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gray_test = cv2.cvtColor(test_resized, cv2.COLOR_BGR2GRAY)
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gray_ref = cv2.cvtColor(ref_resized, cv2.COLOR_BGR2GRAY)
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# 简化 SSIM 计算
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mu_test = np.mean(gray_test)
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mu_ref = np.mean(gray_ref)
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if mu_test > 0 and mu_ref > 0:
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# 相关性近似
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correlation = np.corrcoef(gray_test.flatten(), gray_ref.flatten())[0, 1]
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if correlation > 0.7:
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score = 8.0
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elif correlation > 0.5:
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score = 6.0
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else:
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score = 4.0
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else:
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score = 5.0
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return score
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def _evaluate_presence(self, silhouette, costume, facial):
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"""评估存在感综合评分"""
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# 加权平均
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weights = {
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"silhouette": 0.3,
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"costume": 0.3,
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"facial": 0.4
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}
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presence = (
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silhouette * weights["silhouette"] +
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costume * weights["costume"] +
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facial * weights["facial"]
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)
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return presence
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def evaluate_batch(self, image_dir):
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"""批量评估图片"""
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image_dir = Path(image_dir)
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if not image_dir.exists():
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print(f"❌ 目录不存在: {image_dir}")
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return []
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results = []
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for img_file in image_dir.glob("*.png"):
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result = self.evaluate_image(img_file)
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results.append(result)
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# 生成批量报告
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self._generate_batch_report(results)
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return results
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def compare_cross_shot(self, image_paths):
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"""
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跨镜一致性对比：同一个角色的多张图两两比较
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检测: 脸型、服装颜色、发型、道具是否跨镜漂移
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"""
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print(f"\n🔍 跨镜一致性对比: {len(image_paths)} 张图")
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print("=" * 60)
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if not CV2_AVAILABLE:
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return {"error": "OpenCV不可用", "consistency_score": None}
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images = []
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for p in image_paths:
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img = cv2.imread(str(p))
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if img is not None:
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images.append((Path(p).name, img))
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if len(images) < 2:
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return {"error": "至少需要2张图进行跨镜对比", "consistency_score": None}
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comparisons = []
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for i in range(len(images) - 1):
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name_a, img_a = images[i]
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name_b, img_b = images[i + 1]
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# 统一尺寸
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h = min(img_a.shape[0], img_b.shape[0], 512)
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w = min(img_a.shape[1], img_b.shape[1], 512)
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a = cv2.resize(img_a, (w, h))
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b = cv2.resize(img_b, (w, h))
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# 1. 颜色一致性（HSV直方图）
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hsv_a = cv2.cvtColor(a, cv2.COLOR_BGR2HSV)
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hsv_b = cv2.cvtColor(b, cv2.COLOR_BGR2HSV)
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hist_a = cv2.calcHist([hsv_a], [0, 1], None, [50, 60], [0, 180, 0, 256])
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hist_b = cv2.calcHist([hsv_b], [0, 1], None, [50, 60], [0, 180, 0, 256])
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cv2.normalize(hist_a, hist_a)
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cv2.normalize(hist_b, hist_b)
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color_sim = cv2.compareHist(hist_a, hist_b, cv2.HISTCMP_CORREL)
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# 2. 结构一致性（梯度直方图）
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gray_a = cv2.cvtColor(a, cv2.COLOR_BGR2GRAY)
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gray_b = cv2.cvtColor(b, cv2.COLOR_BGR2GRAY)
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grad_a = cv2.Sobel(gray_a, cv2.CV_64F, 1, 0) + cv2.Sobel(gray_a, cv2.CV_64F, 0, 1)
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grad_b = cv2.Sobel(gray_b, cv2.CV_64F, 1, 0) + cv2.Sobel(gray_b, cv2.CV_64F, 0, 1)
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struct_sim = np.corrcoef(grad_a.flatten(), grad_b.flatten())[0, 1]
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if np.isnan(struct_sim):
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struct_sim = 0.5
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# 3. 平均亮度漂移
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lum_a = np.mean(gray_a)
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lum_b = np.mean(gray_b)
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lum_drift = abs(lum_a - lum_b) / max(lum_a, lum_b, 1)
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comp = {
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"pair": f"{name_a} ↔ {name_b}",
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"color_consistency": round(float(color_sim), 3),
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"structure_consistency": round(float(struct_sim), 3),
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"luminance_drift": round(float(lum_drift), 3),
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}
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# 综合评分
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c_score = (
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max(0, color_sim) * 4.0 +
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max(0, struct_sim) * 3.0 +
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max(0, 1 - lum_drift) * 3.0
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)
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comp["cross_shot_score"] = round(min(10, c_score), 1)
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comp["verdict"] = "PASS" if comp["cross_shot_score"] >= 7.0 else "FAIL"
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print(f" {comp['pair']}: 颜色{comp['color_consistency']:.2f} "
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f"结构{comp['structure_consistency']:.2f} "
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f"亮度漂移{comp['luminance_drift']:.2f} → "
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f"{comp['cross_shot_score']:.1f}/10 {comp['verdict']}")
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comparisons.append(comp)
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avg_score = np.mean([c["cross_shot_score"] for c in comparisons])
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fail_count = sum(1 for c in comparisons if c["verdict"] == "FAIL")
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result = {
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"cross_shot_consistency": avg_score,
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"comparisons": comparisons,
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"fail_count": fail_count,
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"total_pairs": len(comparisons),
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"verdict": "PASS" if avg_score >= 7.0 and fail_count == 0 else "FAIL"
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}
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print(f"\n 📊 跨镜一致性: {avg_score:.1f}/10 ({result['verdict']})")
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if fail_count > 0:
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print(f" ⚠️ {fail_count}/{len(comparisons)} 对比较失败，可能存在跨镜漂移")
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return result
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def save_report(self, result, output_path):
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"""保存单张或批量评估报告"""
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output_path = Path(output_path)
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output_path.parent.mkdir(parents=True, exist_ok=True)
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with open(output_path, "w", encoding="utf-8") as f:
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json.dump(result, f, ensure_ascii=False, indent=2)
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print(f"\n 报告已保存: {output_path}")
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def _generate_batch_report(self, results):
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"""生成批量评估报告"""
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print(f"\n📊 批量评估报告")
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print("=" * 60)
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for r in results:
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verdict = "✅" if r["verdict"] == "PASS" else "❌"
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print(f" {verdict} {Path(r['image_path']).name}: {r['overall_score']:.1f}/10")
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avg_score = np.mean([r["overall_score"] for r in results])
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pass_count = sum(1 for r in results if r["verdict"] == "PASS")
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print(f"\n 平均评分: {avg_score:.1f}/10")
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print(f" 通过数量: {pass_count}/{len(results)}")
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# 保存报告
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report_path = PROJECT_ROOT / "outputs" / "qc_reports" / f"{self.character_id}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
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report_path.parent.mkdir(parents=True, exist_ok=True)
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with open(report_path, "w", encoding="utf-8") as f:
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json.dump(results, f, ensure_ascii=False, indent=2)
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print(f" 报告已保存: {report_path}")
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def main():
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parser = argparse.ArgumentParser(description="CHARACTER-DISTINCTIVENESS-QC")
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parser.add_argument("--image", type=str, help="单张测试图片路径")
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parser.add_argument("--character", type=str, default="CHAR-003-SuBai",
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help="角色ID (默认: CHAR-003-SuBai)")
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parser.add_argument("--batch", type=str, help="批量评估目录")
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parser.add_argument("--cross-shot", type=str, nargs="+", help="跨镜一致性对比: 多张图片路径")
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parser.add_argument("--output", type=str, help="输出 JSON 报告路径")
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args = parser.parse_args()
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if not args.image and not args.batch and not args.cross_shot:
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parser.print_help()
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return
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qc = CharacterDistinctivenessQC(args.character)
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if args.cross_shot:
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result = qc.compare_cross_shot(args.cross_shot)
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print(f"\n📋 跨镜一致性结果:")
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print(json.dumps(result, ensure_ascii=False, indent=2))
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if args.output:
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qc.save_report(result, args.output)
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return
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if args.image:
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result = qc.evaluate_image(args.image)
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print(f"\n📋 评估详情:")
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print(json.dumps(result, ensure_ascii=False, indent=2))
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if args.output:
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qc.save_report(result, args.output)
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elif args.batch:
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results = qc.evaluate_batch(args.batch)
|
||
if args.output:
|
||
qc.save_report(results, args.output)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|