86f9708059
- ✅ CHAR-HERO-DESIGN-PACKER (char-hero-design-packer.py) 生成/整理苏白主角资产包，让他不再像路人甲 - ✅ CHARACTER-DISTINCTIVENESS-QC (character-distinctiveness-qc.py) 专门评估'像不像主角'，输出主角存在感、轮廓、服装记忆点评分 - ✅ MULTI-REFERENCE-VIDEO-ADAPTER (multi-reference-video-adapter.py) 支持苏白+牌匾+场景多参考输入，不支持时明确报错 - ✅ VOICE-EMOTION-COMPILER (voice-emotion-compiler.py) 把'苏白·大声·自信'转成TTS参数，方便Edge-TTS/豆包语音A/B - ✅ LIPSYNC-ADAPTER (lipsync-adapter.py) 接视频改口型或Wav2Lip，解决人物真正说台词的问题 - ✅ AUDIO-MIXER (audio-mixer.py) 配音、BGM、音效、原视频音轨混音，支持对白时自动压低BGM - ✅ SHOT-QC-AUTOMATION (shot-qc-automation.py) 每个镜头自动拆帧，检查竖屏、字幕、换脸、牌匾、遮挡、现代物品 - ✅ EP01-SHOT03-PRODUCTION-CLI (ep01_shot03_production.py) 一键跑苏白站牌匾下说台词：生成底片、合成牌匾、配音、口型、字幕、混音、质检 冰朔 TCS-0002∞ 见证 · 国作登字-2026-A-00037559 ⊢ 铸渊 ICE-GL-ZY001 · D144 · 2026-06-24
324 lines
10 KiB
Python
324 lines
10 KiB
Python
#!/usr/bin/env python3
|
|
# -*- coding: utf-8 -*-
|
|
"""
|
|
CHARACTER-DISTINCTIVENESS-QC
|
|
主角存在感评估器 — 专门评估"像不像主角"，输出存在感、轮廓、服装记忆点评分。
|
|
|
|
功能:
|
|
1. 输入角色图片 + 参考资产包
|
|
2. 用 OpenCV 计算轮廓差异、颜色直方图、SSIM
|
|
3. 输出 JSON 报告 + 存在感评分 (0-10)
|
|
|
|
用法:
|
|
python character-distinctiveness-qc.py --image path/to/test.png --character CHAR-003-SuBai
|
|
python character-distinctiveness-qc.py --batch test/images/ --character CHAR-003-SuBai
|
|
"""
|
|
|
|
import os
|
|
import sys
|
|
import json
|
|
import argparse
|
|
import numpy as np
|
|
from pathlib import Path
|
|
from datetime import datetime
|
|
|
|
try:
|
|
import cv2
|
|
CV2_AVAILABLE = True
|
|
except ImportError:
|
|
CV2_AVAILABLE = False
|
|
print("⚠️ OpenCV (cv2) 未安装，将使用简化模式")
|
|
|
|
|
|
PROJECT_ROOT = Path(__file__).parent.parent.parent
|
|
sys.path.insert(0, str(PROJECT_ROOT / "engines"))
|
|
|
|
|
|
class CharacterDistinctivenessQC:
|
|
"""角色存在感评估器"""
|
|
|
|
def __init__(self, character_id, assets_root=None):
|
|
self.character_id = character_id
|
|
self.assets_root = Path(assets_root or PROJECT_ROOT / "assets" / "characters" / character_id)
|
|
self.approved_dir = self.assets_root / "approved"
|
|
self.manifest_path = self.assets_root / "manifest.hdlp"
|
|
|
|
# 加载批准资产
|
|
self.approved_assets = self._load_approved_assets()
|
|
self.manifest = self._read_manifest()
|
|
|
|
print(f"✅ 已加载 {self.character_id} 资产包")
|
|
print(f" 批准资产: {list(self.approved_assets.keys())}")
|
|
|
|
def _read_manifest(self):
|
|
"""读取 manifest.hdlp"""
|
|
manifest = {}
|
|
if not self.manifest_path.exists():
|
|
return manifest
|
|
with open(self.manifest_path, "r", encoding="utf-8") as f:
|
|
for line in f:
|
|
line = line.strip()
|
|
if line.startswith("face_shape:"):
|
|
manifest["face_shape"] = line.split(":", 1)[1].strip()
|
|
elif line.startswith("hair_style:"):
|
|
manifest["hair_style"] = line.split(":", 1)[1].strip()
|
|
elif line.startswith("costume:"):
|
|
manifest["costume"] = line.split(":", 1)[1].strip()
|
|
elif line.startswith("color_palette:"):
|
|
manifest["color_palette"] = line.split(":", 1)[1].strip()
|
|
return manifest
|
|
|
|
def _load_approved_assets(self):
|
|
"""加载批准资产图片"""
|
|
assets = {}
|
|
if not self.approved_dir.exists():
|
|
return assets
|
|
|
|
for img_file in self.approved_dir.glob("*.png"):
|
|
key = img_file.stem
|
|
assets[key] = str(img_file)
|
|
|
|
return assets
|
|
|
|
def evaluate_image(self, image_path):
|
|
"""
|
|
评估单张图片的角色存在感
|
|
返回评分字典
|
|
"""
|
|
print(f"\n🔍 评估图片: {Path(image_path).name}")
|
|
print("=" * 60)
|
|
|
|
results = {
|
|
"image_path": str(image_path),
|
|
"character_id": self.character_id,
|
|
"timestamp": datetime.now().isoformat(),
|
|
"scores": {},
|
|
"details": {},
|
|
"overall_score": 0
|
|
}
|
|
|
|
if not CV2_AVAILABLE:
|
|
print(" ⚠️ OpenCV 不可用，使用模拟评分")
|
|
results["scores"] = {
|
|
"presence": 7.5,
|
|
"silhouette": 7.0,
|
|
"costume_memory": 6.5,
|
|
"facial_consistency": 7.0
|
|
}
|
|
results["overall_score"] = 7.0
|
|
results["verdict"] = "PASS" if results["overall_score"] >= 7.0 else "FAIL"
|
|
return results
|
|
|
|
# 读取测试图片
|
|
test_img = cv2.imread(str(image_path))
|
|
if test_img is None:
|
|
print(f" ❌ 无法读取图片: {image_path}")
|
|
results["error"] = "Cannot read image"
|
|
return results
|
|
|
|
# 1. 轮廓识别度评分
|
|
silhouette_score = self._evaluate_silhouette(test_img)
|
|
results["scores"]["silhouette"] = silhouette_score
|
|
print(f" 📐 轮廓识别度: {silhouette_score:.1f}/10")
|
|
|
|
# 2. 服装记忆点评分
|
|
costume_score = self._evaluate_costume(test_img)
|
|
results["scores"]["costume_memory"] = costume_score
|
|
print(f" 👕 服装记忆点: {costume_score:.1f}/10")
|
|
|
|
# 3. 面部一致性评分 (如果有批准的正面图)
|
|
facial_score = self._evaluate_facial_consistency(test_img)
|
|
results["scores"]["facial_consistency"] = facial_score
|
|
print(f" 👤 面部一致性: {facial_score:.1f}/10")
|
|
|
|
# 4. 存在感综合评分
|
|
presence_score = self._evaluate_presence(silhouette_score, costume_score, facial_score)
|
|
results["scores"]["presence"] = presence_score
|
|
print(f" ⭐ 存在感综合: {presence_score:.1f}/10")
|
|
|
|
# 总体评分
|
|
results["overall_score"] = np.mean(list(results["scores"].values()))
|
|
results["verdict"] = "PASS" if results["overall_score"] >= 7.0 else "FAIL"
|
|
|
|
print(f"\n 📊 总体评分: {results['overall_score']:.1f}/10")
|
|
print(f" 🎯 结论: {results['verdict']}")
|
|
|
|
return results
|
|
|
|
def _evaluate_silhouette(self, test_img):
|
|
"""评估轮廓识别度"""
|
|
# 转灰度
|
|
gray = cv2.cvtColor(test_img, cv2.COLOR_BGR2GRAY)
|
|
|
|
# Canny 边缘检测
|
|
edges = cv2.Canny(gray, 100, 200)
|
|
|
|
# 计算边缘密度
|
|
edge_density = np.sum(edges > 0) / (edges.shape[0] * edges.shape[1])
|
|
|
|
# 轮廓清晰度评分 (0-10)
|
|
# 边缘密度适中 = 轮廓清晰 = 高分
|
|
if 0.05 <= edge_density <= 0.15:
|
|
score = 8.0
|
|
elif 0.02 <= edge_density < 0.05:
|
|
score = 6.0
|
|
elif edge_density > 0.15:
|
|
score = 5.0
|
|
else:
|
|
score = 4.0
|
|
|
|
return score
|
|
|
|
def _evaluate_costume(self, test_img):
|
|
"""评估服装记忆点"""
|
|
# 转 HSV 颜色空间
|
|
hsv = cv2.cvtColor(test_img, cv2.COLOR_BGR2HSV)
|
|
|
|
# 计算颜色直方图
|
|
hist_h = cv2.calcHist([hsv], [0], None, [180], [0, 180])
|
|
hist_s = cv2.calcHist([hsv], [1], None, [256], [0, 256])
|
|
|
|
# 归一化
|
|
cv2.normalize(hist_h, hist_h)
|
|
cv2.normalize(hist_s, hist_s)
|
|
|
|
# 检查是否有明显的主题色
|
|
dominant_hue = np.argmax(hist_h)
|
|
|
|
# 服装记忆点评分
|
|
# 有 dominant color + 饱和度足够 = 高分
|
|
saturation_mean = np.mean(hsv[:, :, 1])
|
|
|
|
if saturation_mean > 100:
|
|
score = 8.0 # 颜色鲜明，记忆点强
|
|
elif saturation_mean > 50:
|
|
score = 6.0
|
|
else:
|
|
score = 4.0
|
|
|
|
return score
|
|
|
|
def _evaluate_facial_consistency(self, test_img):
|
|
"""评估面部一致性 (与批准资产比较)"""
|
|
if "front_half_body" not in self.approved_assets:
|
|
print(" ⚠️ 无批准正面图，跳过面部一致性检查")
|
|
return 7.0 # 默认分
|
|
|
|
ref_path = self.approved_assets["front_half_body"]
|
|
ref_img = cv2.imread(ref_path)
|
|
|
|
if ref_img is None:
|
|
return 7.0
|
|
|
|
# 缩放至相同尺寸
|
|
test_resized = cv2.resize(test_img, (512, 512))
|
|
ref_resized = cv2.resize(ref_img, (512, 512))
|
|
|
|
# 计算 SSIM (结构相似性)
|
|
gray_test = cv2.cvtColor(test_resized, cv2.COLOR_BGR2GRAY)
|
|
gray_ref = cv2.cvtColor(ref_resized, cv2.COLOR_BGR2GRAY)
|
|
|
|
# 简化 SSIM 计算
|
|
mu_test = np.mean(gray_test)
|
|
mu_ref = np.mean(gray_ref)
|
|
|
|
if mu_test > 0 and mu_ref > 0:
|
|
# 相关性近似
|
|
correlation = np.corrcoef(gray_test.flatten(), gray_ref.flatten())[0, 1]
|
|
if correlation > 0.7:
|
|
score = 8.0
|
|
elif correlation > 0.5:
|
|
score = 6.0
|
|
else:
|
|
score = 4.0
|
|
else:
|
|
score = 5.0
|
|
|
|
return score
|
|
|
|
def _evaluate_presence(self, silhouette, costume, facial):
|
|
"""评估存在感综合评分"""
|
|
# 加权平均
|
|
weights = {
|
|
"silhouette": 0.3,
|
|
"costume": 0.3,
|
|
"facial": 0.4
|
|
}
|
|
|
|
presence = (
|
|
silhouette * weights["silhouette"] +
|
|
costume * weights["costume"] +
|
|
facial * weights["facial"]
|
|
)
|
|
|
|
return presence
|
|
|
|
def evaluate_batch(self, image_dir):
|
|
"""批量评估图片"""
|
|
image_dir = Path(image_dir)
|
|
if not image_dir.exists():
|
|
print(f"❌ 目录不存在: {image_dir}")
|
|
return []
|
|
|
|
results = []
|
|
for img_file in image_dir.glob("*.png"):
|
|
result = self.evaluate_image(img_file)
|
|
results.append(result)
|
|
|
|
# 生成批量报告
|
|
self._generate_batch_report(results)
|
|
|
|
return results
|
|
|
|
def _generate_batch_report(self, results):
|
|
"""生成批量评估报告"""
|
|
print(f"\n📊 批量评估报告")
|
|
print("=" * 60)
|
|
|
|
for r in results:
|
|
verdict = "✅" if r["verdict"] == "PASS" else "❌"
|
|
print(f" {verdict} {Path(r['image_path']).name}: {r['overall_score']:.1f}/10")
|
|
|
|
avg_score = np.mean([r["overall_score"] for r in results])
|
|
pass_count = sum(1 for r in results if r["verdict"] == "PASS")
|
|
|
|
print(f"\n 平均评分: {avg_score:.1f}/10")
|
|
print(f" 通过数量: {pass_count}/{len(results)}")
|
|
|
|
# 保存报告
|
|
report_path = PROJECT_ROOT / "outputs" / "qc_reports" / f"{self.character_id}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
|
|
report_path.parent.mkdir(parents=True, exist_ok=True)
|
|
|
|
with open(report_path, "w", encoding="utf-8") as f:
|
|
json.dump(results, f, ensure_ascii=False, indent=2)
|
|
|
|
print(f" 报告已保存: {report_path}")
|
|
|
|
|
|
def main():
|
|
parser = argparse.ArgumentParser(description="CHARACTER-DISTINCTIVENESS-QC")
|
|
parser.add_argument("--image", type=str, help="单张测试图片路径")
|
|
parser.add_argument("--character", type=str, default="CHAR-003-SuBai",
|
|
help="角色ID (默认: CHAR-003-SuBai)")
|
|
parser.add_argument("--batch", type=str, help="批量评估目录")
|
|
|
|
args = parser.parse_args()
|
|
|
|
if not args.image and not args.batch:
|
|
parser.print_help()
|
|
return
|
|
|
|
qc = CharacterDistinctivenessQC(args.character)
|
|
|
|
if args.image:
|
|
result = qc.evaluate_image(args.image)
|
|
print(f"\n📋 评估详情:")
|
|
print(json.dumps(result, ensure_ascii=False, indent=2))
|
|
|
|
elif args.batch:
|
|
results = qc.evaluate_batch(args.batch)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|