feat: Add hybrid quality evaluation system with CLIP and VLM support
- Add FeatureExtractor for CLIP-based image/text feature extraction - Add ObjectiveMetricsCalculator for technical quality metrics - Add VLMEvaluator for vision language model evaluation - Add HybridQualityGate combining objective + VLM evaluation - Enhance CharacterMemory with visual feature support - Add quality optional dependency (torch, ftfy, regex) - Add unit tests for new modules 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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pixelle_video/services/quality/vlm_evaluator.py
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pixelle_video/services/quality/vlm_evaluator.py
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# Copyright (C) 2025 AIDC-AI
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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VLMEvaluator - Vision Language Model based image quality evaluation
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Supports multiple VLM providers:
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- OpenAI: gpt-4-vision-preview, gpt-4o
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- Qwen-VL: qwen-vl-max, qwen-vl-plus
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- GLM-4V: via OpenAI compatible API
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"""
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import base64
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import json
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import re
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from dataclasses import dataclass, field
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from typing import Optional, List
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from pathlib import Path
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from loguru import logger
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@dataclass
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class VLMEvaluationResult:
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"""Result from VLM evaluation"""
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aesthetic_score: float = 0.0
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text_match_score: float = 0.0
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technical_score: float = 0.0
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issues: List[str] = field(default_factory=list)
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raw_response: Optional[str] = None
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def to_dict(self) -> dict:
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return {
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"aesthetic_score": self.aesthetic_score,
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"text_match_score": self.text_match_score,
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"technical_score": self.technical_score,
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"issues": self.issues,
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}
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@dataclass
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class VLMEvaluatorConfig:
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"""Configuration for VLM evaluator"""
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provider: str = "auto" # "openai", "qwen", "auto"
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model: Optional[str] = None # Auto-select if None
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max_image_size: int = 1024 # Max image dimension
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timeout: int = 30
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temperature: float = 0.1 # Low for consistent evaluation
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class VLMEvaluator:
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"""
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VLM-based image quality evaluator
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Example:
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>>> evaluator = VLMEvaluator(llm_service)
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>>> result = await evaluator.evaluate_image(
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... image_path="frame_001.png",
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... prompt="A sunset over mountains"
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... )
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"""
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EVALUATION_PROMPT = """请评估这张AI生成的图片质量。
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生成提示词: {prompt}
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{narration_section}
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请从以下三个维度评分(0.0-1.0):
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1. **美学质量** (aesthetic_score): 构图、色彩搭配、视觉吸引力
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2. **图文匹配** (text_match_score): 图片与提示词的语义对齐程度
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3. **技术质量** (technical_score): 清晰度、无伪影、无变形
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同时列出发现的问题(如有)。
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请以JSON格式返回:
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```json
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{{
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"aesthetic_score": 0.0-1.0,
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"text_match_score": 0.0-1.0,
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"technical_score": 0.0-1.0,
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"issues": ["问题1", "问题2"]
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}}
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```"""
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def __init__(
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self,
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llm_service=None,
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config: Optional[VLMEvaluatorConfig] = None
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):
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self.llm_service = llm_service
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self.config = config or VLMEvaluatorConfig()
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def _encode_image_base64(self, image_path: str) -> str:
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"""Encode image to base64, with optional resizing"""
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from PIL import Image
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import io
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with Image.open(image_path) as img:
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# Resize if too large
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max_size = self.config.max_image_size
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if max(img.size) > max_size:
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ratio = max_size / max(img.size)
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new_size = (int(img.size[0] * ratio), int(img.size[1] * ratio))
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img = img.resize(new_size, Image.Resampling.LANCZOS)
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# Convert to RGB if needed
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if img.mode in ('RGBA', 'P'):
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img = img.convert('RGB')
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# Encode to base64
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buffer = io.BytesIO()
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img.save(buffer, format='JPEG', quality=85)
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return base64.b64encode(buffer.getvalue()).decode('utf-8')
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def _parse_response(self, response: str) -> VLMEvaluationResult:
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"""Parse VLM response to extract scores"""
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result = VLMEvaluationResult(raw_response=response)
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try:
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# Try to extract JSON from response
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json_match = re.search(r'```json\s*([\s\S]*?)\s*```', response)
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if json_match:
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json_str = json_match.group(1)
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else:
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# Try to find raw JSON
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brace_start = response.find('{')
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brace_end = response.rfind('}')
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if brace_start != -1 and brace_end > brace_start:
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json_str = response[brace_start:brace_end + 1]
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else:
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logger.warning("No JSON found in VLM response")
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return result
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data = json.loads(json_str)
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result.aesthetic_score = float(data.get('aesthetic_score', 0.0))
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result.text_match_score = float(data.get('text_match_score', 0.0))
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result.technical_score = float(data.get('technical_score', 0.0))
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result.issues = data.get('issues', [])
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# Clamp scores to valid range
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result.aesthetic_score = max(0.0, min(1.0, result.aesthetic_score))
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result.text_match_score = max(0.0, min(1.0, result.text_match_score))
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result.technical_score = max(0.0, min(1.0, result.technical_score))
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except (json.JSONDecodeError, ValueError) as e:
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logger.warning(f"Failed to parse VLM response: {e}")
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return result
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async def evaluate_image(
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self,
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image_path: str,
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prompt: str,
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narration: Optional[str] = None
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) -> VLMEvaluationResult:
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"""
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Evaluate image quality using VLM
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Args:
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image_path: Path to image file
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prompt: Generation prompt
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narration: Optional narration text
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Returns:
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VLMEvaluationResult with scores
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"""
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if not Path(image_path).exists():
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return VLMEvaluationResult(issues=["Image file not found"])
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if not self.llm_service:
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logger.warning("No LLM service provided for VLM evaluation")
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return VLMEvaluationResult(issues=["No LLM service"])
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try:
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# Encode image
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image_b64 = self._encode_image_base64(image_path)
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# Build prompt
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narration_section = f"旁白文案: {narration}" if narration else ""
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eval_prompt = self.EVALUATION_PROMPT.format(
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prompt=prompt,
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narration_section=narration_section
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)
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# Call VLM via LLM service with vision
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response = await self._call_vlm(image_b64, eval_prompt)
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return self._parse_response(response)
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except Exception as e:
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logger.error(f"VLM evaluation failed: {e}")
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return VLMEvaluationResult(issues=[f"Evaluation error: {str(e)}"])
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async def _call_vlm(self, image_b64: str, prompt: str) -> str:
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"""Call VLM with image and prompt"""
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from openai import AsyncOpenAI
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# Get config from LLM service
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base_url = self.llm_service._get_config_value("base_url")
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api_key = self.llm_service._get_config_value("api_key")
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model = self.config.model or self.llm_service._get_config_value("model")
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client = AsyncOpenAI(api_key=api_key, base_url=base_url)
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# Build message with image
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "image_url",
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"image_url": {
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"url": f"data:image/jpeg;base64,{image_b64}"
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}
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},
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{
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"type": "text",
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"text": prompt
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}
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]
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}
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]
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response = await client.chat.completions.create(
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model=model,
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messages=messages,
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temperature=self.config.temperature,
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max_tokens=500,
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timeout=self.config.timeout
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)
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return response.choices[0].message.content
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