93 lines
2.8 KiB
Python
93 lines
2.8 KiB
Python
import json
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import re
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from typing import Dict, List, Optional
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from .ollama_client import chat_vision
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from .prompts import build_closedset_prompt
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def _extract_json_obj(text: str):
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"""尽量从返回文本中提取出合法 JSON（支持代码块/前后说明）。"""
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if not isinstance(text, str):
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return None
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s = text.strip()
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# 去掉 Markdown 代码块包裹
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if s.startswith("```"):
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s = re.sub(r"^```[a-zA-Z]*\n|\n```$", "", s).strip()
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# 直接解析
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try:
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obj = json.loads(s)
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if isinstance(obj, dict):
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return obj
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except Exception:
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pass
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# 提取第一个花括号对象
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m = re.search(r"\{[\s\S]*?\}", s)
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if m:
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frag = m.group(0)
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try:
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obj = json.loads(frag)
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if isinstance(obj, dict):
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return obj
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except Exception:
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pass
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return None
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def classify_image(
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image_path: str,
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labels: List[str],
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fewshot_hints: Optional[Dict[str, str]] = None,
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ingredient_only: bool = False,
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) -> Dict:
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if not labels:
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print("[VLM] Empty labels provided, return Unknown")
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return {"label": "Unknown", "confidence": 0.0}
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print(
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f"[VLM] Start classify image={image_path} labels={len(labels)} "
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f"ingredient_only={ingredient_only} fewshot={bool(fewshot_hints)}"
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)
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prompt = build_closedset_prompt(labels, fewshot_hints, ingredient_only)
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_preview_prompt = (prompt[:120]).replace("\n", " ")
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print(f"[VLM] Prompt length={len(prompt)} preview={_preview_prompt}...")
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print("[VLM] Calling chat_vision...")
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text = chat_vision(prompt, [image_path], temperature=0.1)
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_preview_text = (str(text)[:200]).replace("\n", " ")
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print(f"[VLM] Received text length={len(str(text))} preview={_preview_text}...")
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# 解析 JSON（更健壮）
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obj = _extract_json_obj(str(text))
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if obj and "label" in obj:
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label = obj.get("label", "")
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# 若输出不在封闭集，尝试包含匹配；否则 Unknown
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if label not in labels:
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for lb in labels:
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if lb in str(label):
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label = lb
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break
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else:
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label = "Unknown"
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conf = obj.get("confidence", 0.0)
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try:
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conf = float(conf)
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except Exception:
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conf = 0.0
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result = {"label": label, "confidence": conf}
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print(f"[VLM] Parsed JSON result={result}")
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return result
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print("[VLM] JSON parse failed or missing 'label', enter fallback")
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# 回退：字符串包含匹配（无法确定置信度时返回 0.0）
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for lb in labels:
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if lb in str(text):
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print(f"[VLM] Fallback match label={lb}")
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return {"label": lb, "confidence": 0.0}
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print("[VLM] No match, return Unknown")
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return {"label": "Unknown", "confidence": 0.0}
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