feat: 以图搜图功能升级（跨表UNION检索 + 拍照识图入口 + 批量向量初始化脚本）
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@ -71,19 +71,45 @@ def image_search():
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print(f"⚠️ [ImageSearch] 临时文件删除失败: {e}")
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# ---------------------------------------------------------
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# 5. pgvector 余弦相似度检索
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# 5. pgvector 余弦相似度检索（跨表联合检索）
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# ---------------------------------------------------------
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try:
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# 将 Python list 转为 PostgreSQL 向量格式: '[0.1, 0.2, ...]'
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query_vector_str = '[' + ','.join(str(v) for v in embedding) + ']'
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sql = text("""
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SELECT id, name, spec_model, product_image,
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(1 - (img_embedding <=> :query_vector)) AS similarity
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FROM material_base
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WHERE img_embedding IS NOT NULL
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ORDER BY img_embedding <=> :query_vector
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LIMIT 5
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SELECT id, name, spec_model, image_url,
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(1 - (vec <=> :query_vector)) AS similarity
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FROM (
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SELECT id,
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COALESCE(name, '') AS name,
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COALESCE(spec, '') AS spec_model,
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COALESCE(product_image, '') AS image_url,
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img_embedding AS vec
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FROM material_base
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WHERE img_embedding IS NOT NULL
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UNION ALL
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SELECT id,
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'采购入库' AS name,
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'到货照片' AS spec_model,
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COALESCE(arrival_photo, '') AS image_url,
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arrival_image_embedding AS vec
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FROM stock_buy
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WHERE arrival_image_embedding IS NOT NULL
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UNION ALL
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SELECT id,
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'采购入库' AS name,
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'质检报告' AS spec_model,
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COALESCE(qc_report, '') AS image_url,
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qc_report_image_embedding AS vec
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FROM stock_buy
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WHERE qc_report_image_embedding IS NOT NULL
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) AS combined
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ORDER BY vec <=> :query_vector
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LIMIT 10
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""")
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result = db.session.execute(sql, {"query_vector": query_vector_str})
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@ -91,30 +117,31 @@ def image_search():
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results = []
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for row in rows:
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product_id = row[0]
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product_name = row[1] or ""
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item_id = row[0]
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item_name = row[1] or ""
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spec_model = row[2] or ""
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product_image = row[3]
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raw_image = row[3]
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# 解析图片 URL 列表，取第一张
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image_url = ""
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if product_image:
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if raw_image:
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try:
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image_list = json.loads(product_image)
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image_list = json.loads(raw_image)
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if image_list and len(image_list) > 0:
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image_url = image_list[0]
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except Exception:
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image_url = str(product_image)
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# 纯字符串直接使用
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image_url = str(raw_image)
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results.append({
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"product_id": product_id,
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"product_name": product_name,
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"id": item_id,
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"name": item_name,
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"spec_model": spec_model,
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"image_url": image_url,
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"similarity": round(float(row[4]), 4)
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})
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print(f"✅ [ImageSearch] 检索完成，命中 {len(results)} 条结果")
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print(f"✅ [ImageSearch] 跨表检索完成，命中 {len(results)} 条结果")
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return jsonify({
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"code": 200,
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"msg": "检索成功",
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@ -100,7 +100,7 @@ def get_image_embedding(image_path: str) -> list:
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提取图像的 512 维 CLIP embedding 向量
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参数:
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image_path: 图像文件路径（支持本地路径或 URL）
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image_path: 图像文件路径
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返回:
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list: 512 维浮点向量
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@ -108,25 +108,25 @@ def get_image_embedding(image_path: str) -> list:
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if ort_session is None:
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load_clip_model()
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# 加载图像
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try:
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image = Image.open(image_path).convert('RGB')
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except Exception as e:
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raise ValueError(f"图像加载失败: {image_path}, 错误: {e}")
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# 中心裁剪
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# 1. 图片预处理
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image = Image.open(image_path).convert('RGB')
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image = _center_crop_and_resize(image)
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# 归一化
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input_data = _normalize(np.array(image))
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input_data = np.expand_dims(input_data, axis=0) # [1, 3, 224, 224]
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# 添加 batch 维度: (C, H, W) -> (1, C, H, W)
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input_data = np.expand_dims(input_data, axis=0)
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# 2. 构造占位符输入 (关键修复)
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dummy_ids = np.zeros((1, 77), dtype=np.int64)
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dummy_mask = np.zeros((1, 77), dtype=np.int64)
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# 推理
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outputs = ort_session.run(None, {'images': input_data.astype(np.float32)})
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# 输出通常是 (1, 512) 的向量，取第一项并展平为 list
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embedding = outputs[0][0].tolist()
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return embedding
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# 3. 传入模型进行推理
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# 注意: 模型输入名在你的模型里必须叫 'pixel_values', 'input_ids', 'attention_mask'
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# 如果报错找不到输入名，请打印 ort_session.get_inputs()[0].name 确认
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outputs = ort_session.run(
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['image_embeds'],
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{
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'input_ids': dummy_ids,
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'pixel_values': input_data.astype(np.float32),
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'attention_mask': dummy_mask
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}
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)
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return outputs[0][0].tolist()
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220
inventory-backend/scripts/init_all_vectors.py
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inventory-backend/scripts/init_all_vectors.py
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@ -0,0 +1,220 @@
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# -*- coding: utf-8 -*-
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from __future__ import annotations
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"""
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全量历史图片向量初始化脚本
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功能：遍历配置表中所有历史图片字段，批量提取 CLIP 512 维向量并存回数据库。
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用法：python scripts/init_all_vectors.py
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"""
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import os
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import json
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import sys
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from datetime import datetime
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from typing import List, Optional
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# 将项目根目录加入 Python 路径
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
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from tqdm import tqdm
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from sqlalchemy import text
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# Flask 应用环境
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from app import create_app
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from app.extensions import db
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from app.utils.ai_vision import get_image_embedding, load_clip_model
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# ============================================================================
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# 业务配置：表 → 图片字段 → 向量字段 映射
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# ============================================================================
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TARGET_TABLES = [
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# 基础物料
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{"table": "material_base", "img_col": "product_image", "vec_col": "img_embedding"},
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# 采购入库
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{"table": "stock_buy", "img_col": "arrival_photo", "vec_col": "arrival_image_embedding"},
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{"table": "stock_buy", "img_col": "qc_report", "vec_col": "qc_report_image_embedding"},
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]
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# 物理图片根目录（相对于 app 目录的相对路径 ../uploads/）
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APP_DIR = os.path.join(os.path.dirname(__file__), '..', 'app')
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UPLOADS_ROOT = os.path.abspath(os.path.join(APP_DIR, '..', 'uploads'))
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# ============================================================================
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# 核心工具函数
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# ============================================================================
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def parse_img_field(raw_value: str) -> List[str]:
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"""
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健壮解析图片字段，支持以下格式：
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- JSON 数组字符串: ["a.jpg", "b.jpg"]
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- 纯字符串单图片: "a.jpg"
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- 带 /api/v1/files/ 前缀: ["/api/v1/files/a.jpg"]
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返回: 提取出的文件名列表
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"""
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if not raw_value or (isinstance(raw_value, str) and not raw_value.strip()):
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return []
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try:
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# 先尝试按 JSON 解析（处理 JSON 数组字符串）
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parsed = json.loads(raw_value)
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if isinstance(parsed, list):
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items = parsed
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else:
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items = [parsed]
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except (json.JSONDecodeError, TypeError):
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# JSON 解析失败，说明是纯字符串，直接按单图片处理
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items = [raw_value.strip()]
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filenames = []
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for item in items:
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if not item or not isinstance(item, str):
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continue
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item = item.strip()
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if not item:
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continue
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# 去掉可能的 /api/v1/files/ 前缀
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filename = os.path.basename(item)
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filenames.append(filename)
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return filenames
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def build_local_path(filename: str) -> str:
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"""
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将文件名拼装成本地绝对路径
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"""
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return os.path.join(UPLOADS_ROOT, filename)
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def extract_first_valid_vector(raw_img_field: str, table_name: str, img_col: str) -> Optional[str]:
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"""
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读取图片字段，从第一条有效图片提取向量，返回写入 DB 的 JSON 字符串。
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如果所有图片均失败，返回 None。
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"""
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filenames = parse_img_field(raw_img_field)
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if not filenames:
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return None
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for filename in filenames:
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local_path = build_local_path(filename)
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if not os.path.exists(local_path):
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print(f"\033[91m[WARN] {table_name}.{img_col} | 文件不存在: {local_path}\033[0m")
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continue
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try:
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vec = get_image_embedding(local_path)
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if vec is not None:
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return json.dumps(vec)
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except Exception as e:
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print(f"\033[91m[WARN] {table_name}.{img_col} | 推理异常 [{filename}]: {type(e).__name__}: {e}\033[0m")
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continue
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return None
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# ============================================================================
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# 主入口
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# ============================================================================
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def main():
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start = datetime.now()
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total_success = 0
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total_skip = 0
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print("=" * 60)
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print("📦 全量历史图片向量初始化")
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print("=" * 60)
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print(f"图片目录: {UPLOADS_ROOT}")
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print(f"待处理表数: {len(TARGET_TABLES)}")
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print()
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# 1. 初始化 Flask 应用上下文（加载 CLIP 模型）
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app = create_app()
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with app.app_context():
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load_clip_model()
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print("✅ CLIP 模型加载完成")
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print()
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# 2. 遍历目标表
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for config in TARGET_TABLES:
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table_name = config["table"]
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img_col = config["img_col"]
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vec_col = config["vec_col"]
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print(f"正在处理表: {table_name}, 字段: {img_col}")
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# 3. 查询待清洗记录（只选未处理过的）
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sql = text(f"""
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SELECT id, {img_col}
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FROM {table_name}
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WHERE {img_col} IS NOT NULL
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AND {img_col} != '[]'
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AND ({vec_col} IS NULL)
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""")
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rows = db.session.execute(sql).fetchall()
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if not rows:
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print(f"[{table_name}/{img_col}] ⏭ 无待处理记录")
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continue
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print(f"\n[{table_name}/{img_col}] 📋 待处理: {len(rows)} 条")
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# 4. 逐条处理
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processed = 0
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success_count = 0
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for row in tqdm(rows, desc=f"{table_name}/{img_col}", unit="条"):
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record_id = row[0]
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raw_img = row[1]
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try:
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vec_json = extract_first_valid_vector(raw_img, table_name, img_col)
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if vec_json is None:
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total_skip += 1
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continue
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# 更新向量字段
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update_sql = text(f"""
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UPDATE {table_name} SET {vec_col} = :vec_str WHERE id = :id
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""")
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db.session.execute(update_sql, {"vec_str": vec_json, "id": record_id})
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success_count += 1
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# 每 50 条提交一次
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if processed > 0 and processed % 50 == 0:
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db.session.commit()
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print(f"\n ✅ 已提交 {processed} 条")
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except Exception as e:
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print(f"\n\033[91m[WARN] {table_name}/{img_col} | ID={record_id} 处理异常: {type(e).__name__}: {e}\033[0m")
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# 关键：任何异常都不中断，只 continue 下一条
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db.session.rollback()
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continue
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finally:
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processed += 1
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# 循环结束后补一次 commit（处理未凑满50条的剩余数据）
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try:
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db.session.commit()
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except Exception:
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db.session.rollback()
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total_success += success_count
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print(f"[{table_name}/{img_col}] ✅ 完成，成功 {success_count} 条 / 跳过 {len(rows) - success_count} 条")
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# 5. 汇总报告
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elapsed = (datetime.now() - start).total_seconds()
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print()
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print("=" * 60)
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print(f"🏁 全部完成！总计耗时 {elapsed:.1f} 秒")
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print(f" ✅ 成功写入向量: {total_success} 条")
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print(f" ⏭ 无有效图片（跳过）: {total_skip} 条")
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print("=" * 60)
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if __name__ == "__main__":
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main()
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Reference in New Issue
Block a user