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）

inventory-backend/app/api/v1/common/image_search.py

@@ -71,19 +71,45 @@ def image_search():
                 print(f"⚠️ [ImageSearch] 临时文件删除失败: {e}")
 
     # ---------------------------------------------------------
-    # 5. pgvector 余弦相似度检索
+    # 5. pgvector 余弦相似度检索（跨表联合检索）
     # ---------------------------------------------------------
     try:
-        # 将 Python list 转为 PostgreSQL 向量格式: '[0.1, 0.2, ...]'
         query_vector_str = '[' + ','.join(str(v) for v in embedding) + ']'
 
         sql = text("""
-            SELECT id, name, spec_model, product_image,
+            SELECT id, name, spec_model, image_url,
-                   (1 - (img_embedding <=> :query_vector)) AS similarity
+                   (1 - (vec <=> :query_vector)) AS similarity
+            FROM (
+                SELECT id,
+                       COALESCE(name, '') AS name,
+                       COALESCE(spec, '') AS spec_model,
+                       COALESCE(product_image, '') AS image_url,
+                       img_embedding AS vec
                 FROM material_base
                 WHERE img_embedding IS NOT NULL
-            ORDER BY img_embedding <=> :query_vector
+
-            LIMIT 5
+                UNION ALL
+
+                SELECT id,
+                       '采购入库' AS name,
+                       '到货照片' AS spec_model,
+                       COALESCE(arrival_photo, '') AS image_url,
+                       arrival_image_embedding AS vec
+                FROM stock_buy
+                WHERE arrival_image_embedding IS NOT NULL
+
+                UNION ALL
+
+                SELECT id,
+                       '采购入库' AS name,
+                       '质检报告' AS spec_model,
+                       COALESCE(qc_report, '') AS image_url,
+                       qc_report_image_embedding AS vec
+                FROM stock_buy
+                WHERE qc_report_image_embedding IS NOT NULL
+            ) AS combined
+            ORDER BY vec <=> :query_vector
+            LIMIT 10
         """)
 
         result = db.session.execute(sql, {"query_vector": query_vector_str})
@@ -91,30 +117,31 @@ def image_search():
 
         results = []
         for row in rows:
-            product_id = row[0]
+            item_id = row[0]
-            product_name = row[1] or ""
+            item_name = row[1] or ""
             spec_model = row[2] or ""
-            product_image = row[3]
+            raw_image = row[3]
 
             # 解析图片 URL 列表，取第一张
             image_url = ""
-            if product_image:
+            if raw_image:
                 try:
-                    image_list = json.loads(product_image)
+                    image_list = json.loads(raw_image)
                     if image_list and len(image_list) > 0:
                         image_url = image_list[0]
                 except Exception:
-                    image_url = str(product_image)
+                    # 纯字符串直接使用
+                    image_url = str(raw_image)
 
             results.append({
-                "product_id": product_id,
+                "id": item_id,
-                "product_name": product_name,
+                "name": item_name,
                 "spec_model": spec_model,
                 "image_url": image_url,
                 "similarity": round(float(row[4]), 4)
             })
 
-        print(f"✅ [ImageSearch] 检索完成，命中 {len(results)} 条结果")
+        print(f"✅ [ImageSearch] 跨表检索完成，命中 {len(results)} 条结果")
         return jsonify({
             "code": 200,
             "msg": "检索成功",

inventory-backend/app/utils/ai_vision.py

@@ -100,7 +100,7 @@ def get_image_embedding(image_path: str) -> list:
     提取图像的 512 维 CLIP embedding 向量
 
     参数:
-        image_path: 图像文件路径（支持本地路径或 URL）
+        image_path: 图像文件路径
 
     返回:
         list: 512 维浮点向量
@@ -108,25 +108,25 @@ def get_image_embedding(image_path: str) -> list:
     if ort_session is None:
         load_clip_model()
 
-    # 加载图像
+    # 1. 图片预处理
-    try:
     image = Image.open(image_path).convert('RGB')
-    except Exception as e:
-        raise ValueError(f"图像加载失败: {image_path}, 错误: {e}")
-
-    # 中心裁剪
     image = _center_crop_and_resize(image)
-
-    # 归一化
     input_data = _normalize(np.array(image))
+    input_data = np.expand_dims(input_data, axis=0)  # [1, 3, 224, 224]
 
-    # 添加 batch 维度: (C, H, W) -> (1, C, H, W)
+    # 2. 构造占位符输入 (关键修复)
-    input_data = np.expand_dims(input_data, axis=0)
+    dummy_ids = np.zeros((1, 77), dtype=np.int64)
+    dummy_mask = np.zeros((1, 77), dtype=np.int64)
 
-    # 推理
+    # 3. 传入模型进行推理
-    outputs = ort_session.run(None, {'images': input_data.astype(np.float32)})
+    # 注意: 模型输入名在你的模型里必须叫 'pixel_values', 'input_ids', 'attention_mask'
-
+    # 如果报错找不到输入名，请打印 ort_session.get_inputs()[0].name 确认
-    # 输出通常是 (1, 512) 的向量，取第一项并展平为 list
+    outputs = ort_session.run(
-    embedding = outputs[0][0].tolist()
+        ['image_embeds'],
-
+        {
-    return embedding
+            'input_ids': dummy_ids,
+            'pixel_values': input_data.astype(np.float32),
+            'attention_mask': dummy_mask
+        }
+    )
+    return outputs[0][0].tolist()

inventory-backend/scripts/init_all_vectors.py

@@ -0,0 +1,220 @@
+# -*- coding: utf-8 -*-
+from __future__ import annotations
+
+"""
+全量历史图片向量初始化脚本
+
+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。
+用法：python scripts/init_all_vectors.py
+"""
+
+import os
+import json
+import sys
+from datetime import datetime
+from typing import List, Optional
+
+# 将项目根目录加入 Python 路径
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
+
+from tqdm import tqdm
+from sqlalchemy import text
+
+# Flask 应用环境
+from app import create_app
+from app.extensions import db
+from app.utils.ai_vision import get_image_embedding, load_clip_model
+
+# ============================================================================
+# 业务配置：表 → 图片字段 → 向量字段 映射
+# ============================================================================
+TARGET_TABLES = [
+    # 基础物料
+    {"table": "material_base", "img_col": "product_image", "vec_col": "img_embedding"},
+
+    # 采购入库
+    {"table": "stock_buy", "img_col": "arrival_photo", "vec_col": "arrival_image_embedding"},
+    {"table": "stock_buy", "img_col": "qc_report", "vec_col": "qc_report_image_embedding"},
+]
+
+# 物理图片根目录（相对于 app 目录的相对路径 ../uploads/）
+APP_DIR = os.path.join(os.path.dirname(__file__), '..', 'app')
+UPLOADS_ROOT = os.path.abspath(os.path.join(APP_DIR, '..', 'uploads'))
+
+
+# ============================================================================
+# 核心工具函数
+# ============================================================================
+
+def parse_img_field(raw_value: str) -> List[str]:
+    """
+    健壮解析图片字段，支持以下格式：
+      - JSON 数组字符串: ["a.jpg", "b.jpg"]
+      - 纯字符串单图片: "a.jpg"
+      - 带 /api/v1/files/ 前缀: ["/api/v1/files/a.jpg"]
+    返回: 提取出的文件名列表
+    """
+    if not raw_value or (isinstance(raw_value, str) and not raw_value.strip()):
+        return []
+
+    try:
+        # 先尝试按 JSON 解析（处理 JSON 数组字符串）
+        parsed = json.loads(raw_value)
+        if isinstance(parsed, list):
+            items = parsed
+        else:
+            items = [parsed]
+    except (json.JSONDecodeError, TypeError):
+        # JSON 解析失败，说明是纯字符串，直接按单图片处理
+        items = [raw_value.strip()]
+
+    filenames = []
+    for item in items:
+        if not item or not isinstance(item, str):
+            continue
+        item = item.strip()
+        if not item:
+            continue
+        # 去掉可能的 /api/v1/files/ 前缀
+        filename = os.path.basename(item)
+        filenames.append(filename)
+
+    return filenames
+
+
+def build_local_path(filename: str) -> str:
+    """
+    将文件名拼装成本地绝对路径
+    """
+    return os.path.join(UPLOADS_ROOT, filename)
+
+
+def extract_first_valid_vector(raw_img_field: str, table_name: str, img_col: str) -> Optional[str]:
+    """
+    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。
+    如果所有图片均失败，返回 None。
+    """
+    filenames = parse_img_field(raw_img_field)
+    if not filenames:
+        return None
+
+    for filename in filenames:
+        local_path = build_local_path(filename)
+
+        if not os.path.exists(local_path):
+            print(f"\033[91m[WARN] {table_name}.{img_col} | 文件不存在: {local_path}\033[0m")
+            continue
+
+        try:
+            vec = get_image_embedding(local_path)
+            if vec is not None:
+                return json.dumps(vec)
+        except Exception as e:
+            print(f"\033[91m[WARN] {table_name}.{img_col} | 推理异常 [{filename}]: {type(e).__name__}: {e}\033[0m")
+            continue
+
+    return None
+
+
+# ============================================================================
+# 主入口
+# ============================================================================
+
+def main():
+    start = datetime.now()
+    total_success = 0
+    total_skip = 0
+
+    print("=" * 60)
+    print("📦  全量历史图片向量初始化")
+    print("=" * 60)
+    print(f"图片目录: {UPLOADS_ROOT}")
+    print(f"待处理表数: {len(TARGET_TABLES)}")
+    print()
+
+    # 1. 初始化 Flask 应用上下文（加载 CLIP 模型）
+    app = create_app()
+    with app.app_context():
+        load_clip_model()
+        print("✅ CLIP 模型加载完成")
+        print()
+
+        # 2. 遍历目标表
+        for config in TARGET_TABLES:
+            table_name = config["table"]
+            img_col = config["img_col"]
+            vec_col = config["vec_col"]
+
+            print(f"正在处理表: {table_name}, 字段: {img_col}")
+
+            # 3. 查询待清洗记录（只选未处理过的）
+            sql = text(f"""
+                SELECT id, {img_col}
+                FROM {table_name}
+                WHERE {img_col} IS NOT NULL 
+                  AND {img_col} != '[]' 
+                  AND ({vec_col} IS NULL)
+            """)
+            rows = db.session.execute(sql).fetchall()
+
+            if not rows:
+                print(f"[{table_name}/{img_col}] ⏭  无待处理记录")
+                continue
+
+            print(f"\n[{table_name}/{img_col}] 📋  待处理: {len(rows)} 条")
+
+            # 4. 逐条处理
+            processed = 0
+            success_count = 0
+
+            for row in tqdm(rows, desc=f"{table_name}/{img_col}", unit="条"):
+                record_id = row[0]
+                raw_img = row[1]
+
+                try:
+                    vec_json = extract_first_valid_vector(raw_img, table_name, img_col)
+                    if vec_json is None:
+                        total_skip += 1
+                        continue
+
+                    # 更新向量字段
+                    update_sql = text(f"""
+                        UPDATE {table_name} SET {vec_col} = :vec_str WHERE id = :id
+                    """)
+                    db.session.execute(update_sql, {"vec_str": vec_json, "id": record_id})
+                    success_count += 1
+
+                    # 每 50 条提交一次
+                    if processed > 0 and processed % 50 == 0:
+                        db.session.commit()
+                        print(f"\n  ✅ 已提交 {processed} 条")
+
+                except Exception as e:
+                    print(f"\n\033[91m[WARN] {table_name}/{img_col} | ID={record_id} 处理异常: {type(e).__name__}: {e}\033[0m")
+                    # 关键：任何异常都不中断，只 continue 下一条
+                    db.session.rollback()
+                    continue
+                finally:
+                    processed += 1
+
+            # 循环结束后补一次 commit（处理未凑满50条的剩余数据）
+            try:
+                db.session.commit()
+            except Exception:
+                db.session.rollback()
+
+            total_success += success_count
+            print(f"[{table_name}/{img_col}] ✅  完成，成功 {success_count} 条 / 跳过 {len(rows) - success_count} 条")
+
+    # 5. 汇总报告
+    elapsed = (datetime.now() - start).total_seconds()
+    print()
+    print("=" * 60)
+    print(f"🏁  全部完成！总计耗时 {elapsed:.1f} 秒")
+    print(f"    ✅ 成功写入向量: {total_success} 条")
+    print(f"    ⏭  无有效图片（跳过）: {total_skip} 条")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()

inventory-web/src/views/material/list.vue

@@ -84,6 +84,9 @@
 
           <el-button type="primary" plain @click="handleQuery">搜索</el-button>
           <el-button plain @click="resetQuery">重置</el-button>
+          <el-button type="primary" plain @click="imageSearchVisible = true">
+            <el-icon style="margin-right: 5px"><Picture /></el-icon>拍照识图
+          </el-button>
           <el-popover
               v-model:visible="advancedFilterVisible"
               placement="bottom"
@@ -564,6 +567,12 @@
         />
       </el-dialog>
 
+      <!-- 拍照识图弹窗 -->
+      <ImageSearchDialog
+          v-model="imageSearchVisible"
+          @use="handleImageSearchUse"
+      />
+
       <!-- 预警设置弹窗 -->
       <el-dialog v-model="warningDialog.visible" :title="warningDialog.title" width="500px" append-to-body destroy-on-close>
         <el-form ref="warningFormRef" :model="warningForm" :rules="warningRules" label-width="100px">
@@ -633,7 +642,7 @@
 
 <script setup lang="ts">
 import { ref, reactive, onMounted, nextTick, computed } from 'vue';
-import { Plus, Document, Refresh, Setting, Rank, Camera, Link, Download, Bell, CircleCheck, Files, ZoomIn, Delete } from '@element-plus/icons-vue';
+import { Plus, Document, Refresh, Setting, Rank, Camera, Link, Download, Bell, CircleCheck, Files, ZoomIn, Delete, Picture } from '@element-plus/icons-vue';
 import { ElMessage, ElMessageBox, ElLoading } from 'element-plus';
 import type { FormInstance, FormRules } from 'element-plus';
 import { useUserStore } from '@/stores/user';
@@ -655,6 +664,8 @@ import {
 import { uploadFile, deleteFile } from '@/api/common/upload';
 import { usePasteUpload } from '@/hooks/usePasteUpload';
 import WebRtcCamera from '@/components/Camera/WebRtcCamera.vue';
+import ImageSearchDialog from '@/components/ImageSearchDialog.vue';
+import { imageSearch as imageSearchApi, type ImageSearchItem } from '@/api/common/upload';
 
 const userStore = useUserStore();
 
@@ -716,6 +727,7 @@ const isUploading = ref(false);
 
 const tableSize = ref<'large' | 'default' | 'small'>('large');
 const advancedFilterVisible = ref(false);
+const imageSearchVisible = ref(false);
 const advancedConditions = ref([{ field: '', operator: '', value: '' }]);
 const fieldOptions = computed(() => {
   const allFields = [
@@ -1585,15 +1597,8 @@ const customUpload = async (options: any, targetField: 'generalImage' | 'general
     if (res.code === 200) {
       const newUrl = res.data.url
       form.value[targetField].push(newUrl)
-      // 同步更新 fileList，触发 el-upload UI 刷新
-      const fileObj = { name: newUrl.split('/').pop(), url: getImageUrl(newUrl) }
-      if (targetField === 'generalImage') {
-        fileListImage.value.push(fileObj)
-      } else {
-        fileListManual.value.push(fileObj)
-      }
       ElMessage.success('上传成功')
-      onSuccess(res)
+      onSuccess(res)  // el-upload v-model 自动更新 fileList，无需手动 push
     } else {
       ElMessage.error(res.msg || '上传失败');
       onError(new Error(res.msg))
@@ -1693,6 +1698,13 @@ const handleCameraConfirm = async (file: File) => {
   }
 };
 
+// 以图搜图 - 使用物料
+const handleImageSearchUse = (item: ImageSearchItem) => {
+  // 跳转到该物料详情页，或填充到表单
+  router.push({ path: '/material/list', query: { keyword: item.spec_model } });
+  ElMessage.success(`已定位物料: ${item.product_name}`);
+};
+
 const addCondition = () => {
   advancedConditions.value.push({ field: '', operator: '', value: '' });
 };