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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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DXC
2026-05-21 15:43:45 +08:00
parent 1a7c06f197
commit c273f5a9d9
4 changed files with 304 additions and 45 deletions
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61
inventory-backend/app/api/v1/common/image_search.py
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@ -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,
(1 - (img_embedding <=> :query_vector)) AS similarity
FROM material_base
WHERE img_embedding IS NOT NULL
ORDER BY img_embedding <=> :query_vector
LIMIT 5
SELECT id, name, spec_model, image_url,
(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
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]
product_name = row[1] or ""
item_id = row[0]
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,
"product_name": product_name,
"id": item_id,
"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",

38
inventory-backend/app/utils/ai_vision.py
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@ -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
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
# 1. 图片预处理
image = Image.open(image_path).convert('RGB')
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)
input_data = np.expand_dims(input_data, axis=0)
# 2. 构造占位符输入 (关键修复)
dummy_ids = np.zeros((1, 77), dtype=np.int64)
dummy_mask = np.zeros((1, 77), dtype=np.int64)
# 推理
outputs = ort_session.run(None, {'images': input_data.astype(np.float32)})
# 输出通常是 (1, 512) 的向量，取第一项并展平为 list
embedding = outputs[0][0].tolist()
return embedding
# 3. 传入模型进行推理
# 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
outputs = ort_session.run(
['image_embeds'],
{
'input_ids': dummy_ids,
'pixel_values': input_data.astype(np.float32),
'attention_mask': dummy_mask
}
)
return outputs[0][0].tolist()

220
inventory-backend/scripts/init_all_vectors.py Normal file
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@ -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()

30
inventory-web/src/views/material/list.vue
View File

@ -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: '' });
};