diff --git a/inventory-backend/app/api/v1/common/image_search.py b/inventory-backend/app/api/v1/common/image_search.py
index aad1355..d1a33ae 100644
--- a/inventory-backend/app/api/v1/common/image_search.py
+++ b/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,
- (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",
diff --git a/inventory-backend/app/utils/ai_vision.py b/inventory-backend/app/utils/ai_vision.py
index cf1854b..353f1da 100644
--- a/inventory-backend/app/utils/ai_vision.py
+++ b/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
- 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
\ No newline at end of file
+ # 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()
\ No newline at end of file
diff --git a/inventory-backend/scripts/init_all_vectors.py b/inventory-backend/scripts/init_all_vectors.py
new file mode 100644
index 0000000..12d1460
--- /dev/null
+++ b/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()
\ No newline at end of file
diff --git a/inventory-web/src/views/material/list.vue b/inventory-web/src/views/material/list.vue
index eecb890..9ee65c2 100644
--- a/inventory-web/src/views/material/list.vue
+++ b/inventory-web/src/views/material/list.vue
@@ -84,6 +84,9 @@
搜索
重置
+
+ 拍照识图
+
+
+
+
@@ -633,7 +642,7 @@