153 lines
5.6 KiB
Python
153 lines
5.6 KiB
Python
# -*- coding: utf-8 -*-
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"""
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以图搜图 API - CLIP Vision Embedding + pgvector 余弦距离检索
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"""
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import os
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import uuid
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import json
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from flask import Blueprint, request, jsonify
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from sqlalchemy import text
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from app.extensions import db
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from app.utils.ai_vision import load_clip_model, get_image_embedding
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# 注册蓝图
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image_search_bp = Blueprint('image_search', __name__)
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# ============================================================================
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# POST /api/v1/common/image-search
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# 以图搜图：上传图片 → CLIP embedding → pgvector 余弦相似度检索
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# ============================================================================
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@image_search_bp.route('/image-search', methods=['POST'])
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def image_search():
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# ---------------------------------------------------------
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# 1. 检查文件
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# ---------------------------------------------------------
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if 'file' not in request.files:
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return jsonify({"code": 400, "msg": "未找到图片文件"}), 400
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file = request.files['file']
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if file.filename == '':
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return jsonify({"code": 400, "msg": "未选择文件"}), 400
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# ---------------------------------------------------------
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# 2. 安全保存临时文件
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# ---------------------------------------------------------
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ext = file.filename.rsplit('.', 1)[-1].lower()
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if ext not in {'png', 'jpg', 'jpeg', 'gif', 'bmp', 'webp'}:
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return jsonify({"code": 400, "msg": "不支持的图片格式"}), 400
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tmp_filename = f"{uuid.uuid4().hex}.{ext}"
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tmp_dir = os.path.join(os.path.dirname(__file__), '..', '..', '..', 'uploads')
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os.makedirs(tmp_dir, exist_ok=True)
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tmp_path = os.path.join(tmp_dir, tmp_filename)
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try:
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file.save(tmp_path)
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print(f"💾 [ImageSearch] 临时文件已保存: {tmp_path}")
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# ---------------------------------------------------------
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# 3. 提取 CLIP embedding
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# ---------------------------------------------------------
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load_clip_model()
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embedding = get_image_embedding(tmp_path)
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print(f"✅ [ImageSearch] Embedding 提取成功，维度: {len(embedding)}")
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except Exception as e:
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print(f"❌ [ImageSearch] 图像处理失败: {e}")
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return jsonify({"code": 500, "msg": f"图像处理失败: {str(e)}"}), 500
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finally:
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# ---------------------------------------------------------
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# 4. 无论成功与否，都删除临时文件
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# ---------------------------------------------------------
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if os.path.exists(tmp_path):
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try:
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os.remove(tmp_path)
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print(f"🗑️ [ImageSearch] 临时文件已清理: {tmp_path}")
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except Exception as e:
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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（跨表联合检索）
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# ---------------------------------------------------------
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try:
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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, 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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rows = result.fetchall()
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results = []
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for row in rows:
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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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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 raw_image:
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try:
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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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# 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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"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] 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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"data": results
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})
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except Exception as e:
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print(f"❌ [ImageSearch] 数据库检索失败: {e}")
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return jsonify({"code": 500, "msg": f"检索失败: {str(e)}"}), 500 |