231 lines
8.0 KiB
Python
231 lines
8.0 KiB
Python
# -*- 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 (已全面修复)
|
||
# ============================================================================
|
||
TARGET_TABLES = [
|
||
# 1. 基础物料
|
||
{"table": "material_base", "img_col": "product_image", "vec_col": "img_embedding"},
|
||
|
||
# 2. 采购入库
|
||
{"table": "stock_buy", "img_col": "arrival_photo", "vec_col": "arrival_image_embedding"},
|
||
{"table": "stock_buy", "img_col": "inspection_report", "vec_col": "qc_report_image_embedding"}, # 已修复: qc_report -> inspection_report
|
||
|
||
# 3. 半成品入库 (新增)
|
||
{"table": "stock_semi", "img_col": "arrival_photo", "vec_col": "arrival_image_embedding"},
|
||
{"table": "stock_semi", "img_col": "quality_report_link", "vec_col": "qc_report_image_embedding"},
|
||
|
||
# 4. 成品入库 (新增)
|
||
{"table": "stock_product", "img_col": "product_photo", "vec_col": "arrival_image_embedding"},
|
||
{"table": "stock_product", "img_col": "quality_report_link", "vec_col": "qc_report_image_embedding"}
|
||
|
||
# 注意：成品入库表还有一个 inspection_report_link，但由于数据库中成品表目前只加了两个向量字段，
|
||
# 暂不将该字段加入遍历，以免覆盖 quality_report_link 的特征。
|
||
]
|
||
|
||
# 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() |