第二代仪评指标联识别接口
This commit is contained in:
BBIT-Kai
@@ -485,7 +485,7 @@ def get_ticket_image_list(user_id):
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"created_at": MyUtils.format_datetime(row[0]),
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"file_name": row[1],
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"resolution": row[2],
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"size": round(row[3], 2),
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"size": round(row[3] / 1024, 2),
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"name": row[4],
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"moisture_content": row[5],
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"cocoon_weight": row[6],
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@@ -493,7 +493,6 @@ def get_ticket_image_list(user_id):
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"fresh_shell_weight": row[8],
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"sample_count": row[9],
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"barcode": row[10],
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# "oss_url": f"http://{SERVER_PATH_OSS}:9000/image-ticket/{row[11]}",
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"oss_url": get_temp_url("image-ticket", row[11]),
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"net_weight_total": row[12],
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"evaluator": row[13],
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@@ -513,6 +512,7 @@ def insert_ticket_image(
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moisture_content,
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cocoon_weight,
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defective_pupa_count,
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dead_pupa_count,
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fresh_shell_weight,
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sample_count,
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barcode,
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@@ -527,11 +527,11 @@ def insert_ticket_image(
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"""
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INSERT INTO ticket_images (
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created_by, file_name, resolution, size, name,
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moisture_content, cocoon_weight, defective_pupa_count,
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moisture_content, cocoon_weight, defective_pupa_count, dead_pupa_count,
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fresh_shell_weight, sample_count, barcode, oss,
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net_weight_total, evaluator, reviewer, created_at
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)
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VALUES (%s, %s, %s, %s, %s,
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VALUES (%s, %s, %s, %s, %s, %s,
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%s, %s, %s, %s, %s,
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%s, %s, %s, %s, %s, NOW())
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RETURNING id
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@@ -545,6 +545,7 @@ def insert_ticket_image(
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moisture_content,
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cocoon_weight,
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defective_pupa_count,
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dead_pupa_count,
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fresh_shell_weight,
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sample_count,
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barcode,
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@@ -0,0 +1,67 @@
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import json
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import re
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from langchain.schema import HumanMessage
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from config.llm import *
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from llm.ticketLLM import decode_barcode
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def get_ticket_response_v2(image_url: str, needBarcode: bool = False):
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# 构建 prompt
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prompt_text = f"""
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你是一位专业的图像分析AI。你的任务是仔细分析提供的图片内容，并按JSON格式输出结果。
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## JSON输出结构及字段说明:
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# 蚕茧检测信息数据模型
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该模型用于描述蚕茧检测信息，每条记录包含以下字段：
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## 1. 下足茧重量 (`cocoon_weight`)
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- **类型**：浮点数
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- **描述**：下足茧的重量，单位为克（可带小数）
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## 2. 非蛹粒数 (`defective_pupa_count`)
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- **类型**：整数
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- **描述**：不合格蛹的数量，即非好蛹的个数
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## 3. 僵蛹粒数 (`dead_pupa_count`)
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- **类型**：整数
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- **描述**：僵蚕蛹的数量
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## 4. 鲜壳量 (`fresh_shell_weight`)
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- **类型**：浮点数
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- **描述**：鲜壳重量，单位为克（可带小数）
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## 5. 小样粒数 (`sample_count`)
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- **类型**：整数
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- **描述**：检测使用的小样数量，即用于检测的茧粒数
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---
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### 注意事项
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- 所有字段都是必填的（required），在 JSON 实例中必须提供值
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- 浮点数字段可以包含小数，整数字段只能是整数
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- `evaluator` 和 `reviewer` 可以为空字符串，但字段必须存在
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- 如果图片与上述内容无关，则字段值为默认值，例如0、0.0、""
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最后，只输出严格的 JSON 格式，不要包含其他文字、markdown等内容。
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"""
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messages = [
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HumanMessage(
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content=[
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{"type": "text", "text": prompt_text},
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{"type": "image_url", "image_url": {"url": image_url}},
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]
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)
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]
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# 直接调用模型
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llmRes = llmVision.invoke(messages).content
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# 去掉 ```json 和 ``` 包裹
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cleaned = re.sub(r"^```json\s*|\s*```$", "", llmRes.strip())
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# 解析 JSON
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jsonRes = json.loads(cleaned)
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print("needBarcode:", needBarcode)
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if needBarcode:
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jsonRes["barcode"] = decode_barcode(image_url)
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return jsonRes
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@@ -0,0 +1,7 @@
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from pydantic import BaseModel
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class F8ImageRequestV2(BaseModel):
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title: str
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image: str
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needBarcode: bool
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@@ -5,6 +5,7 @@ from fastapi import APIRouter
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from config.app import F8_SERVER_USER_ID
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from models.BaseResponse import BaseResponse
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from models.F8ImageRequest import F8ImageRequest
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from models.F8ImageRequestV2 import F8ImageRequestV2
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from service.vision import process_ticket_image
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from utils import MyUtils
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@@ -20,6 +21,29 @@ async def cocoonTicket(data: F8ImageRequest):
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img_bytes = base64.b64decode(input_data)
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json_data = await MyUtils.async_task(
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process_ticket_image,
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1,
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True,
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img_bytes,
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f"{data.title}.jpg",
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data.title,
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F8_SERVER_USER_ID,
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)
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return BaseResponse(data=json_data)
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except Exception as e:
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return BaseResponse(status=False, message=f"解析失败: {str(e)}", data=None)
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@f8Router.post("/createTicketImageTaskV2")
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async def cocoonTicket(data: F8ImageRequestV2):
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input_data = data.image
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if "," in input_data:
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input_data = input_data.split(",")[1]
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try:
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img_bytes = base64.b64decode(input_data)
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json_data = await MyUtils.async_task(
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process_ticket_image,
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2,
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data.needBarcode,
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img_bytes,
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f"{data.title}.jpg",
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data.title,
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@@ -36,7 +36,7 @@ async def createTicketImageTask(
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try:
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contents = await file.read()
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json_data = await MyUtils.async_task(
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process_ticket_image, contents, file.filename, projectName, user_id
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process_ticket_image, 1, True, contents, file.filename, projectName, user_id
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)
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return BaseResponse(data=json_data)
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except Exception as e:
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@@ -5,10 +5,13 @@ import config.minIO as minIO
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import db.postgres as pg
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from config.minIO import minio_client
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from llm.ticketLLM import *
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from llm.ticketLLMv2 import get_ticket_response_v2
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def process_ticket_image(
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img_bytes: bytes,
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version: int,
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needBarcode: bool = False,
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img_bytes=None,
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file_name: str = None,
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project_name: str = None,
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user_id: UUID = None,
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@@ -18,6 +21,8 @@ def process_ticket_image(
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"""
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# 上传到 OSS，使用 UUID 做对象名
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if img_bytes is None:
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img_bytes = []
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object_name = str(uuid.uuid4())
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file_bytes = BytesIO(img_bytes)
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bucket_name = "image-ticket"
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@@ -28,15 +33,16 @@ def process_ticket_image(
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oss_url = minIO.get_temp_url(bucket_name, object_name)
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# 调用分析方法获取 JSON
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json_data = get_ticket_response(oss_url)
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# 解析条码
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barcode = decode_barcode(BytesIO(img_bytes))
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json_data["barcode"] = barcode
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if version == 1:
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json_data = get_ticket_response(oss_url)
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elif version == 2:
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json_data = get_ticket_response_v2(oss_url, needBarcode)
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else:
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json_data = get_ticket_response(oss_url)
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# 获取图片分辨率和大小
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img = Image.open(BytesIO(img_bytes))
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resolution = f"{img.width}x{img.height}"
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size_kb = len(img_bytes) / 1024
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size_kb = round(len(img_bytes) / 1024, 2)
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# 插入数据库
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pg.insert_ticket_image(
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@@ -45,12 +51,13 @@ def process_ticket_image(
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resolution=resolution,
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size=size_kb,
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name=project_name if project_name else object_name[:8],
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dead_pupa_count=json_data.get("moisture_content") if version == 2 else 0,
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moisture_content=json_data.get("moisture_content"),
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cocoon_weight=json_data.get("cocoon_weight"),
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defective_pupa_count=json_data.get("defective_pupa_count"),
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fresh_shell_weight=json_data.get("fresh_shell_weight"),
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sample_count=json_data.get("sample_count"),
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barcode=barcode,
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barcode=json_data.get("barcode"),
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oss=object_name,
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net_weight_total=json_data.get("net_weight_total"),
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evaluator=json_data.get("evaluator"),
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