后端新增《蚕茧识别V2》模块

bbit_ai/app/app.py

@@ -4,6 +4,7 @@ from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 from uvicorn import Config, Server
 
+from config.yolo import YOLOSingleton
 from mcp_local.mcp_pipe import init_mcp_server
 from routers.Bot import botRouter
 from routers.Chat import chatRouter
@@ -21,7 +22,7 @@ async def ai_lab():
     app = FastAPI(title="BBIT_AI")
 
     origins = [
-        "http://localhost:8090",  # Vite dev 默认端口
+        "http://localhost:8091",  # Vite dev 默认端口
         "https://ai.ronsunny.cn:8090",
         "*",  # ⚠️ 生产环境不要用
     ]
@@ -40,11 +41,11 @@ async def ai_lab():
         reportDataRouter,
         serviceRouter,
         botRouter,
-        visionRouter,
         rqRouter,
     ]
     for r in routers:
         app.include_router(r, prefix="/llm", tags=["llm"])
+    app.include_router(visionRouter, prefix="/cv", tags=["cv"])
     app.include_router(publicRouter, prefix="/api/public", tags=["api"])
     config = Config(app=app, host="0.0.0.0", port=13011, log_level="info")
     server = Server(config)
@@ -52,12 +53,14 @@ async def ai_lab():
 
 
 async def main():
+    # 初始化模型
+    YOLOSingleton.init_model()
     # 主干AI实验室FastAPI服务
     task_api = asyncio.create_task(ai_lab())
 
     # MCP服务-ailab
-    # endpoint_url = "wss://ai.ronsunny.cn:8090/aimcp/mcp_endpoint/mcp/?token=TsSP9lBq6Oa1WMkachHoS2TtNt4GKV/Gli24pk5Rjpk%3D"
-    endpoint_url_ai_lab = "ws://ce_bot_mcp:8004/mcp_endpoint/mcp/?token=TsSP9lBq6Oa1WMkachHoS2TtNt4GKV/Gli24pk5Rjpk%3D"
+    endpoint_url_ai_lab = "wss://ai.ronsunny.cn:8090/aimcp/mcp_endpoint/mcp/?token=TsSP9lBq6Oa1WMkachHoS2TtNt4GKV/Gli24pk5Rjpk%3D"
+    # endpoint_url_ai_lab = "ws://ce_bot_mcp:8004/mcp_endpoint/mcp/?token=TsSP9lBq6Oa1WMkachHoS2TtNt4GKV/Gli24pk5Rjpk%3D"
     task_mcp1 = asyncio.create_task(init_mcp_server(endpoint_url_ai_lab))
 
     # MCP服务-ql

bbit_ai/app/config/milvus.py

@@ -1,8 +1,9 @@
 from langchain_milvus import Milvus
 
 from config.llm import llmEmbeddings
+from utils.GlobalVariable import LOCAL_IP
 
-URI = "http://ce_milvus:19530"
+URI = "http://" + LOCAL_IP + ":19530"
 
 knVectorstore = Milvus(
     embedding_function=llmEmbeddings,

bbit_ai/app/config/pgDb.py

@@ -5,6 +5,8 @@ from contextlib import contextmanager
 import psycopg
 from psycopg_pool import ConnectionPool
 
+from utils.GlobalVariable import LOCAL_IP
+
 logger = logging.getLogger("PGPool")
 logger.setLevel(logging.INFO)
 
@@ -66,7 +68,7 @@ class PGPool:
 
 
 pg_pool = PGPool(
-    uri="postgresql://postgres:123456@ce_postgres/ktor2",
+    uri="postgresql://postgres:123456@" + LOCAL_IP + "/ktor2",
     min_size=1,
     max_size=20,
 )

bbit_ai/app/config/rabbitMQ.py

@@ -1,4 +1,6 @@
-RABBIT_HOST = "ce_rabbitmq"
+from utils.GlobalVariable import LOCAL_IP
+
+RABBIT_HOST = LOCAL_IP
 RABBIT_VHOST = "/bbit_ai"
 RABBIT_USER = "bbit_ai"
 RABBIT_PASSWORD = "123456"

bbit_ai/app/config/yolo.py

@@ -0,0 +1,156 @@
+import logging
+
+import cv2
+import numpy as np
+import torch
+from PIL import Image, ImageDraw, ImageFont
+from ultralytics import YOLO
+
+from utils import MyUtils
+
+
+def draw_annotations(
+    img_bgr,
+    boxes,
+    labels,
+    confidences=None,
+    font_path="/usr/share/fonts/truetype/wqy/wqy-zenhei.ttc",
+):
+    """
+    绘制带中文、置信度、不同颜色的标注框
+    :param img_bgr: np.ndarray, BGR 图像
+    :param boxes: list of xyxy
+    :param labels: list of str
+    :param confidences: list of float, 可选，用于显示置信度
+    :param font_path: 字体路径
+    :return: np.ndarray, BGR 标注图
+    """
+    h, w, _ = img_bgr.shape
+    line_width = max(2, int(w / 400))
+    font_size = max(20, int(w / 40))
+
+    img = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
+    pil_img = Image.fromarray(img)
+    draw = ImageDraw.Draw(pil_img)
+    font = ImageFont.truetype(font_path, font_size)
+
+    # 定义类别颜色映射
+    color_map = {
+        "正茧": "green",
+        "双宫茧": "grey",
+        "黄斑茧": "red",
+        "毛茧": "white",
+        "蛆壳茧": "purple",
+    }
+
+    for idx, (b, label) in enumerate(zip(boxes, labels), start=1):
+        conf = confidences[idx - 1] if confidences else None
+        display_text = f"{label}#{idx}"
+        if conf is not None:
+            display_text += f" {conf:.2f}"
+
+        x1, y1, x2, y2 = map(int, b)
+        box_color = color_map.get(label, "red")
+
+        # 画框
+        draw.rectangle([x1, y1, x2, y2], outline=box_color, width=line_width)
+
+        # 文本边界
+        bbox = draw.textbbox((0, 0), display_text, font=font)
+        text_width = bbox[2] - bbox[0]
+        text_height = bbox[3] - bbox[1]
+
+        # 文本背景
+        draw.rectangle(
+            [x1, y1 - text_height - 4, x1 + text_width + 4, y1], fill="black"
+        )
+        draw.text((x1 + 2, y1 - text_height - 2), display_text, fill="white", font=font)
+
+    annotated = cv2.cvtColor(np.array(pil_img), cv2.COLOR_RGB2BGR)
+    return annotated
+
+
+logger = logging.getLogger("yolo_service")
+
+
+class YOLOSingleton:
+    _model = None
+    _ready = False
+
+    @classmethod
+    def instance(cls):
+        """获取模型单例"""
+        if cls._model is None:
+            logger.warning("模型尚未初始化")
+        return cls._model
+
+    @classmethod
+    def is_ready(cls):
+        return cls._ready
+
+    @classmethod
+    def init_model(cls):
+        """初始化模型，失败时不抛出异常"""
+        try:
+            cls._model = YOLO("/app/models/yolo/yolo_silkworm_cocoon_detect_v1.pt")
+            cls._model.to("cuda" if torch.cuda.is_available() else "cpu")
+            cls._ready = True
+            logger.info("✅ YOLO 模型加载完成")
+        except Exception as e:
+            cls._model = None
+            cls._ready = False
+            logger.error(f"❌ YOLO 模型加载失败: {e}")
+
+    @classmethod
+    def detect(cls, img_bytes: bytes):
+        if not cls._ready or cls._model is None:
+            raise RuntimeError("模型未加载或不可用")
+
+        label_map = {
+            "normal": "正茧",
+            "double_pupa": "双宫茧",
+            "spot": "黄斑茧",
+            "hairy": "毛茧",
+            "maggot_shell": "蛆壳茧",
+        }
+
+        img = cv2.imdecode(np.frombuffer(img_bytes, np.uint8), cv2.IMREAD_COLOR)
+        results = cls._model(img, conf=0.45)
+        r = results[0]
+        boxes = r.boxes
+        names = cls._model.names
+
+        total = len(boxes)
+        class_counts = {}
+        confidences = []
+        box_list = []
+        label_list = []
+
+        for idx, b in enumerate(boxes):
+            cls_id = int(b.cls)
+            conf = float(b.conf)
+            label_en = names.get(cls_id, str(cls_id))
+            label_cn = label_map.get(label_en, label_en)
+            class_counts[label_cn] = class_counts.get(label_cn, 0) + 1
+            confidences.append(conf)
+            box_list.append(b.xyxy[0])
+            label_list.append(label_cn)
+
+        # 用 PIL 绘制中文
+        annotated = draw_annotations(img, box_list, label_list, confidences)
+
+        _, buffer = cv2.imencode(".jpg", annotated)
+        img_bytes_out = buffer.tobytes()
+
+        result_json = {
+            "total_objects": total,
+            "class_counts": class_counts,
+            "min_confidence": MyUtils.safe_round(min(confidences), 4),
+            "max_confidence": MyUtils.safe_round(max(confidences), 4),
+            "avg_confidence": (
+                MyUtils.safe_round(sum(confidences) / len(confidences), 4)
+            ),
+            "speed_ms": r.speed,  # 直接来自 YOLO
+        }
+
+        return img_bytes_out, result_json

bbit_ai/app/db/postgres.py

@@ -635,3 +635,121 @@ def get_license_image_list(user_id, page=1, page_size=10):
                 )
 
     return total, result
+
+
+# ————————————————————————————————————————————————————蚕茧质量识别———————————————————————————————
+def insert_sca_image(
+    file_name,
+    resolution,
+    size,
+    cocoon_count,
+    max_confidence,
+    min_confidence,
+    average_confidence,
+    other_info,
+    preprocess_time_ms,
+    inference_time_ms,
+    postprocess_time_ms,
+    name,
+    image_pre,
+    image_after,
+    created_by,
+):
+    with pg_pool.getConn() as conn:
+        with conn.cursor() as cursor:
+            other_info = json.dumps(other_info)
+            cursor.execute(
+                """
+                INSERT INTO sca_images (
+                    upload_datetime, file_name, resolution, size, cocoon_count, max_confidence, min_confidence,
+                    average_confidence, other_info, preprocess_time_ms, inference_time_ms, postprocess_time_ms, name, image_pre, image_after, created_by
+                )
+                VALUES (NOW(), %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s )
+                RETURNING id
+                """,
+                (
+                    file_name,
+                    resolution,
+                    size,
+                    cocoon_count,
+                    max_confidence,
+                    min_confidence,
+                    average_confidence,
+                    other_info,
+                    preprocess_time_ms,
+                    inference_time_ms,
+                    postprocess_time_ms,
+                    name,
+                    image_pre,
+                    image_after,
+                    created_by,
+                ),
+            )
+            new_id = cursor.fetchone()[0]
+            conn.commit()
+            return new_id
+
+
+def get_sca_image_list(user_id, name, page=1, page_size=10):
+    """
+    获取用户已分析图片列表，带分页
+    """
+    offset = (page - 1) * page_size
+
+    with pg_pool.getConn() as conn:
+        with conn.cursor() as cursor:
+            # 1️⃣ 查询总条数
+            # ✅ 改进版：支持 name 为空时统计全部，不为空时模糊统计
+            cursor.execute(
+                """
+                SELECT COUNT(*)
+                FROM sca_images
+                WHERE created_by = %s 
+                  AND (%s = '' OR name LIKE '%%' || %s || '%%')
+                """,
+                (user_id, name, name),
+            )
+
+            total = cursor.fetchone()[0]
+
+            # 2️⃣ 查询当前页数据
+            # ✅ 改进版
+            cursor.execute(
+                """
+                SELECT id, name, upload_datetime, file_name, image_pre, image_after, resolution, 
+                       size, cocoon_count, max_confidence, min_confidence, average_confidence, other_info, preprocess_time_ms, inference_time_ms, postprocess_time_ms
+                FROM sca_images
+                WHERE created_by = %s 
+                  AND (%s = '' OR name LIKE '%%' || %s || '%%')
+                ORDER BY upload_datetime DESC
+                LIMIT %s OFFSET %s
+                """,
+                (user_id, name, name, page_size, offset),
+            )
+
+            rows = cursor.fetchall()
+
+            result = []
+            for row in rows:
+                result.append(
+                    {
+                        "id": row[0],
+                        "name": row[1],
+                        "upload_datetime": MyUtils.format_datetime(row[2]),
+                        "file_name": row[3],
+                        "image_pre": get_temp_url("image-sca", "raw/" + row[4]),
+                        "image_after": get_temp_url("image-sca", "ai/" + row[5]),
+                        "resolution": row[6],
+                        "size": MyUtils.safe_round(row[7] / 1024, 2),
+                        "cocoon_count": row[8],
+                        "max_confidence": row[9],
+                        "min_confidence": row[10],
+                        "average_confidence": row[11],
+                        "other_info": row[12],
+                        "preprocess_time_ms": MyUtils.safe_round(row[13], 4),
+                        "inference_time_ms": MyUtils.safe_round(row[14], 4),
+                        "postprocess_time_ms": MyUtils.safe_round(row[15], 4),
+                    }
+                )
+
+    return total, result

bbit_ai/app/requirements.txt

@@ -21,6 +21,8 @@ pyzxing==1.1.1
 Pillow==11.3.0
 python-multipart==0.0.20
 aio_pika==9.5.7
+ultralytics==8.3.227
+
 # MCP服务
 python-dotenv>=1.0.0
 websockets>=11.0.3

bbit_ai/app/routers/Public.py

@@ -6,7 +6,11 @@ from config.app import F8_SERVER_USER_ID
 from models.BaseResponse import BaseResponse
 from models.F8ImageRequest import F8ImageRequest
 from models.F8ImageRequestV2 import F8ImageRequestV2
-from service.vision import process_ticket_image, process_license_image
+from service.vision import (
+    process_ticket_image,
+    process_license_image,
+    process_silkworm_cocoon_image,
+)
 from utils import MyUtils
 
 publicRouter = APIRouter()
@@ -55,3 +59,22 @@ async def cocoon_license(data: F8ImageRequest):
         return BaseResponse(data=data)
     except Exception as e:
         return BaseResponse(status=False, message=f"解析失败: {str(e)}", data=None)
+
+
+@publicRouter.post("/recognize-silkworm-cocoon")
+async def recognize_silkworm_cocoon(data: F8ImageRequest):
+    input_data = data.image
+    if "," in input_data:
+        input_data = input_data.split(",")[1]
+    try:
+        img_bytes = base64.b64decode(input_data)
+        json_data = await MyUtils.async_task(
+            process_silkworm_cocoon_image,
+            img_bytes,
+            f"{data.title}.jpg",
+            data.title,
+            F8_SERVER_USER_ID,
+        )
+        return BaseResponse(data=json_data)
+    except Exception as e:
+        return BaseResponse(status=False, message=f"解析失败: {str(e)}", data=None)

bbit_ai/app/routers/Vision.py

@@ -7,7 +7,11 @@ from config.security import get_user_id_from_token
 from llm.ticketLLM import *
 from models.BaseResponse import BaseResponse
 from models.ImageRequest import ImageRequest
-from service.vision import process_ticket_image, process_license_image
+from service.vision import (
+    process_ticket_image,
+    process_license_image,
+    process_silkworm_cocoon_image,
+)
 from utils import MyUtils
 
 visionRouter = APIRouter()
@@ -25,6 +29,8 @@ def cocoonTicket(data: ImageRequest, user_id: UUID = Depends(get_user_id_from_to
         return BaseResponse(status=False, message="unknown error", data=None)
 
 
+# ————————————————————————————————仪评指标联识别任务————————————————————————————————————————————————
+# 一代
 @visionRouter.post("/createTicketImageTask")
 async def createTicketImageTask(
     file: UploadFile = File(...),
@@ -44,13 +50,7 @@ async def createTicketImageTask(
         return BaseResponse(status=False, message=f"解析失败: {str(e)}", data=None)
 
 
-@visionRouter.get("/getTicketImageList")
-def cocoonTicket(user_id: UUID = Depends(get_user_id_from_token)):
-    if not user_id:
-        return {"error": "userId is required"}
-    return BaseResponse(data=pg.get_ticket_image_list(user_id))
-
-
+# 二代
 @visionRouter.post("/createTicketImageTaskV2")
 async def createTicketImageTask(
     file: UploadFile = File(...),
@@ -70,24 +70,15 @@ async def createTicketImageTask(
         return BaseResponse(status=False, message=f"解析失败: {str(e)}", data=None)
 
 
-@visionRouter.post("/createTicketImageTaskV2")
-async def createTicketImageTask(
-    file: UploadFile = File(...),
-    projectName: str = Form(...),
-    user_id: UUID = Depends(get_user_id_from_token),
-):
+# 获取仪评指标联识别任务列表
+@visionRouter.get("/getTicketImageList")
+def cocoonTicket(user_id: UUID = Depends(get_user_id_from_token)):
     if not user_id:
         return {"error": "userId is required"}
-    try:
-        contents = await file.read()
-        json_data = await MyUtils.async_task(
-            process_ticket_image, 2, True, contents, file.filename, projectName, user_id
-        )
-        return BaseResponse(data=json_data)
-    except Exception as e:
-        return BaseResponse(status=False, message=f"解析失败: {str(e)}", data=None)
+    return BaseResponse(data=pg.get_ticket_image_list(user_id))
 
 
+# ————————————————————————————————证件照片识别任务————————————————————————————————————————————————
 @visionRouter.post("/createLicenseImageTask")
 async def createLicenseImageTask(
     file: UploadFile = File(...),
@@ -107,7 +98,7 @@ async def createLicenseImageTask(
 
 
 @visionRouter.get("/getLicenseImageList")
-def cocoonLicense(
+def getLicenseImageList(
     user_id: UUID = Depends(get_user_id_from_token),
     page: int = Query(1, ge=1),
     page_size: int = Query(10, ge=1, le=100),
@@ -121,3 +112,40 @@ def cocoonLicense(
             "items": items,
         }
     )
+
+
+# ————————————————————————————————蚕茧识别任务————————————————————————————————————————————————
+@visionRouter.post("/createSilkwormCocoonAnalysisTask")
+async def createSilkwormCocoonAnalysisTask(
+    file: UploadFile = File(...),
+    projectName: str = Form(...),
+    user_id: UUID = Depends(get_user_id_from_token),
+):
+    if not user_id:
+        return {"error": "userId is required"}
+    try:
+        contents = await file.read()
+        await MyUtils.async_task(
+            process_silkworm_cocoon_image, contents, file.filename, projectName, user_id
+        )
+        return BaseResponse(data=None)
+    except Exception as e:
+        return BaseResponse(status=False, message=f"解析失败: {str(e)}", data=None)
+
+
+@visionRouter.get("/getSilkwormCocoonAnalysisTasks")
+def getSilkwormCocoonAnalysisTasks(
+    user_id: UUID = Depends(get_user_id_from_token),
+    name: str = "",
+    page: int = Query(1, ge=1),
+    page_size: int = Query(10, ge=1, le=100),
+):
+    if not user_id:
+        return {"error": "userId is required"}
+    total, items = pg.get_sca_image_list(user_id, name, page=page, page_size=page_size)
+    return BaseResponse(
+        data={
+            "total": total,
+            "items": items,
+        }
+    )

bbit_ai/app/service/vision.py

@@ -5,6 +5,7 @@ import config.minIO as minIO
 import db.postgres as pg
 from agent.licenseImageAgent import get_license_response
 from config.minIO import minio_client
+from config.yolo import YOLOSingleton
 from llm.ticketLLM import *
 from llm.ticketLLMv2 import get_ticket_response_v2
 
@@ -106,3 +107,86 @@ def process_license_image(
     )
 
     return json_data
+
+
+def process_silkworm_cocoon_image(
+    img_bytes=None,
+    file_name: str = None,
+    project_name: str = None,
+    user_id: UUID = None,
+):
+    # 上传到 OSS，使用 UUID 做对象名
+    if img_bytes is None:
+        img_bytes = []
+    pre_object_name = str(uuid.uuid4())
+    after_object_name = str(uuid.uuid4())
+    file_bytes = BytesIO(img_bytes)
+
+    bucket_name = "image-sca"
+    if not minio_client.bucket_exists(bucket_name):
+        minio_client.make_bucket(bucket_name)
+    minIO.push_file(
+        bucket_name, "raw/" + pre_object_name, file_bytes, img_bytes, "image/jpeg"
+    )
+
+    # YOLO检测
+    img_bytes_out, results_json = YOLOSingleton.detect(img_bytes)
+
+    # results_json = {
+    #     "total_objects": "",
+    #     "max_confidence": "",
+    #     "min_confidence": "",
+    #     "avg_confidence": "",
+    #     "class_counts": "",
+    #     "speed_ms": {
+    #         "preprocess": "",
+    #         "inference": "",
+    #         "postprocess": "",
+    #     },
+    # }
+    speed_json = results_json.get("speed_ms")
+
+    file_bytes_out = BytesIO(img_bytes_out)
+    minIO.push_file(
+        bucket_name,
+        "ai/" + after_object_name,
+        file_bytes_out,
+        img_bytes_out,
+        "image/jpeg",
+    )
+
+    # 获取图片分辨率和大小
+    img = Image.open(BytesIO(img_bytes))
+    resolution = f"{img.width}x{img.height}"
+    size_kb = round(len(img_bytes) / 1024, 2)
+
+    # 插入数据库
+    pg.insert_sca_image(
+        file_name=file_name,
+        resolution=resolution,
+        size=size_kb,
+        cocoon_count=results_json.get("total_objects"),
+        max_confidence=results_json.get("max_confidence"),
+        min_confidence=results_json.get("min_confidence"),
+        average_confidence=results_json.get("avg_confidence"),
+        other_info=results_json.get("class_counts"),
+        preprocess_time_ms=speed_json.get("preprocess"),
+        inference_time_ms=speed_json.get("inference"),
+        postprocess_time_ms=speed_json.get("postprocess"),
+        name=project_name if project_name else pre_object_name[:8],
+        image_pre=pre_object_name,
+        image_after=after_object_name,
+        created_by=user_id,
+    )
+    return {
+        "resolution": resolution,
+        "size": size_kb,
+        "cocoon_count": results_json.get("total_objects"),
+        "max_confidence": results_json.get("max_confidence"),
+        "min_confidence": results_json.get("min_confidence"),
+        "average_confidence": results_json.get("avg_confidence"),
+        "preprocess_time_ms": speed_json.get("preprocess"),
+        "inference_time_ms": speed_json.get("inference"),
+        "postprocess_time_ms": speed_json.get("postprocess"),
+        "details": results_json.get("class_counts"),
+    }

bbit_ai/app/utils/GlobalVariable.py

@@ -0,0 +1 @@
+LOCAL_IP = "10.10.12.101"

bbit_ai/app/utils/MyUtils.py

@@ -20,3 +20,7 @@ def format_datetime(dt: datetime, tz="Asia/Shanghai"):
     tz_obj = pytz.timezone(tz)
     dt = dt.astimezone(tz_obj)
     return dt.strftime("%Y-%m-%d %H:%M:%S")
+
+
+def safe_round(value, ndigits=2, default=None):
+    return round(value, ndigits) if value is not None else default