315 lines
11 KiB
Python
315 lines
11 KiB
Python
from fastapi import FastAPI, HTTPException, File, UploadFile
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from fastapi.responses import StreamingResponse, JSONResponse
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from fastapi.middleware.cors import CORSMiddleware
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from ultralytics import YOLO
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import cv2
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import numpy as np
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import json
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import uvicorn
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from kafka import KafkaProducer, KafkaConsumer
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from redis import Redis
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import io
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import uuid
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import os
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from datetime import datetime, timedelta
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import threading
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import torch
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torch.cuda.set_device(1)
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import colorsys
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app = FastAPI()
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yolo_app = FastAPI()
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app.mount("/yolo", yolo_app)
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# CORS配置
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ALLOWED_ORIGINS = 'https://beta.obscura.work'
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# 只为主应用添加CORS中间件
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app.add_middleware(
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CORSMiddleware,
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allow_origins=ALLOWED_ORIGINS,
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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# 配置
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MODEL_PATH = "/home/zydi/models/yolov8x.pt" # 请替换为您的模型路径
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KAFKA_BROKER = "222.186.10.253:9092"
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KAFKA_TOPIC = "yolo" # 指定Kafka topic
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KAFKA_GROUP_ID = "yolo_group" # 指定消费者组ID
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REDIS_HOST = "222.186.10.253"
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REDIS_PORT = 6379
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REDIS_PASSWORD = "Obscura@2024"
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REDIS_DB = 6
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UPLOAD_DIR = "/www/wwwroot/beta.obscura.work/upload_files/upload"
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RESULT_DIR = "/www/wwwroot/beta.obscura.work/upload_files/result"
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MAX_FILE_AGE = timedelta(hours=1)
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# 确保目录存在
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os.makedirs(UPLOAD_DIR, exist_ok=True)
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os.makedirs(RESULT_DIR, exist_ok=True)
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# 初始化 Kafka
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producer = KafkaProducer(bootstrap_servers=[KAFKA_BROKER])
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consumer = KafkaConsumer(
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KAFKA_TOPIC,
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bootstrap_servers=[KAFKA_BROKER],
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group_id=KAFKA_GROUP_ID,
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auto_offset_reset='earliest',
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enable_auto_commit=True,
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value_deserializer=lambda x: json.loads(x.decode('utf-8'))
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)
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# 初始化 Redis
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redis_client = Redis(
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host=REDIS_HOST,
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port=REDIS_PORT,
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password=REDIS_PASSWORD,
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db=REDIS_DB
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)
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class yoloDetector:
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def __init__(self, model_path):
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self.model = YOLO(model_path)
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def detect(self, frame):
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results = self.model(frame)
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return results
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def format_results(self, results, original_shape):
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formatted_results = []
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for r in results:
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boxes = r.boxes
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for box in boxes:
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x1, y1, x2, y2 = box.xyxy[0].tolist()
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# 缩放坐标到原始图像尺寸
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x1, x2 = [x * original_shape[1] / 640 for x in [x1, x2]]
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y1, y2 = [y * original_shape[0] / 640 for y in [y1, y2]]
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conf = box.conf.item()
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cls = int(box.cls.item())
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name = self.model.names[cls]
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formatted_results.append({
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"class": name,
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"confidence": conf,
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"bbox": [x1, y1, x2, y2]
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})
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return formatted_results
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def draw_results(self, frame, formatted_results):
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for result in formatted_results:
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x1, y1, x2, y2 = map(int, result['bbox'])
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name = result['class']
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conf = result['confidence']
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cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2) # 使用固定的绿色
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label = f"{name} {conf:.2f}"
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(text_width, text_height), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 1, 2)
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cv2.rectangle(frame, (x1, y1 - text_height - 5), (x1 + text_width, y1), (0, 255, 0), -1)
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cv2.putText(frame, label, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)
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return frame
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detector = yoloDetector(MODEL_PATH)
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def process_image(image_data, filename):
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try:
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original_img = cv2.imdecode(np.frombuffer(image_data, np.uint8), cv2.IMREAD_COLOR)
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original_shape = original_img.shape
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img = cv2.cvtColor(original_img, cv2.COLOR_BGR2RGB)
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img = cv2.resize(img, (640, 640))
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img = img.transpose((2, 0, 1))
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img = np.ascontiguousarray(img)
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img = torch.from_numpy(img).float()
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img /= 255.0
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img = img.unsqueeze(0)
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results = detector.detect(img)
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json_results = detector.format_results(results, original_shape)
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annotated_img = detector.draw_results(original_img, json_results)
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annotated_filename = f"yolo_{filename}"
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annotated_path = os.path.join(RESULT_DIR, annotated_filename)
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cv2.imwrite(annotated_path, annotated_img)
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return json_results, annotated_filename
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except Exception as e:
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print(f"处理图像时出错: {str(e)}")
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return None, None
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def process_video(video_data, filename):
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try:
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temp_video_path = os.path.join(UPLOAD_DIR, f"yolo_{filename}")
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with open(temp_video_path, 'wb') as temp_video:
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temp_video.write(video_data)
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cap = cv2.VideoCapture(temp_video_path)
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frame_count = 0
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json_results = []
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fps = int(cap.get(cv2.CAP_PROP_FPS))
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width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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original_shape = (height, width)
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annotated_filename = f"yolo_{filename}"
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output_path = os.path.join(RESULT_DIR, annotated_filename)
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out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*'mp4v'), 1, (width, height))
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while cap.isOpened():
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ret, frame = cap.read()
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if not ret:
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break
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# 每秒只处理一帧
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if frame_count % fps == 0:
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preprocessed_frame = preprocess_frame(frame)
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results = detector.detect(preprocessed_frame)
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frame_json_results = detector.format_results(results, original_shape)
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json_results.append({"frame": frame_count, "detections": frame_json_results})
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annotated_frame = detector.draw_results(frame, frame_json_results)
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out.write(annotated_frame)
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frame_count += 1
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cap.release()
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out.release()
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os.remove(temp_video_path)
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return json_results, annotated_filename
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except Exception as e:
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print(f"处理视频时出错: {str(e)}")
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return None, None
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def preprocess_frame(frame):
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# 预处理单个视频帧
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frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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frame_resized = cv2.resize(frame_rgb, (640, 640)) # 调整为YOLO输入尺寸
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frame_transposed = frame_resized.transpose((2, 0, 1)) # HWC转为CHW
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frame_contiguous = np.ascontiguousarray(frame_transposed)
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frame_tensor = torch.from_numpy(frame_contiguous).float()
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frame_normalized = frame_tensor / 255.0 # 归一化到[0, 1]
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frame_batched = frame_normalized.unsqueeze(0) # 添加批次维度
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return frame_batched
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@yolo_app.post("/upload")
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async def upload_file(file: UploadFile = File(...)):
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content = await file.read()
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file_extension = os.path.splitext(file.filename)[1].lower()
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new_filename = f"{uuid.uuid4()}{file_extension}"
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# Save the original file
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original_file_path = os.path.join(UPLOAD_DIR, new_filename)
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with open(original_file_path, "wb") as f:
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f.write(content)
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# Send processing task to Kafka
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producer.send(KAFKA_TOPIC, json.dumps({
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"filename": new_filename,
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"file_type": "image" if file_extension in ['.jpg', '.jpeg', '.png'] else "video"
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}).encode('utf-8'))
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# Set initial status in Redis
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redis_key = f"yolo_result:{new_filename}"
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redis_client.set(redis_key, json.dumps({"status": "queued"}))
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return JSONResponse(content={"message": "File uploaded and queued for processing", "filename": new_filename})
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@yolo_app.get("/result/{filename}")
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async def get_yolo_result(filename: str):
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redis_key = f"yolo_result:{filename}"
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result = redis_client.get(redis_key)
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if result:
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result_data = json.loads(result)
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return JSONResponse(content=result_data) # 直接返回整个结果，包括 status
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else:
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raise HTTPException(status_code=404, detail="Result not found")
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@yolo_app.get("/annotated/{filename}")
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async def get_annotated_file(filename: str):
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redis_key = f"yolo_result:{filename}"
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result = redis_client.get(redis_key)
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if result:
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result_data = json.loads(result)
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if result_data["status"] == "completed":
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annotated_filename = result_data["annotated_filename"]
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file_path = os.path.join(RESULT_DIR, annotated_filename)
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if os.path.exists(file_path):
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def iterfile():
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with open(file_path, mode="rb") as file_like:
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yield from file_like
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file_extension = os.path.splitext(annotated_filename)[1].lower()
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return StreamingResponse(iterfile(), media_type=f"image/{file_extension[1:]}" if file_extension in ['.jpg', '.jpeg', '.png'] else "video/mp4")
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raise HTTPException(status_code=404, detail="Annotated file not found")
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def process_task():
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for message in consumer:
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task = message.value
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filename = task['filename']
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file_type = task['file_type']
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file_path = os.path.join(UPLOAD_DIR, filename)
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# Update status to "processing"
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redis_key = f"yolo_result:{filename}"
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redis_client.set(redis_key, json.dumps({"status": "processing"}))
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try:
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if file_type == "image":
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with open(file_path, 'rb') as f:
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content = f.read()
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json_results, annotated_filename = process_image(content, filename)
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else:
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with open(file_path, 'rb') as f:
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content = f.read()
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json_results, annotated_filename = process_video(content, filename)
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if json_results and annotated_filename:
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redis_client.set(redis_key, json.dumps({
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"json_results": json_results,
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"status": "completed",
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"annotated_filename": annotated_filename
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}))
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else:
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redis_client.set(redis_key, json.dumps({"status": "failed"}))
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except Exception as e:
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print(f"Error processing task: {str(e)}")
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redis_client.set(redis_key, json.dumps({"status": "failed", "error": str(e)}))
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def listen_redis_changes():
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pubsub = redis_client.pubsub()
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pubsub.psubscribe('__keyspace@3__:yolo_result:*') # 监听所有yolo_result键的变化
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for message in pubsub.listen():
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if message['type'] == 'pmessage':
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key = message['channel'].decode('utf-8').split(':')[-1]
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operation = message['data'].decode('utf-8')
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if operation == 'set':
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value = redis_client.get(f"yolo_result:{key}")
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if value:
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result = json.loads(value)
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print(f"Status update for {key}: {result['status']}")
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# 这里可以添加其他处理逻辑，比如发送通知等
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if __name__ == "__main__":
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# 启动处理任务的线程
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threading.Thread(target=process_task, daemon=True).start()
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# 启动Redis监听线程
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threading.Thread(target=listen_redis_changes, daemon=True).start()
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uvicorn.run(app, host="0.0.0.0", port=7003) |