Add YOLO11 Nailong Killer detection app

NailongKiller.yolo11n.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f1fe4dcb1f98bd02882a151f0c97a889e05884d7b24a7a347667359334cc3dc0
+size 16040468

app.py

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+import gradio as gr
+from ultralytics import YOLO
+import torch
+import cv2
+import numpy as np
+from fastapi import FastAPI, File, UploadFile
+from PIL import Image
+import io
+
+# 初始化 FastAPI
+app = FastAPI()
+
+# 加载模型
+model = YOLO("NailongKiller.yolo11n.pt")
+
+def detect_objects(image):
+    # 运行推理
+    results = model(image)
+    
+    # 获取第一个结果
+    result = results[0]
+    
+    # 在图像上绘制检测框
+    annotated_image = result.plot()
+    
+    # 转换为RGB格式
+    annotated_image = cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)
+    
+    return annotated_image
+
+# API端点
+@app.post("/detect/")
+async def detect_api(file: UploadFile = File(...)):
+    # 读取上传的图片
+    contents = await file.read()
+    image = Image.open(io.BytesIO(contents))
+    
+    # 转换为numpy数组
+    image_np = np.array(image)
+    
+    # 运行推理
+    results = model(image_np)
+    result = results[0]
+    
+    # 获取检测结果
+    detections = []
+    for box in result.boxes:
+        detection = {
+            "bbox": box.xyxy[0].tolist(),  # 边界框坐标
+            "confidence": float(box.conf[0]),  # 置信度
+            "class": int(box.cls[0])  # 类别
+        }
+        detections.append(detection)
+    
+    return {"detections": detections}
+
+# 创建Gradio界面
+demo = gr.Interface(
+    fn=detect_objects,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Image(),
+    title="奶龙杀手 (Nailong Killer)",
+    description="上传图片来检测奶龙 (Upload an image to detect Nailong)",
+    examples=["example1.jpg", "example2.jpg"]
+)
+
+# 将Gradio接口挂载到FastAPI
+app = gr.mount_gradio_app(app, demo, path="/")
+
+# 启动应用
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)

requirements.txt

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+ultralytics>=8.0.0
+torch>=2.0.0
+opencv-python-headless
+numpy>=1.22.2
+fastapi
+python-multipart
+uvicorn
+pillow
+gradio>=3.0.0