Hugging Face's logo

Spaces:
Hakureirm
/
mice-pose-gpu
Running on Zero

App Files Community
mice-pose-gpu
File size: 3,337 Bytes 
8d9f842
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0f3261d
 
 
8d9f842
 
 
 
 
 
 
 
0f3261d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8d9f842
 
 
 
 
 
 
 
 
 
 
 
 
0f3261d
 
 
8d9f842
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
 
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):
    if image is None:
        return None
    
    # 运行推理
    results = model(image)
    result = results[0]
    annotated_image = result.plot()
    annotated_image = cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)
    
    return annotated_image

# 创建自定义CSS
custom_css = """
#component-0 {
    max-width: 900px;
    margin: auto;
    padding: 20px;
    border-radius: 15px;
    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
}
.gradio-container {
    font-family: 'Source Sans Pro', sans-serif;
}
.gr-button {
    background: linear-gradient(45deg, #FF6B6B, #FF8E53) !important;
    border: none !important;
}
.gr-button:hover {
    transform: translateY(-2px);
    box-shadow: 0 5px 15px rgba(255, 107, 107, 0.4);
}
"""

# 创建Gradio界面
with gr.Blocks(css=custom_css) as demo:
    gr.Markdown(
        """
        # 🐉 奶龙杀手 (Nailong Killer)
        ## 一个基于YOLO的奶龙检测系统
        """
    )
    
    with gr.Row():
        with gr.Column():
            input_image = gr.Image(
                label="上传图片 (Upload Image)", 
                type="numpy",
                tool="select"
            )
            with gr.Row():
                clear_btn = gr.Button("清除 (Clear)", variant="secondary")
                submit_btn = gr.Button("检测 (Detect)", variant="primary")
                
        with gr.Column():
            output_image = gr.Image(label="检测结果 (Detection Result)")
            
    gr.Markdown(
        """
        ### 使用说明 (Instructions):
        1. 点击上传或拖拽图片到左侧区域
        2. 点击"检测"按钮开始识别
        3. 右侧将显示检测结果
        
        ### 注意事项 (Notes):
        - 支持常见图片格式 (jpg, png, etc.)
        - 建议上传清晰的图片以获得更好的检测效果
        """
    )
    
    # 事件处理
    submit_btn.click(
        fn=detect_objects,
        inputs=input_image,
        outputs=output_image
    )
    
    clear_btn.click(
        lambda: (None, None),
        outputs=[input_image, output_image]
    )
    
    # 添加示例
    gr.Examples(
        examples=["example1.jpg", "example2.jpg"],
        inputs=input_image,
        outputs=output_image,
        fn=detect_objects,
        cache_examples=True
    )

# API端点
@app.post("/detect/")
async def detect_api(file: UploadFile = File(...)):
    contents = await file.read()
    image = Image.open(io.BytesIO(contents))
    image_np = np.array(image)
    
    results = model(image_np)
    result = results[0]
    
    detections = []
    for box in result.boxes:
        detection = {
            "bbox": box.xyxy[0].tolist(),
            "confidence": float(box.conf[0]),
            "class": int(box.cls[0])
        }
        detections.append(detection)
    
    return {"detections": detections}

# 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)