IT爱学堂-计算机视觉项目课：基于Django和YoloV8的鸟类识别智能平台-Django + YOLOv8 项目系统教程

bash1# 创建环境（Python 3.8，兼容性最优）
2conda create -n yolov8_env python=3.8 -y
3
4# 激活环境（之后所有操作都在这里）
5conda activate yolov8_env
6

bash1python -c "import torch; print(torch.cuda.is_available())"
2# 显示 True = GPU可用，False = 用CPU版，完全没问题
3

bash1# 安装YOLOv8（Ultralytics官方库，一行搞定）
2pip install ultralytics -i https://pypi.tuna.tsinghua.edu.cn/simple
3
4# 安装Django
5pip install django -i https://pypi.tuna.tsinghua.edu.cn/simple
6
7# 安装标注工具LabelImg
8pip install labelimg -i https://pypi.tuna.tsinghua.edu.cn/simple
9
10# 安装视频处理依赖
11pip install moviepy -i https://pypi.tuna.tsinghua.edu.cn/simple
12

bash1# 验证YOLOv8
2python -c "from ultralytics import YOLO; print('YOLOv8 安装成功！')"
3
4# 验证Django
5python -c "import django; print('Django 版本:', django.VERSION)"
6
7# 验证LabelImg
8labelimg  # 弹出界面即成功
9

python1from ultralytics import YOLO
2
3# 加载官方预训练模型（首次运行自动下载，约200MB）
4model = YOLO('yolov8n.pt')  # nano版，CPU也能跑，35ms/张
5
6# 检测一张图片
7results = model('test.jpg')
8
9# 显示结果（自动弹窗）
10results[0].show()
11
12# 保存结果
13results[0].save(filename='result.jpg')
14

1datasets/
2├── images/
3│   ├── train/      # 70%~80%的图片
4│   └── val/        # 20%~30%的图片
5├── labels/
6│   ├── train/      # 对应train图片的.txt标注
7│   └── val/        # 对应val图片的.txt标注
8└── data.yaml       # 数据集配置文件
9

yaml1path: datasets/
2train: images/train
3val: images/val
4
5nc: 2  # 类别数量（比如cat和dog，nc=2）
6names: ['cat', 'dog']  # 类别名，必须和标注一致
7

bash1yolo detect train model=yolov8n.pt data=datasets/data.yaml epochs=50 imgsz=640 batch=4
2

1runs/detect/train/weights/best.pt  ← 这就是你训练好的模型！
2

bash1# 进入你的工作目录
2cd D:\projects
3
4# 创建项目
5django-admin startproject mysite
6
7# 进入项目
8cd mysite
9
10# 创建检测应用
11python manage.py startapp detector
12

1mysite/
2├── mysite/          # 项目配置
3│   ├── settings.py  # 核心配置
4│   ├── urls.py      # 路由入口
5│   └── wsgi.py
6├── detector/        # 检测应用（我们的核心代码在这里）
7│   ├── views.py     # 视图函数
8│   ├── urls.py      # 应用路由
9│   └── models.py
10└── manage.py
11

python1INSTALLED_APPS = [
2    'django.contrib.admin',
3    'django.contrib.auth',
4    'django.contrib.contenttypes',
5    'django.contrib.sessions',
6    'django.contrib.messages',
7    'django.contrib.staticfiles',
8    'detector',  # ← 加上我们的应用
9]
10
11# 媒体文件配置（存放上传的图片）
12MEDIA_URL = '/media/'
13MEDIA_ROOT = os.path.join(BASE_DIR, 'media')
14

python1from django.shortcuts import render
2
3def index(request):
4    return render(request, 'detector/index.html')
5

python1from django.urls import path
2from . import views
3
4urlpatterns = [
5    path('', views.index, name='index'),
6]
7

python1from django.contrib import admin
2from django.urls import path, include
3from django.conf import settings
4from django.conf.urls.static import static
5
6urlpatterns = [
7    path('admin/', admin.site.urls),
8    path('', include('detector.urls')),
9] + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)
10

bash1python manage.py runserver
2

python1import os
2from django.shortcuts import render
3from django.conf import settings
4from ultralytics import YOLO
5from django.core.files.storage import FileSystemStorage
6
7# 加载训练好的模型（放在项目根目录）
8MODEL_PATH = os.path.join(settings.BASE_DIR, 'best.pt')
9model = YOLO(MODEL_PATH)
10
11def index(request):
12    return render(request, 'detector/index.html')
13
14def detect(request):
15    if request.method == 'POST' and request.FILES.get('image'):
16        # 1. 保存上传的图片
17        uploaded_file = request.FILES['image']
18        fs = FileSystemStorage()
19        filename = fs.save(uploaded_file.name, uploaded_file)
20        file_path = os.path.join(settings.MEDIA_ROOT, filename)
21
22        # 2. 调用YOLOv8检测
23        results = model(file_path, conf=0.5)
24
25        # 3. 保存检测结果图片
26        result_path = os.path.join(settings.MEDIA_ROOT, 'result.jpg')
27        results[0].save(filename=result_path)
28
29        # 4. 统计检测结果
30        detections = []
31        for r in results:
32            for box in r.boxes:
33                cls_id = int(box.cls)
34                conf = float(box.conf)
35                label = model.names[cls_id]
36                detections.append(f"{label} ({conf:.2f})")
37
38        return render(request, 'detector/result.html', {
39            'result_image': f'{settings.MEDIA_URL}result.jpg',
40            'detections': detections,
41            'count': len(detections)
42        })
43
44    return render(request, 'detector/index.html')
45

html1<!DOCTYPE html>
2<html lang="zh-CN">
3<head>
4    <meta charset="UTF-8">
5    <title>YOLOv8 视觉检测系统</title>
6    <style>
7        body { font-family: Arial, sans-serif; max-width: 800px; margin: 50px auto; text-align: center; }
8        .upload-box { border: 2px dashed #ccc; padding: 40px; border-radius: 10px; margin: 20px 0; }
9        button { background: #007bff; color: white; border: none; padding: 12px 30px; font-size: 16px; border-radius: 5px; cursor: pointer; }
10        button:hover { background: #0056b3; }
11        img { max-width: 100%; margin-top: 20px; border-radius: 8px; }
12    </style>
13</head>
14<body>
15    <h1>🎯 YOLOv8 视觉检测系统</h1>
16    <p>上传一张图片，AI 帮你识别图中的目标</p>
17
18    <form method="post" enctype="multipart/form-data" class="upload-box">
19        {% csrf_token %}
20        <input type="file" name="image" accept="image/*" required>
21        <br><br>
22        <button type="submit">🔍 开始检测</button>
23    </form>
24</body>
25</html>
26

html1<!DOCTYPE html>
2<html lang="zh-CN">
3<head>
4    <meta charset="UTF-8">
5    <title>检测结果</title>
6    <style>
7        body { font-family: Arial, sans-serif; max-width: 800px; margin: 50px auto; text-align: center; }
8        img { max-width: 100%; border-radius: 8px; box-shadow: 0 4px 15px rgba(0,0,0,0.2); }
9        .info { background: #f0f8ff; padding: 20px; border-radius: 10px; margin: 20px 0; }
10        .back { display: inline-block; margin-top: 20px; color: #007bff; text-decoration: none; }
11    </style>
12</head>
13<body>
14    <h1>✅ 检测完成！共发现 {{ count }} 个目标</h1>
15
16    <img src="{{ result_image }}" alt="检测结果">
17
18    <div class="info">
19        <h3>📋 检测详情：</h3>
20        {% for d in detections %}
21            <p>{{ d }}</p>
22        {% endfor %}
23    </div>
24
25    <a href="/" class="back">← 返回首页继续检测</a>
26</body>
27</html>
28

python1from django.urls import path
2from . import views
3
4urlpatterns = [
5    path('', views.index, name='index'),
6    path('detect/', views.detect, name='detect'),
7]
8

html1<form method="post" enctype="multipart/form-data" class="upload-box" action="{% url 'detector:detect' %}">
2

bash1python manage.py runserver
2

python1from moviepy.editor import VideoFileClip
2
3def detect_video(request):
4    if request.method == 'POST' and request.FILES.get('video'):
5        video_path = os.path.join(settings.MEDIA_ROOT, request.FILES['video'].name)
6        output_path = os.path.join(settings.MEDIA_ROOT, 'output.mp4')
7
8        clip = VideoFileClip(video_path)
9
10        def process_frame(frame):
11            results = model(frame, conf=0.5, verbose=False)
12            return results[0].plot()
13
14        modified_clip = clip.fl_image(process_frame)
15        modified_clip.write_videofile(output_path, codec='libx264')
16
17        return render(request, 'detector/result.html', {
18            'result_image': f'{settings.MEDIA_URL}output.mp4',
19            'detections': ['视频检测完成'],
20            'count': 1
21        })
22

bash1conda create -n yolov8_env python=3.8 -y
2conda activate yolov8_env
3pip install ultralytics django -i https://pypi.tuna.tsinghua.edu.cn/simple
4