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-# 4-2 图像标注实战（Label Studio）
-
-## 学习目标
-
-1. 掌握 Label Studio 的安装、启动与界面使用
-2. 学会配置三种图像标注类型（分类、目标检测、语义分割）
-3. 独立完成边界框、多边形、关键点的标注
-4. 理解 VOC / YOLO / COCO 三种导出格式的区别
-
----
-
-## 1. Label Studio 简介
-
-### 1.1 一句话定义
-
-**Label Studio**：一个开源的、**多模态**数据标注平台，支持图像、文本、语音、视频等几乎所有数据类型的标注与团队协作。
-
-### 1.2 为什么选它
-
-我们这门课统一用 Label Studio 的原因：
-
-| 需求 | Label Studio 对应能力 |
-|------|---------------------|
-| 多种数据类型 | 图像 / 文本 / 语音 / 视频 / 时序，一站式 |
-| 不用写代码 | 通过 XML 标签可视化配置标注界面 |
-| 团队协作 | 内置成员管理、角色权限、任务分配 |
-| 格式灵活 | 内置 YOLO、COCO、VOC、JSON、CSV 导出 |
-| 本地部署 | 数据不出学校，开源免费 |
-
-```
-传统方案（多工具切换）：
-  图像 → LabelImg
-  文本 → Doccano
-  语音 → Audacity / ELAN
-  协作 → Excel 邮件
-
-Label Studio 一站式：
-  图像 / 文本 / 语音 / 协作 → Label Studio
-```
-
-### 1.3 与其他工具的对比
-
-| 工具 | 类型 | 适用场景 | 本课程 |
-|------|------|---------|--------|
-| **Label Studio** | 开源/多模态 | 课程学习 / 中小团队 / 综合项目 | 主选 |
-| LabelImg | 开源/图像 | 极简目标检测 | 仅做对比 |
-| CVAT | 开源/图像视频 | 大型分割、视频跟踪 | 了解 |
-| Doccano | 开源/文本 | 简单文本分类 | 已弃用 |
-| Scale AI | 商业 | 企业级标注 | 了解 |
-
----
-
-## 2. Label Studio 安装与启动
-
-### 2.1 环境要求
-
-- Python 3.8 及以上
-- 浏览器（Chrome / Edge / Firefox）
-- 建议 8G 以上内存
-
-### 2.2 pip 安装（推荐）
-
-```bash
-# 在命令行中执行
-pip install label-studio
-
-# 启动 Label Studio
-label-studio
-```
-
-启动后命令行会显示：
-
-```
-[INFO] Label Studio is running at http://localhost:8080
-[INFO] Open it in your browser to start labeling
-```
-
-### 2.3 第一次打开
-
-1. 浏览器访问 `http://localhost:8080`
-2. 第一次进入需要 **Sign Up** 注册管理员账号（用你的邮箱 + 密码）
-3. 注册后自动登录到主界面
-
-```
-┌────────────────────────────────────────────────────────┐
-│  Label Studio                                           │
-│  Sign Up                                                │
-├────────────────────────────────────────────────────────┤
-│                                                         │
-│   Email:    [___________________________]               │
-│   Username: [___________________________]               │
-│   Password: [___________________________]               │
-│                                                         │
-│             [  Create Account  ]                        │
-│                                                         │
-└────────────────────────────────────────────────────────┘
-```
-
-### 2.4 安装常见问题
-
-| 问题 | 解决 |
-|------|------|
-| 端口 8080 被占用 | `label-studio --port 8001` |
-| 启动报错找不到模块 | `pip install --upgrade label-studio` |
-| 中文乱码 | 系统区域设置改为 UTF-8 |
-| 浏览器无法打开 | 检查防火墙、放行端口 |
-
----
-
-## 3. 主界面导览
-
-登录后看到的主界面：
-
-```
-┌──────────────────────────────────────────────────────────────┐
-│ [Label Studio]  Projects  Members  Settings  [用户头像]      │
-├──────────────────────────────────────────────────────────────┤
-│                                                              │
-│   My Projects                                                │
-│                                                              │
-│   ┌────────────────────────────────────────────────┐        │
-│   │  + Create Project                              │        │
-│   └────────────────────────────────────────────────┘        │
-│                                                              │
-│   没有任何项目，点上面按钮开始                              │
-│                                                              │
-└──────────────────────────────────────────────────────────────┘
-```
-
-**主要功能区**：
-- **Projects**：项目列表，每个项目对应一个标注任务
-- **Members**：成员管理（管理员可见）
-- **Settings**：个人设置
-
----
-
-## 4. 创建第一个项目：图像分类
-
-### 4.1 任务说明
-
-**任务**：对 10 张动物图片进行分类，类别有：cat / dog / bird
-
-### 4.2 创建项目
-
-1. 点击 `+ Create Project`
-2. 填写：
-   - **Project Name**：`animal_classification`
-   - **Description**：动物图片三分类
-3. 点击 `Save`
-
-### 4.3 配置标注界面
-
-进入项目后，先点 `Settings` → `Labeling Interface` → `Code`：
-
-```xml
-<View>
-  <Image name="image" value="$image"/>
-  <Choices name="label" toName="image" choice="single">
-    <Choice value="cat"/>
-    <Choice value="dog"/>
-    <Choice value="bird"/>
-  </Choices>
-</View>
-```
-
-**逐行解释**：
-
-```
-<View>            ← 整个标注界面的根标签
-  <Image ...>     ← 显示图像（$image 是任务数据里的字段名）
-  <Choices ...>   ← 单选/多选控件
-    <Choice/>     ← 一个选项
-</View>
-```
-
-**关键参数**：
-- `toName="image"`：告诉 Label Studio，这个控件是对上面 `Image` 控件的结果
-- `choice="single"`：单选（可选 `single-radio`、`multiple`）
-
-保存后点 `Label All Tasks` 就能看到标注界面。
-
-### 4.4 导入数据
-
-在项目页 → `Settings` → `Data Import`：
-
-**方式 A：上传本地文件**
-
-```
-1. 准备文件夹
-   images/
-     ├── cat_001.jpg
-     ├── cat_002.jpg
-     ├── dog_001.jpg
-     └── bird_001.jpg
-
-2. 拖拽到上传区域
-   或点击 Upload Files 选中文件夹
-```
-
-**方式 B：上传 CSV / JSONL（推荐，标签稳定）**
-
-`tasks.csv`：
-
-```csv
-image
-images/cat_001.jpg
-images/cat_002.jpg
-images/dog_001.jpg
-images/bird_001.jpg
-```
-
-**方式 C：粘贴 URL**
-
-```csv
-image
-https://example.com/cat_001.jpg
-https://example.com/dog_001.jpg
-```
-
-上传完后点 `Save` → 任务列表里就会看到每一张图对应一行任务。
-
-### 4.5 开始标注
-
-1. 点击 `Label All Tasks`
-2. 进入标注界面：
-   - 上面是图片
-   - 下面是三个单选按钮
-3. 选中正确类别
-4. 点击右下角 `Submit` 进入下一张
-5. 全部完成后回到项目页，可以看到完成进度
-
-```
-┌──────────────────────────────────────────────────┐
-│  Project: animal_classification                   │
-├──────────────────────────────────────────────────┤
-│  Tasks:     10 total / 7 done / 3 skipped         │
-│  Progress:  ████████████░░░░░░  70%               │
-└──────────────────────────────────────────────────┘
-```
-
-### 4.6 导出数据
-
-项目页 → `Export`：
-
-- 格式选 `JSON`
-- 下载得到 `project-1-at-2024-05-20.json`
-
-导出内容示例：
-
-```json
-[
-  {
-    "id": 1,
-    "data": {"image": "images/cat_001.jpg"},
-    "annotations": [{
-      "result": [{
-        "from_name": "label",
-        "to_name": "image",
-        "type": "choices",
-        "value": {"choices": ["cat"]}
-      }]
-    }]
-  }
-]
-```
-
----
-
-## 5. 创建第二个项目：目标检测（边界框）
-
-### 5.1 任务说明
-
-**任务**：对 10 张街景图进行目标检测，标出每张图中的 `person`、`car`、`bicycle`
-
-### 5.2 项目配置 XML
-
-```xml
-<View>
-  <Image name="image" value="$image"/>
-  <RectangleLabels name="label" toName="image" strokeWidth="3">
-    <Label value="person" background="#FF0000"/>
-    <Label value="car"    background="#00FF00"/>
-    <Label value="bicycle" background="#0000FF"/>
-  </RectangleLabels>
-</View>
-```
-
-**新增标签**：
-- `<RectangleLabels>`：矩形框 + 标签（Label Studio 专门用于目标检测）
-- `<Label value="..." background="...">`：每个标签的颜色
-- `strokeWidth="3"`：边框线宽
-
-### 5.3 标注操作
-
-进入标注界面后：
-
-```
-┌──────────────────────────────────────────────────┐
-│  [image]                                          │
-│  ┌────────┐                                       │
-│  │  人    │ ← 拖动鼠标画框                        │
-│  └────────┘                                       │
-│         ┌─────────┐                                │
-│         │   车    │                                │
-│         └─────────┘                                │
-├──────────────────────────────────────────────────┤
-│  标签栏:                                          │
-│  [ person ]  [ car ]  [ bicycle ]                 │
-└──────────────────────────────────────────────────┘
-```
-
-**操作步骤**：
-
-1. 选中上方标签（点击 person/car/bicycle 之一）
-2. 在图片上 **按住鼠标左键拖动**，画矩形框
-3. 松开后，框会自动带上刚才选中的标签
-4. 继续选下一个标签，再画下一个框
-5. 同一张图可以有多个框
-6. 标完后点 `Submit`
-
-### 5.4 快捷键速查
-
-| 快捷键 | 功能 |
-|--------|------|
-| `Ctrl + Enter` | 提交当前任务 |
-| `H` | 切换显示已画框 |
-| `Ctrl + Z` | 撤销上一个框 |
-| `Delete` | 删除选中的框 |
-| 数字 `1-9` | 快速选择第 N 个标签 |
-| `+ / -` | 缩放图像 |
-| 滚轮 | 上下滚动 |
-
-### 5.5 边界框标注规范
-
-**基本规则**：
-
-```
-正确（紧贴目标）           错误（框太大）            错误（截断）
-
-┌──────────┐               ┌────────────────┐        ┌──────────
-│ ▓▓▓▓▓▓▓ │               │ ▓▓▓▓▓▓▓▓▓▓▓▓▓ │        │ ▓▓▓▓▓▓▓ │
-│ ▓ 人 ▓▓ │               │ ▓  人  ▓▓▓▓▓▓ │        │ ▓ 人 ▓▓ │
-│ ▓▓▓▓▓▓▓ │               │ ▓▓▓▓▓▓▓▓▓▓▓▓▓ │        │ ▓▓▓▓▓▓▓ │
-└──────────┘               └────────────────┘        └──────────┘
-```
-
-**多目标处理**：
-
-- 一张图有几个就标几个，每个独立画框
-- 互相重叠的目标也要分别标
-- 远处很小的目标可以标 "difficult" 标记
-
-**遮挡处理**：
-
-```
-情况1：目标被部分遮挡
-   → 标可见部分（标框框住能看到的人）
-
-情况2：目标被严重遮挡（< 20% 可见）
-   → 建议不标（标记为非标注目标）
-
-情况3：多人重叠
-   → 每个独立标，画框按可见边界
-```
-
-### 5.6 YOLO 格式导出
-
-项目页 → `Export` → 选 `YOLO`：
-
-下载得到压缩包，解压后结构：
-
-```
-project-1-at-2024-05-20-yolo/
-├── images/
-│   ├── street_001.jpg
-│   └── street_002.jpg
-├── labels/
-│   ├── street_001.txt
-│   └── street_002.txt
-├── classes.txt
-└── notes.json
-```
-
-`classes.txt`（类别映射）：
-
-```
-0: person
-1: car
-2: bicycle
-```
-
-`street_001.txt`（一张图的标注）：
-
-```
-# 格式: <class_id> <x_center> <y_center> <width> <height>
-# 所有值都是相对图片尺寸的归一化值 (0-1)
-0 0.35 0.45 0.18 0.55
-1 0.75 0.50 0.30 0.20
-```
-
-**逐字段解释**：
-
-```
-0      → 类别 ID（0=person）
-0.35   → 框中心点 x / 图片宽度
-0.45   → 框中心点 y / 图片高度
-0.18   → 框宽度 / 图片宽度
-0.55   → 框高度 / 图片高度
-```
-
-### 5.7 Pascal VOC 格式导出
-
-项目页 → `Export` → 选 `PascalVOC`：
-
-`street_001.xml`：
-
-```xml
-<?xml version="1.0" encoding="UTF-8"?>
-<annotation>
-    <filename>street_001.jpg</filename>
-    <size>
-        <width>1920</width>
-        <height>1080</height>
-        <depth>3</depth>
-    </size>
-    <object>
-        <name>person</name>
-        <bndbox>
-            <xmin>134</xmin>
-            <ymin>216</ymin>
-            <xmax>478</xmax>
-            <ymax>810</ymax>
-        </bndbox>
-    </object>
-    <object>
-        <name>car</name>
-        <bndbox>
-            <xmin>1100</xmin>
-            <ymin>450</ymin>
-            <xmax>1680</xmax>
-        </bndbox>
-    </object>
-</annotation>
-```
-
-注意 VOC 用的是**绝对像素坐标**（xmin、ymin、xmax、ymax），而 YOLO 是**归一化坐标**（0-1）。
-
-### 5.8 COCO 格式导出
-
-项目页 → `Export` → 选 `COCO`：
-
-`result.json`：
-
-```json
-{
-  "images": [
-    {"id": 1, "file_name": "street_001.jpg", "width": 1920, "height": 1080},
-    {"id": 2, "file_name": "street_002.jpg", "width": 1920, "height": 1080}
-  ],
-  "categories": [
-    {"id": 0, "name": "person"},
-    {"id": 1, "name": "car"},
-    {"id": 2, "name": "bicycle"}
-  ],
-  "annotations": [
-    {
-      "id": 1, "image_id": 1, "category_id": 0,
-      "bbox": [134, 216, 344, 594],
-      "area": 204336,
-      "iscrowd": 0
-    }
-  ]
-}
-```
-
-注意 COCO 的 `bbox` 格式是 `[x_min, y_min, width, height]`（**前两个是左上角，后两个是宽高**），和 VOC 略有区别。
-
-### 5.9 三种格式对比
-
-| 特性 | VOC | YOLO | COCO |
-|------|-----|------|------|
-| 文件类型 | 每个图一个 XML | 每个图一个 TXT | 所有图一个 JSON |
-| 坐标系 | 绝对像素 (xmin,ymin,xmax,ymax) | 归一化 0-1（中心点+宽高）| 绝对像素 (x,y,w,h) |
-| 多目标 | 一个 object 一对 bndbox | 一行一个目标 | 一个 annotation 一个目标 |
-| 适用框架 | Faster R-CNN, SSD | YOLO 系列 | Mask R-CNN, Detectron2 |
-| 优缺点 | 直观但文件多 | 紧凑、YOLO 专用 | 强大、适合大项目 |
-| 人类可读 | ⭐⭐⭐ | ⭐ | ⭐⭐ |
-
-**怎么选**：
-- 学 YOLO 模型 → 导 YOLO
-- 用 MMDetection / Detectron2 → 导 COCO
-- 通用、自己写训练脚本 → 导 VOC 或 JSON
-
----
-
-## 6. 创建第三个项目：语义分割（多边形）
-
-### 6.1 任务说明
-
-**任务**：对 5 张街景图做**像素级**语义分割，类别：road、building、sky、vegetation
-
-### 6.2 项目配置 XML
-
-```xml
-<View>
-  <Image name="image" value="$image"/>
-  <PolygonLabels name="label" toName="image" strokeWidth="2">
-    <Label value="road"        background="#808080"/>
-    <Label value="building"    background="#A52A2A"/>
-    <Label value="sky"         background="#87CEEB"/>
-    <Label value="vegetation"  background="#228B22"/>
-  </PolygonLabels>
-</View>
-```
-
-`<PolygonLabels>`：多边形 + 标签，专门用于语义分割。
-
-### 6.3 标注操作
-
-1. 选中标签（点击 road / building / sky / vegetation）
-2. 在图像上**连续点击多个点**画出多边形
-3. **双击或按回车**结束当前多边形
-4. 一个类别画完，再选下一个类别继续
-5. 一张图可能有很多个多边形
-
-```
-标注过程示意：
-
-点 1 ──→ 点 2
-         │
-         ↓
-点 4 ←── 点 3
-(双击结束)
-
-┌──────────────────┐
-│                  │
-│   1────────2     │
-│   │        │     │
-│   │  区域  │     │
-│   │        │     │
-│   4────────3     │
-│                  │
-└──────────────────┘
-```
-
-### 6.4 分割标注注意事项
-
-| 情况 | 处理 |
-|------|------|
-| 两个区域紧贴 | 各画一个多边形，不要合成一个 |
-| 区域很小 | 至少 3 个点画出三角形 |
-| 形状不规则 | 多点描边，越细越准 |
-| 类别不确定 | 选最接近的，宁可错记也不要漏 |
-
-### 6.5 导出多边形
-
-项目页 → `Export` → 选 `JSON`：
-
-```json
-{
-  "id": 1,
-  "data": {"image": "street_001.jpg"},
-  "annotations": [{
-    "result": [{
-      "from_name": "label",
-      "to_name": "image",
-      "type": "polygonlabels",
-      "value": {
-        "points": [[0.1, 0.8], [0.9, 0.8], [0.9, 1.0], [0.1, 1.0]],
-        "polygonlabels": ["road"]
-      }
-    }]
-  }]
-}
-```
-
-`points` 数组里每个点都是 `[x, y]` 的归一化坐标（0-1），是按顺时针或逆时针排列的多边形顶点。
-
-> 💡 想转成 mask（像素级 PNG 图）？需要用 `label-studio-converter` 工具，详见 4-4 节。
-
----
-
-## 7. 课堂演示：Python SDK
-
-### 7.1 简介
-
-Label Studio 提供 `label-studio-sdk`，可以用 Python 编程式管理项目、查询任务、写入预标注。
-
-### 7.2 安装
-
-```bash
-pip install label-studio-sdk
-```
-
-### 7.3 常用代码
-
-```python
-from label_studio_sdk import Client
-
-# 连接本地服务
-ls = Client(url='http://localhost:8080', api_key='YOUR_API_KEY')
-
-# 获取 API Key：
-# Label Studio → Settings → Account → Access Token
-
-# 列出所有项目
-projects = ls.list_projects()
-for p in projects:
-    print(p.id, p.title)
-
-# 获取某个项目的任务
-project = ls.get_project(id=1)
-tasks = project.get_tasks()
-print(f"项目里有 {len(tasks)} 个任务")
-
-# 导入新任务
-project.import_tasks([
-    {"image": "images/new_001.jpg"},
-    {"image": "images/new_002.jpg"}
-])
-
-# 导出标注
-project.export_tasks(export_type='JSON')
-```
-
-> 这一节以了解为主，详细用法 4-4 节会展开。
-
----
-
-## 8. 实战练习
-
-### 8.1 任务一：图像分类（必做）
-
-**任务**：在 Label Studio 创建项目 `fruit_classification`
-
-**步骤**：
-1. 准备 10 张水果图片（apple、banana、orange）
-2. 启动 Label Studio，创建项目
-3. 配置 `<Choices>` 三分类界面
-4. 上传 10 张图
-5. 完成 10 张标注
-6. 导出 JSON 并截图
-
-### 8.2 任务二：目标检测（必做）
-
-**任务**：创建项目 `vehicle_detection`
-
-**步骤**：
-1. 准备 10 张街景图
-2. 配置 `<RectangleLabels>` 目标检测界面
-3. 类别：person / car / bicycle / traffic_light
-4. 标完所有目标
-5. 导出 YOLO 格式
-6. 检查导出的 .txt 文件坐标是否正确
-
-### 8.3 任务三（选做）：语义分割
-
-**任务**：创建项目 `scene_segmentation`
-
-**步骤**：
-1. 准备 3-5 张风景图
-2. 配置 `<PolygonLabels>` 分割界面
-3. 类别：sky / vegetation / road / building
-4. 画多边形
-5. 导出 JSON
-
-### 8.4 标注自检清单
-
-```
-图像分类：
-□ 类别选择是否正确
-□ 是否有漏标的任务
-□ 导出 JSON 是否完整
-
-目标检测：
-□ 边界框是否紧贴目标
-□ 是否每个目标都标了
-□ 框内类别是否正确
-□ 导出 YOLO 格式后坐标值是否在 0-1 之间
-□ classes.txt 是否齐全
-
-语义分割：
-□ 多边形顶点是否足够（至少 3 个）
-□ 类别判断是否准确
-□ 不同区域之间是否有遗漏
-```
-
----
-
-## 9. 本节知识小结
-
-```
-┌────────────────────────────────────────────────────────────┐
-│                4-2 图像标注实战（Label Studio）             │
-├────────────────────────────────────────────────────────────┤
-│                                                            │
-│  📌 Label Studio 是多模态标注平台，一站式解决图像/文本/语音 │
-│                                                            │
-│  📌 安装：                                                  │
-│     pip install label-studio                               │
-│     label-studio                                           │
-│     浏览器访问 http://localhost:8080                       │
-│                                                            │
-│  📌 三种图像标注类型：                                       │
-│     分类      → <Choices>                                   │
-│     目标检测  → <RectangleLabels>                          │
-│     语义分割  → <PolygonLabels>                            │
-│                                                            │
-│  📌 三种导出格式：                                           │
-│     VOC    → XML，像素绝对坐标                              │
-│     YOLO   → TXT，归一化 0-1 坐标                          │
-│     COCO   → JSON，[x,y,w,h] 像素坐标                      │
-│                                                            │
-│  📌 标注规范：框要紧贴目标、不多不少                       │
-│                                                            │
-└────────────────────────────────────────────────────────────┘
-```
-
----
-
-## 课后任务
-
-1. 安装 Label Studio，截图证明能正常打开
-2. 完成"任务一"图像分类 10 张图
-3. 完成"任务二"目标检测 10 张图，导出 YOLO 格式
-4. （选做）完成"任务三"语义分割
-
----
-
-## 下节预告
-
-下一节我们将学习 **4-3 文本标注与语音标注（Label Studio）**：
-- 文本分类、NER 命名实体识别
-- 意图识别与槽位标注
-- 语音转写 ASR
-- 说话人分离与音频分类
diff --git a/data/images/标注练习1.jpg b/data/images/标注练习1.jpg
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diff --git a/data/reviews.json b/data/reviews.json
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--- /dev/null
+++ b/data/reviews.json
@@ -0,0 +1,7 @@
+[
+  {"id": 1, "text": "外卖小哥送得超快，餐盒还是热的，炸鸡酥脆多汁，酸辣粉也很正宗，分量足，五星好评！"},
+  {"id": 2, "text": "等了一个半小时才送到，汤全洒了，面坨成一坨，联系客服也不回，太让人失望了。"},
+  {"id": 3, "text": "奶茶是用料很扎实的现煮茶，珍珠Q弹有嚼劲，配送员态度也好，下次还会再点。"},
+  {"id": 4, "text": "配送速度一般，但披萨味道不错，芝士拉丝效果好，性价比高，值得推荐。"},
+  {"id": 5, "text": "点的麻辣烫食材不新鲜，有股怪味，吃完拉肚子，商家推卸责任，再也不点了。"}
+]
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--- a/project-14-22.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/project-14-22coco.zip b/project-14-22coco.zip
deleted file mode 100644
index 2912512..0000000
Binary files a/project-14-22coco.zip and /dev/null differ
diff --git a/q2_1_crawler/move.html b/q2_1_crawler/move.html
new file mode 100644
index 0000000..667e109
--- /dev/null
+++ b/q2_1_crawler/move.html
@@ -0,0 +1,102 @@
+[
+  {
+    "id": "1",
+    "title": "泰坦尼克号",
+    "director": "Frank Darabont",
+    "year": "2015",
+    "rating": "6.8",
+    "duration": "91",
+    "genre": "科幻",
+    "actors_count": "3"
+  },
+  {
+    "id": "2",
+    "title": "星际穿越",
+    "director": "陈凯歌",
+    "year": "2021",
+    "rating": "6.2",
+    "duration": "113",
+    "genre": "科幻",
+    "actors_count": "2"
+  },
+  {
+    "id": "3",
+    "title": "三傻大闹宝莱坞",
+    "director": "Robert Zemeckis",
+    "year": "2004",
+    "rating": "7.4",
+    "duration": "95",
+    "genre": "悬疑",
+    "actors_count": "4"
+  },
+  {
+    "id": "4",
+    "title": "阿甘正传",
+    "director": "James Cameron",
+    "year": "2013",
+    "rating": "6.9",
+    "duration": "93",
+    "genre": "爱情",
+    "actors_count": "4"
+  },
+  {
+    "id": "5",
+    "title": "放牛班的春天",
+    "director": "宫崎骏",
+    "year": "2005",
+    "rating": "7.1",
+    "duration": "127",
+    "genre": "悬疑",
+    "actors_count": "3"
+  },
+  {
+    "id": "6",
+    "title": "千与千寻",
+    "director": "Christopher Nolan",
+    "year": "2024",
+    "rating": "6.4",
+    "duration": "147",
+    "genre": "动画",
+    "actors_count": "3"
+  },
+  {
+    "id": "7",
+    "title": "忠犬八公的故事",
+    "director": "Lasse Hallström",
+    "year": "2002",
+    "rating": "6.2",
+    "duration": "166",
+    "genre": "剧情",
+    "actors_count": "4"
+  },
+  {
+    "id": "8",
+    "title": "霸王别姬",
+    "director": "Rajkumar Hirani",
+    "year": "2005",
+    "rating": "7.9",
+    "duration": "149",
+    "genre": "冒险",
+    "actors_count": "2"
+  },
+  {
+    "id": "9",
+    "title": "肖申克的救赎",
+    "director": "Christophe Barratier",
+    "year": "2008",
+    "rating": "9.3",
+    "duration": "91",
+    "genre": "冒险",
+    "actors_count": "2"
+  },
+  {
+    "id": "10",
+    "title": "盗梦空间",
+    "director": "Christopher Nolan",
+    "year": "2019",
+    "rating": "7.1",
+    "duration": "132",
+    "genre": "剧情",
+    "actors_count": "5"
+  }
+]
\ No newline at end of file
diff --git a/q2_1_crawler/movie.json b/q2_1_crawler/movie.json
new file mode 100644
index 0000000..667e109
--- /dev/null
+++ b/q2_1_crawler/movie.json
@@ -0,0 +1,102 @@
+[
+  {
+    "id": "1",
+    "title": "泰坦尼克号",
+    "director": "Frank Darabont",
+    "year": "2015",
+    "rating": "6.8",
+    "duration": "91",
+    "genre": "科幻",
+    "actors_count": "3"
+  },
+  {
+    "id": "2",
+    "title": "星际穿越",
+    "director": "陈凯歌",
+    "year": "2021",
+    "rating": "6.2",
+    "duration": "113",
+    "genre": "科幻",
+    "actors_count": "2"
+  },
+  {
+    "id": "3",
+    "title": "三傻大闹宝莱坞",
+    "director": "Robert Zemeckis",
+    "year": "2004",
+    "rating": "7.4",
+    "duration": "95",
+    "genre": "悬疑",
+    "actors_count": "4"
+  },
+  {
+    "id": "4",
+    "title": "阿甘正传",
+    "director": "James Cameron",
+    "year": "2013",
+    "rating": "6.9",
+    "duration": "93",
+    "genre": "爱情",
+    "actors_count": "4"
+  },
+  {
+    "id": "5",
+    "title": "放牛班的春天",
+    "director": "宫崎骏",
+    "year": "2005",
+    "rating": "7.1",
+    "duration": "127",
+    "genre": "悬疑",
+    "actors_count": "3"
+  },
+  {
+    "id": "6",
+    "title": "千与千寻",
+    "director": "Christopher Nolan",
+    "year": "2024",
+    "rating": "6.4",
+    "duration": "147",
+    "genre": "动画",
+    "actors_count": "3"
+  },
+  {
+    "id": "7",
+    "title": "忠犬八公的故事",
+    "director": "Lasse Hallström",
+    "year": "2002",
+    "rating": "6.2",
+    "duration": "166",
+    "genre": "剧情",
+    "actors_count": "4"
+  },
+  {
+    "id": "8",
+    "title": "霸王别姬",
+    "director": "Rajkumar Hirani",
+    "year": "2005",
+    "rating": "7.9",
+    "duration": "149",
+    "genre": "冒险",
+    "actors_count": "2"
+  },
+  {
+    "id": "9",
+    "title": "肖申克的救赎",
+    "director": "Christophe Barratier",
+    "year": "2008",
+    "rating": "9.3",
+    "duration": "91",
+    "genre": "冒险",
+    "actors_count": "2"
+  },
+  {
+    "id": "10",
+    "title": "盗梦空间",
+    "director": "Christopher Nolan",
+    "year": "2019",
+    "rating": "7.1",
+    "duration": "132",
+    "genre": "剧情",
+    "actors_count": "5"
+  }
+]
\ No newline at end of file
diff --git a/q2_1_crawler/q2_1.py b/q2_1_crawler/q2_1.py
new file mode 100644
index 0000000..2d782cf
--- /dev/null
+++ b/q2_1_crawler/q2_1.py
@@ -0,0 +1,65 @@
+import requests
+from bs4 import BeautifulSoup as bs
+import json
+
+url = 'https://exam.detr.top/exam-b/movies'
+headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/149.0.0.0 Safari/537.36 Edg/149.0.0.0',
+    'Referer':'https://exam.detr.top/exam-b/movies'}
+req = requests.get(url, headers=headers)
+req.encoding="utf-8"
+
+data=[]
+
+soup=bs(req.text,"html.parser")
+# print(soup)
+#id, title, director, year, rating, duration, genre, actors_count
+
+item=soup.select("table tbody tr" )
+movie_list=[]
+
+for tr in item:
+    tds=tr.find_all("td")
+    tds=list(tds)
+    # print(tds)
+    if len(tds)<8:
+        continue
+    movie={
+        "id":tds[0].get_text(strip=True),
+        "title":tds[1].get_text(strip=True),
+        "director":tds[2].get_text(strip=True),
+        "year":tds[3].get_text(strip=True),
+        "rating":tds[4].get_text(strip=True),
+        "duration":tds[5].get_text(strip=True),
+        "genre":tds[6].get_text(strip=True),
+        "actors_count":tds[7].get_text(strip=True)
+        }
+    movie_list.append(movie)
+print(movie_list)
+    
+
+with open('movie.json', 'w', encoding='utf-8') as f:
+    json.dump(movie_list, f, ensure_ascii=False, indent=2)
+
+with open("move.html","w",encoding='utf-8') as f:
+     json.dump(movie_list, f, ensure_ascii=False, indent=2)
+    
+
+
+
+
+# for i in range(len(items)):
+#     rank=i+1
+#     title=items[i].find("span",class_="title").get_text()
+#     actors=items[i].find("div",class_="bd").get_text().strip()
+#     try:
+#        actors=actors.split("主演：")[1].split("\n")[0]
+#     except:
+#        actors="无"
+#     quote=items[i].find("p",class_="quote").get_text().strip()
+
+#     data.append({
+#         "rank":rank,
+#         "title":title,
+#         "actors":actors,
+#         "quote":quote
+#     })
\ No newline at end of file
diff --git a/q2_1_crawler/q2_2.py b/q2_1_crawler/q2_2.py
new file mode 100644
index 0000000..f30c2f7
--- /dev/null
+++ b/q2_1_crawler/q2_2.py
@@ -0,0 +1,43 @@
+# ① 找出评分最高和最低的电影，打印电影名 + 评分。
+# ② 统计各类型的电影数量，用字典格式输出。
+# ③ 统计各导演的电影数量，用字典格式输出。
+# ④ 统计 2020 年（含）以后上映的电影数量。
+
+import json
+
+
+
+with open('movie.json', 'r', encoding='utf-8') as f:
+    data=json.load(f)
+    # print(data)
+   
+
+sort_movie=sorted(data,key=lambda x:x["rating"])
+min=sort_movie[0]
+max=sort_movie[-1]
+print("评分最低的电影",min["title"],min["rating"])
+print("评分最高的电影",max["title"],max["rating"])
+
+genre_shu={}
+for g in data:
+    ge=g["genre"]
+    if ge in genre_shu:
+        genre_shu[ge]+=1
+    else:
+        genre_shu[ge]=1
+print("各类型的电影数量",genre_shu)
+
+director_shu={}
+for d in data:
+    di=d["director"]
+    if di in director_shu:
+        director_shu[di]+=1
+    else:
+        director_shu[di]=1
+print("各导演的电影数量",director_shu)
+
+a=0
+for y in data:
+    if int(y["year"]) >= 2020:
+        a+=1
+print("2020 年（含）以后上映的电影数量",a)
\ No newline at end of file
diff --git a/q3/q3_1/q3_1_image_label.zip b/q3/q3_1/q3_1_image_label.zip
new file mode 100644
index 0000000..5b08d84
Binary files /dev/null and b/q3/q3_1/q3_1_image_label.zip differ
diff --git a/q3/q3_2/q3_2_takeout_reviews.json b/q3/q3_2/q3_2_takeout_reviews.json
new file mode 100644
index 0000000..94c7925
--- /dev/null
+++ b/q3/q3_2/q3_2_takeout_reviews.json
@@ -0,0 +1 @@
+[{"id":32,"annotations":[{"id":20,"completed_by":1,"result":[{"value":{"choices":["正面"]},"id":"Onq13IcOtQ","from_name":"sentiment","to_name":"text","type":"choices","origin":"manual"}],"was_cancelled":false,"ground_truth":false,"created_at":"2026-06-25T07:10:52.845728Z","updated_at":"2026-06-25T07:10:52.845818Z","draft_created_at":"2026-06-25T07:10:22.361376Z","lead_time":12.684999999999999,"prediction":{},"result_count":1,"unique_id":"0dd85e72-c68d-4302-ab92-11c8114bd5bb","import_id":null,"last_action":null,"bulk_created":false,"task":32,"project":12,"updated_by":1,"parent_prediction":null,"parent_annotation":null,"last_created_by":null}],"file_upload":"9ce97d9b-reviews.json","drafts":[],"predictions":[],"data":{"id":1,"text":"外卖小哥送得超快，餐盒还是热的，炸鸡酥脆多汁，酸辣粉也很正宗，分量足，五星好评！"},"meta":{},"created_at":"2026-06-25T07:10:04.754168Z","updated_at":"2026-06-25T07:10:52.903174Z","allow_skip":true,"inner_id":1,"total_annotations":1,"cancelled_annotations":0,"total_predictions":0,"comment_count":0,"unresolved_comment_count":0,"last_comment_updated_at":null,"project":12,"updated_by":1,"comment_authors":[]},{"id":33,"annotations":[{"id":21,"completed_by":1,"result":[{"value":{"choices":["负面"]},"id":"cJxGS89PGV","from_name":"sentiment","to_name":"text","type":"choices","origin":"manual"}],"was_cancelled":false,"ground_truth":false,"created_at":"2026-06-25T07:10:56.177683Z","updated_at":"2026-06-25T07:10:56.177702Z","draft_created_at":"2026-06-25T07:10:40.725815Z","lead_time":4.317,"prediction":{},"result_count":1,"unique_id":"ec19a0ee-0062-4fcb-be7e-3885bbb0e630","import_id":null,"last_action":null,"bulk_created":false,"task":33,"project":12,"updated_by":1,"parent_prediction":null,"parent_annotation":null,"last_created_by":null}],"file_upload":"9ce97d9b-reviews.json","drafts":[],"predictions":[],"data":{"id":2,"text":"等了一个半小时才送到，汤全洒了，面坨成一坨，联系客服也不回，太让人失望了。"},"meta":{},"created_at":"2026-06-25T07:10:04.754256Z","updated_at":"2026-06-25T07:10:56.235324Z","allow_skip":true,"inner_id":2,"total_annotations":1,"cancelled_annotations":0,"total_predictions":0,"comment_count":0,"unresolved_comment_count":0,"last_comment_updated_at":null,"project":12,"updated_by":1,"comment_authors":[]},{"id":34,"annotations":[{"id":22,"completed_by":1,"result":[{"value":{"choices":["正面"]},"id":"TEYHnwsP5p","from_name":"sentiment","to_name":"text","type":"choices","origin":"manual"}],"was_cancelled":false,"ground_truth":false,"created_at":"2026-06-25T07:11:02.709432Z","updated_at":"2026-06-25T07:11:02.709456Z","draft_created_at":"2026-06-25T07:10:44.119328Z","lead_time":5.162,"prediction":{},"result_count":1,"unique_id":"e99cc8d6-b0ec-4ba7-95c9-75e600010972","import_id":null,"last_action":null,"bulk_created":false,"task":34,"project":12,"updated_by":1,"parent_prediction":null,"parent_annotation":null,"last_created_by":null}],"file_upload":"9ce97d9b-reviews.json","drafts":[],"predictions":[],"data":{"id":3,"text":"奶茶是用料很扎实的现煮茶，珍珠Q弹有嚼劲，配送员态度也好，下次还会再点。"},"meta":{},"created_at":"2026-06-25T07:10:04.754319Z","updated_at":"2026-06-25T07:11:02.772529Z","allow_skip":true,"inner_id":3,"total_annotations":1,"cancelled_annotations":0,"total_predictions":0,"comment_count":0,"unresolved_comment_count":0,"last_comment_updated_at":null,"project":12,"updated_by":1,"comment_authors":[]},{"id":35,"annotations":[{"id":23,"completed_by":1,"result":[{"value":{"choices":["正面"]},"id":"WSCVtAKzJa","from_name":"sentiment","to_name":"text","type":"choices","origin":"manual"}],"was_cancelled":false,"ground_truth":false,"created_at":"2026-06-25T07:11:05.544799Z","updated_at":"2026-06-25T07:11:05.544821Z","draft_created_at":"2026-06-25T07:10:46.400039Z","lead_time":3.517,"prediction":{},"result_count":1,"unique_id":"b72bb1ff-9547-471d-8edd-58f5277dca12","import_id":null,"last_action":null,"bulk_created":false,"task":35,"project":12,"updated_by":1,"parent_prediction":null,"parent_annotation":null,"last_created_by":null}],"file_upload":"9ce97d9b-reviews.json","drafts":[],"predictions":[],"data":{"id":4,"text":"配送速度一般，但披萨味道不错，芝士拉丝效果好，性价比高，值得推荐。"},"meta":{},"created_at":"2026-06-25T07:10:04.754376Z","updated_at":"2026-06-25T07:11:05.601129Z","allow_skip":true,"inner_id":4,"total_annotations":1,"cancelled_annotations":0,"total_predictions":0,"comment_count":0,"unresolved_comment_count":0,"last_comment_updated_at":null,"project":12,"updated_by":1,"comment_authors":[]},{"id":36,"annotations":[{"id":24,"completed_by":1,"result":[{"value":{"choices":["负面"]},"id":"qLglBmVk44","from_name":"sentiment","to_name":"text","type":"choices","origin":"manual"}],"was_cancelled":false,"ground_truth":false,"created_at":"2026-06-25T07:11:08.643150Z","updated_at":"2026-06-25T07:11:08.643172Z","draft_created_at":"2026-06-25T07:10:48.222193Z","lead_time":2.6100000000000003,"prediction":{},"result_count":1,"unique_id":"7e05411c-9818-4359-872a-ed1e588d0687","import_id":null,"last_action":null,"bulk_created":false,"task":36,"project":12,"updated_by":1,"parent_prediction":null,"parent_annotation":null,"last_created_by":null}],"file_upload":"9ce97d9b-reviews.json","drafts":[],"predictions":[],"data":{"id":5,"text":"点的麻辣烫食材不新鲜，有股怪味，吃完拉肚子，商家推卸责任，再也不点了。"},"meta":{},"created_at":"2026-06-25T07:10:04.754432Z","updated_at":"2026-06-25T07:11:08.699069Z","allow_skip":true,"inner_id":5,"total_annotations":1,"cancelled_annotations":0,"total_predictions":0,"comment_count":0,"unresolved_comment_count":0,"last_comment_updated_at":null,"project":12,"updated_by":1,"comment_authors":[]}]
\ No newline at end of file
diff --git a/q3/q3_3_质量自评.md b/q3/q3_3_质量自评.md
new file mode 100644
index 0000000..f8dde57
--- /dev/null
+++ b/q3/q3_3_质量自评.md
@@ -0,0 +1,13 @@
+1.标注前准备：
+在图片标注中
+（1）边界框必须紧贴目标物轮廓
+（2）标签必须为 cat、dog、car（必须小写英文，不能写成"猫/狗/车"或"Cat/Dog/Car"）
+在文本标注中
+（1）标注必须包含每条评论的 id 和 text 字段
+（2）每个标注必须有一个 sentiment 字段，值为 "正面" 或 "负面"
+2.标注过程
+在图像的标注中，边界框内的留白较多，解决方法：最大限度地贴近目标轮廓
+在文本的标注中，可能会遇到情感模糊的问题，解决办法：抓住关键词进行标注。
+3.标注后检查
+在图像标注中，再次检查边界框与目标轮廓是否贴合，检查导出的 yolo 文件内容是否完整
+在文本标注中，对照关键词检查情感是否判断正确，检查导出的 json文件内容是否完整
\ No newline at end of file
diff --git a/q4/q4_1/q4_1.py b/q4/q4_1/q4_1.py
new file mode 100644
index 0000000..b7526d6
--- /dev/null
+++ b/q4/q4_1/q4_1.py
@@ -0,0 +1,33 @@
+import matplotlib.pyplot as plt
+import json
+
+
+with open('movie.json', 'r', encoding='utf-8') as f:
+    data=json.load(f)
+    # print(data)
+    
+genre_shu={}
+for g in data:
+    ge=g["genre"]
+    if ge in genre_shu:
+        genre_shu[ge]+=1
+    else:
+        genre_shu[ge]=1
+# print("各类型的电影数量",genre_shu)
+
+genre_lei=list(genre_shu.keys())
+genre_liang=list(genre_shu.values())
+
+print(genre_liang)
+
+plt.figure(figsize=(14, 12))
+plt.bar(genre_lei,  genre_liang,        # 类别, 数值
+        width=0.6)                   # 柱子宽度（0~1之间）
+
+# 标题和标签
+plt.title('类型电影数量分布', fontsize=14)
+plt.xlabel('类型名称', fontsize=12)
+plt.ylabel('电影数量', fontsize=12)
+
+plt.show()
+
diff --git a/q4/q4_1/q4_1_bar.png b/q4/q4_1/q4_1_bar.png
new file mode 100644
index 0000000..0ed7e05
Binary files /dev/null and b/q4/q4_1/q4_1_bar.png differ
diff --git a/q4/q4_2/q4_2.py b/q4/q4_2/q4_2.py
new file mode 100644
index 0000000..a3e4b3d
--- /dev/null
+++ b/q4/q4_2/q4_2.py
@@ -0,0 +1,24 @@
+import matplotlib.pyplot as plt
+import json
+
+rating=[]
+duration=[]
+
+with open('movie.json', 'r', encoding='utf-8') as f:
+    data=json.load(f)
+    # print(data)
+    for i in data:
+        rating.append(i["rating"])
+        duration.append(i["duration"])
+plt.figure(figsize=(12, 8))
+plt.scatter(duration, rating,
+            c='red',
+            s=80,                      # 点的大小
+            alpha=0.6,                 # 透明度
+            edgecolors='white')         # 点的边框
+plt.title('时长与评分关系散点图', fontsize=14)
+plt.xlabel('时长', fontsize=12)
+plt.ylabel('评分', fontsize=12)
+plt.grid(True, linestyle='--', alpha=0.5)
+plt.show()
+       
diff --git a/q4/q4_2/q4_2_scatter.png b/q4/q4_2/q4_2_scatter.png
new file mode 100644
index 0000000..5dbac63
Binary files /dev/null and b/q4/q4_2/q4_2_scatter.png differ
diff --git a/q4/q4_3a/q4_3a.py b/q4/q4_3a/q4_3a.py
new file mode 100644
index 0000000..bc84d8b
--- /dev/null
+++ b/q4/q4_3a/q4_3a.py
@@ -0,0 +1,19 @@
+import matplotlib.pyplot as plt
+import json
+rating=[]
+
+with open('movie.json', 'r', encoding='utf-8') as f:
+    data=json.load(f)
+    # print(data)
+    for i in data:
+        rating.append(i["rating"])
+
+plt.figure(figsize=(12,8))
+plt.hist(rating,                     # 数据
+         bins=3,                    # 分成几个柱子
+         color='#3498DB',            # 颜色
+         edgecolor='white')          # 柱子边框颜色
+plt.title('评分分布', fontsize=14)
+plt.xlabel('评分', fontsize=13)
+plt.grid(True, linestyle='--', alpha=0.5, axis='y')
+plt.show()
\ No newline at end of file
diff --git a/q4/q4_3a/q4_3a_hist.png b/q4/q4_3a/q4_3a_hist.png
new file mode 100644
index 0000000..6c68736
Binary files /dev/null and b/q4/q4_3a/q4_3a_hist.png differ
diff --git a/q4/q4_3b/q4_3b.py b/q4/q4_3b/q4_3b.py
new file mode 100644
index 0000000..38865a2
--- /dev/null
+++ b/q4/q4_3b/q4_3b.py
@@ -0,0 +1,19 @@
+import matplotlib.pyplot as plt
+import json
+duration=[]
+
+with open('movie.json', 'r', encoding='utf-8') as f:
+    data=json.load(f)
+    # print(data)
+    for i in data:
+       duration.append(i["duration"])
+
+plt.figure(figsize=(12,8))
+plt.hist( duration,                     # 数据
+         bins=3,                    # 分成几个柱子
+         color='#3498DB',            # 颜色
+         edgecolor='white')          # 柱子边框颜色
+plt.title('时长分布', fontsize=14)
+plt.xlabel('时长（分钟）', fontsize=13)
+plt.grid(True, linestyle='--', alpha=0.5, axis='y')
+plt.show()
\ No newline at end of file
diff --git a/q4/q4_3b/q4_3b_hist.png b/q4/q4_3b/q4_3b_hist.png
new file mode 100644
index 0000000..b142813
Binary files /dev/null and b/q4/q4_3b/q4_3b_hist.png differ