add videoclas module and pipeline

docs/data_annotations/video_modules/video_classification.en.md

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+---
+comments: true
+---
+
+# PaddleX Video Classification Task Module Data Annotation Tutorial
+
+This document will introduce how to use the [BILS](https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/annotation_tools.md) annotation tool to complete data annotation for video classification-related single models.
+Click the above link to install the data annotation tool and view the detailed usage process by referring to the homepage documentation.
+
+## 1. BILS Annotation
+### 1.1 Introduction to BILS Annotation Tool
+`BILS` (Baidu Intelligent Labeling System) is a video annotation software that supports tagging on the timeline and can be used for annotation tasks such as video event localization and short video classification. The user interface is simple, and the operation is easy and intuitive.
+### 1.2 BILS Installation
+Click the download link to download the installation package locally, and then follow the prompts to install it step-by-step.
+
+macOS: [dmg package download](https://videotag.bj.bcebos.com/Annotation-tools/4.11-EIVideo-0.0.0.dmg)
+
+Windows: [exe file download](https://videotag.bj.bcebos.com/Annotation-tools/EIVideo-Setup-0.0.0.exe)
+
+Instructional video: [video download](https://videotag.bj.bcebos.com/Annotation-tools/4.11-%E4%BA%A7%E5%93%81%E8%AF%B4%E6%98%8E.mp4)
+### 1.3 BILS Annotation Process
+#### 1.3.1 Prepare Data to Be Annotated
+* Create a root directory for the dataset, such as `video_cls`.
+* Create a `videos` directory within `video_cls` and store the videos to be annotated in the `videos` directory, as shown below:
+
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/data_prepare/video_classification/01.png">
+
+#### 1.3.2 BILS
+Click the BILS software icon to launch the `BILS` annotation tool.
+
+#### 1.3.3 Start Video Annotation
+* After launching `BILS`, it will look like this:
+
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/data_prepare/video_classification/02.png">
+
+* Click the `Settings` button in the right navigation bar, fill in the project name, and set both the project directory and dataset directory to the storage directory of the videos to be annotated, i.e., `video_cls/videos`. Click the `Update Files` button to read the videos to be annotated. After updating the files, the first video in the video folder will play automatically, and you can click the arrow icons to pause, play, or replay.
+
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/data_prepare/video_classification/03.png">
+
+* Click the `Annotation` button in the right navigation bar. The default labels are `Goal, Three-pointer, Two-pointer`. If you need to create a new label, click the `Edit` icon, create a new label, and rename it. Here, `Drumming` is used as an example.
+
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/data_prepare/video_classification/04.png">
+
+* Hold down the `Option` key and click somewhere in the video clip to set the start and end times of the video. Click the trash can icon to delete previously annotated labels. After determining the start and end times of the video clip, check `Drumming`, and the action category of the current time segment will be marked as `Drumming`. Finally, click the `OK` icon to save the current label to the `BILS` format annotation file.
+
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/data_prepare/video_classification/05.png">
+
+* After completing the annotation of the current video, click the `Folder Mode` icon to switch to the video file mode. Then, click on the next video that needs annotation and repeat the above annotation process.
+
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/data_prepare/video_classification/06.png">
+
+* After annotating all videos, click the `Export` icon to export the annotated label files.
+
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/data_prepare/video_classification/07.png">
+
+* After obtaining the exported JSON file (the default name is `ai.json`), use the [convert_to_videocls.py](https://paddle-model-ecology.bj.bcebos.com/paddlex/PaddleX3.0/doc_images/applications/video_classification_dataset_prepare/convert_to_videocls.py) script to convert the exported dataset into the `Video Classification` dataset format. Generate `train.txt`, `val.txt`, and `label.txt`.
+
+```bash
+python convert_to_videocls.py --dataset_path /path/to/dataset
+```
+`dataset_path` is the annotated `BILS` format classification dataset.
+
+## 2. Data Format
+* The dataset defined by PaddleX for the video classification task is named **VideoClsDataset**, with the following organization structure and annotation format:
+
+```bash
+dataset_dir    # Root directory of the dataset, the directory name can be changed
+├── videos     # Directory for storing videos, the directory name can be changed, but note the correspondence with the content of train.txt and val.txt
+├── label.txt  # Correspondence between annotation IDs and category names, the file name cannot be changed. Each line gives the category ID and category name, for example: 0 abseiling
+├── train.txt  # Training set annotation file, the file name cannot be changed. Each line gives the video path and video category ID, separated by a space, for example: videos/Qbo_tnzfjOY.mp4 2
+└── val.txt    # Validation set annotation file, the file name cannot be changed. Each line gives the video path and video category ID, separated by a space, for example: videos/3caPS4FHFF8.mp4 0
+```
+
+Annotation files are in video format. Please prepare your data by referring to the above specifications, and you can also refer to the [example dataset](https://paddle-model-ecology.bj.bcebos.com/paddlex/data/k400_examples.tar).

docs/data_annotations/video_modules/video_classification.md

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+---
+comments: true
+---
+
+# PaddleX视频分类任务模块数据标注教程
+
+本文档将介绍如何使用 [BILS](https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/annotation_tools.md) 标注工具完成视频分类相关单模型的数据标注。
+点击上述链接，参考⾸⻚⽂档即可安装数据标注⼯具并查看详细使⽤流程。
+
+## 1. BILS 标注
+### 1.1 BILS 标注工具介绍
+`BILS` (Baidu Intelligent Labeling System) 是一款支持时间轴打标签的视频标注软件，可被用于视频事件定位 、短视频分类等任务的标注工作。用户界面简约，操作简单、易上手。
+### 1.2 BILS 安装
+点击下载链接，将安装包下载到本地，之后按照提示一步步安装即可。
+
+mac端: [dmg包下载](https://videotag.bj.bcebos.com/Annotation-tools/4.11-EIVideo-0.0.0.dmg)
+
+windows端： [exe文件下载](https://videotag.bj.bcebos.com/Annotation-tools/EIVideo-Setup-0.0.0.exe)
+
+使用教学视频: [视频下载](https://videotag.bj.bcebos.com/Annotation-tools/4.11-%E4%BA%A7%E5%93%81%E8%AF%B4%E6%98%8E.mp4)
+### 1.3 BILS 标注过程
+#### 1.3.1 准备待标注数据
+* 创建数据集根目录，如 `video_cls`。
+* 在 `video_cls` 中创建 `videos` 目录，并将待标注视频存储在 `videos` 目录下，如下图所示：
+
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/data_prepare/video_classification/01.png">
+
+#### 1.3.2 BILS
+点击BILS软件图标，启动` BILS` 标注工具
+
+#### 1.3.3 开始图片标注
+* 启动 `` BILS`` 后如图所示：
+
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/data_prepare/video_classification/02.png">
+
+* 点击右侧导航栏中 `设置` 按钮，填写项目名称，并将项目目录、数据集目录均设置成标注视频的存储目录，即  `video_cls/video` 。点击更新文件按钮，读取待标注视频。更新文件后，视频文件夹中的第一个视频会自动播放，可以点击箭头图标进行暂停、播放或重播。
+
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/data_prepare/video_classification/03.png">
+
+* 点击右侧导航栏中 `标注` 按钮，默认标签为 `进球、三分球、二分球`。如果需要新建标签，可以点击 `编辑` 图标，新建标签，并对其进行重命令。这里，以 `打鼓`为例。
+
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/data_prepare/video_classification/04.png">
+
+*  按住 ` option` 键点击视频片段的某处，可以设置视频的起始时间和终止时间。点击垃圾桶图标可以删除之前标注的标签。确定视频片段的起始、终止时间后，勾选 `打鼓`， 即可将当前时间片段的动作类别标记为 `打鼓`。最后，点击 `确定` 图标，将当前标签保存到 `BILS` 格式的标注文件中。
+
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/data_prepare/video_classification/05.png">
+
+
+* 当前视频标注完成后, 点击 `文件夹模式` 图标，切换到视频文件模式。然后，点击下一个需要标注的视频重复上述标注。
+
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/data_prepare/video_classification/06.png">
+
+
+* 所有视频标注完成后，点击`导出`图标，可以导出标注好的标签文件。
+
+
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/data_prepare/video_classification/07.png">
+
+
+* 获得导出的json文件后（默认名称是 `ai.json` ），使用 [convert_to_videocls.py](https://paddle-model-ecology.bj.bcebos.com/paddlex/PaddleX3.0/doc_images/applications/video_classification_dataset_prepare/convert_to_videocls.py) 脚本，将导出的数据集转化为 `视频分类` 数据集格式。生成 `train.txt`，`val.txt` 和`label.txt`。
+
+```bash
+python convert_to_videocls.py --dataset_path /path/to/dataset
+```
+ `dataset_path` 为标注的 `BILS` 格式分类数据集。
+
+
+##  2. 数据格式
+* PaddleX 针对视频分类任务定义的数据集，名称是 <b>VideoClsDataset</b>，组织结构和标注格式如下：
+
+```bash
+dataset_dir    # 数据集根目录，目录名称可以改变
+├── videos     # 视频的保存目录，目录名称可以改变，但要注意与train.txt、val.txt的内容对应
+├── label.txt  # 标注id和类别名称的对应关系，文件名称不可改变。每行给出类别id和类别名称，内容举例：0 abseiling
+├── train.txt  # 训练集标注文件，文件名称不可改变。每行给出视频路径和视频类别id，使用空格分隔，内容举例：videos/Qbo_tnzfjOY.mp4 2
+└── val.txt    # 验证集标注文件，文件名称不可改变。每行给出视频路径和视频类别id，使用空格分隔，内容举例：videos/3caPS4FHFF8.mp4 0
+```
+标注文件采用视频格式。请大家参考上述规范准备数据，此外可以参考[示例数据集](https://paddle-model-ecology.bj.bcebos.com/paddlex/data/k400_examples.tar)。