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d3dbf03 | 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 | # Preparing UCF-101
## Introduction
<!-- [DATASET] -->
```BibTeX
@article{Soomro2012UCF101AD,
title={UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild},
author={K. Soomro and A. Zamir and M. Shah},
journal={ArXiv},
year={2012},
volume={abs/1212.0402}
}
```
For basic dataset information, you can refer to the dataset [website](https://www.crcv.ucf.edu/research/data-sets/ucf101/).
Before we start, please make sure that the directory is located at `$MMACTION2/tools/data/ucf101/`.
## Step 1. Prepare Annotations
First of all, you can run the following script to prepare annotations.
```shell
bash download_annotations.sh
```
## Step 2. Prepare Videos
Then, you can run the following script to prepare videos.
```shell
bash download_videos.sh
```
For better decoding speed, you can resize the original videos into smaller sized, densely encoded version by:
```
python ../resize_videos.py ../../../data/ucf101/videos/ ../../../data/ucf101/videos_256p_dense_cache --dense --level 2 --ext avi
```
## Step 3. Extract RGB and Flow
This part is **optional** if you only want to use the video loader.
Before extracting, please refer to [install.md](/docs/en/get_started/installation.md) for installing [denseflow](https://github.com/open-mmlab/denseflow).
If you have plenty of SSD space, then we recommend extracting frames there for better I/O performance. The extracted frames (RGB + Flow) will take up about 100GB.
You can run the following script to soft link SSD.
```shell
# execute these two line (Assume the SSD is mounted at "/mnt/SSD/")
mkdir /mnt/SSD/ucf101_extracted/
ln -s /mnt/SSD/ucf101_extracted/ ../../../data/ucf101/rawframes
```
If you only want to play with RGB frames (since extracting optical flow can be time-consuming), consider running the following script to extract **RGB-only** frames using denseflow.
```shell
bash extract_rgb_frames.sh
```
If you didn't install denseflow, you can still extract RGB frames using OpenCV by the following script, but it will keep the original size of the images.
```shell
bash extract_rgb_frames_opencv.sh
```
If Optical Flow is also required, run the following script to extract flow using "tvl1" algorithm.
```shell
bash extract_frames.sh
```
## Step 4. Generate File List
you can run the follow script to generate file list in the format of rawframes and videos.
```shell
bash generate_videos_filelist.sh
bash generate_rawframes_filelist.sh
```
## Step 5. Check Directory Structure
After the whole data process for UCF-101 preparation,
you will get the rawframes (RGB + Flow), videos and annotation files for UCF-101.
In the context of the whole project (for UCF-101 only), the folder structure will look like:
```
mmaction2
├── mmaction
├── tools
├── configs
├── data
│ ├── ucf101
│ │ ├── ucf101_{train,val}_split_{1,2,3}_rawframes.txt
│ │ ├── ucf101_{train,val}_split_{1,2,3}_videos.txt
│ │ ├── annotations
│ │ ├── videos
│ │ │ ├── ApplyEyeMakeup
│ │ │ │ ├── v_ApplyEyeMakeup_g01_c01.avi
│ │ │ ├── YoYo
│ │ │ │ ├── v_YoYo_g25_c05.avi
│ │ ├── rawframes
│ │ │ ├── ApplyEyeMakeup
│ │ │ │ ├── v_ApplyEyeMakeup_g01_c01
│ │ │ │ │ ├── img_00001.jpg
│ │ │ │ │ ├── img_00002.jpg
│ │ │ │ │ ├── ...
│ │ │ │ │ ├── flow_x_00001.jpg
│ │ │ │ │ ├── flow_x_00002.jpg
│ │ │ │ │ ├── ...
│ │ │ │ │ ├── flow_y_00001.jpg
│ │ │ │ │ ├── flow_y_00002.jpg
│ │ │ ├── ...
│ │ │ ├── YoYo
│ │ │ │ ├── v_YoYo_g01_c01
│ │ │ │ ├── ...
│ │ │ │ ├── v_YoYo_g25_c05
```
For training and evaluating on UCF-101, please refer to [Training and Test Tutorial](/docs/en/user_guides/train_test.md).
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