[Feature] UCF101-24 preparation (#219)

* init docs

* update

* update changelog

* Update changelog.md

Co-authored-by: lizz <innerlee@users.noreply.github.com>

docs/changelog.md

@@ -6,7 +6,8 @@
 
 **New Features**
 - Support to run real-time action recognition from web camera ([#171](https://github.com/open-mmlab/mmaction2/pull/171))
-- Support to export the pytorch models to onnx ones. ([#160](https://github.com/open-mmlab/mmaction2/pull/160))
+- Support to export pytorch models to onnx ([#160](https://github.com/open-mmlab/mmaction2/pull/160))
+- Support UCF101-24 preparation ([#219](https://github.com/open-mmlab/mmaction2/pull/219))
 - Support to report mAP for ActivityNet with [CUHK17_activitynet_pred](http://activity-net.org/challenges/2017/evaluation.html). ([#176](https://github.com/open-mmlab/mmaction2/pull/176))
 - Add the data pipeline for ActivityNet, which includes downloading videos, extracting RGB and Flow frames, finetuning TSN and extracting feature. ([#190](https://github.com/open-mmlab/mmaction2/pull/190))
 

docs/data_preparation.md

@@ -23,6 +23,7 @@ To ease usage, we provide tutorials of data deployment for each dataset.
 - [Moments in Time](http://moments.csail.mit.edu/): See [preparing_mit.md](/tools/data/mit/preparing_mit.md)
 - [Multi-Moments in Time](http://moments.csail.mit.edu/challenge_iccv_2019.html): See [preparing_mmit.md](/tools/data/mmit/preparing_mmit.md)
 - ActivityNet_feature: See [praparing_activitynet.md](/tools/data/activitynet/preparing_activitynet.md)
+- [UCF101-24](http://www.thumos.info/download.html): See [preparing_ucf101_24.md](/tools/data/ucf101_24/preparing_ucf101_24.md)
 
 Now, you can switch to [getting_started.md](getting_started.md) to train and test the model.
 

tools/data/ucf101_24/preparing_ucf101_24.md

@@ -0,0 +1,70 @@
+# Preparing UCF101-24
+
+For basic dataset information, you can refer to the dataset [website](http://www.thumos.info/download.html).
+Before we start, please make sure that the directory is located at `$MMACTION2/tools/data/ucf101_24/`.
+
+## Download and Extract
+
+You can download the RGB frames, optical flow and ground truth annotations from [google drive](https://drive.google.com/drive/folders/1BvGywlAGrACEqRyfYbz3wzlVV3cDFkct).
+The data are provided from [MOC](https://github.com/MCG-NJU/MOC-Detector/blob/master/readme/Dataset.md), which is adapted from [act-detector](https://github.com/vkalogeiton/caffe/tree/act-detector) and [corrected-UCF101-Annots](https://github.com/gurkirt/corrected-UCF101-Annots).
+
+**Note**: The annotation of this UCF101-24 is from [here](https://github.com/gurkirt/corrected-UCF101-Annots), which is more correct.
+
+After downloading the `UCF101_v2.tar.gz` file and put it in `$MMACTION2/tools/data/ucf101_24/`, you can run the following command to extract.
+
+```shell script
+tar -zxvf UCF101_v2.tar.gz
+```
+
+## Check Directory Structure
+
+After extracting, you will get the `rgb-images` directory, `brox-images` directory and `UCF101v2-GT.pkl` for UCF101-24.
+
+In the context of the whole project (for UCF101-24 only), the folder structure will look like:
+
+```
+mmaction2
+├── mmaction
+├── tools
+├── configs
+├── data
+│   ├── ucf101_24
+│   |   ├── brox-images
+│   |   |   ├── Basketball
+│   |   |   |   ├── v_Basketball_g01_c01
+│   |   |   |   |   ├── 00001.jpg
+│   |   |   |   |   ├── 00002.jpg
+│   |   |   |   |   ├── ...
+│   |   |   |   |   ├── 00140.jpg
+│   |   |   |   |   ├── 00141.jpg
+│   |   |   ├── ...
+│   |   |   ├── WalkingWithDog
+│   |   |   |   ├── v_WalkingWithDog_g01_c01
+│   |   |   |   ├── ...
+│   |   |   |   ├── v_WalkingWithDog_g25_c04
+│   |   ├── rgb-images
+│   |   |   ├── Basketball
+│   |   |   |   ├── v_Basketball_g01_c01
+│   |   |   |   |   ├── 00001.jpg
+│   |   |   |   |   ├── 00002.jpg
+│   |   |   |   |   ├── ...
+│   |   |   |   |   ├── 00140.jpg
+│   |   |   |   |   ├── 00141.jpg
+│   |   |   ├── ...
+│   |   |   ├── WalkingWithDog
+│   |   |   |   ├── v_WalkingWithDog_g01_c01
+│   |   |   |   ├── ...
+│   |   |   |   ├── v_WalkingWithDog_g25_c04
+│   |   ├── UCF101v2-GT.pkl
+
+```
+
+**Note**: The `UCF101v2-GT.pkl` exists as a cache, it contains 6 items as follows:
+1. `labels` (list): List of the 24 labels.
+2. `gttubes` (dict): Dictionary that contains the ground truth tubes for each video.
+  A **gttube** is dictionary that associates with each index of label and a list of tubes.
+  A **tube** is a numpy array with `nframes` rows and 5 columns, each col is in format like `<frame index> <x1> <y1> <x2> <y2>`.
+3. `nframes` (dict): Dictionary that contains the number of frames for each video, like `'HorseRiding/v_HorseRiding_g05_c02': 151`.
+4. `train_videos` (list): A list with `nsplits=1` elements, each one containing the list of training videos.
+5. `test_videos` (list): A list with `nsplits=1` elements, each one containing the list of testing videos.
+6. `resolution` (dict): Dictionary that outputs a tuple (h,w) of the resolution for each video, like `'FloorGymnastics/v_FloorGymnastics_g09_c03': (240, 320)`.