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https://github.com/jinyu121/video2frame

Yet another easy-to-use tool to extract frames from videos, for deep learning and computer vision.
https://github.com/jinyu121/video2frame

Last synced: 6 days ago
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Yet another easy-to-use tool to extract frames from videos, for deep learning and computer vision.

Host: GitHub
URL: https://github.com/jinyu121/video2frame
Owner: jinyu121
License: gpl-3.0
Created: 2019-01-05T06:16:45.000Z (almost 6 years ago)
Default Branch: master
Last Pushed: 2019-11-09T13:33:41.000Z (almost 5 years ago)
Last Synced: 2024-08-02T09:22:47.153Z (3 months ago)
Language: Python
Homepage:
Size: 87.9 KB
Stars: 131
Watchers: 4
Forks: 27
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

awesome-github-star - video2frame - to-use tool to extract frames from videos, for deep learning and computer vision. | jinyu121 | 121 | (Python)

README

# video2frame

Video2frame is also an easy-to-use tool to extract frames from video.

## Why this tool

[Forwchen's vid2frame tool](https://github.com/forwchen/vid2frame) is great, but I am always confused by their parameters. At the same time, I also want to add something I need to the tool.

So I re-wrote the code. And now, it is a new wheel. It is hard to make a PR since I changed the code style.

## How to use

1. ### Establish the environment

We recommend using [conda](https://conda.io/) to establish the environment. Just using

```sh
conda env create -f install/conda-environment.yml
```

You can also do it manually. This project relays on the following packages:

- Python
- FFmpeg
- Python packages (can be installed using `pip install -r install/pip-requirements.txt`)
+ h5py
+ lmdb
+ numpy
+ easydict
+ tqdm

1. ### Make the annotation json file

The json file should like

```json
{
"meta": {
"class_num": 2,
"class_name": [
"class_1",
"class_2"
]
},
"annotation": {
"label1_abcdefg": {
"path": "path/to/the/video/file_1.mp4",
"class": 1
},
"label2_asdfghj": {
"path": "path/to/the/video/file_2.mp4",
"class": 2
}
}
}
```

1. ### Extract frames using `video2frame.py`

#### Examples

+ Using the default options:

```sh
python video2frame.py dataset.json
```

+ Specify the output file name:

```sh
python video2frame.py dataset.json --db_name my_dataset
```

+ Using lmdb rather than hdf5:

```sh
python video2frame.py dataset.json --db_type LMDB
```
or
```sh
python video2frame.py dataset.json --db_name my_dataset.lmdb
```

+ Random clip 5 seconds:

```sh
python video2frame.py dataset.json --duration 5.0
```

+ Get 3 video clips with a length of 5 seconds:

```sh
python video2frame.py dataset.json --clips 3 --duration 5.0
```

+ Resize the frames to 320x240:

```sh
python video2frame.py dataset.json --resize_mode 1 --resize 320x240
```

+ Keep the aspect ration, and resize the shorter side to 320:

```sh
python video2frame.py dataset.json --resize_mode 2 --resize S320
```

+ Keep the aspect ration, and resize the longer side to 240:

```sh
python video2frame.py dataset.json --resize_mode 2 --resize L240
```

+ Extract 5 frames per second:

```sh
python video2frame.py dataset.json --fps 5
```

+ Uniformly sample 16 frames per video:

```sh
python video2frame.py dataset.json --sample_mode 1 --sample 16
```

+ Randomly sample 16 frames per video:

```sh
python video2frame.py dataset.json --sample_mode 2 --sample 16
```

+ Use 16 threads to speed-up:

```sh
python video2frame.py dataset.json --threads 16
```

+ Resize the frames to 320x240, extract one frame every two seconds, uniformly sample 32 frames per video, and using 20 threads:

```sh
python video2frame.py dataset.json \
--resize_mode 1 \
--resize 320x240 \
--fps 0.5 \
--sample_mode 1 \
--sample 32 \
--threads 20
```

#### All parameters

```text
usage: video2frame.py [-h] [--db_name DB_NAME]
[--db_type {LMDB,HDF5,FILE,PKL}] [--tmp_dir TMP_DIR]
[--clips CLIPS] [--duration DURATION]
[--resize_mode {0,1,2}] [--resize RESIZE] [--fps FPS]
[--sample_mode {0,1,2,3}] [--sample SAMPLE]
[--threads THREADS] [--keep]
annotation_file

positional arguments:
annotation_file The annotation file, in json format

optional arguments:
-h, --help show this help message and exit
--db_name DB_NAME The database to store extracted frames (default: None)
--db_type {LMDB,HDF5,FILE,PKL}
Type of the database (default: HDF5)
--tmp_dir TMP_DIR Temporary folder (default: /tmp)
--clips CLIPS Num of clips per video (default: 1)
--duration DURATION Length of each clip (default: -1)
--resize_mode {0,1,2}
Resize mode
0: Do not resize
1: 800x600: Resize to WxH
2: L600 or S600: keep the aspect ration and scale the longer/shorter side to s (default: 0)
--resize RESIZE Parameter of resize mode (default: None)
--fps FPS Sample the video at X fps (default: -1)
--sample_mode {0,1,2,3}
Frame sampling options
0: Keep all frames
1: Uniformly sample n frames
2: Randomly sample n continuous frames
3: Randomly sample n frames
4: Sample 1 frame every n frames (default: 0)
--sample SAMPLE How many frames (default: None)
--threads THREADS Number of threads (default: 0)
--keep Do not delete temporary files at last (default: False)
```

## Tools

1. `video_folder_to_json.py`

A json generator where the videos are arranged in this way:

```text
root/swimming/xxx.mp4
root/swimming/xxy.avi
root/swimming/xxz.flv

root/dancing/123.mkv
root/dancing/nsdf3.webm
root/dancing/asd932_.mov
```
1. `something_to_json.py`

A json generator that converts the `Something-Something` dataset.

1. `ucf101_to_json.py`

A json generator that converts the `UCF101` dataset.

## Examples

1. `pytorch_skvideo_dataset.py`

Get frames using `skvideo` package, when training and evaluating. It is OKay when your batch size is small, and your CPUs are powerful enough.

1. `pytorch_lmdb_video_dataset.py`

A PyTorch `Dataset` example to read LMDB dataset.

1. `pytorch_hdf5_video_dataset.py`

A PyTorch `Dataset` example to read HDF5 dataset.

__ALWAYS ENSURE `num_workers=0` OR `num_workers=1` OF YOUR DATA LOADER.__

1. `pytorch_pkl_video_dataset.py`

A PyTorch `Dataset` example to read pickle dataset.

1. `pytorch_file_video_dataset.py`

A PyTorch `Dataset` example to read image files dataset.