keras-video-generators 1.0.14
Keras sequence generators for video data
License: MIT

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# Keras Sequence Video generators

This package proposes some classes to work with Keras (included in TensorFlow) that generates batches of frames from video files.

It is useful to work with Time Distributed Layer with Video input (frames). Then you may use GRU or LSTM. 
See articles:

- [The basics of Video frame as input](https://medium.com/smileinnovation/how-to-work-with-time-distributed-data-in-a-neural-network-b8b39aa4ce00)
- [Usage of these generators here](https://medium.com/smileinnovation/training-neural-network-with-image-sequence-an-example-with-video-as-input-c3407f7a0b0f)

An provided example of usage can be [displayed in nbviewer here](https://nbviewer.jupyter.org/github/metal3d/keras-video-generators/blob/master/Example%20of%20usage.ipynb).

Requirements are:

- Python >= 3.6 (Python 2 will never been supported)
- OpenCV
- numpy
- Keras >= 2
- TensorFlow >= 1.15 (or other backend, not tested, TensorFlow is needed by Keras)

TensorFlow 2 works as well. This requirements **is not integrated in the setup.py** to let you choose the version, or to let you try with other backend. We mean that you will need to install a backend yourself (e.g. `pip install tensorflow`)

If you want to compile the package, you need:

- sphinx to compile doc (work in progress)
- setuptools

# Installation

You can install the package via `pip`:

```bash
pip install keras-video-generators
```

If you want to build from sources, clone the repository then:

```bash
python setup.py build
```

# Usage

The package contains 3 generators that inherits `Sequence` interface. So they may be used with `model.fit_generator()`:

- `VideoFrameGenerator` that will take the choosen number of frames from the entire video
- `SlidingFrameGenerator` that takes frames with decay for the entire video or with a sequence time
- `OpticalFlowGenerator` that gives optical flow sequence from frames with different methods (experimental)

Each of these generators accepts parameters:

- `glob_pattern` that must contain `{classname}`, e.g. './videos/{classname}/*.avi' - the "classname" in string is used to detect classes
- `nb_frames` that is the number of frame in the sequence
- `batch_size` that is the number of sequence in one batch
- `transformation` that can be `None` or or ImageDataGenerator to make data augmentation
- `use_frame_cache` to use with caution, if set to `True`, the class will keep frames in memory (without augmentation). You need **a lot of memory**
- and many more, see class documentation


# Changelog

## v1.0.14
- Changes to get first and last frames in sequence

## v1.0.13
- try to fix SageMaker problem by avoiding usage of internal keras from tensorflow

## v1.0.12
- fix transformation error with SlidingFrameGenerator

## v1.0.11
- set generator to be Iterable
- frame cache was disabled by error, it's back now
- fixup import Sequence from `tensorflow.keras`
- fix frame count problems for video with bad headers

## v1.0.10
- fix Windows problems with path using backslashes
- add auto discovery for classes if "None" is sent
- add travis tests


## v1.0.9
- fix frame counter in SlidingFrameGenerator

## v1.0.8
- fix tiny video frame count
- refactorisation
- pep8
- fix problems for video without headers

## v1.0.7
- fix name check in classes to get them from filename
- add `split_test` and `split_val`

## v1.0.5
- fix package generation

## v1.0.4 
- fix assertion