ntt is a Python module that provides simple and consistent interfaces for common image and video processing tasks. It wraps around popular libraries such as Pillow, OpenCV, imageio, and scikit-image to simplify their usage and make them interchangeable, to build complex pipelines.

• Pillow – image file handling
• OpenCV – computer vision, image and video processing
• imageio – read/write images and videos
• scikit-image – scientific image processing

1. Create a virtual environment:

python -m venv venv

2. Activate the environment:

• On macOS/Linux:

source venv/bin/activate

• On Windows:

venv\Scripts\activate

3. Install the module:

pip install ntt

import ntt
 
ntt.__version__  # Check the version

Assuming you have cloned the repository or installed the source package, you can run tests with pytest:

$ pytest tests

To download the data samples (videos, images, sounds, etc.) used in tests and examples, clone the repository and update the .env file with the path to the cloned folder:

git clone https://github.com/centralelyon/ntt-samples.git

Alternatively, you can generate fake videos samples by running the following script:

from ntt.videos.video_generation import random_video

video = random_video(320, 240, 10, 2)

The ultimate goal of ntt is to build complex pipelines for video and image processing. For that, we also built a separate tool, the pipeoptz library, which provides a simple way to create and manage pipelines of functions.

import random

from ntt.frames.frame_generation import random_frame
from ntt.frames.display import display_frame
from pipeoptz import Pipeline, Node

def random_number():
    num = random.randint(100, 600)
    return num

pipeline = Pipeline("Simple Pipeline", "Generate a random image.")

node_gen_width = Node("GenWidth", random_number) 
node_gen_height = Node("GenHeight", random_number)
node_random_frame = Node(
    "random_frame", random_frame, fixed_params={"width": 10, "height": 3}
)

pipeline.add_node(node_gen_width)
pipeline.add_node(node_gen_height)
pipeline.add_node(
    node_random_frame, predecessors={"width": "GenWidth", "height": "GenHeight"}
)

outputs = pipeline.run()
display_frame(outputs[1][pipeline.static_order()[-1]])

You may look at the examples folder to see how to use ntt functions. Also a look a the tests folder to see how functions are tested. And of course, the documentation at https://ntt.readthedocs.io.

Assuming you have a crop.mp4  video in a samples folder and an output folder, here is how to use extract_first_frame function.

import os
from dotenv import load_dotenv
from ntt.frames.frame_extraction import extract_first_frame

if __name__ == "__main__":
    load_dotenv()

    output = extract_first_frame(
        video_path_in=os.environ.get("NTT_SAMPLES_PATH"),
        video_name_in="crop.mp4",
        frame_path_out=os.environ.get("PATH_OUT"),
        frame_name_out="crop-ex.jpg",
    )

    print(f"Frame successfully extracted at {output}") if output is not None else print(
        "Frame extraction failed"
    )

.
├── .circleci: configuration for CircleCI
│   ├── config.yml
│   └── ...
├── examples: simple examples on how to use ntt functions
│   ├── (files)
│   └── ...
├── samples: sample videos, images and data
│   ├── (files)
│   └── ...
├── src: the package source code
│   └── ntt: the main module
│       ├── README.md
│       ├── __init__.py
│       ├── frames: module for frame extraction
│       │   └── ...
│       ├── ...
│       └── ...
├── tests: pytest files
│   ├── (files)
│   └── ...
├── .gitignore
├── Dockerfile
├── README.md
├── pyproject.toml: ntt Python packaging file, contains ntt dependencies
├── requirements.txt
└──

Each module structure is as follows:

.
├── ...
├── ntt/
│   ├── __init__.py
│   ├── README.md
│   ├── name_of_the_module/
│   │   ├── __init__.py
│   │   ├── README.md
│   │   ├── name_of_the_function1.py
│   │   ├── name_of_the_function2.py
│   │   └── ...
│   ├── ...
│   └── ...
└── ...

The project is configured to run tests on CircleCI. The configuration file is .circleci/config.yml.

• build the image

docker build -t ntt .

$ docker build -t ntt .

• run the image (rm is to remove the container after it is stopped)

docker run --rm -v ${PWD}:/app ntt

• show the list of containers

docker ps -a

• run a custom script

docker run --rm -v ${PWD}:/app -e PYTHONPATH=/app/src ntt python tests/test_random_strings.py

• run in interactive mode

docker run --rm -it -v ${PWD}:/app -e PYTHONPATH=/app/src ntt bash

• run the image

docker run -v "$(pwd)":/app ntt python ntt/frames/test/test_frame_extraction.py