image-colorization-api

Description

The Image Colorization API is a RESTful API that allows users to colorize black and white images using a pre-trained machine learning model. With this API, you can easily integrate image colorization capabilities into your own applications, websites, or services.

Overview

The Image Colorization API provides a simple and efficient way to add colorization functionality to your projects. It utilizes a state-of-the-art machine learning model that has been trained on a large dataset of colored images. The model takes a black and white image as input and produces a colorized version of the image as output.

Installation

• Clone the repository:

git clone https://github.com/yasharora102/image-colorization-api.git

• Install the dependencies:

pip install -r requirements.txt

• Get the models from Google Drive: Link

• Extract the models and place them in the models directory.

• Run the server:

uvicorn app:app --reload --port 8080

• Go to http://127.0.0.1/docs to view the Swagger UI.

• Go to http://127.0.0.1/ to view the application.

Usage

Sample Request

import requests
import os

url = "http://127.0.0.1:8080/upload" 

file_path = "images.jpeg"  # Replace with the path to your image file

with open(file_path, "rb") as file:
    files = {"file": file}
    response = requests.post(url, files=files)

if response.status_code == 200:
    with open("output.jpg", "wb") as file:
        file.write(response.content)
    print("Image saved successfully")
else:
    print("Error:", response.text)

Running the Web App

• Run the server:

uvicorn app:app --reload --port 8080

• Go to http://127.0.0.1:8080/ to view the application.

Web App

Download Page

View the tutorial on YouTube.

Sample Images

Original Image	Colorized Image

Development

• For creating new features, create new branch locally and work on it.
• After testing the feature, create a PR.
• To fetch new changes

    $ git fetch upstream
    $ git rebase upstream/main

Formatting

• Use black for formatting the code.
• black is already installed in the project dependencies.
• To format the code, run black . in the project root directory.