iOS lockscreen python || Image Segmentation with MediaPipe

Craft Your Photos with Style: Image Segmentation with Templates using Python!

Ever wished you could instantly transform your photos with a touch of artistic flair? This blog post introduces you to a captivating Python project that accomplishes just that using image segmentation and templates! (Unleash your creativity with the project video here: https://www.youtube.com/watch?v=XcdtFqYt5RA&t=123s)

The Power of Image Segmentation with MediaPipe

This project leverages MediaPipe, a powerful open-source framework developed by Google. MediaPipe provides pre-built models for various computer vision tasks, including image segmentation. In this project, MediaPipe is employed to separate the foreground object (like a person) from the background in your image.

Template Magic: Transforming Your Photos

But wait, there's more! This project goes beyond basic segmentation. It incorporates the concept of templates. After segmenting the foreground object, the project allows you to seamlessly apply different templates onto the background. Imagine placing your portrait on a stylish black and white background or a vibrant artistic template – the possibilities are endless!

Getting Started: Shape Your Photos

Here's a roadmap to set up and experience the photo manipulation magic of this project:

Prerequisites:

• Python 3.x

• OpenCV (for image processing)

• NumPy (for numerical computations)

• Pillow (for image manipulation)

• MediaPipe (for image segmentation)

• PyYAML (for reading configuration files)

Installation:

1. Clone the Codebase (Optional):  If you prefer using Git version control, clone the project's code using:

Bash

git clone https://github.com/sairam-penjarla/ioslockscreen.git

1. Manual Download (Optional): Alternatively, you can download the project's source code directly.

2. Install Dependencies:  Once you have the code, install the required libraries using pip:

Bash

pip install -r requirements.txt

Usage:

1. Place Your Image:  Ensure your photo is placed in the directory specified within the project (usually named input).

2. Run the Script:  Now that everything is set up, launch the project using:

Bash

python image_segmentation_app.py

This script will process your image, perform segmentation, and apply the default templates.

1. Explore Customization (Optional):  Feeling adventurous? The project allows you to customize various aspects using the config.yaml file. Here, you can specify:

• Input and output paths for your images.

• Different template paths for creating unique styles.

• The path to the MediaPipe segmentation model.

Example config.yaml file:

YAML

input_path: "../input/b.jpg"  # Path to your image
output_path: "../output"  # Where the output image will be saved
white_template_path: "../assets/white.png"  # Path to white template
black_template_path: "../assets/black.png"  # Path to black template
model_path: "../model/deeplab_v3.tflite"  # Path to MediaPipe segmentation model

This project empowers you to unleash your creativity and transform your photos with the power of image segmentation and templates.  So, grab your photos and get ready to create unique and artistic masterpieces!

Happy Coding and Happy Segmenting!