Image Blending with Multiple Methods

Summary

Introduction

Image blending is a fundamental technique in computer vision, used to merge multiple images into a single, seamless output. It plays a key role in applications such as panorama stitching, image editing, object insertion, and style transfer. In this task, various blending methods are explored, ranging from basic alpha blending, to more advanced techniques like pyramid blending and Poisson blending.

Each method offers different advantages in terms of smoothness, edge handling, and structural consistency. Through hands-on implementation and comparison, learners will understand how different algorithms deal with challenges like boundary artifacts and intensity mismatches. The task also involves working with preprocessing steps like image alignment and masking, and evaluating results both visually and quantitatively.

By completing this task, students will gain essential knowledge in image compositing techniques, preparing them for more advanced topics in image synthesis, computational photography, and visual effects.

Parameters

Inputs

bg_img - (image -.png|.jpg|.jpeg): The background image where the object will be blended. This serves as the target canvas.
obj_img - (image -.png|.jpg|.jpeg): The object image (or foreground) that will be inserted/blended into the background.
mask_img - (image -.png|.jpg|.jpeg): A binary or soft mask indicating the region of the object image to be used during blending. Pixel values typically range from 0 (background) to 255 (object), or [0,1] for soft blending.
method - (text): The blending method to apply. Supported values may include: Poisson, Laplacian, Mixed Gradient

Output

output - (image -.png): The final blended image where the object is seamlessly merged into the background according to the selected method.

Examples

bg_img	obj_img	mask_img	method	output
			Mixed Gradient

Usage for developers

Please find below the details to track the information and access the code for processing the model on our platform.

Requirements

pip install -r requirements.txt

Code based on AIOZ structure

import cv2
import numpy as np
from .utils import *

...
def blend_images(bg_img, obj_img, mask_img, method):
...

def do_ai_task(
        bg_img: Union[str, Path],
        obj_img: Union[str, Path],
        mask_img: Union[str, Path],
        method: Union[str, Path],
        model_storage_directory: Union[str, Path],
        device: Literal["cpu", "cuda", "gpu"] = "cpu",
        *args, **kwargs) -> Any:
    """Define AI task: load model, pre-process, post-process, etc ..."""
    # Define AI task workflow. Below is an example

    img = blend_images(bg_img, obj_img, mask_img, method) 
    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
    cv2.imwrite("output.png", img)
    output_image = open("output.png", "rb")  # io.BufferedReader
    return output_image

Reference

This repository is based on and inspired by Gokay Aydogan's work. We sincerely appreciate their generosity in sharing the code.

License

We respect and comply with the terms of the author's license cited in the Reference section.