Image Blending and Overlay Techniques with OpenCV

Overview

Image blending and overlay are fundamental techniques in computer vision and image processing. They allow you to combine multiple images in various ways, from simple blending to precise overlay positioning.

Prerequisites

To follow along with this tutorial, you'll need to install these Python libraries:

• opencv-python: For image processing and blending operations
• matplotlib: For displaying images
• numpy: For numerical operations

You can install them using:

pip install opencv-python matplotlib numpy

Basic Setup

Let's start by importing the necessary libraries:

import cv2
import numpy as np
from matplotlib import pyplot as plt

Step 1: Reading and Preparing Images

Loading Images

First, we'll read two different images that we want to blend:

# Read the image in BGR Mode 
swiftImg = cv2.imread('Assets/adv.jpeg')
shutterImg = cv2.imread('Assets/shutter.webp')

Converting Color Spaces

OpenCV reads images in BGR format, so we need to convert them to RGB for proper display:

# Convert the image from BGR to RGB
swiftImg = cv2.cvtColor(swiftImg, cv2.COLOR_BGR2RGB)
shutterImg = cv2.cvtColor(shutterImg, cv2.COLOR_BGR2RGB)

Step 2: Image Resizing

Before blending, we need to ensure both images have the same dimensions:

# Resize the images
swiftImg = cv2.resize(swiftImg, (600, 600))
shutterImg = cv2.resize(shutterImg, (600, 600))

Step 3: Weighted Image Blending

Understanding cv2.addWeighted()

The cv2.addWeighted() function blends two images together by calculating a weighted sum of their pixel values. This is useful for creating smooth transitions or overlays between images.

# Blending the images, alpha = 0.5, beta = 0.5, gamma = 0
blendedImg = cv2.addWeighted(src1=swiftImg, alpha=0.5, src2=shutterImg, beta=0.5, gamma=0)

Parameters explained:

• src1: First source image
• alpha: Weight of the first image (0.0 to 1.0)
• src2: Second source image
• beta: Weight of the second image (0.0 to 1.0)
• gamma: Scalar added to the weighted sum

Displaying the Blended Image

# Display the blended image
plt.imshow(blendedImg)
plt.show()

Step 4: Image Overlay with Positioning

Reading Images for Overlay

# Read Images in RGB Mode
swiftImg2 = cv2.imread('Assets/adv.jpeg')
shutterImg2 = cv2.imread('Assets/shutter.webp')

swiftImg2 = cv2.cvtColor(swiftImg2, cv2.COLOR_BGR2RGB)
shutterImg2 = cv2.cvtColor(shutterImg2, cv2.COLOR_BGR2RGB)

Resizing for Overlay

We resize the smaller image to fit within the larger one:

# Resize the images
shutterImg2 = cv2.resize(shutterImg2, (300, 300))

Setting Overlay Position

# Set the small images' starting position that will be placed on the larger image
xOffset = 0
yOffset = 0

# Set the small images' ending position that will be placed on the larger image
xEnd = xOffset + shutterImg2.shape[1]
yEnd = yOffset + shutterImg2.shape[0]

Performing the Overlay

# Place the small image on the larger image
swiftImg2[yOffset:yEnd, xOffset:xEnd] = shutterImg2

# Display the image
plt.imshow(swiftImg2)
plt.show()

Understanding Image Overlay

Coordinate System

• xOffset, yOffset: Starting position (top-left corner)
• xEnd, yEnd: Ending position (bottom-right corner)
• shape[1]: Width of the image
• shape[0]: Height of the image