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In this blog, we will learn how to implement the **transition effect** in **OpenCV**.

Let’s Code **Transition Effect in OpenCV**!

## Steps for Transition Effect in OpenCV

- Load two images
**,**which will take part in the**transition effect**. **Image manipulation**(If you want a certain effect)**Transition**between the two images using**addWeighted()****OpenCV**function.

## Transition Effect Algorithm

### Import Packages

For Implementing the **Transition effect** in OpenCV Python, we will have to import the Computer Vision package.

import cv2 as cv

The second package is **NumPy** for working with image matrix and creation of** nD arrays**.

For **Transition Effect**, **NumPy** is very important as it is used in the creation of the rotation matrix and also **athematic **and **logical operations** on image matrices.

import numpy as np

We also need to import the time package as it will help in resting the **transition** **effect**.

import time

### Load Images

Read images in **OpenCV **using **imread()** function. Read two images that will take part in the transition effect.

img1 = cv.imread(‘./img1.jpg’)

img2 = cv.imread(‘./img2.jpg’)

### Blending Logic

There is a function in OpenCV that helps blend two images on basis of **percentage**.

**addWeighted**() function in OpenCV takes five parameters:

- First Image
**Alpha**value (**Opacity**for the first image)- Second Image
**Beta**Value (**Opacity**for the second image)**Gamma**Value (Weight for every pixel after blend, 0 for normal output)

The **alpha** value represents the opacity value of the first image.

The **beta** value represents the opacity value of the second image.

The **gamma** value represents the weight added to every pixel after the blend.

### Maths behind the function

We have to make the **percentage **such that it follows the rule of:

alpha + beta = 1

If we choose the **alpha value **as 0.7 I.e 70%. The **beta value **then should be 0.3 I.e 30%.

0.7 + 0.3 = 1.0

### Creating Transition Effect

**np.linspace()** is a **NumPy** function for generating **linearly spaced numbers** between two numbers.

**np.linspace(0,10,5)** – This function will generate 5 numbers between 1 – 10 and all will be evenly spaced.

We will use **linspace function** in the loop to generate different values for alpha and beta for the opacity of the images.

for i in np.linspace(0,1,100):

alpha = i

beta = 1 – alpha

output = cv.addWeighted(img1,alpha,img2,beta,0)

Alpha is assigned the value of ‘i’ that will change alpha’s value in every iteration.

The beta value will also change with each iteration as beta depends on the value of alpha. Beta = **1 – alpha**.

But, the **alpha **and **beta values **will** always sum to 1**.

The **addWeighted()** function then takes the two images, alpha, beta, and gamma values to generate a new blend image.

This process continues till the loop ends or we forcefully end the process with an ‘**ESC**‘ keypress.

## Simple Transition Effect Source code

```
import cv2 as cv
import numpy as np
import time
while True:
img1 = cv.imread('./img1.jpg')
img2 = cv.imread('./img2.jpg')
for i in np.linspace(0,1,100):
alpha = i
beta = 1 - alpha
output = cv.addWeighted(img1,alpha,img2,beta,0)
cv.imshow('Transition Effect ',output)
time.sleep(0.02)
if cv.waitKey(1) == 27:
break
cv.destroyAllWindow()
```

## Create Trackbar for Transition Effect in OpenCV

Earlier we were dependent on the loop to see the transition effect.

We can also create a **trackbar **that will control the alpha value and on that basis, the **transition **will be applied.

You can change the range of the value of the trackbar in order to get a smoother or fast transition.

Change **sleep** time as well when you change the range of the trackbar.

## Transition Effect OpenCV Documentation

Learn more about the transition and blending of OpenCV functions from official OpenCV Documentation.