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This post will be helpful in learning OpenCV using Python programming. Here I will show how to implement OpenCV functions and apply them in various aspects using some great examples. Then the output will be visualized along with the comparisons.

We will also discuss the basic of image processing and provide the detail explanation related to the OpenCV functions.

Requirements:

• OpenCV 3.4+
• Python 3.6+
• Numpy
• Image, Webcam or Video input
• Documentation Source: OpenCV Official Documentation

First, you need to setup your Python Environment with OpenCV. You can easily do it by following Life2Coding’s tutorial on YouTube: Linking OpenCV with Python 3

Goals:

The goal is to combine or merge multiple images vertically using OpenCV python.

Documentation:

imread()

Loads an image from a file.

Parameters

filename	Name of file to be loaded.
flags	Flag that can take values of cv::ImreadModes

imshow()

Displays an image in the specified window.

Parameters

winname	Name of the window.
mat	Image to be shown.

imwrite()

Saves an image to a specified file.

Parameters

filename	Name of the file.
img	Image to be saved.
params	Format-specific parameters encoded as pairs (paramId_1, paramValue_1, paramId_2, paramValue_2, … .) see cv::ImwriteFlags

waitKey()

Waits for a pressed key.

Parameters

delay

Delay in milliseconds. 0 is the special value that means “forever”.

destroyAllWindows()

Destroys all of the HighGUI windows.

Steps:

• Open the list of images using cv2.imread()
• Get the max_width between all the images
• Also need to calcuate the max height by adding all the images’ height
• Then need to define the new image height using the max width and total height
• Also padding is added between the images which can be adjusted easily
• Display the combined image using cv2.imshow()
• Save the output image using cv2.imwrite()
• Wait for keyboard button press using cv2.waitKey()
• Exit window and destroy all windows using cv2.destroyAllWindows()

Example Code:

import cv2
import numpy as np

def combine_vertically(image_names,padding=40):
    images = []
    max_width = 0  # find the max width of all the images
    total_height = 0  # the total height of the images (vertical stacking)

    for name in image_names:
        # open all images and find their sizes
        img=cv2.imread(name)
        images.append(img)

        image_width=img.shape[1]
        image_height=img.shape[0]

        if image_width > max_width:
            max_width = image_width

        #add all the images heights
        total_height += image_height



    # create a new array with a size large enough to contain all the images
    # also add padding size for all the images except the last one
    final_image = np.zeros((total_height+(len(image_names)-1)*padding,max_width,3),dtype=np.uint8)

    current_y = 0 # keep track of where your current image was last placed in the y coordinate
    for image in images:
        # add an image to the final array and increment the y coordinate
        height = image.shape[0]
        width = image.shape[1]
        final_image[current_y:height+current_y,:width,:] = image
        # add the padding between the images
        current_y += height+ padding
    return final_image




if __name__ == '__main__':

    image_names = ['./opencv.jpg','./python.jpg','./windows.jpg']
    final_image=combine_vertically(image_names,padding=0)
    cv2.imshow('out',final_image)
    cv2.imwrite('vertical.PNG',final_image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

Output:

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