In this article, we are going to write a python script using the OpenCV library to Resize Multiple Images and save them as an image file. Resizing the image refers to the growth of the images. Measurement works best in the use of multiple images and in machine learning applications. It helps to reduce the number of pixels from an image and that has several benefits e.g. It can reduce neural network training time as the number of pixels in the image greatly increases the number of input nodes which also improves the model difficulty.
Approach:
- Firstly, load the required libraries into a Python file (argparse, OpenCV, etc.).
- We are using argparse() function to get the path of the directory of images on which we need to perform the resizing.
- Use for loop to iterate every image in the directory.
- Load image in a variable using cv2.imread() function.
- Define a resizing scale and set the calculated height and width.
- Resize the image using cv2.resize() function.
- Place the output file inside the output folder using cv2.imwrite() function.
All the images inside the Images folder will be resized and will be saved in an output folder.
Below is the implementation:
Python3
# Required Libraries import cv2 import numpy as np from os import listdir from os.path import isfile, join from pathlib import Path import argparse import numpy # Argument parsing variable declared ap = argparse.ArgumentParser() ap.add_argument("-i", "--image", required=True, help="Path to folder") args = vars(ap.parse_args()) # Find all the images in the provided images folder mypath = args["image"] onlyfiles = [f for f in listdir(mypath) if isfile(join(mypath, f))] images = numpy.empty(len(onlyfiles), dtype=object) # Iterate through every image # and resize all the images. for n in range(0, len(onlyfiles)): path = join(mypath, onlyfiles[n]) images[n] = cv2.imread(join(mypath, onlyfiles[n]), cv2.IMREAD_UNCHANGED) # Load the image in img variable img = cv2.imread(path, 1) # Define a resizing Scale # To declare how much to resize resize_scaling = 50 resize_width = int(img.shape[1] * resize_scaling/100) resize_hieght = int(img.shape[0] * resize_scaling/100) resized_dimensions = (resize_width, resize_hieght) # Create resized image using the calculated dimensions resized_image = cv2.resize(img, resized_dimensions, interpolation=cv2.INTER_AREA) # Save the image in Output Folder cv2.imwrite( 'output/' + str(resize_width) + str(resize_hieght) + str(n) + '_resized.jpg', resized_image) print("Images resized Successfully") |
Open the terminal in the folder where this Python Script is saved and type the below command.
python resize.py --image path/to/images/folder/
Output:
