Loading and Displaying Images with OpenCV

OpenCV is a popular computer vision library that provides a wide range of functionality for image and video processing. In this article, we will explore how to load and display images using OpenCV in Python.

Loading an Image

To load an image using OpenCV, we can use the imread() function. This function takes the image file path as input and returns an array representing the image.

import cv2

# Load an image
image = cv2.imread('path/to/image.jpg')

It's important to provide the correct file path to the image. If the image is not located in the same directory as the Python script, you need to provide the full path or specify the relative path.

The imread() function loads the image in the BGR color format by default. If you prefer working with the RGB color format, you can convert the image using the cvtColor() function.

# Convert the image to RGB
rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

Displaying an Image

Once we have loaded an image, we can display it using the imshow() function. This function takes two arguments: the window name as a string and the image array.

# Display the image in a window
cv2.imshow('Image', image)

To view the image, we need to create a named window using the namedWindow() function before displaying it.

# Create a named window

# Display the image in a window
cv2.imshow('Image', image)

To hold the image window, we need to use the waitKey() function. This function keeps the window open until a key is pressed.

# Wait until a key is pressed to close the window

By passing 0 as an argument to waitKey(), we ensure that it waits indefinitely until a key is pressed. Alternatively, you can provide a number of milliseconds to wait for a key press.


In this article, we have learned how to load and display images using OpenCV in Python. We first utilized the imread() function to load an image and further explored converting it to the RGB color format using cvtColor(). Then, we displayed the image by creating a named window with namedWindow() and using imshow() to show the image. Finally, we held the image window using waitKey() until a key press is encountered. OpenCV provides a powerful set of tools to work with images efficiently and effectively.

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