5 Best Ways to Display an Image Using cv2 in Python

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πŸ’‘ Problem Formulation: You have an image stored on your local machine or server and you want to display it on your screen using the Python library OpenCV (cv2). The input is the path to the image file and the desired output is to see the image rendered in a window created by cv2.

Method 1: Basic Display Using cv2.imshow()

This method involves using the cv2.imshow() function to create a window that displays the image. The first argument is the window name, and the second argument is the image to be displayed. The cv2.waitKey() function is used afterward to wait for a key event, and cv2.destroyAllWindows() closes the window.

Here’s an example:

import cv2
# Load the image
image = cv2.imread('path_to_image.jpg')
# Display the image in a window
cv2.imshow('Image', image)
# Wait for a key press
cv2.waitKey(0)
# Close window
cv2.destroyAllWindows()

The output will be a window labeled “Image” displaying the loaded picture.

This snippet loads an image from the specified path and uses cv2.imshow() to create a named window to display this image. The script will wait indefinitely due to cv2.waitKey(0) until a key is pressed before it finally closes all windows.

Method 2: Resizing the Display Window

If an image is too large, you can resize the window using the cv2.namedWindow() function, followed by setting the window property with cv2.WINDOW_NORMAL, which allows for window resizing.

Here’s an example:

import cv2
# Load the image
image = cv2.imread('path_to_image.jpg')
# Create a resizable window
cv2.namedWindow('Resized Image', cv2.WINDOW_NORMAL)
# Display the image
cv2.imshow('Resized Image', image)
# Wait for a key press
cv2.waitKey(0)
# Close all the windows
cv2.destroyAllWindows()

The output is a resizable window titled “Resized Image”, where the displayed image can be resized with mouse dragging.

This code example sets up a resizable window using cv2.namedWindow() and cv2.WINDOW_NORMAL, then continues similarly to Method 1. It ensures the image fits your screen or suits your desired window size.

Method 3: Displaying an Image in Grayscale

To display an image in grayscale, load the image with the flag cv2.IMREAD_GRAYSCALE. This will read the image directly in grayscale mode, which is particularly useful for image processing tasks.

Here’s an example:

import cv2
# Load the image in grayscale
gray_image = cv2.imread('path_to_image.jpg', cv2.IMREAD_GRAYSCALE)
# Display the grayscale image
cv2.imshow('Grayscale Image', gray_image)
# Wait for a key press
cv2.waitKey(0)
# Close all the windows
cv2.destroyAllWindows()

The output is a window labeled “Grayscale Image” where the image is displayed in shades of gray.

By setting the cv2.IMREAD_GRAYSCALE flag, OpenCV converts the image to grayscale before displaying it. This can be a useful preprocessing step in many vision applications.

Method 4: Displaying Multiple Images in Different Windows

If you wish to display multiple images simultaneously, you can call cv2.imshow() multiple times with unique window names for each displayed image.

Here’s an example:

import cv2
# Load the images
image1 = cv2.imread('path_to_image1.jpg')
image2 = cv2.imread('path_to_image2.jpg')
# Display first image
cv2.imshow('Image 1', image1)
# Display second image
cv2.imshow('Image 2', image2)
# Wait for a key press
cv2.waitKey(0)
# Close all the windows
cv2.destroyAllWindows()

The output consists of two separate windows, each displaying a different image.

This snippet demonstrates how to open multiple images in separate windows using OpenCV, which is useful when comparing images or when simultaneous visualization is required.

Bonus One-Liner Method 5: Display Using Matplotlib

Although not a pure cv2 method, using Matplotlib’s pyplot module in combination with cv2 to display images is popular, especially for its compatibility with Jupyter notebooks or when you need to plot graphs alongside images.

Here’s an example:

import cv2
import matplotlib.pyplot as plt
# Load the image in BGR format
image = cv2.imread('path_to_image.jpg')
# Convert it to RGB format
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Display the image
plt.imshow(image)
plt.show()

The output will be a Matplotlib window displaying the image in RGB format.

This code snippet reads an image using OpenCV, converts it from BGR (the default format used by OpenCV) to RGB (used by Matplotlib), and displays it using Matplotlib. This method is particularly useful for embedding images within a larger visualization context or in notebooks.

Summary/Discussion

  • Method 1: Basic Display. Quick and simple. Limited to basic visualization.
  • Method 2: Resizable Window. Flexible for large images. Requires additional setup.
  • Method 3: Grayscale Display. Ideal for processing tasks. Loses color information.
  • Method 4: Multiple Windows. Useful for comparisons. Each image requires its own window.
  • Method 5: Matplotlib Display. Good for complex visualizations. Not pure cv2 and may add overhead.