To compare two images using OpenCV and Python, you can utilize various image comparison methods provided by OpenCV. Here’s an example comparing two images using the Structural Similarity Index (SSIM):
import cv2 # Load the images image1 = cv2.imread('image1.jpg') image2 = cv2.imread('image2.jpg') # Convert the images to grayscale gray_image1 = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY) gray_image2 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY) # Compute the SSIM between the two images ssim_score, diff = cv2.compareSSIM(gray_image1, gray_image2, full=True) # Display the SSIM score print("SSIM:", ssim_score) # Show the difference image (optional) cv2.imshow('Difference', diff) cv2.waitKey(0) cv2.destroyAllWindows()
In this example, we assume you have two images,
image2.jpg, in the same directory. You’ll need to replace these file names with the actual paths to your images.
First, we use the
cv2.imread() function to load the images as BGR images. Then, we convert the images to grayscale using the
Next, we use the
cv2.compareSSIM() function to compute the SSIM score between the two grayscale images. The SSIM score measures the structural similarity between two images and ranges from -1 to 1, with 1 indicating a perfect match.
Finally, we print the SSIM score and optionally display the difference image using
cv2.waitKey(). The difference image highlights the areas of dissimilarity between the two images.
Keep in mind that the SSIM method is just one of many approaches you can use to compare images using OpenCV. Depending on your specific requirements, you may explore other methods such as Mean Squared Error (MSE) or Normalized Cross-Correlation (NCC).
Note that in the provided example, the images are compared based on their pixel values. If you want to compare images based on their visual similarity, you might need to explore more advanced techniques such as feature extraction and matching algorithms.
Remember to install the OpenCV library if you haven’t already by running
pip install opencv-python.