Image Processing and Object Comparison using Python – Part 2

Image Comparison and Similarity Measurement


Welcome to the second part of our tutorial on Image Processing and Object Comparison using Python. In this section, we’ll delve into image comparison and explore techniques for measuring the similarity between two images. Understanding these methods is crucial for various applications, such as image retrieval, object recognition, and quality assessment.

Setting Up the Environment:

Before we proceed, make sure you have the required libraries installed. The code assumes you have already loaded the images and performed basic manipulations as covered in Part 1.

from skimage.metrics import structural_similarity as ssim
from scipy.stats import wasserstein_distance

Structural Similarity Index (SSI):

SSI is a metric that quantifies the similarity between two images. Let’s calculate the SSI between two images and visualize the results.

im1 = + "hline.jpg")
im2 = + "rotate.jpg")

ssi_index, _ = ssim(np.array(im1), np.array(im2), full=True)

print(f"Structural Similarity Index: {ssi_index}")

Histogram Comparison:

Histogram comparison is another approach to measure image similarity. We’ll use the Earth Mover’s Distance (Wasserstein Distance) to compare histograms.

hist1, _ = np.histogram(np.array(im1).ravel(), bins=256, density=True)
hist2, _ = np.histogram(np.array(im2).ravel(), bins=256, density=True)

w_distance = wasserstein_distance(hist1, hist2)

print(f"Wasserstein Distance: {w_distance}")

Visualizing Results:

Now, let’s visualize the results of the image comparison.

fig, axes = plt.subplots(1, 2, figsize=(10, 5))

axes[0].set_title("Image 1")

axes[1].set_title("Image 2")

print("SSI: Structural Similarity Index")
print(f"SSI between Image 1 and Image 2: {ssi_index}")

print("\nWasserstein Distance:")
print(f"Wasserstein Distance between Image 1 and Image 2: {w_distance}")


In this part, we explored techniques for image comparison and similarity measurement. The Structural Similarity Index (SSI) and Wasserstein Distance provide valuable insights into the likeness of two images. These metrics play a crucial role in various computer vision applications, helping to determine the resemblance between objects in different images.

In the final part, we will apply these concepts to compare and analyze images, providing practical insights and applications for your projects.


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