img = imread('cameraman.tif');
edges = edge(img, 'Canny');
imshow(edges);
title('Edges found with Canny Detector');

Output: A figure window displays a binary image showing only the detected edges from the original image in white.

---
#MATLAB #ImageProcessing #Segmentation #Morphology

#21. graythresh()
Computes a global image threshold from a grayscale image using Otsu's method.

img = imread('coins.png');
level = graythresh(img);
disp(['Optimal threshold level (Otsu): ', num2str(level)]);

Optimal threshold level (Otsu): 0.49412

#22. imbinarize()
Converts a grayscale image to a binary image based on a threshold.

img = imread('coins.png');
level = graythresh(img); % Find optimal threshold
bw_img = imbinarize(img, level);
imshow(bw_img);
title('Binarized Image (Otsu Method)');

Output: A figure window displays a black and white image of the coins.

#23. strel()
Creates a morphological structuring element (SE), which is used to probe an image in morphological operations.

se = strel('disk', 5);
disp('Created a disk-shaped structuring element with radius 5.');
disp(se);

Created a disk-shaped structuring element with radius 5.
(Output describes the strel object and shows its matrix representation)

#24. imdilate()
Dilates a binary image, making objects larger and filling small holes.

img = imread('text.png');
se = strel('line', 3, 90); % A vertical line SE
dilated_img = imdilate(img, se);
imshow(dilated_img);
title('Dilated Text');

Output: A figure window shows the text characters appearing thicker, especially in the vertical direction.

#25. imerode()
Erodes a binary image, shrinking objects and removing small noise.

img = imread('text.png');
se = strel('line', 3, 0); % A horizontal line SE
eroded_img = imerode(img, se);
imshow(eroded_img);
title('Eroded Text');

Output: A figure window shows the text characters appearing thinner, with horizontal parts possibly disappearing.

---
#MATLAB #ImageProcessing #Analysis

#26. imopen()
Performs morphological opening (erosion followed by dilation). It smooths contours and removes small objects.

original = imread('circbw.tif');
se = strel('disk', 10);
opened_img = imopen(original, se);
imshow(opened_img);
title('Morphologically Opened Image');

Output: A figure window displays the image with small protrusions removed and gaps between objects widened.

#27. bwareaopen()
Removes all connected components (objects) from a binary image that have fewer than a specified number of pixels.

img = imread('text.png');
cleaned_img = bwareaopen(img, 50); % Remove objects with fewer than 50 pixels
imshow(cleaned_img);
title('Image after removing small objects');

Output: A figure window shows the text image with small noise specks or broken parts of characters removed.

#28. bwlabel()
Labels connected components in a binary image.

img = imread('text.png');
[L, num] = bwlabel(img);
disp(['Number of connected objects found: ', num2str(num)]);

Number of connected objects found: 114

#29. regionprops()
Measures a set of properties for each labeled region in an image.

💡 Applying Image Filters with Pillow

Pillow's ImageFilter module provides a set of pre-defined filters you can apply to your images with a single line of code. This example demonstrates how to apply a Gaussian blur effect, which is useful for softening images or creating depth-of-field effects.

from PIL import Image, ImageFilter

try:
    # Open an existing image
    with Image.open("your_image.jpg") as img:
        # Apply the Gaussian Blur filter
        # The radius parameter controls the blur intensity
        blurred_img = img.filter(ImageFilter.GaussianBlur(radius=5))

        # Display the blurred image
        blurred_img.show()

        # Save the new image
        blurred_img.save("blurred_image.png")

except FileNotFoundError:
    print("Error: 'your_image.jpg' not found. Please provide an image.")

Code explanation: The script opens an image file, applies a GaussianBlur filter from the ImageFilter module using the .filter() method, and then displays and saves the resulting blurred image. The blur intensity is controlled by the radius argument.

#Python #Pillow #ImageProcessing #ImageFilter #PIL

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By: @DataScienceM ✨