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  1. Digital image processing involves manipulating and analyzing images using algorithms. Python, with its extensive libraries, simplifies this process, making it accessible for practical applications like computer vision, medical imaging, and more. Below are some essential techniques and their implementations in Python.

    1. Reading and Displaying an Image

    You can use libraries like OpenCV or Matplotlib to load and display images.

    import cv2
    from matplotlib import pyplot as plt

    # Read the image
    image = cv2.imread('image.jpg')

    # Convert BGR to RGB for proper display
    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

    # Display the image
    plt.imshow(image_rgb)
    plt.axis('off')
    plt.show()
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    2. Image Resizing

    Resize images for specific dimensions using OpenCV.

    resized_image = cv2.resize(image, (200, 200)) # Resize to 200x200 pixels
    cv2.imwrite('resized_image.jpg', resized_image)
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    3. Converting to Grayscale

    Convert an image to grayscale for simpler processing.

    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    cv2.imwrite('gray_image.jpg', gray_image)
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