# Plot the original image and the extracted color channels MImgCh3 = omarray(imgAr圜roppedCh3) # Create an image from the blue channel array MImgCh2 = omarray(imgAr圜roppedCh2) # Create an image from the green channel array MImgCh1 = omarray(imgAr圜roppedCh1) # Create an image from the red channel array # Convert the extracted color channels back to images ImgAr圜roppedCh3 = imgAry, 0:dim, 2] # Extract the blue channel ImgAr圜roppedCh2 = imgAry, 0:dim, 1] # Extract the green channel ImgAr圜roppedCh1 = imgAry, 0:dim, 0] # Extract the red channel # Extract each color channel from the array MImg = omarray(imgAr圜ropped) # Create a new image from the cropped arrayĭim = imgAry.shape # Store the dimensions of the array # Convert the cropped array back to an image # Crop the array by selecting rows from 400 to 498 and columns from 300 to 398 Import PIL # Import the Python Imaging Library (PIL)įrom PIL import Image # Import the Image module from PILįrom matplotlib import pyplot # Import the pyplot module from matplotlibįrom numpy import asarray # Import the asarray function from numpy # Create a new image from the modified array # Convert the modified array back to an image # Multiply the array by 2 and subtract 100 from each element Print('Size = ', imgAry.size, ',shape=', imgAry.shape) Print('Format = ',img_resized.format, ' ,Mode = ',img_resized.mode, ' ,Size = ', img_resized.size)įormat = None ,Mode = RGBA ,Size = (200, 200) Reading and Displaying Image with matplotlib Img.show() # Show the image in the default image viewer Print('Format = ', img.format, ' ,Mode = ', img.mode, ' ,Size = ', img.size) #Open the image with the specified filename
# Import the Python Imaging Library (PIL)
However, for more advanced image processing tasks or computer vision applications, specialized libraries like OpenCV or scikit-image are more suitable.įollowings are list of topics and examples on this note. For example, you can use Matplotlib to display images alongside plots, create image-based visualizations, or preprocess images for use in other data analysis tasks. While Matplotlib is not specifically designed for image processing like some other libraries (e.g., Pillow or OpenCV), it is useful for working with images in the context of data visualization and analysis. Matplotlib offers functions for reading, displaying, and saving images, as well as basic manipulation like resizing, cropping, and color mapping. Matplotlib : Matplotlib is a popular Python library for data visualization and plotting, which can also be used for basic image processing tasks.The Wand library makes it easy to access ImageMagick's functionality from Python code. ImageMagick supports various image formats and offers powerful features like image conversion, resizing, and filtering. ImageMagick : ImageMagick is a versatile command-line tool for image manipulation that also provides Python bindings through the Wand library.
scikit-image is suitable for users who need advanced image processing functionality and are familiar with the scientific Python ecosystem. It offers a collection of algorithms for image processing, such as edge detection, segmentation, and morphological operations. scikit-image : scikit-image is a library built on top of NumPy, SciPy, and matplotlib, focusing on image processing.It is ideal for building sophisticated applications that require real-time image processing or computer vision capabilities. OpenCV offers advanced features such as object recognition, feature detection, and tracking. OpenCV : OpenCV (Open Source Computer Vision) is a powerful library primarily designed for computer vision tasks but also supports various image processing operations.Pillow is an easy-to-use library suitable for beginners and experts alike. It provides functions for opening, saving, and manipulating images, as well as for applying various filters and transformations. Pillow (PIL - Python Imaging Library) : Pillow is a widely-used, user-friendly library that supports a range of image file formats.Some popular Python libraries for image handling and manipulation are: These libraries provide a powerful set of tools for working with image data, making it easy to create, process, and analyze images in your Python applications. Python offers multiple libraries for image handling and manipulation, allowing you to perform a variety of operations on images, such as reading, writing, resizing, filtering, and transforming.
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