Package: fast-skimage
fast-skimageWelcome tofast-skimage, an image acquisition and processing library. This powerful library offers a wide range of tools for advanced image manipulation and analysis, wrapped up in the accessibleImageclass.FeaturesAdvanced Manipulation: Apply complex operations like adding watermarks, noise detection, auto-enhancement, and saturation increase with simple method calls.Filtering and Thresholding: Includes mean, median filtering, Otsu's thresholding, and custom thresholding methods for image segmentation and noise reduction.Fourier Transforms: Utilize Fourier-based methods for reducing image dithering and other artifacts.Histogram Operations: Equalize and stretch image histograms to improve contrast and visibility.Texture Analysis: Perform texture segmentation using a variety of descriptors.Small Image Library: 7 various pictures for testing are provided with the package (see section "Image Library" below).Getting StartedInstallation: Clone the repository or download theImageclass module to your project.Dependencies: Ensure all dependencies such asnumpy,matplotlib,scikit-image, andPyWaveletsare installed.Usage: Import theImageclass from the module and instantiate it with the path to your image or a NumPy array.Examplefromfast_skimageimportImagefromfast_skimageimportetretatfromskimage.dataimportimmunohistochemistryimg=Image("Pictures/camera.jpg")# Load an image with path...img2=Image(immunohistochemistry())# ... or numpy array ...colored_image_array=etretat()# ... or a library image.img3=Image(colored_image_array.get())img2.auto_enhance()# Apply auto-enhancementimg3.auto_enhance()img3.show(subplots=(1,2,1),size=12)# Display the resultimg2.show(subplots=(1,2,2),title='Immunochemistry Image')img.show(size=(12,6),type_of_plot='hist',axis=True)# Plot histogramImage LibraryA small image library is provided along with theImageclass. These can be manually extracted with the following lines:fromfast_skimageimportimage_nameimage_array=image_name()image=Image(image_array.get())Note that all images listed below come from the INFO-H500 course of Prof. Olivier Debeir at ULB (Université Libre de Bruxelles).Grayscale Noisy Imagefast-skimage.astronaut_noisyGrayscale Clean Imagesfast-skimage.camerafast-skimage.walkingGrayscale Clean Watermarkfast-skimage.watermark(the ULB logo)Colored Clean Imagesfast-skimage.etretatfast-skimage.nycfast-skimage.zebraCollection LibraryTheCollectionclass allows you to handle multiple images (Imageobjects) at the same time. For instance:fromfast_skimage.CollectionimportCollection# Loads all images as Image objects in a single Collection objecttr_glioma=Collection(directory_path="Training/glioma",name="Glioma (training)",verbose=1)keys,images=tr_glioma.images.items()# Extract all image pixels back into a Pandas dataframeglioma=tr_glioma.extract_images(label="Glioma",to_df=True)DocumentationRefer to the in-line comments and method docstrings for detailed usage of each feature.ContributionContributions are welcome! Feel free to submit pull requests, suggest features, or report bugs.LicenseThis library is distributed under the MIT license. SeeLICENSEfor more information.ContactAuthor: Alexandre Le MercierDate: November 21, 2023 (last update on February 11, 2024)Email:alexandre.le.mercier@ulb.beLinkedIn:Alexandre Le MercierHappy Image Processing!