![]() Please cite Imagededup in your publications if this is useful for your research. See the Contribution guide for more details. All deduplication methods fare well on datasets containing exact duplicates, but Difference hashing is the fastest.CNN works best for near duplicates and datasets containing transformations.Generally speaking, following conclusions can be made: The next releases have significant changes to all methods, so the current benchmarks may not hold.ĭetailed benchmarks on speed and classification metrics for different methods have been provided in the documentation. Update: Provided benchmarks are only valid upto imagededup v0.2.2. It is also possible to use your own custom models for finding duplicates using the CNN method.įor more detailed usage of the package functionality, refer: ⏳ Benchmarks utils import plot_duplicates plot_duplicates( image_dir = 'path/to/image/directory', # plot duplicates obtained for a given file using the duplicates dictionary from imagededup. find_duplicates( encoding_map = encodings) It features many of the same ROI and analysis tools as Mango and uses interoperable file formats and customization files such as ROIs and user-defined color tables. # Find duplicates using the generated encodings duplicates = phasher. iMango is a mobile-friendly medical imaging research application developed for the Apple iPad. encode_images( image_dir = 'path/to/image/directory') # Generate encodings for all images in an image directory encodings = phasher. Install imagededup from PyPI (recommended):įrom imagededup.There are two ways to install imagededup: It is distributed under the Apache 2.0 license. Imagededup is compatible with Python 3.8+ and runs on Linux, MacOS X and Windows. Plotting duplicates found for a given image file.ĭetailed documentation for the package can be found at:.Framework to evaluate effectiveness of deduplication given a ground truth mapping.Generation of encodings for images using one of the above stated algorithms.Convolutional Neural Network (CNN) - Select from several prepackaged models or provide your own custom model.Finding duplicates in a directory using one of the following algorithms:.Furthermore, by working with a native iPad app, you are untethered from your computer (you can sit in front of the TV and edit for hours). If you use the Apple pencil, or even a stylus, iMango is much nicer than drawing with a mouse. An evaluationįramework is also provided to judge the quality of deduplication for a given dataset.įollowing details the functionality provided by the package: Mango has a companion iPad app called iMango which is very nice for viewing NIFTI images and editing masks. This package provides functionality to make use of hashing algorithms that are particularly good at finding exactĭuplicates as well as convolutional neural networks which are also adept at finding near duplicates. With the Umango Extract Client for iOS, profiling of documents and images can be done anywhere a network connection is available.Imagededup is a python package that simplifies the task of finding exact and near duplicates in an image collection. Data entry based on preset image regions (key from image) Real time browsing of network folders, backoffice data structures and 3rd party systems from the tablet Real time ODBC data lookups into backoffice databases from the tablet Once complete, the images and their related metadata are sent to their final destination according to the jobs pre-existing configuration. Umango provides a seamless experience whether at the panel of a multifunction device, a desktop browser or mobile tablet, users can begin processing a batch of documents on one device, defer until later and then resume their work on another device platform. Take photos or select files on the tablet and push them into job based business processes. With Umango Extract for tablet devices gives you more freedom and flexibility in where images are captured and profiling work is completed.
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