But I do suggest you to change the default location to a place where you will be storing the data, unless you are going to store the data in the same system volume in Windows. You can choose to use all default settings along the way. Dokian is included in the download package and will be installed automatically.ĭouble-click the downloaded package to start installation. You will need both OpenDedup and Dokian library to work together. Go to OpenDedup download page, scroll down to the bottom of the page, and click to download the Windows binaries. The online Quick Start Guide is no longer exist so I am outlining a few steps here to help those who are interested in seeing how cool this technique is. Both 32-bit and 64-bit are also supported. But the good news is that it also has a Windows port that works on both Windows 7 and Windows Server 2008 R2 systems, though not officially supported. Well, this isn’t much helpful because it’s for Linux. It is basically a file system for Linux, known as SDFS, with deduplication capability built-in. OpenDedup is an open source deduplication solution that was designed for enterprises with virtual environments looking for a high-performance, scalable, and low-cost deduplication solution. It would be very nice if we can adopt this cool technique into our backup solution or even built-in in the client operating system. But unfortunately, you don’t see it around in lower-end computing level, such as client system like Windows 7. See how much spaces are saved?Īs you can see, it’s a very efficient methodology that would save tons of your storage spaces. Normally, it takes more than 400GB after a month but with Data Deduplication, it would take as less as 100GB data storage, only the changes being made during the month will take extra space in the backup. It can also be applied to network data transfer to reduce the number of bytes of data being sent. For example, let’s say I have a set of 100GB data that’s rarely changed but still needs to be included in the weekly data backup. Please cite Imagededup in your publications if this is useful for your research.Data Deduplication is a specialized data compression technique used mostly in high-end data storage solutions, such as SANs and Backups, to reduce the storage space by eliminating duplicate copies of repeating data. 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: utils import plot_duplicates plot_duplicates( image_dir = 'path/to/image/directory',įor more examples, refer this part of theįor more detailed usage of the package functionality, refer: ⏳ Benchmarksĭetailed benchmarks on speed and classification metrics for different methods have been provided in the documentation. # plot duplicates obtained for a given file using the duplicates dictionary from imagededup. find_duplicates( encoding_map = encodings) # Find duplicates using the generated encodings duplicates = phasher. 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 3.6+ 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.Finding duplicates in a directory using one of the following algorithms:.An evaluationįramework is also provided to judge the quality of deduplication for a given dataset.įollowing details the functionality provided by the package: 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. Imagededup is a python package that simplifies the task of finding exact and near duplicates in an image collection.
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