{ "info": { "author": "Pier Carlo Cadoppi", "author_email": "piercarlo.cadoppi@studenti.unipr.it", "bugtrack_url": null, "classifiers": [ "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: Python :: 3" ], "description": "A Mask R-CNN Keras implementation with Modanet annotations on the Paperdoll dataset\n\n# Mask R-CNN with ModaNet\n\nMy bachelor's thesis project.\n\n![ModaNet](https://github.com/eBay/modanet/blob/master/logo/modanet_logo2.png)\n\nTo sum it all up, I created a program that enables you to quickly train any model using [fizyr's keras-maskrcnn](https://github.com/fizyr/keras-maskrcnn) (I spent around a month to make it work).\nAnd in particular to train it using [ModaNet](https://github.com/eBay/modanet).\nModaNet, I discovered, had its flaws, particularly on the footwear and boots. They had the bounding boxes overlap with each other.\nYou can check them out by running `maskrcnn-modanet viewimage --all-set --original`\nWith and without the \"original\" parameter, in parallel in two different terminal tabs/windows.\n\nSo I fixed them (although help is much appreciated to refine it).\n\nThen I ran some tests to check the results and footwear and boots recognition were dramatically improved.\n\nI then formulated a simple application to analyze how many shoes, or skirts, or one of the other 13 labels, are in the user's instagram account, only analyzing images in which there is only one person in the frame. More details again on the [release notes for v1.0](https://github.com/cad0p/maskrcnn-modanet/releases/tag/v1.0).\n\nBelow is the home screen of the program.\n\n```\nUsage: maskrcnn-modanet [OPTIONS] COMMAND [ARGS]...\n\n Main CLI.\n\nOptions:\n --help Show this message and exit.\n\nCommands:\n datasets Manage your datasets run 1 -> maskrcnn-modanet datasets...\n evaluate Evaluate any trained model, average precision and recall.\n instagram Simple implementation to track instagram metrics per...\n processimage View and save processed image and annotations from input...\n savedvars Show and edit saved variables\n train Train using the dataset downloaded usage: maskrcnn-\n modanet...\n viewannotation View and (not yet needed) save dataset images, plain (not...\n viewimage View and (not yet needed) save dataset images, plain (not...\n\n\n```\nI'll be very happy to merge your pull requests that add new implementations, or link to them in a section here!\n\nRegarding the Instagram analyzer, I started from the [Instaloader](https://github.com/instaloader) classes and overrode some methods to get the urls of the posts instead of downloading them.\n\nIt then runs through the COCO model to determine the images that have only one person that is bigger than 10% of the image, and on those images I run the ModaNet model to show some statistics about what type of apparel the user is wearing and even display the instances of them, if you request it.\n\nSay you want to quickly find what skirt (or footwear) your instagram star always wears. With this tool you can! And you can also see how often the instagram user shows himself alone in their images, and what he/she usually shares of him (always pictures with shoes? always only the top part?)\n\n\n[Link to the Thesis Presentation](https://docs.google.com/uc?id=1IPyoPsAxFk7EXtFL3K4AbUWVy_VjKb6K)\n\n[Short Version](https://docs.google.com/uc?id=1Tua2xs1DoY4Kv_bHwbF_rflgV8E7iTUq)\n\n## Getting Started\n\nThis project is written in Python 3, so it works in all major OSes. Although only Linux and MacOS are fully supported.\nKeep in mind to use pip or pip3 depending on your settings.\n\nClone this repo\n\nRun `pip install maskrcnn-modanet`\n\nOr go to the repo you just cloned on the terminal and run `pip install -e .`\n\nIf you see any errors, just install the dependencies manually, just like this: `pip3 install --upgrade cython`\n\nNow that you've installed it, run `maskrcnn-modanet datasets download the/folder/you/want/to/put/data/in`\n\nIt will take a while, about 40GB to download!\nEDIT: it is now reduced to just 2-3 GB. See the [release notes for v1.0](https://github.com/cad0p/maskrcnn-modanet/releases/tag/v1.0) for details on this and on the instagram application. \n\nThen you can explore its features and commands by running `maskrcnn-modanet`\n\n### Prerequisites\n\nInstall Python and Keras\n\nInstall [Git LFS (Large File Storage)](https://github.com/git-lfs/git-lfs/wiki/Installation) to get all the files!\n\n\n\n## Built With\n\n* [Sublime Text](https://www.sublimetext.com/) - The text editor used\n* [Python 3](https://www.python.org) - Language utilized\n* [GitHub Desktop](https://desktop.github.com/) - To manage developement\n* [Sublime Merge](https://www.sublimemerge.com/) - To manage developement\n\n## Contributing\n\nThe following is a copy of PurpleBooth\n> Please read [CONTRIBUTING.md](https://gist.github.com/PurpleBooth/b24679402957c63ec426) for details on our code of conduct, and the process for submitting pull requests to us.\n\n## Versioning\n\nFor the versions available, see the 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