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face-mask's Introduction

Classification of Masked and Unmasked Faces using Transfer Learning Concept.

CodeQL

Principle Dataset Link:

Uploaded by Sunil Singh ([email protected]) - https://drive.google.com/drive/folders/1pAxEBmfYLoVtZQlBT3doxmesAO7n3ES1 which is publicly available. From this database we separate faces.

Obtaining & Configuring the dataset (from Google Drive to Computer)

Steps:
  1. Use: pip install google-api-python-client.
  2. Please refer to the Google Documentation on OAuth 2.0 & Drive API v3.
  3. Please enter: http://localhost:8080/ as a redirect_uri in the cloud console of Google for your Client ID & Secret Pair.
  4. Please set up: the OAuth 2.0 screen with one of the sensitive scopes as ./auth/drive for full access to Google Drive API.

After registering the client keys please do not forget to download and save client_secrets.json, at the same path as your program. That section is uploaded to https://github.com/formula21/face-mask/blob/main/croppie.py.

Other notes:
  1. The imagebuffer once stored as a file, stays on your computer, unless you define the FLAG_UPLOAD = true and FLAG_UPLOAD_PARENT_ID = [None, None] or [str, str].
  • If either None is defined, a new folder with the names with_mask and without_mask are created or the id's are used to upload. If a failure of finding the directories in drive, the program terminates with an Exception.
  • If either or both are found, we will send the buffer immediately to upload.
  1. By default, the file is supposed to be also saved locally, however this can be omitted by defining FLAG_DOWNLOAD_AND_SAVE = false.

Google Drive Authorization

We are authorizing Google Colab with Google Drive for us to get access to our dataset. This authorization is native to Google's Documentation.

Please note: You need to change the variable dir below to the appropriate path. If you are using a "Shared With Me" folder please set up a "Add Shortcut to Drive", to set shortcut and get easy access to “My Drive”.

Jupyter Notebook & Google Colab

For training the model, the system did not have enough memory. We had to write a modified code on Google Colab, only to train & test the model.

License

The project is licensed with an open source license from MIT. A copy of this license should be there exported with every copy of the library you clone or download. However the license reads as follows:

Copyright (c) 2021 Anweshan Roy Chowdhury [email protected], Sayon Roy [email protected].

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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