Real Time Drowsiness And Yawn Detection

Last Updated on May 3, 2021

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We implemented the drowsiness detection algorithm detailed above using OpenCV, dlib, and Python .We calculate EAR Ratio for eyes and Convex hull to find the distance and determine if an individual is yawning.

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Python Project: Pillow, Tesseract, And Opencv

Last Updated on May 3, 2021

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The Project

Take a ZIP file) of images and process them, using a library built into python that you need to learn how to use. A ZIP file takes several different files and compresses them, thus saving space, into one single file. The files in the ZIP file we provide are newspaper images (like you saw in week 3). Your task is to write python code which allows one to search through the images looking for the occurrences of keywords and faces. E.g. if you search for "pizza" it will return a contact sheet of all of the faces which were located on the newspaper page which mentions "pizza". This will test your ability to learn a new (library), your ability to use OpenCV to detect faces, your ability to use tesseract to do optical character recognition, and your ability to use PIL to composite images together into contact sheets.

Each page of the newspapers is saved as a single PNG image in a file called images.zip. These newspapers are in english, and contain a variety of stories, advertisements and images. Note: This file is fairly large (~200 MB) and may take some time to work with, I would encourage you to use small_img.zip for testing.

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Breast Cancer Analysis And Prediction Using Ml

Last Updated on May 3, 2021

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Project EDA-

Done by using module called Pandas Profiling


Data Set Information:

Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. n the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23-34].

This database is also available through the UW CS ftp server: ftp ftp.cs.wisc.edu cd math-prog/cpo-dataset/machine-learn/WDBC/

Also can be found on UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29


Attribute Information:

  1. ID number
  2. Diagnosis (M = malignant, B = benign) 3-32)

Ten real-valued features are computed for each cell nucleus:

a) radius (mean of distances from center to points on the perimeter) b) texture (standard deviation of gray-scale values) c) perimeter d) area e) smoothness (local variation in radius lengths) f) compactness (perimeter^2 / area - 1.0) g) concavity (severity of concave portions of the contour) h) concave points (number of concave portions of the contour) i) symmetry j) fractal dimension ("coastline approximation" - 1)

The mean, standard error and "worst" or largest (mean of the three largest values) of these features were computed for each image, resulting in 30 features. For instance, field 3 is Mean Radius, field 13 is Radius SE, field 23 is Worst Radius.

All feature values are recoded with four significant digits.

Missing attribute values: none

Class distribution: 357 benign, 212 malignant

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Disease Prediction System

Last Updated on May 3, 2021

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ML-Model-Flask-Deployment

This is a demo project to elaborate how Machine Learn Models are deployed on production using Flask API


Prerequisites

You must have Scikit Learn, Pandas (for Machine Leraning Model) and Flask (for API) installed.


Project Structure

This project has four major parts :

  1. model.py - This contains code fot our Machine Learning model to predict employee salaries absed on trainign data in 'hiring.csv' file.
  2. app.py - This contains Flask APIs that receives employee details through GUI or API calls, computes the precited value based on our model and returns it.
  3. request.py - This uses requests module to call APIs already defined in app.py and dispalys the returned value.
  4. templates - This folder contains the HTML template to allow user to enter employee detail and displays the predicted employee salary.


Running the project

  1. Ensure that you are in the project home directory. Create the machine learning model by running below command -
python model.py

This would create a serialized version of our model into a file model.pkl

  1. Run app.py using below command to start Flask API
python app.py

By default, flask will run on port 5000.

  1. Navigate to URL http://localhost:5000

You should be able to view the homepage as below : 

Enter valid numerical values in all 3 input boxes and hit Predict.

If everything goes well, you should be able to see the predcited salary vaule on the HTML page! 

  1. You can also send direct POST requests to FLask API using Python's inbuilt request module Run the beow command to send the request with some pre-popuated values -
python request.py


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Scholastic

Last Updated on May 3, 2021

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Problem Statement

Develop tools that would increase Productivity for students and teachers. In the past 10-15 years we have seen the transition of things around us from offline to online, whether it's business, entertainment activities, daily needs, and now even education. Productivity tools have been a success with businesses and firms. Develop productivity tools for students and teachers in any domain of your choice that can achieve the same success in the educational field in the future.


Problem Solution

In this post - covid era, the education sector has erupted, with a plethora of new opportunities. Scholastic provides a complete and comprehensive education portal for students as well as staff.

  • The USP of the application are lab sessions simulated using Augmented Reality.
  • Other features include usage of virtual assistants like Alexa to provide reminders, complete timetable and file integration
  • A blockchain based digital report card system where teachers can upload report cards for students & send it to parents.
  • Plagiarism checker for assignments. It is a one - stop solution to all needs such as announcements and circulars from institution or a staff member, fee payment and even a chatbot for additional support.


Tech Stack

  • Google Assistant For Chatbot
  • Via the Actions Console
  • Python3 for Plagiarism Checker
  • Gensim
  • NumPy
  • NLP Models ( Word Embedding)
  • Heroku (For Deployment & making API Calls)
  • Android Studio with Java For Main Android App
  • AR Foundation For Simulated Lab Sessions with Blender & Unity
  • Ethereum, Solidity & React.js For Blockchain Based Storage for Report Cards (Along with Ganache & Truffle Suite)


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Ai Based Attendance System Using Neural Net

Last Updated on May 3, 2021

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This repositoy contains two folders : Problem Statement Part 1 (For Hackathon Model) and Problem Statement Part 2 (For report on FCN in Autonomous Vehicle)

The model for Hackathon is trained using the idea that each folder in Trainset is a department for the comapny with image of each employee (both passport and selfie). This was necessary for now as training the model using each employee's picture was not possible due to availability of less documents.

The above code for task 1 of Deep Learning CV Hackathon is trained on the data set in the link https://drive.google.com/file/d/12_WTFi9ppvD-loaWUWpUar25Z3nT5k9P/view

Hackathon.ipynb is the trained model that was trained in GOOGLE COLAB, so I would recommend you to run the hackathon.ipynb on Google Colab, in case you don't want to use !mkdir or !unzip in your code, you can run the file in jupyter even, after commenting these statements.

hackathon.py is the required python file that takes in two arguments : the selfies image and passport image along with the extension and directory ( if not present in the same folder as the hackathon.py model). The file should be run on python IDE , not on Command Prompt as this has been programmed accordingly.

haarcasccade_frontalface_default.xml file has been used for Face Region of Interest detection. This needs to be in the same folder as Hackathon.ipynb and hackathon.py

The model is saved by the name of saved_model .

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