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copper-engine

COPPER - a high performance Java workflow engine

coronavirus-tracker-api

🦠 A simple and fast (< 200ms) API for tracking the global coronavirus (COVID-19, SARS-CoV-2) outbreak. It's written in python using the 🔥 FastAPI framework. Supports multiple sources!

country-list

:globe_with_meridians: List of all countries with names and ISO 3166-1 codes in all languages and data formats.

country-list-1

List of all countries with names and ISO 3166-1 codes in all languages and data formats

covid-ct

COVID-CT-Dataset: A CT Scan Dataset about COVID-19

covid19_tracker_android

Coronavirus COVID-19 Tracker Android Application

currency

Android currency conversion

deeplearning_plantdiseases

Training and evaluating state-of-the-art deep learning CNN architectures for plant disease classification task.

deepsentiment

Speech Emotion Recognition using FFT and SVM

deepspeech

A TensorFlow implementation of Baidu's DeepSpeech architecture

dentalcare-app

This is an basic android app for the dentists to share it with their patients so they can get facilities like getting appointment, learn cost of different surgery, precautions and much more.

dentalmanagementsystem

Android based dental management system that helps dentists manage patients, organize dental records, insurance claim management, handle scheduling and billing all through one easy -to -use platform.

dentisterymanagement

dentistrymanager

dentistware

dl-face-detection

Repository for face detection using deep learning project.

dokan

Multivendor marketplace platform

dokan-app-build-script

This repo contains the build scripts for both Dokan Android and iOS app

dokan-invoice

dokanee

The new default theme for Dokan

dolibarr

Dolibarr ERP CRM is a modern software package to manage your company or foundation activity (contacts, suppliers, invoices, orders, stocks, agenda, accounting, ...). It is open source software written in PHP and designed for small and medium businesses, foundations and freelancers. You can freely install, use and distribute it as a standalone application or as a web application to use it from every internet access and media.

droid-maze

Android maze game

droidar

DroidAR Mobile Locationbased Augmented Reality Framework for Android

drone-simulation

Drone Simulation with realistic controls made using Unity

dupeguru

Find duplicate files

emerald-dialer

Lightweight dialer with T9 search

emotion-conv-net

Real-time emotion recognition using convolutional neural nets.

emotion-detection-in-videos

The aim of this work is to recognize the six emotions (happiness, sadness, disgust, surprise, fear and anger) based on human facial expressions extracted from videos. To achieve this, we are considering people of different ethnicity, age and gender where each one of them reacts very different when they express their emotions. We collected a data set of 149 videos that included short videos from both, females and males, expressing each of the the emotions described before. The data set was built by students and each of them recorded a video expressing all the emotions with no directions or instructions at all. Some videos included more body parts than others. In other cases, videos have objects in the background an even different light setups. We wanted this to be as general as possible with no restrictions at all, so it could be a very good indicator of our main goal. The code detect_faces.py just detects faces from the video and we saved this video in the dimension 240x320. Using this algorithm creates shaky videos. Thus we then stabilized all videos. This can be done via a code or online free stabilizers are also available. After which we used the stabilized videos and ran it through code emotion_classification_videos_faces.py. in the code we developed a method to extract features based on histogram of dense optical flows (HOF) and we used a support vector machine (SVM) classifier to tackle the recognition problem. For each video at each frame we extracted optical flows. Optical flows measure the motion relative to an observer between two frames at each point of them. Therefore, at each point in the image you will have two values that describes the vector representing the motion between the two frames: the magnitude and the angle. In our case, since videos have a resolution of 240x320, each frame will have a feature descriptor of dimensions 240x320x2. So, the final video descriptor will have a dimension of #framesx240x320x2. In order to make a video comparable to other inputs (because inputs of different length will not be comparable with each other), we need to somehow find a way to summarize the video into a single descriptor. We achieve this by calculating a histogram of the optical flows. This is, separate the extracted flows into categories and count the number of flows for each category. In more details, we split the scene into a grid of s by s bins (10 in this case) in order to record the location of each feature, and then categorized the direction of the flow as one of the 8 different motion directions considered in this problem. After this, we count for each direction the number of flows occurring in each direction bin. Finally, we end up with an s by s by 8 bins descriptor per each frame. Now, the summarizing step for each video could be the average of the histograms in each grid (average pooling method) or we could just pick the maximum value of the histograms by grid throughout all the frames on a video (max pooling For the classification process, we used support vector machine (SVM) with a non linear kernel classifier, discussed in class, to recognize the new facial expressions. We also considered a Naïve Bayes classifier, but it is widely known that svm outperforms the last method in the computer vision field. A confusion matrix can be made to plot results better.

emotion-recognition

Emotion Recognition based on Open Source Biometrics (OpenBr)

emotion-recognition-android-app-and-server

This is an Android APP for emotion recognition project.