alexivaner/Applied-CV-Zero-DCE-master alexivaner/Applied-CV-Zero-DCE-master

  • Stars
    star
    2
  • Language
    Jupyter Notebook
  • License
    MIT License
  • Created almost 4 years ago
  • Updated almost 4 years ago
Twitter logo Share LinkedIn logo Share Facebook logo Share
alexivaner/Applied-CV-Zero-DCE-master
github

alexivaner

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

This is the final project for NCTU Applied CV Course Fall 2020

More Repositories

1

Deep-Learning-Based-Radio-Signal-Classification

Final Project for AI Wireless
Jupyter Notebook
80
star
2

DeepSort-Yolo-V4-with-CSV-Logging-and-Picture-Cropping

I added csv tracking logging using Pandas in DeepSort Yolo v4. The csv tracking is very useful if you want to train the csv inside GRU neural network or other kind of deep learning train. I also added person cropping so we could get each person tracked and each person frames picture one by one.
Python
7
star
3

3d_pose_baseline_pytorch_Alpha_Pose

Jupyter Notebook
5
star
4

Applied-CV-Handling-CIFAR10-Imbalance-Datasets

Handling Long-Tail CIFAR-10 Dataset using Resampling and Reweighting
Python
3
star
5

Smart-Door-with-Face-Recognition-and-Google-Assistant

This thesis will be focused on the integration of smart door with face recognition and Google Assistant. The smart door will be unlocked if face already recognized but the smart door will stay locked if the face is not recognized and the system will send an email notification to the house owner. The system will be using Raspberry Pi 3 as main microcontroller and servo as locking door actuator. The program will be developed using node-red, Blynk and MQTT platform which are very powerful for developing Internet of Things devices. All of the programs will be coded using Python language that commonly used in Raspbian OS for Raspberry Pi 3. Face detection method will be using Haar Cascade features and face recognition method will using Local Binary Pattern Histogram. Google Assistant integration will use Dialogflow and firebase as Google Cloud services. Integration of Face Recognition and the smart door is successful. Smart door will be unlocked if faces are recognized with an average trust of more than 60%, If the face is not recognized, an email notification containing a face image also successfully sent to the house owner. The Google Assistant could also handle user request successfully with a success rate of 92.8% from 147 trials.
Python
2
star
6

ApplicationGithubUser

This is Android application for see Github followers and followings using Github API
Kotlin
1
star
7

WebRTC_Octon_106

Custom WebRTC for Octon
C++
1
star