allenzhaoxin,github

3d_video_converter_for_vr_box

This is a simple python script which converts a 2D video to 3D video for VR boxes. It is all about our mind's judgment about the picture. As we see normally we need to make our mind believe that some things are away and some are close. That just can be done by modifying the 2D image and that's it.

android-chat

开源即时通讯(野火IM)系统Android端高仿微信

android-liveliness-detection

An android application which detects if the faces belong to alive humans or fake posters , using Support Vector Machines with LBP features.

android-process-button

Android Buttons With Built-in Progress Meters.

androidcamerademowithbaidufacerecognitionapi

百度人脸识别（闸机、门禁类型）Android Demo

architecture-samples

A collection of samples to discuss and showcase different architectural tools and patterns for Android apps.

awesome-android-ui

A curated list of awesome Android UI/UX libraries

bookkeeping-ios

模仿鲨鱼记账（iOS版）

cameradetectfaceforandroid

Android手机调用相机并且识别人脸

car-detection-and-speed-

Important Imports : OpenCv , Pandas

car-make-model-classifier-yolo3-python

Car make and model classification with YOLOv3 object detector - Python example

car-speed-detection-opencv-python

In this tutorial, you will learn how to detect the speed of a car using Python OpenCV library and some basic mathematical calculations. In the area of traffic management, checking the speed of a vehicle passing through a road becomes very crucial when there is the rule of speed limitations. This Python program exactly checks the speed of the car passing through a specified lane and prints it on the console window.

dangerous_driving_behavior_detection

利用SSD目标检测算法判断是否闭眼或者张开嘴和吸烟打电话等手势行为，通过PERCLOS准则判断是否疲劳等等，功能涵盖7类情绪识别，眨眼判断，哈欠判断，吸烟，打电话等，达到危险驾驶检测的功能

deeplearning

深度学习入门教程&&优秀文章&&Deep Learning Tutorial

downloadprogressbar

DownloadProgressBar is an android library that delivers awesome custom progress bar. You can manipulate it's state in every way.

draggable-example

vue.draggable example

electric_meter_reader

Within this project, we trained a model that automatically detects area of electric numbers and then recognizes each digit. Our model accuracy reaches 89%.