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eclipse-gef

A simple RCP application with a GEF Editor

eclipse.platform.ui

Eclipse Platform/UI

eclipse4book

Eclipse 4 Book

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.

exercise.gef

Exercise for Eclipse GEF

flownet2

FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

gitcmdtest

gitcmdtest

hof

Histogram of Optical Flow by OpenCV

jetson-reinforcement

Deep reinforcement learning libraries for NVIDIA Jetson TX1/TX2 with pyTorch, OpenAI Gym, and Gazebo robotics simulator.

jetson-tx2-pytorch

Installing PyTorch on the Nvidia Jetson TX1/TX2

libsockcanpp

A C++ wrapper around Linux's socketcan featureset.

libsocketcan

From http://git.pengutronix.de/?p=tools/libsocketcan.git, but with added online docs

linuxcanbus

mhof

一篇paper的原理，基于多尺度光流直方图的异常检测，自己用python写了下，基本能看出演示效果

motion-ground-truth

Matlab functions to calculate ground truth visual motion estimates taking into account lens calibration

mydatavisualization

3D Data Visualization based on Qt and QML

ndk-samples

Android NDK samples with Android Studio

packtpub-xtext-book-2nd-examples

This contains the examples for the book "Implementing Domain-Specific Languages with Xtext and Xtend, 2nd edition"

pypl.github.io

PYPL Popularity of programming language index

qmltreewidget

A TreeView component, implemented by QML, providing convenient interfaces and customizable stylesheet, also available for iOS and Android.

qt

The knowledge point of Qt

qtquickexamples

QtQuick相关的技术分享

reduplication-of-single-image-crowd-counting-via-mcnn-on-ucf-dataset

reinspect

End to end detection in crowded scenes

resnet-18-caffemodel-on-imagenet

ResNet-18 Caffemodel @ilsvrc12 shrt 256 with Top-1 69% Top-5 89%

shadertoy

socketcan-cpp

Simple c++ interface to read/write can sockets

socketcan-wio

CAN-BUS controller driver for Linux using socketcan

socketcangui

GUI for the linux socketcan-API

stackoverflow

my answers in Stack Overflow :bowtie: :octocat: