westke Goto Github PK

collectionviewclassifymenu

CollectionView做的两级菜单，可以折叠第二级菜单

conf

Code and presentations used in conferences I attend

contentfulwatchkitexample

Example for a WatchKit app using the Contentful SDK

coreanimationcode

Code examples of the book "iOS Core Animation Advanced Techniques"

corestore

Simple, elegant, and smart Core Data programming with Swift

counter

A simple example of the VIPER architecture for iOS apps

css-tutorial

CSS 教程

cssketch

Plugin that adds CSS support to Sketch 3 for a faster design workflow.

d2l-zh

《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被全球200所大学采用教学。

developing_ios_8_apps_with_swift

Stanford 公开课，Developing iOS 8 Apps with Swift 字幕翻译

developmentstack

System development basics, analysis, project/planning, documentation, wireframe/mockup, design/modeling, implementation, quality, management, build, testing, deployment, maintenance, troubleshooting, learning

dgrunkeeperswitch

Runkeeper design switch control (two part segment control)

diplomat

整合第三方 SDK 微信、微博、 QQ 等为统一的 Diplomat 接口。

dkliveblur

dopdropdownmenu

Drop down menu like we see on website for iPhone

dotfiles

My OS X dotfiles.

dsymtools

dSYM analyze

durexkit

An open source SafeKit for iOS . Never never crash.

eidolon

The Artsy Auction Kiosk App

eigen

The Art World in Your Pocket or Your Trendy Tech Company's Tote, Artsy's iOS app.

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.

emotions

A simple detector of prototypic emotions from facial images captured in video, written in Python.

facade

Programmatic view layout for the rest of us.

face-emotion-recognition-in-images-videos

facial emotion recognition with CNN and LSTM

face-recognition

Facial recognition system learning source

faiss

A library for efficient similarity search and clustering of dense vectors.

fastimagecache

iOS library for quickly displaying images while scrolling

fdfullscreenpopgesture

A UINavigationController's category to enable fullscreen pop gesture with iOS7+ system style.

ffmpeg-compilation

ffmpeg-ios-build-script

Shell script to build FFmpeg for iOS