جستجوی ریپازیتوری‌های کد

Go开发的金融证券交易所系统

A demo microservice with Clean Architecture in practice

The course material for our React Hooks workshop

React components for the Web Animations API - http://react-web-animation.surge.sh

A cross-platform markdown editor.

イラストに「命」を吹き込むソフトウェア

3D fluid simulation on the in Unity

PyTorch implementation of Wasserstein Auto-Encoders

完整版的ijkplayer，现在自己改一下

This is an efficient implementation of a fully connected neural network in NumPy. The network can be trained by a variety of learning algorithms: backpropagation, resilient backpropagation and scaled conjugate gradient learning. The network has been developed with PYPY in mind.

Communicate with others and broadcast Bitcoin transactions off-grid without internet access or even power grid.

blog base on express,co,mongoose,react,redux,react-router...

小程序组件-canvas拖拽组件

Python Software to use the Topdon TC001 Thermal Camera on Linux and the Raspberry Pi

Converts Postman collections to k6 script code

基于Netty、Nacos实现的网关以及RPC。学习完毕当前项目可以帮助你深入理解Netty。

bazel build rules to use boost in bazel projects

Food Delivery app built with React Native, TypeScript, and Tailwind CSS. Featuring Google Auth, smart search, cart, and smooth navigation, with Appwrite handling the backend and storage.

Share code with LLMs via Model Context Protocol or clipboard. Rule-based customization enables easy switching between different tasks (like code review and documentation). Includes smart code outlining.

Музыкальный плеер для социальной сети VK

一款模仿易企秀制作的编辑器

World generation augmenter

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.

the black magic Discord framework 🧙‍♂️

Integration of the Hateoas library into Symfony.

NuCypher's reference implementation of Umbral (threshold proxy re-encryption) using OpenSSL and Cryptography.io

Let’s go in-depth and understand the functionalities you can achieve with the help of Microsoft Identity. We will build a small yet practical implementation of Custom User Management in ASP.NET Core MVC with Identity.

A python script that can automatically scrape other people followers on instagram and save them in a txt file.

tp android open source project