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DeepVis Toolbox
make a better chinese character recognition OCR than tesseract
A collection of various deep learning architectures, models, and tips
吴恩达《深度学习》学习笔记(xmind)、作业代码、代码视频讲解
Automatically remove the mosaics in images and videos, or add mosaics to them.
support deepsort and bytetrack MOT(Multi-object tracking) using yolov5 with C++
FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.
本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。
Django App for Facial Expression Recognition
Ready-to-use OCR with 40+ languages supported including Chinese, Japanese, Korean and Thai
An easy, flexible, and accurate plate recognition project for Chinese licenses in unconstrained situations.
EasyPR移植到Android版本
Pytorch🍊🍉 is delicious, just eat it! 😋😋
The code for Expectation-Maximization Attention Networks for Semantic Segmentation (ICCV'2019 Oral)
Real-time emotion recognition using convolutional neural nets.
Train a neural network to detect and classify emotion from a photograph of a face.
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.
multi-modal emotion recognition
Emotion recognition using DNN with tensorflow
Recurrent Neural Networks for Emotion Recognition in Video
A software which detect a human face through live webcam feed and identifies the emotion of the person (i.e. the person is happy or sad).
Real time emotion recogniser using web camera based on FACS.
MATLAB implementation of Emotion recognition engine based on CNNs
Emotion recognizing tool for RGB-D camera (Kinect)
多标签分类,端到端的中文车牌识别基于mxnet, End-to-End Chinese plate recognition base on mxnet
Facial detection, landmark tracking and expression transfer library for Windows, Linux and Mac
1M人脸检测模型(含关键点)
face-expression prediction using python
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.