Pinned Repositories
Algorithms
:computer: Data Structures and Algorithms in Python
ANNetGPGPU
A GPU (CUDA) based Artificial Neural Network library
awesome-python
A curated list of awesome Python frameworks, libraries, software and resources
blender-chemicals
Draws chemicals in Blender using common input formats (smiles, molfiles, cif files, etc.)
brain
[UNMAINTAINED] Neural networks in JavaScript
captchacker2
一个基于SVM的验证码破解程序
cdkr
Integrating R and the CDK
ChemApp
ChemDataExtractor
Automatically extract chemical information from scientific documents
chemistry-css
A chemistry CSS library for creating Chemical Structures and Equations with just HTML.
yccai's Repositories
yccai/rdkit-react
Script to perform reactions on molecules using RDKIT
yccai/pyCalc
:computer: A GUI Calculator using Tkinter
yccai/svm
Tools that implement of the classic SMO (Sequential Minimal Optimization) algorithm for traning SVMs
yccai/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.
yccai/convert
Set of tools which help to convert data from chemical table files into graph format
yccai/GLP
Graph Learning Package
yccai/chemistry-css
A chemistry CSS library for creating Chemical Structures and Equations with just HTML.
yccai/RDKitjs
wrapping RDKit to js using emscritpen
yccai/molecule_graph_matcher
posey application to search library of organic molecules using posey kit
yccai/HERG-QSAR
Using RDKIT and SciKit-Learn to analyse Herg data from Chembl
yccai/molecules
chemical graph theory library
yccai/DataPortal
A Django application for compound registration, similarity and substructure searches utilizing RDKit
yccai/UGM_2015
Materials from the 2015 RDKit UGM
yccai/cinfony
Simplified and standard interface to a number of cheminformatics toolkits
yccai/object-detector
Object Detection Framework using HOG as descriptor and Linear SVM as classifier.
yccai/Smiley
An OpenSMILES compliant C++ SMILES/SMARTS parser
yccai/captchacker2
一个基于SVM的验证码破解程序
yccai/easyAI
Python artificial intelligence framework for games
yccai/UGM_2014
Materials from the 2014 RDKit UGM
yccai/Frog2
2D/3D generation for small compounds
yccai/RDKitSV
A simple program for viewing 2D pictures of chemical structures and matching SMARTS patterns against them.
yccai/Molecules3D
3D chemical structure visualisation with three.js
yccai/ketcher
Ketcher: web-based chemical sketcher
yccai/K-Nearest-Neighbors-with-Dynamic-Time-Warping
Python implementation of KNN and DTW classification algorithm
yccai/PCV
Open source Python module for computer vision
yccai/learningpaperone
yccai/UGM_2013
yccai/snake-ai
a snake AI written in python
yccai/svmjs
Support Vector Machine in Javascript (SMO algorithm, supports arbitrary kernels) + GUI demo
yccai/imago
Imago OCR: chemical image recognition toolkit