Pinned Repositories
AR-lab
Cut and paste someone using AR
awesome-comfyui-workflow
awesome-design.ai
Useful resources for creating Design Artificial Intelligence
awesome-metaverse
Useful resources for creating Meta-verse
Comfyui-ChatTTS
comfyui-liveportrait
LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control
comfyui-mixlab-nodes
Workflow-to-APP、ScreenShare&FloatingVideo、GPT & 3D、SpeechRecognition&TTS
comfyui-moondream
tiny vision language model
comfyui-ps-plugin
Turn your workflow into a Photoshop plugin.把你的工作流变成Photoshop插件。
comfyui-sound-lab
A node collection for sound design, supporting MusicGen and Stable Audio. Welcome to use and experience it.
shadowcz007's Repositories
shadowcz007/DynamicUpdateDemo
实现 react-native 动态更新的 Demo
shadowcz007/lightH5-2
shadowcz007/DragAndDrop
React Native Drag and Drop Tutorial
shadowcz007/cnodejs-ionic
The mobile app of https://cnodejs.org, web demo https://lanceli.github.io/cnodejs-ionic
shadowcz007/github-current-user
💁 verify access to the current GitHub user
shadowcz007/svg2gif
Converts svg images to gif images with animations
shadowcz007/jiaocheng1
shadowcz007/Python4DataScience.CH
从0开始接触Python处理数据科学问题。包含Python0基础入门、科学计算工具入门、数学与计算机基础入门、统计学习入门。
shadowcz007/code-blast-codemirror
✨Particles blasts while typing in Codemirror
shadowcz007/get-pixels
Reads an image into an ndarray
shadowcz007/codeology
Codeology brings to life the art and science of code. https://codeology.braintreepayments.com/
shadowcz007/electron-microscope
use electron-microscope to inspect websites and extract data
shadowcz007/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.
shadowcz007/whatsthis
A smart camera app using react-native and tensorflow
shadowcz007/TextToSVG
Sketch plugin that converts text to svg path
shadowcz007/gravit
Gravit - The versatile Design Tool for Windows, Mac, Chrome OS and the Browser
shadowcz007/WhoAmI
A mind-reading website.
shadowcz007/svg2canvas
SVG to Canvas Converter based on canvg.js, Can be used for the node image in Qunee for HTML5
shadowcz007/cluster-kmeans
Clusters an array of vectors into k clusters using k-means (using random initial centroids and Euclidean as the distance function).
shadowcz007/clusterfck
[UNMAINTAINED] K-means and hierarchical clustering
shadowcz007/ZVulDrill
Web漏洞演练平台
shadowcz007/t3
Create ThreeJS demos with little code
shadowcz007/chinese-conv
簡轉繁,繁轉簡。Conversion between Traditional and Simplified Chinese
shadowcz007/silence-chromium
🙊 filter out spurious log messages for chromium/electron stderr
shadowcz007/cppnjs
Compositional Pattern Producing Networks, now in a javascript library near you!
shadowcz007/sketch-i18n
Translate pages in Sketch
shadowcz007/hog-descriptor
[UNMAINTAINED] Histogram of Oriented Gradients (HOG) descriptor extractor
shadowcz007/layout
Organize and layout items based on various algorithms
shadowcz007/html2md
HTML to Markdown converter in JavaScript.
shadowcz007/voronoi-map-js
JavaScript port of Amit Patel's mapgen2 https://github.com/amitp/mapgen2