📏 Compute measurements of a React component.

📱⚡️ Lighthouse for Mobile - audits your app and gives a performance score to your Android apps (native, React Native, Flutter..). Measure performance on CLI, E2E tests, CI...

📐 Monitor and measure React Native performance

🙌 Utility to measure view bounds

😎 📏 React hook to measure an element's size and handle responsive components.

A React hook that allows you to use a ResizeObserver to measure an element's size.

A React Hook to measure DOM nodes

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.

Easily implement usual security measures in React Native Expo apps. Made by BAM ❤️💙💛

A component for measuring & annotating images.

Measure text width and/or height without laying it out.

measure startup time of your react-native app

An open source utility library for measuring React component render times.

A package to dynamically measure sound input level in React Native applications. Can be used to help user to adjust microphone sensitivity.

A collection of hooks to measure things in React

React hook to measure elapsed time using requestAnimationFrame

:zap: :cyclone: Measure React rendering lifecycles using controls

📏 A devtool for measuring pixel dimensions on your React Native screens

A React component for measuring 3D objects.

A HOC to make your React-Native components aware of their width and height

React Native Jsi view helpers for measure text and view.

library I created to measure performance in react applications.

React wrapper of leaflet-measure for react-leaflet. Coordinate, linear, and area measure control for Leaflet.

React hook to measure text width

Image Preloader for React & React Virtualized

React PD-Meter App (measures distance between pupils) with MediaPipe Face Mesh - internship project (2023)

📏 Experimental off-thread position calculator built for React 📐

[LIVE ON IOS APP STORE] Quick Measure - GPS is a mobile app to quickly and easily measure area and distance. Users can measure automatically with GPS and save their maps to view later. Quick Measure was built with React Native, Expo-Router, Nativewind (Tailwind), and React-Native-Maps.

Alert MUI React Datagrid for Streamlit with filter measure for sensor monitoring

A react-virtualized alternative without measuring