This guide walks through installing OpenCV and php-facedetect, writing PHP code to capture webcam images, crop them, run Pico face detection, and overlay bounding boxes, providing a complete example for object detection using PHP.
This guide walks you through installing necessary drivers and libraries, capturing webcam frames with PHP and FFmpeg, and using the Fer2013 model to recognize facial emotions, providing a complete example that displays both the image and detected mood on a web page.
This tutorial walks you through installing PHP‑GD and Video4Linux, creating a web page that previews the webcam using getUserMedia, adding a filter selector, and processing the image on the server with PHP‑GD to apply real‑time visual effects.
This tutorial explains how to set up a PHP environment, use OpenCV and the GD library to capture webcam video frames, apply real-time image filters such as grayscale, blur, contrast, and edge detection, and switch effects via keyboard input, enabling personalized photo creation.
This tutorial explains how to set up a PHP environment, use OpenCV and GD libraries to capture live webcam frames, apply various image filters such as grayscale, Gaussian blur, contrast adjustments, edge detection, and switch effects via keyboard input, providing complete code examples.
This article demonstrates how to set up a PHP environment, use OpenCV and GD libraries to capture webcam video, apply various real-time image effects such as grayscale, Gaussian blur, contrast adjustments, edge detection, and implement keyboard-controlled effect switching, providing complete code examples.
This tutorial explains how to install PHP‑GD and Video4Linux, create a web page that streams webcam video with getUserMedia, capture frames to a canvas, send them to a PHP script, and apply real‑time filters such as grayscale, sepia, or invert using the GD library.
This tutorial explains how to use PHP with OpenCV and the GD library to capture webcam video, save each frame as a JPEG, and apply various real‑time image filters such as grayscale, Gaussian blur, contrast adjustments, and edge detection, including keyboard‑controlled filter switching.
This tutorial explains how to set up a PHP development environment, capture live video from a webcam using OpenCV, and apply various real‑time image effects with the GD library, including keyboard‑controlled effect switching.
This tutorial explains how to set up a PHP environment, capture live video from a webcam using OpenCV, and apply various real‑time image effects with the GD library, including grayscale, Gaussian blur, contrast adjustments, edge detection, and keyboard‑controlled effect switching.
This tutorial explains how to set up a PHP environment, capture live video from a webcam with OpenCV, and apply various real‑time image effects such as grayscale, Gaussian blur, contrast adjustments, and edge detection, including keyboard‑controlled effect switching.
This tutorial demonstrates how to use PHP on a Linux system with V4L2 and FFmpeg to capture webcam video, integrate the Fer2013 emotion‑recognition model via Python, and display real‑time emotion analysis on a web page.
This tutorial explains how to set up a PHP environment, use OpenCV and GD libraries to capture live webcam frames, apply various image‑processing effects such as grayscale, blur, contrast adjustments, and implement keyboard‑controlled effect switching, all with complete code examples.
This article demonstrates how to set up a PHP environment, use OpenCV and GD libraries to capture webcam video, apply various real-time image effects such as grayscale, Gaussian blur, contrast adjustments, and implement keyboard-controlled effect switching, providing complete code examples.
This tutorial explains how to set up a PHP environment, use OpenCV and GD libraries to capture webcam video, apply various real‑time image effects such as grayscale, blur, contrast, and edge detection, and add keyboard controls for switching filters, enabling creative photo processing.
This tutorial explains how to set up a PHP environment, capture live webcam video with OpenCV, and apply various real‑time image effects using the GD library, including code examples for grayscale, blur, contrast, edge detection, and keyboard‑controlled effect switching.
This tutorial explains how to set up a PHP environment, capture live video from a webcam using OpenCV, and apply various real-time image effects with the GD library, including keyboard-controlled switching of filters.
This tutorial explains how to install the necessary PHP‑GD and Video4Linux components, create a web page that shows a live webcam preview with JavaScript, add a filter selector, and process the selected filter on the server side using PHP‑GD to produce real‑time filtered images.
This tutorial explains how to build a mobile web app that captures video with the phone camera, loads a custom LEGO detection model via Roboflow, identifies selected brick types in real time, and visualizes the results on a p5.js canvas, including deployment steps and improvement suggestions.
This tutorial explains how to access a webcam with JavaScript, display the live video in a web page, capture a frame onto a canvas, overlay custom text as a watermark, and export the result as a data URL for further processing.
This tutorial explains how to set up a PHP environment, capture live webcam frames with OpenCV, and apply various real‑time image effects using the GD library, including code examples for switching effects via keyboard input.
This guide explains how to install Python and OpenCV, write a script that captures a webcam image and emails it using SMTP, and then package the script into a standalone executable with PyInstaller, including all required commands and code snippets.
Jason Mayes, a Google web engineer, open‑sourced a TensorFlow.js demo that removes people from live webcam video in real time using a lightweight MobileNet model, with only about 200 lines of code, and provides GitHub and CodePen links for experimentation.
During pandemic remote work, this guide shows engineers how to use a webcam, TensorFlow.js pose detection, and simple web APIs to monitor posture, automate meal and water reminders, and generate audio alerts, turning everyday health risks into a programmable personal wellness system.
