Open Source Mac Image Recognition Software - Page 2

clmtrackr is a javascript library for fitting facial models to faces in videos or images. It currently is an implementation of constrained local models fitted by regularized landmark mean-shift, as described in Jason M. Saragih's paper. clmtrackr tracks a face and outputs the coordinate positions of the face model as an array. The library provides some generic face models that were trained on the MUCT database and some additional self-annotated images. Check out clmtools for building your own models. For tracking in video, it is recommended to use a browser with WebGL support, though the library should work on any modern browser. For some more information about Constrained Local Models, take a look at Xiaoguang Yan's excellent tutorial, which was of great help in implementing this library.

howmanypeoplearearound calculates the number of people in the vicinity using the approximate number of smartphones as a proxy (since ~70% of people have smartphones nowadays). A cellphone is determined to be in proximity to the computer based on sniffing WiFi probe requests. Possible uses of howmanypeoplearearound include, monitoring foot traffic in your house with Raspberry Pis, seeing if your roommates are home, etc. There are a number of possible USB WiFi adapters that support monitor mode. Namely you want to find a USB adapter with one of the following chipsets: Atheros AR9271, Ralink RT3070, Ralink RT3572, or Ralink RT5572. You will be prompted for the WiFi adapter to use for scanning. Make sure to use an adapter that supports "monitor" mode. You can modify the scan time, designate the adapter, or modify the output using some command-line options.

This is a tool that can convert any image into a pure CSS image. I also made a per-pixel animation experiment using the box-shadow idea, see morphin. Pure CSS, this output was created by resizing and setting each pixel as a box shadow of a single-pixel div, so no IMG tag or background image is needed. This can result in huge outputs, and the use of this output is not recommended for production unless there is no other option. Base64, the entire image file is embedded inside the <img> tag using base64, so no need for external hosting is needed.

NextCaptcha is a powerful captcha solving service that supports various types of captchas including reCAPTCHA v2, reCAPTCHA v2 Enterprise, reCAPTCHA v3, reCAPTCHA Mobile, hCaptcha, and FunCaptcha. With NextCaptcha, you can easily solve a variety of captcha challenges in your automation scripts and programs. This SDK provides a simple and easy-to-use Golang interface for interacting with the NextCaptcha API. It supports all available captcha types and offers intuitive methods for solving different types of captchas. Install Instructions - https://nextcaptcha.com

NextCaptcha is a powerful captcha solving service that supports various types of captchas including reCAPTCHA v2, reCAPTCHA v2 Enterprise, reCAPTCHA v3, reCAPTCHA Mobile, hCaptcha, hCaptcha Enterprise, and FunCaptcha. With NextCaptcha, you can easily solve a variety of captcha challenges in your automation scripts and programs. This SDK provides a simple and easy-to-use Node.js interface for interacting with the NextCaptcha API. It supports all available captcha types and offers intuitive methods for solving different types of captchas.

retina.js makes it easy to serve high-resolution images to devices with displays that support them. You can prepare images for as many levels of pixel density as you want and let retina.js dynamically serve the right image to the user. retina.js assumes you are using Apple's prescribed high-resolution modifiers (@2x, @3x, etc) to denote high-res image variants on your server. It also assumes that if you have prepared a variant for a given high-res environment, that you have also prepared variants for each environment below it. For example, if you have prepared 3x variants, retina.js will assume that you have also prepared 2x variants. If the environment does have 3x capabilities, retina.js will serve up the 3x image. It will expect that url to be /images/my_image@3x.png. If the environment has the ability to display images at higher densities than 3x, retina.js will serve up the image of the highest resolution that you've provided, in this case 3x.