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ScreenCat is named after cats, but also for the idea of 'catting' a screen (as in unix cat). It has two C/C++ dependencies, Electron (which includes node.js) and robotjs for creating mouse + keyboard events.To download the latest build visit the releases page. Builds for your OS may not be available and you may have to build it yourself, sorry.
How simple is it to cause a deep neural network to misclassify an image if an attacker is only allowed to modify the color of one pixel and only see the prediction probability? Turns out it is very simple. In many cases, an attacker can even cause the network to return any answer they want. The following project is a Keras reimplementation and tutorial of "One pixel attack for fooling deep neural networks".
I used this challenge to learn more about neural networks and machine learning. A neural network consists of layers, and each layer has neurons. My network has three layers: an input layer, a hidden layer, and an output layer. The input to my network has 64 binary numbers. These inputs are connected to the neurons in the hidden layer. The hidden layer performs some computation and passes the result to the output layer neuron out. This also performs a computation and then outputs a 0 or a 1. The input layer doesn’t actually do anything, they are just placeholders for the input value. Only the neurons in the hidden layer and the output layer perform computations. The neurons from the input layer are connected to the neurons in the hidden layer. Likewise, both neurons from the hidden layer are connected to the output layer. These kinds of layers are called fully-connected because every neuron is connected to every neuron in the next layer. Each connection between two neurons has a weight, which is just a number. These weights form the brain of my network. For the activation function in my network, I use the sigmoid function. Sigmoid is a mathematical function. The sigmoid takes in some number x and converts it into a value between 0 and 1. That is ideal for my purposes, since I am dealing with binary numbers. This will turn a linear equation into something that is non-linear. This is important because without this, the network wouldn’t be able to learn any interesting things. I have already mentioned that the input to this network are 64 binary numbers. I resize the drawn image to 8x8 pixels which makes together 64 pixels. I go through the image and check each pixel if the pixel has a pink color I add a 1 to my array else I add a 0. At the end I will have 64 binary numbers which I can add to my input layer.
AutoPy is a simple, cross-platform GUI automation toolkit for Python. It includes functions for controlling the keyboard and mouse, finding colors and bitmaps on-screen, and displaying alerts -- all in a cross-platform, efficient, and simple manner. Works on Mac OS X, Windows, and X11.
A python library built to empower developers to build applications and systems with self-contained Deep Learning and Computer Vision capabilities using simple and few lines of code. Built with simplicity in mind, ImageAI supports a list of state-of-the-art Machine Learning algorithms for image prediction, custom image prediction, object detection, video detection, video object tracking and image predictions trainings. ImageAI currently supports image prediction and training using 4 different Machine Learning algorithms trained on the ImageNet-1000 dataset. ImageAI also supports object detection, video detection and object tracking using RetinaNet, YOLOv3 and TinyYOLOv3 trained on COCO dataset. Eventually, ImageAI will provide support for a wider and more specialized aspects of Computer Vision including and not limited to image recognition in special environments and special fields.
XBImageFilters allows you to obtain filtered versions of any image or from the camera in realtime. It uses OpenGL ES 2 to filter the images through fragment shaders you write yourself so you can filter your images in whatever way you want and it is super fast. In this screenshot of the sample we have on the top half of the screen a regular UIImageView with contentMode set to UIViewContentModeTop, and on the bottom half a XBFilteredImageView with the same image with contentMode set to UIViewContentModeBottom and a filter [a GLSL fragment shader] that outputs the luminance of the pixel color.
The Embedded Learning Library (ELL) allows you to build and deploy machine-learned pipelines onto embedded platforms, like Raspberry Pis, Arduinos, micro:bits, and other microcontrollers. The deployed machine learning model runs on the device, disconnected from the cloud. Our APIs can be used either from C++ or Python.A good place to start is the tutorial, which allows you to do image recognition on a Raspberry Pi with a web cam, disconnected from the cloud. The software you deploy to the Pi will recognize a variety of common objects on camera and print a label for the recognized object on the Pi's screen.
A generic image detection program that uses Google's Machine Learning library, Tensorflow and a pre-trained Deep Learning Convolutional Neural Network model called Inception. This model has been pre-trained for the ImageNet Large Visual Recognition Challenge using the data from 2012, and it can differentiate between 1,000 different classes, like Dalmatian, dishwasher etc. The program applies Transfer Learning to this existing model and re-trains it to classify a new set of images.
SOD is an embedded, modern cross-platform computer vision and machine learning software library that expose a set of APIs for deep-learning, advanced media analysis & processing including real-time, multi-class object detection and model training on embedded systems with limited computational resource and IoT devices. SOD was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in open source as well commercial products.
Another desktop magnifier and ruler for easy and accurate on the screen measuring. (Features: exact measurement, zoom: 1x, 2x, 4x, 8x, opacity, resizeable, color picker, screenshot of area, easy mouse handling, shortcuts for all functions)
AutoHotkey is a free, open source macro-creation and automation software utility that allows users to automate repetitive tasks. It is driven by a custom scripting language that is aimed specifically at providing keyboard shortcuts, otherwise known as hotkeys. AutoHotkey_L started as a fork of AutoHotkey but has been the main branch for some time.
Automate almost anything by sending keystrokes amp; mouse clicks (macros). Create hotkeys for keyboard, mouse, joystick, amp; handheld remote controls. Define abbreviations that expand as you type them (AutoText). Create graphical user interfaces amp; menu bars.
Kur is a system for quickly building and applying state-of-the-art deep learning models to new and exciting problems. Kur was designed to appeal to the entire machine learning community, from novices to veterans. It uses specification files that are simple to read and author, meaning that you can get started building sophisticated models without ever needing to code. Even so, Kur exposes a friendly and extensible API to support advanced deep learning architectures or workflows.
PyAutoGUI is a cross-platform GUI automation Python module for human beings. Used to programmatically control the mouse & keyboard. Windows has no dependencies. The Win32 extensions do not need to be installed.
Keyboard Image Viewer is built to be run in full-screen and controlled by a keyboard. Includes ability to tag and rate images, search, detect duplicates and use appropriate background color, which makes it ideal for managing large collections of images.