Open Source Python Raspberry Pi Software for Mac

KeexyBox allows you to do parental control, block ads, limit telemetry, and browse the Internet anonymously from your home network without installing any software on your devices. It also can be used to create a public wireless access point with the captive portal. It is a software program which requires a Raspberry PI box and which is installed on Raspbian. It constitutes a cut-off point between your devices (computers, tablets, smartphones, etc.) and your router or Internet box. KeexyBox acts as a default gateway and DNS server for the devices in your home network. It thus intercepts all connections to the Internet to carry out website filtering or to activate browsing via the Tor anonymity network according to connection profiles which you have configured.

Image of OpenWrt OS, with snort community featured in, for Raspberry Pi 4/ 4B+ and 400, basically for Processor BCM2711. Installed Snort Community Model to Intrusion Detection system. Prevention system not installed. eth0 used as output/ LAN socket, to run internet and access router. eth1, which can be USB -> Ethernet port, is used as WAN port, to connect Pi board to Internet IP. to flash img file, you can restore in SD card, or use Balena Etcher to flash. or you can use DD commend of UNIX. **USERNAME** - root **PASSWORD** - bing.google12

Face Mask Detection system based on computer vision and deep learning using OpenCV and Tensorflow/Keras. Face Mask Detection System built with OpenCV, Keras/TensorFlow using Deep Learning and Computer Vision concepts in order to detect face masks in static images as well as in real-time video streams. Amid the ongoing COVID-19 pandemic, there are no efficient face mask detection applications which are now in high demand for transportation means, densely populated areas, residential districts, large-scale manufacturers and other enterprises to ensure safety. The absence of large datasets of ‘with_mask’ images has made this task cumbersome and challenging. Our face mask detector doesn't use any morphed masked images dataset and the model is accurate. Owing to the use of MobileNetV2 architecture, it is computationally efficient, thus making it easier to deploy the model to embedded systems (Raspberry Pi, Google Coral, etc.).