- #RASPBERRY PI 2 FFMPEG BUILD PLEX INSTALL#
- #RASPBERRY PI 2 FFMPEG BUILD PLEX SOFTWARE#
- #RASPBERRY PI 2 FFMPEG BUILD PLEX CODE#
Make sure you use a resistor with the LED!ġ2.) Connect Pi Camera Module to Raspberry Pi.ġ5.) After all your hardware and software is configured correctly run the following command: python TFLite_detection_webcam_loop.py -modeldir=TFLite_model_bbd -output_path=processed_images
This LED will turn on to indicate when the program is running. This will be used as input.ġ1.) Connect an LED to GPIO PIN 4. Setting Up Hardwareġ0.) Connect a push button to GPIO pin 17.
#RASPBERRY PI 2 FFMPEG BUILD PLEX INSTALL#
Setting Up SoftwareĢ.) Change directory to source code: cd rpi_road_object_detectionģ.) Open command prompt and make sure pi is up to date: sudo apt-get update & sudo apt-get upgradeĤ.) Install virtual environment: sudo pip3 install virtualenvĥ.) Make virtual environment: python3.7 -m venv TFLite-venvĦ.) Activate Environment: source TFLite-venv/bin/activateħ.) Install the dependencies: bash get_py_requirements.shĨ.) Make sure the camera module is enabled: sudo raspi-configĩ.) Go to Interface Options and make sure the Pi Camera is enabled. The software setup steps should install OpenCV, but sometimes installing it on the Raspberry Pi can be finicky. Try using this tutorial to install and build opencv: Issuesġ.) If you get an error when trying to run the program showing the following: ImportError: No module named cv2 I then used the USB-C cable plugged into the AC outlet of my car while I drove around to record and process footage. This tissue box setup isn't the greatest, but it's what I used to mount the PI on the dashboard of my car.
#RASPBERRY PI 2 FFMPEG BUILD PLEX CODE#
Special thanks to Evan from EdjeElectronics for the instructions and the majority of the code used in this project! :) Results Reference for Source Code for the Project: Explanation of Machine Learning/Deep Learning/Object Detection:.Post Describing the Training Procedure:.Raspberry PI 4) to see how it performs in terms of processing speed and detection accuracy. Then to test the trained network on lightweight hardware (i.e. bus, traffic light, traffic sign, person, bike, truck, motor, car, train, rider). The goal of this project was to train a neural network to detect things on the road that an autonomous driving vehicle would see (eg. Training Dataset:Berkely Deep Drive (BBD100K).This repository contains code and instructions to configure the necessary hardware and software for running autonomous driving object detection on the Raspberry Pi 4!ĭetails of Software and Neural Network Model for Object Detection: Repository to run object detection on a model trained on an autonomous driving dataset.Īutonomous Driving Object Detection on the Raspberry Pi 4
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