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Autonomous Driving Object Detection on the Raspberry Pi 4!
Autonomous Driving Object Detection on the Raspberry Pi 4!
In this tutorial, the instructor demonstrates the steps required to configure the Raspberry Pi 4 for object detection with an autonomous driving trained neural network. This includes cloning the repository, setting up a virtual environment, installing dependencies such as GPIO, OpenCV, and TensorFlow, and configuring the Raspberry Pi camera module. Next, the instructor demonstrates connecting an LED and push button to the Pi and running a Python script to capture images with object detection. Finally, the user can make adjustments to the batch rc file to run the script on boot and record footage with the images saved to the output path.
How To Run TensorFlow Lite on Raspberry Pi for Object Detection
How To Run TensorFlow Lite on Raspberry Pi for Object Detection
The tutorial explains how to set up TensorFlow Lite on a Raspberry Pi for object detection. This involves updating the Pi, enabling the camera interface, downloading the GitHub repository, creating a virtual environment, installing TensorFlow and OpenCV, and running a shell script to install all required packages and dependencies. Users can download a sample model provided by Google or train their own custom model. Once the model is ready, users can run a code on Python 3 to see their real-time webcam detection script, detection on videos and images. The improved speed of TensorFlow Lite make it useful for real-time detection applications, such as smart cameras or alarm systems. The creator also mentions their own pet detector project and encourages viewers to stay tuned for their next video on setting up the Coral USB accelerator.
in the description and run it on the terminal using "W git" for download and "unzip" for extraction. Additionally, the speaker provides a written guide on GitHub for users who want to train a detection model and convert it to TensorFlow Lite. Once the model is ready, users can run a code on Python 3 to see their real-time webcam detection script, detection on videos and images. The speaker also mentioned that they will explain how to get a huge boost in detection speed by using Google's choral USB accelerator in their next video.
Raspberry Pi Object Detection Tutorial
Raspberry Pi Object Detection Tutorial
In this Raspberry Pi object detection tutorial, the presenter shows how to install Tensorflow Lite on a Raspberry Pi and use it for image classification with real-time classification demonstration included. They also explain what lib atlas is, a crucial component of machine learning for linear algebra, and how to fix related errors on a Raspberry Pi. The presenter notes that a Coral USB accelerator can be used to increase the speed of the project but is not required. Overall, the presenter emphasizes the flexibility of the script to fit different use cases or models.
Object Detection OpenCV Python | Easy and Fast (2020)
Object Detection OpenCV Python | Easy and Fast (2020)
In this video tutorial titled "Object Detection OpenCV Python | Easy and Fast (2020)," the presenter demonstrates how to create an object detector using the OpenCV library in Python. The video focuses on creating a detector with a balance between accuracy and speed that can detect multiple commonly found objects in real-time. The MobileNet SSD model is used for object detection due to its speed and accuracy, and the coco dataset is used to detect classes like person, bicycle, and car. The video shows how to loop through various variables using the zip function to create a rectangle around the detected object and how to modify the code to run object detection on a webcam feed. The presenter also explains how to adjust the threshold value and add confidence values to detected objects to understand the probability of each object.
How to Set Up TensorFlow Object Detection on the Raspberry Pi
How to Set Up TensorFlow Object Detection on the Raspberry Pi
In this video, the process of setting up TensorFlow Object Detection API on a Raspberry Pi is explained step-by-step. First, the required packages are installed, including TensorFlow, OpenCV, and protobuf. Then, the TensorFlow structure is set up, and SSD Lite models are downloaded from the TensorFlow detection models zoo. A Python script for object detection is provided, and viewers are shown how to use it with a Pi camera or USB webcam. The video also covers more advanced topics, such as downloading and using a custom model. The Raspberry Pi is recommended for creative projects that require low-cost and portability, such as a digital cat flap that can send a message when it detects the resident cat outside.
Face Recognition With Raspberry Pi + OpenCV + Python
Face Recognition With Raspberry Pi + OpenCV + Python
Core Electronics showcases how to create a facial recognition system using OpenCV and Python's face recognition package on a Raspberry Pi. The tutorial includes training the system using a Python code named "train_model.py" and testing it through an identification code called "facial_req.py." The system can differentiate unfamiliar and known faces, and it can rotate the servo as well once the system recognizes a known face. The creator credits the OpenCV and facial recognition package teams, along with Carolyn Dunn, for making this kind of software possible and has high hopes for its potential in their future projects.
How to Install TensorFlow 2 and OpenCV on a Raspberry Pi
How to Install TensorFlow 2 and OpenCV on a Raspberry Pi
This video provides a step-by-step guide on how to install TensorFlow 2 and OpenCV on a Raspberry Pi. The presenter emphasizes the importance of having a newer Pi, specifically a Pi 4 that is 64-bit, and provides instructions on how to install Raspberry Pi OS, update and upgrade the system, and select the appropriate TensorFlow shell script for their system. The video also explains how to change the Python version to 3.7 for those experiencing issues with installation and provides detailed instructions on installing virtual environments, system packages, TensorFlow, and OpenCV. Throughout the video, the presenter provides helpful tips and solutions to potential errors. The video concludes by testing the installation of OpenCV and TensorFlow using import commands and encourages viewers to leave feedback or requests.
Object Identification & Animal Recognition With Raspberry Pi + OpenCV + Python
Object Identification & Animal Recognition With Raspberry Pi + OpenCV + Python
The video showcases a Raspberry Pi 4 project that utilizes a trained library and a Pi camera to identify an extensive range of 91 animals and objects in real-time with a confidence rating. The presenter provides a thorough demonstration of how to set up the hardware, configure the Raspberry Pi, and install OpenCV software to enable real-time computer vision and imaging processing operations. Through the example of a cup as a target, viewers learn how to modify the code to send signals via the Raspberry Pi's GPIO pins to execute specific actions when OpenCV identifies the target. The presenter highlights the software's potential for exciting projects and expresses gratitude towards OpenCV and CoCo teams.
Object Detection Raspberry Pi using OpenCV Python
Object Detection Raspberry Pi using OpenCV Python
The YouTube video "Object Detection Raspberry Pi using OpenCV Python" demonstrates how to access and modify a code for object detection, specifically the MobileNet SSD. The tutorial emphasizes modular coding and provides tips for using the code on different platforms, including Raspberry Pi. The video shows how to turn the code into a module and create a function that detects specific objects and controls what the model outputs. The presenter also demonstrates how to modify the code for object detection by adding parameters like threshold value and non-maximum suppression. The video provides the necessary files and instructions for setting up object detection on a Raspberry Pi and offers a demonstration of the detection of specific objects. The presenter invites viewers to visit their website for download and subscription information.
Install and build OpenCV python From Source on Raspberry pi 4 and 3
Install and build OpenCV python From Source on Raspberry pi 4 and 3
The YouTube video explains two methods of installing OpenCV for Python on a Raspberry Pi, with the first one involving a single terminal command to install pre-built binaries and the second method requiring building OpenCV from source. After downloading the source from the Github repository, the final steps of building OpenCV from source on a Raspberry Pi involve running the cmake and make commands, which may take several hours to complete, before typing the "sudo make install" command. The video demonstrates how to check the successful installation using a Python command. The video ends with an encouragement to like, subscribe and ask any questions in the comment section.