Learning ONNX for trading - page 10

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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.

• 00:00:00 In this section, the video instructor walks through the software configuration steps to perform object detection with an autonomous driving trained neural network on Raspberry Pi 4. The instructor starts by ensuring the Raspberry Pi is up to date, cloning the repository provided in the video description, and installing a virtual environment to keep dependencies for this project separate from other projects on the system. The instructor then activates the virtual environment before installing dependencies such as a gpio python package, OpenCV, and TensorFlow, which are all things needed for the project, by running a bash script. Finally, the instructor shows how to configure the Raspberry Pi camera module by entering the configuration settings in the terminal and connecting it to the Raspberry Pi 4.
  
  
• 00:05:00 In this section, the presenter demonstrates how to set up the camera module, LED, and push button on the Raspberry Pi for autonomous driving object detection. The camera module is simply plugged into the Pi and secured in place. An LED is connected to the Pi with a 470 ohm resistor to provide visual feedback while the program runs. Similarly, a push button is added to the breadboard to allow starting and stopping the processing of images. The presenter then shows the software side of the setup by running a Python script with arguments for the monitor neural network and output path, which saves the processed images to a specified location.
  
  
• 00:10:00 In this section, we see the output path and where the images captured by the Raspberry Pi with object detection are saved. The user goes on to make adjustments to the batch rc file so that the python script runs on boot, without the need for a keyboard or mouse. The final step is to put the Raspberry Pi in the car, plug it into an AC outlet, and start recording footage with the processed images saved to the output path specified. The user suggests that the captured images can be turned into a gif or a video.

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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.

• 00:00:00 In this section, the video tutorial gives a step-by-step guide on how to set up TensorFlow Lite on a Raspberry Pi for object detection. TensorFlow Lite is a lightweight machine learning model optimized to run on low-powered devices like the Pi, with faster inference times and less processing power required. The tutorial covers 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. The video also includes tips for dealing with errors and getting help, and the GitHub guide includes a list of common errors and solutions.
  
  
• 00:05:00 In this section, the speaker explains how to set up the detection model for TensorFlow Lite. Users can either download a sample model provided by Google or train their own custom model. Google's sample model is a quantized SSD mobile net model trained on the MS cocoa dataset, allowing it to detect 80 common objects with minimal accuracy drop. To download the sample model, users can right-click the link
  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.
  
  
• 00:10:00 In this section, the video creator mentions that the improved speed of TensorFlow Lite makes it useful for real-time detection applications such as smart cameras or alarm systems. They also mention their own project, a pet detector video where they used object detection to alert them if their cat wants to be let outside, and say they will be posting more TensorFlow computer vision projects. They end by thanking viewers for watching and encourage them to stay tuned for their next video on setting up the Coral USB accelerator.

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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.

• 00:00:00 In this section of the video, the presenter provides a tutorial for installing Tensorflow Lite on a Raspberry Pi and using it for image classification. The presenter uses Tensorflow's example library and notes that a Coral USB accelerator can be used to increase the speed of the project, although it is not required. To begin, the presenter upgrades the Raspberry Pi and creates a virtual environment. The presenter demonstrates how to activate the environment and install the necessary packages before installing the Tensorflow Lite runtime. Finally, the presenter checks the version to ensure that everything is properly installed.
  
  
• 00:05:00 In this section, the speaker runs an object detection example on a Raspberry Pi, but encounters an error related to lib atlas. They explain that lib atlas is crucial for linear algebra, which is an important component of machine learning. They show how they fixed the issue by running sudo apt-get install lib atlas bass dash dev. The speaker then demonstrates real-time classification using the Raspberry Pi and emphasizes that the script can be modified to fit different use cases or models.

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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.

• 00:00:00 In this section of the video, the creator discusses how to create an object detector with a good balance between accuracy and speed. The detector can run in real-time while detecting multiple common objects and does not require any third-party libraries to run other than OpenCV. The video goes through the codes of creating an object detector and explains in detail the usage of the MobileNet SSD, which has a good balance between accuracy and speed, and can run on a CPU almost in real-time, making it an excellent choice for detecting common objects. Finally, opencv-python is the only library needed to run the detector, and the coco dataset is used to detect classes such as person, bicycle, and car.
  
  
• 00:05:00 In this section of the video tutorial, the presenter demonstrates how to display an image using the OpenCV library in Python. They import names of objects from the coco dataset using the with open function and read it in as an array. They then import configuration files and weights and use the OpenCV function to create the model. Unlike in the YOLO video tutorial, where techniques had to be applied to extract the bounding box, the function does all the processing for us, and all we need to do is pass the image, and it will display the bounding box and ID names.
  
  
• 00:10:00 In this section, the video tutorial explains how to perform object detection using OpenCV and Python. After configuring the detection model, the code sends the input image to the model and returns the class ids, confidence levels, and bounding boxes. The tutorial focuses on getting an object detector up and running as quickly and easily as possible without extensive installations or formalities. The code can be utilized for various applications such as self-driving cars or robotic devices. The tutorial also touches on the importance of class ids and the significance of subtracting one from their values when referring to the class names.
  
  
• 00:15:00 In this section, the instructor explains how to loop through three different variables or information using the zip function. They use zip to flatten the confidence and bounding box variables and then use a for loop to create a rectangle around the detected object. They also write the name of the detected object using the putText function and use the class names variable to subtract one from the class id to get the appropriate name. The instructor adds other parameters to make the label more visible and even changes the text to be all in capital letters. Finally, they successfully detect a person in the image.
  
  
• 00:20:00 In this section, the tutorial demonstrates how to modify the code to run object detection on a webcam feed instead of static images. The code uses "cv2.videoCapture" to initialize the webcam and set the image size parameters. The while loop is used to constantly capture and display the webcam feed, and a condition is added to check if any object has been detected before displaying it. The system accurately detects objects such as a keyboard, monitor, cell phone, and mouse in real-time using the webcam feed.
  
  
• 00:25:00 In this section, the author explains that the object detection algorithm can detect objects with good speed and accuracy, even though it might not be the best accuracy. The video then goes on to show how to change the threshold value and add confidence values to the detected objects. The YouTuber then runs the program and demonstrates how the confidence values are displayed for the detected objects, which are visible and clear enough to understand the probability of each object.

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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.

• 00:00:00 In this section of the video, the narrator provides a guide on how to set up TensorFlow Object Detection API on a Raspberry Pi. The steps include updating the Raspberry Pi, installing TensorFlow, OpenCV, and protobuf, setting up the TensorFlow directory structure, and testing out the object detector. The narrator also recommends installing additional dependencies, such as numpy, pillow, scipy, and matplotlib. Additionally, the video provides helpful tips such as installing libatlas and libAV codec for smooth processing.
  
  
• 00:05:00 In this section, the speaker provides a step-by-step guide on how to install the TensorFlow Object Detection API on the Raspberry Pi. They start by installing the necessary packages, including lib xvid core dev, lib x264 dev, and QT for dev tools, followed by OpenCV. The speaker then explains the challenges that come with installing protobuf on the Raspberry Pi and guides the viewer on how to compile it from source, including getting the packages needed to compile proto bruh fun source and downloading the protobuf releases page. Finally, the speaker provides the necessary path commands and issues the command to install the protocol buffers Python implementation.
  
  
• 00:10:00 In this section, the speaker explains the process for setting up the TensorFlow directory on the Raspberry Pi. This involves creating a directory for TensorFlow and downloading the TensorFlow repository from GitHub. The Python path environment variable needs to be modified to point at some directories inside the TensorFlow repository, and this is done by modifying the bash RC file. The speaker also explains how to download the SSD Lite model from the TensorFlow detection models zoo and the use of Pro Talk to compile the protocol buffer files used by the object detection API. Finally, a Python script for detecting objects on live feeds from a Pi camera or USB webcam is provided, the code of which is available on the speaker's GitHub repository.
  
  
• 00:15:00 In this section, the speaker guides the viewers on how to use TensorFlow Object Detection on Raspberry Pi. They first instruct the viewers to download and run the python script for object detection, ensuring that the PI camera is enabled in the configuration menu. They also explain that it is recommended to close all other applications, especially the web browser, as tensorflow uses a lot of memory. The speaker also shows how to use a model that the user has trained themselves and provides a Dropbox link for their own playing card detection model as an example. The viewers are advised to run the object detection script, which may take up to a minute to initialize, and then will detect common objects and display them in a window with a rectangle. Lastly, the speaker recommends the Raspberry Pi for creative applications that require low-cost and portability, such as a digital cat flap that sends a message when it detects the resident cat outside.

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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.

• 00:00:00 In this section, Core Electronics demonstrates how to use OpenCV and Python's face recognition package on a Raspberry Pi to create a facial recognition system. First, they gather the necessary materials, including the Raspberry Pi, Official Camera, Micro SD card, HDMI cord, and power supply. After configuring the raspberry pi and installing the packages, they show how to train the facial recognition system using a python code called "train_model.py" and then test it using an identification code named "facial_req.py." The program enables the Raspberry Pi camera to search live for faces and identify them correctly once it locates them. The system can also distinguish between unknown and known faces, displaying "unknown" or the subject's name, respectively.
  
  
• 00:05:00 In this section, the video creator explains how to add six lines of code to the script to control a servo using GPIO pins of the Raspberry Pi, which can rotate only when the Raspberry Pi system recognizes its owner's face. The system will not activate the servo if it recognizes no face or an unknown face. The video creator hides his face and shows how the servo moves when recognizing his face. He credits the OpenCV and facial recognition package teams and Carolyn Dunn for creating the software that makes these systems work together so well. The video creator believes this software has immense potential to take projects to incredible places.

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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.

• 00:00:00 In this section of the video, the presenter explains how to set up a Raspberry Pi for an all-in-one installation of TensorFlow and OpenCV, starting with the importance of having a newer Pi, specifically a Pi 4 that is 64-bit. The video covers the process of installing Raspberry Pi OS and setting up a host name, username, password, and Wi-Fi configurations using the Raspberry Pi imager. After booting up the Pi, the presenter instructs viewers to update and upgrade before checking their Python version and "uname -m" output, which are important for selecting the appropriate TensorFlow shell script for their system. The presenter also directs viewers to a privately hosted shell scripts and wheel files that can make TensorFlow work with the Raspberry Pi.
  
  
• 00:05:00 In this section of the video, the presenter discusses how to change your Python version to 3.7 for those running into issues installing TensorFlow 2 and OpenCV on a Raspberry Pi. To do this, viewers need to use pi m and install the required Python version. The presenter demonstrates how to install pi m, add lines to the dot bash rc file, install system packages, and update pi m. The presenter then explains how to install Python version 3.7.12 and make a project directory. Finally, the presenter shows viewers how pi m works and checks the Python version.
  
  
• 00:10:00 In this section, the speaker explains how to install TensorFlow 2 and OpenCV on a Raspberry Pi. The speaker suggests using Python3.9 or Python3.7 with the appropriate TensorFlow wheel shell command. They walk through installing a virtual environment package and creating an environment in which to work. The speaker then explains how to install system packages and TensorFlow. There's a simple test provided to determine whether the installation is successful. The speaker also discusses an error that users may encounter and presents the solution to the problem.
  
  
• 00:15:00 In this section, the speaker provides instructions for installing OpenCV on Raspberry Pi. For Raspberry Pi 3 users, he recommends following a particular method detailed in a specific video and then running a single command: pip install opencv-python. This command takes just ten to twenty seconds to execute, and the speaker recommends not adding any optional features unless you are an advanced user. The video ends by testing the OpenCV and TensorFlow installation using import commands, and the speaker encourages viewers to leave comments, requests or feedback.

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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.

• 00:00:00 In this section of the video, the presenter introduces the project of using a Raspberry Pi 4 in combination with a trained library and a Pi camera to identify 91 unique objects and animals in real-time, with an updating confidence rating. Opencv software is utilized to provide resources to help solve real-time computer vision and imaging processing problems. The presenter walks through the steps necessary to set up the hardware, configure the Raspberry Pi, and install the software. The viewer is then shown how to run the code, and how to tinker with several of the code's values to refine the object and animal identification process.
  
  
• 00:05:00 In this section, the presenter demonstrates how to modify code to send signals through the GPIO pins of a Raspberry Pi whenever a particular target, in this case a cup, is seen by the OpenCV software. The modified code commands the Raspberry Pi to rotate whenever the cup is detected. The potential of this software to take on amazing projects is highlighted, along with a thanks to the OpenCV and CoCo teams for their work on this software.

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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.

• 00:00:00 The video shows how to access the code for the object detection project and create a function that allows users to get information on specific objects. The code used is from a previous video that detects different objects using the MobileNet SSD. The tutorial also emphasizes writing modular code to make it easier to remove and add code. In addition to explaining the code, the tutor also gives useful tips on how to write and use the code on different platforms, including Raspberry Pi.
  
  
• 00:05:00 In this section, the speaker describes how to turn the code that was previously written into a module that can be accessed by other scripts. The speaker demonstrates by creating a function called "get_objects" that takes an image as input and returns an image with rectangles and object detection labels as output. The speaker also shows how to use the "nms" parameter to remove overlapping object detections. By the end of the section, the speaker has created a modular function that can be used to detect objects in an image with OpenCV and Python.
  
  
• 00:10:00 In this section, the video demonstrates how to add functionality to control whether to display the bounding boxes and class names to increase or decrease the frame rate. The video explains that a boolean value can be set to determine whether to draw the bounding boxes and then shows how to implement this in the for loop. The video also adds the feature to send information about the bounding box and class name, allowing you to receive the actual information rather than just displaying it. Finally, the video shows how to add functionality to detect specific objects and control what the model outputs.
  
  
• 00:15:00 In this section of the video, the presenter explains how to customize the object detection module to detect specific objects. The user can create a list of objects to detect by writing them down in the objects list. If the user leaves the list empty, it will detect all the classes it was trained on. The presenter shows an example of how to detect only cups by adding "cup" to the objects list. Multiple objects can be added to the list, and the program will detect only those objects. The presenter also provides a way to run the object detection module from another module using the main module.
  
  
• 00:20:00 In this section of the video, the presenter explains how to modify the code for object detection by adding parameters such as a threshold value and non-maximum suppression (NMS). The NMS parameter helps to remove duplicate detections in the image. The presenter shows how to add these parameters to the code and demonstrates the effects of changing their values. Later in the video, the presenter explains that in order to run the code on a Raspberry Pi, OpenCV version 4.3 or later is needed. If the user hasn't installed this version before, they can follow the presenter's instructions on their website.
  
  
• 00:25:00 In this section, the instructor demonstrates how to set up object detection on a Raspberry Pi using OpenCV and Python. This involves replacing several files with the latest version, importing cv2, and checking the version number. The instructor also provides the necessary files for object detection and demonstrates how to edit the file path so that it works correctly. Additionally, the instructor shows an example of object detection using an external camera and notes that it may take some time to process. The code is run successfully, and the model is able to detect objects such as bottles, cups, and remotes.
  
  
• 00:30:00 In this section, the presenter demonstrates the ability to detect specific objects using OpenCV and Python on a Raspberry Pi. They test the detection by changing the label from "remote" to "cup" and then to "bottle" and proceed to run the detection again. The detection seems to work well on the Raspberry Pi, but it is slow. The presenter mentions that they will try the same detection on a Jetson Nano in the next video to see how much better it performs. They also invite viewers to visit their website to download the files and codes for free and to subscribe to their channel.

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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.

• 00:00:00 In this section of the video, the presenter explains two methods to install OpenCV for Python on a Raspberry Pi. The first method involves installing pre-built binaries with a single terminal command, which is simple but may not guarantee the latest version of OpenCV. The second method is to build OpenCV from the source, which requires installing some dependencies first, downloading the source from the Github repository, and creating and running a command to build the source. Both methods are shown step-by-step in the video.
  
  
• 00:05:00 In this section, the video discusses the final steps of installing and building OpenCV Python from source on a Raspberry Pi 4 or 3. After running the cmake command and then the make command, which can take several hours to complete, the final step is to type "sudo make install". To check if the installation was successful, the video demonstrates how to enter the command "python3" and then "import cv2 as cv" followed by a print statement. If the terminal returns a message with the OpenCV version, then the installation was successful. The video encourages viewers to like and subscribe to the channel and to ask any questions in the comment section.