Opencv led tracking

Opencv led tracking


  • Knowledge Base
  • Multiple Color Detection in Real-Time using Python-OpenCV
  • Multiple Object Tracking using Deep Learning with YOLO V5
  • Controlling Arduino using OpenCV Interface
  • Simple object tracking with OpenCV
  • Computer Vision : Object detection and tracking using OpenCV and Python
  • Knowledge Base

    Project Architecture for Pros Everyone has seen pictures of TouchDesigner projects with hundreds of operators and wires all over the place. Impressive, right? In fact, the opposite is true. The messier your project architecture is, the harder it is to collaborate. Eventually, working with a team becomes impossible. Even working alone, keeping everything straight in your head becomes a recipe for disaster.

    Finally, your project is far more likely to break when you update it. This is because everything is coupled together more on this later. If you want to create large-scale installations or consistently work on projects in a professional capacity, you need a project architecture that is clean, organized, and easy to use.

    The best project architectures — those used by the pros — are so streamlined that they make programming TouchDesigner look boring. With my project architecture system at your disposal, you will: Dramatically shorten your development cycles. By reducing complexity in your project architecture, you eliminate unnecessary steps in the development process.

    Shorter development cycles let you adapt to client demands, as well as take on more advanced — and better paying — gigs. Collaborate effectively. Once projects get bigger, a simplified architecture like mine is the only way to work with a team.

    And effective collaboration is a requirement for anyone looking to work long-term professionally with TouchDesigner. Never refactor again. When I first started with TouchDesigner, I would sometimes spend days rebuilding my projects. With my current project architecture, I never have to refactor — and neither will you. We accomplish this through my 3 core project architecture concepts: Standardize: This is part of what enables effective collaboration, which is necessary if you want to create complex, large-scale installations.

    When you standardize your project architectures, you speed up your development cycle while also minimizing the chances of introducing bugs into your workflow. Compartmentalize: By isolating nodes and features into compartments, we can introduce logic into our workflow. This reduces the need to refactor projects down the road, makes it easier to optimize nodes and wires, and promotes the re-use of specific components i. Decouple: The cornerstone of collaboration, decoupling lets us separate our functionality from our controls.

    This enables you to create multiple user interfaces so teams can work on different things in the same project. Without decoupling, any number of issues can occur. Someone could add, rename or change something without the others knowing, creating a chain of bugs down the line.

    Decoupling also results in far less refactoring of code is necessary. You will also have the confidence you need to land better gigs and meet challenging client demands with flexibility and ease. How about installations that span forty-story high-rises that use Twitter posts to prompt generative designs? Big clients — with big budgets — demand a level of immersion deeper than the use of Microsoft Kinect and Leap Motion interaction. In short, they want to use technology to become part of the broader conversation.

    The catch? Figuring out how to integrate data natively in TouchDesigner is particularly challenging. Made for the complete Python beginner, the training provides you with everything you need to begin integrating external data sources with your TouchDesigner projects. In this 1.

    Walk through how to register, get credentials, and read API documentation. This can be a daunting process and typically deters newcomers.

    Get answers to common problems, such as how to work with Big Data without experiencing performance issues. Receive 2 project file templates that you can use in your projects however you like. Many people quit out of frustration. Then you need this training. When I first started working with TouchDesigner in , I thought the most valuable skill I had to offer was my ability to code beautiful interactive and immersive media projects for my clients.

    While this IS important, I quickly realized that that what my clients valued most was my ability to create an installation that performed perfectly — no tearing, stuttering, judder, or any other issues.

    When I first started, I encountered all the issues mentioned above. I overcame them with a combination of all-nighters, hiring the right and expensive experts, and in some cases, luck. I also wasted a lot of time and money. With experience, I was able to preemptively solve for all these performance issues. After this training, you will have the confidence you need to deploy immersive design and interactive technology installations for big brands who pay top dollar for your skills.

    I learned these directly from the source: NVidia and Derivative. These settings have helped me avoid disaster many, many times. How to ensure you purchase the right hardware for an installation. We want to eliminate tearing, stuttering or judder — the main causes of a failed installation — before they occur. A deeper understanding of VSYNC, hertz, FPS so that you understand how to identify and troubleshoot the causes of tearing, stuttering, and judder, should they happen to your installation.

    How to configure windows 7 and 10 for optimal performance, so that your project runs at a high, stable frame rate. How about installations that use GPU particle systems, volumetric lighting, and multi-composite operators?

    As lots of you know, this is all possible with TouchDesigner — sort of. But as your interactive installations grow larger and your clients begin to want more generative and technical content, there are several challenges that arise and the cracks begin to show.

    Problems typically fall into two broad categories: Workflows that should be simple become bloated and tedious. Anyone who has tried to composite a large number of icons on screen when working on info displays has experienced this first-hand..

    Performance issues such as low framerate become unmanageable, requiring time-consuming workarounds. In some cases, these kinds of technical issues become unresolvable. When problems due to scale such as these inevitably occur, the standard TouchDesigner functionality and nodes only gets you so far.

    Lucky for us, we can leverage the code that powers a lot of TouchDesigner to create installations of virtually unlimited scale and technical possibility. GLSL is the programming language on which many of the features of TouchDesigner are created even now.

    Once you learn how to do this, you can customize TouchDesigner however you like. This is the knowledge required to overcome the problems faced when trying to scale your projects Never programmed C — let alone GLSL Shaders — a day in your life? No worries. I start right from the beginning and assume zero knowledge of either language.

    I walk you through everything necessary to begin importing GPU code from Shadertoy. This gives you access to thousands of shaders that you can use for inspiration or in your installations. I provide you with 9 of example project file templates. The techniques in these templates have made me thousands of dollars and are the result of years of trial and error.

    Elburz Sorkhabi explores and explains concepts in TouchDesigner revolving around network optimization and performance bottlenecks.

    Multiple Color Detection in Real-Time using Python-OpenCV

    It has various uses like object detection, counting objects, security tools ,etc. The Object tracking is a prominent technology in image processing which has a large future scope. The MOT has made significant growth in a few years due to deep learning, computer vision, machine learning, etc. This paper aims to provide a software solution that keeps track of the objects so that it can handle object list and count.

    Also unlike the general yolo object detection tool which detects all objects at the same time ,this MOT system also detects only objects which are needed to be detected by the user and thus helps in improving the performance of the system.

    Multiple Object Tracking MOT plays an important role in solving many basic problems in computer vision [1]. Tracking multiple objects in videos requires detection of objects in individual frames and combining those across multiple frames.

    Many Computer Vision techniques have been used to build MOT systems, and day to day the technology is growing rapidly providing an area of opportunities called image processing is done by providing a labeled dataset which is trained and is used as a model for the system which can detect objects in different frames comparing to the objects that was provided in the model by mapping the same pattern of model in the frame [2].

    The proposed system uses the Latest YoloV5 which is used to detect the objects. YoloV5 uses pytorch classifier for training as well as detection. Yolo begins its journey with darknet technology ,which was later developed to yolov2 ,then yolo v3 and later to yolo v4 [9].

    And now for easy building of object detection yolo v5 was introduced leading to better performance of object detection [6]. Yolo V5 is constructed with Pytorch Classifier in deep learning and after object detection the opencv module is used for inputting real-time or file format video input to the algorithm and also tracks, and counts the objects detected in the output obtained making the system an efficient MOT system.

    And also the Tkinter makes the MOT easy for user interaction making it user to choose particular model according to the user requirements and only the particular model object is detected. The proposed system can be used for various object crowded environments for detection of particular classified objects according to the environment.

    The MOT makes users take count of the same type of objects and also used to detect the particular object from the bulk crowd helping users to save time in searching for a particular object. Classification is the to classify the image with a unique id in order to identify it while object detection. Detection is the next step where using the trained model the object is detected in frames. This gives a good perception for the system giving the idea about the image.

    This step detects the objects using the model and provides the location of the object in that frame. The next stage is the segmentation where the detected object is described and segmented for better understanding commonly used coordinate representation of rectangular detection box as shown in Fig. When he started the yolo algorithm construction and when it didn't have significant progress in another author Alexey Bochkovskiy published a paper on yolo and then after that a series of yolo arrived which led to yolov2,yolov3 and then upto yolov4.

    Yolo V5 has the same advantages and has almost similar architecture as yolo v4. Yet Yolov5 makes it convenient to train and detect objects compared to yolov4. It begins with object segmentation where image division takes place in order to discover image boundaries and objects in it. It is used for labelling using bounding boxes in image. After that in recognition in context, a basic correlation architecture is represented between the image and the objects in it. After this we collect or gather the same type of color or grey levels.

    This is super pixel stuff segmentation where they help in featuring important areas and also they can reduce the input element for calculations. Open CV Opencv is an open source library for computer vision modules like image processing, camera access,etc [4].

    Now Gpu is also included in the Opencv module which is an essential element for pytorch [5]. Opencv technology developed in a fast rate and supports many algorithms to make the algorithm efficient,especially in image processing field. Opencv in python supports libraries like numpy, matplot,utilis,etc. Opencv does few applications like video image stitching, navigation, Medical analysis, etc.

    Then the inputted data is processed into frames for detection. During detection the The Yolo V5 uses the Model or dataset to detect the object in the input data. By using the model after detecting objects the detected objects are classified and represented by using labels and bounding boxes around the detected objects and then it is processed into output video format As shown in Fig.

    Input Using OpenCV we can access camera module and also we can add video files in different formats. Using OpenCV the the real-time video frame is collected from camera lenses. Before collecting the real-time video frame we assigned a Tkinter window prompt which signifies or collects from the user about which dataset or model should be used for detection.

    Once the input option is received the user option is sent to neural network module and the camera s enabled using OpenCV and starts collecting the video frames from the camera lens. General working system Neural Network YOLO V5 Here using the input received from the camera or video the input data is classified into frames and each frame is sent to yolo detection algorithm with the model which user selected.

    The model can be a predefined model that is, COCO dataset model or we can create custom models for detection. Once the detection is done, it is bounded with boxes and labels where the object is found and is send to output section where the detected frames are collected and are then compressed into output format. Before merging, the detected frames are used for tracking, counting and sorting using OpenCV and also for better results DeepSORT is also used for sorting and tracking of objects [8].

    Dataset This field is used for creating custom dataset from raw images in order for creating a custom model which can be used for detection. For this the first thing is used to collect the raw images from various sources and create a dataset. Then from the dataset images the objects must be annotated and labelled from the images.

    For this Python frameworks like Labeling is used for annotating and labelling of the objects. Once this is done it can send to yolo training algorithm where the dataset can be trained and model can be created using COCO dataset model. Once the YAML file is configured it is set up in algorithm and using pytorch the given dataset gets trained using GPU according to the epochs given in algorithm for training and with test dataset the testing of trained model after completion also takes place, predicting the objects in test image.

    Once the objects are predicted the model is compressed into the yolo model format which is configured using the pre- trained model that is using COCO dataset model. Once this is done the model file with the corresponding test result potted in graphs and texts are written in output folder where the evaluation and testing of the model can be done by using it in a detection algorithm.

    The activity structure or system architecture of the proposed MOT model is shown in Fig. Integration Here the above five modules are intersected to make a single system where at front a login page using MySQL can also be set up but it is considered as optional as opencv mostly works under a secure arena.

    After integrating the modules it is compressed into an executable file which is in.

    Multiple Object Tracking using Deep Learning with YOLO V5

    PySerial was installed either with the rest of the OS or one of the other packages. In any event, it was already present on my system after following all the above steps. I use the Linux utility screen as a terminal emulator.

    Controlling Arduino using OpenCV Interface

    Screen is not installed by default. The following command will install it: sudo apt-get install screen Finally, any user that wishes to access the serial port needs to be placed into the dialout group. Once the command is executed, the user needs to log off then log back into the system for it to take effect. Software Installed! That concludes the installation of the OS and all the software packages required to run the Python face detection script and communicate with the Arduino. The next steps are to install a sketch on the Arduino to move the motors in response to commands on the serial port and to run the Python face detection script.

    While the motors are running, it listens for the next position and will change the destination on the fly if the previous move has not completed. Download the Arduino sketch from my github repository here. If needed, post two of this series contains links to download the IDE and some tutorials.

    It should match the baud rate set in the Arduino sketch. If screen starts then immediately exits, your user account needs to be added the dialout group using the usermod command a few paragraphs above.

    Using screen to test connectivity to the Arduino and googly eyes. My script is based off this sample code. To run the sample as is, cd to opencv Green rectangles should appear around any faces in the video. Blue rectangles should appear around nested eyeballs. Hit escape to exit. Output of the default facedetect.

    Simple object tracking with OpenCV

    Detected faces are highlighted in green. Detected nested eyeballs are highlighted in blue. I made the following changes to the script to make it work with the Arduino and the googly eyes: Adjust video size — Capture the input video at the full resolution of the webcam. Find the biggest face — What happens when multiple people are in the frame? I modified the code to calculate the areas of all the faces found and use the largest face.

    All the faces are highlighted in green and the face used for the position of the eyes is highlighted in red. Create a new directory, cd to it, and download my script to it from my github repository here.

    The script is dependent on several files that are in the python2 samples directory. Copy the following files so they are in the same directory as the file fd2. Now launch the Python script using the following command.

    Computer Vision : Object detection and tracking using OpenCV and Python

    LayoutParams RelativeLayout. Open FdView. But you can try it with both — simply rewrite the filename. As face is found, it reduces our ROI region of interest — where we will finding eyes to face rectangle only. From face anatomy we can exclude the bottom part of face with mouth and some top part with forehead and hair. I could be computed relatively to the face size. It saves computing power. Get template — find eye in desired roi by haar classifier, if eye is found, reduce roi to the eye only and search for the darkness point — pupil.

    Create rectangle of desired size, centered in pupil — our new eye template. Tutorial


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