Python is the ideal programming language for rapidly prototyping and developing production-grade codes for image processing and Computer Vision with its robust syntax and wealth of powerful libraries. We insert the sleep to make sure the camera stream is warmed up and providing us data before we move on. You’ll start to notice a pattern here. So you may ask, "Why?” Or even, “How are we using these if they’re not written in Python?” The key points to take away are, while these libraries are written in a different language, they have Python bindings, which allow us to take full advantage of the speed that C++ offers us! Computer vision takes a lot of math... and I’m a big fan of using the right tool for the job. Since I’m finishing this tutorial around the holidays, it seems appropriate to create a Santa hat projector! There are several times while running this code where we will need to have a test pattern or image fill the available area. Finally we return the sprite and its calculated placement in the final image. Using our new corner data, we call our function that computes the destination array for our perspective transform. Beginner level computer vision projects: 1. Instead we'll project a white image, finding the edges and contours in what our camera sees, and pick the largest one with four corners as our projected image. Our first parameter, manual_adjust, I added after the fact because we want our sprite to extend a little on both sides of the detected face. If it is, we bail on the while loop. We use the Python sorted() function to allow us to do a custom sort in the following way: sorted(what_to_sort, key=how_to_sort, reverse=biggest_first)[:result_limit]. Another important general note about camera calibration that doesn't apply to our project, but can be very helpful is that camera calibration also allows us to determine the relationship between the camera's pixels and real world units (like millimeters or inches). This code is run at import time, and before any of our main code is run! From skills that will help you to develop and future proof your career to immediate solutions to every day tech challenges, Packt is a go-to resource to make you a better, smarter developer. Now we need to identify our projection region. After you have the module physically installed, you’ll need to power on the Pi. Finally we return the important data back to the calling function. You’ll learn state-of-the-art techniques to classify images and find and identify humans within videos. If you don't use this, Python will give you a syntax error. Install and run the major computer vision packages within Python, Apply powerful support vector machines for simple digit classification, Work with human faces and perform identification and orientation estimation, Know how to read text from real-world images, See how to extract human pose data from images, Downloading and Installing Python 3/Anaconda, Acquiring and Processing MNIST Digit Data, Creating and Training a Support Vector Machine, Applying the Support Vector Machine to New Data, Introducing TensorFlow with Digit Classification, Example One – Finding 68 Facial Landmarks in Images, Using a Pre-Trained Model (Inception) for Image Classification, Pose Estimation with DeeperCut and ArtTrack, AWS Certified Solutions Architect - Associate. There are two ways to do this, either from the command line, or from the desktop menu. For this project we’re going to be using two computer vision libraries. To overcome this, we will create a black image that matches our projector resolution that we will add our sprite into. Next, we do the same with our y coordinates on the left and right side of the image. technolomaniac. Let's run through the complete code for our project. If everything is set up correctly, you should be able to look at you awesome new test image in all its glory. Our last step is to reshape our data into a nicer matrix and put the corner points into the correct order for processing later, before returning it to the calling function. The alternative is to compile OpenCV from source, which is a topic for another tutorial. We start by reducing the number of channels we need to process by converting the image to grayscale. We don't really need to check every frame here, so it can help speed up our frame rate to only check every fifth frame. The Raspberry Pi has a built-in command line application called raspistill that will allow you to capture a still image. So in order to make this transition, we need to execute the following command. In this function, we scale our sprite to match the size of our detected face. Next, you’ll understand how to set up Anaconda Python 3 for the major OSes (Windows, Mac, and Linux) and augment it with the powerful vision and machine learning tools OpenCV and TensorFlow, as well as Dlib. Packt Udemy courses continue this tradition, bringing you comprehensive yet concise video courses straight from the experts. Normally I would make a shared module where I would put these helper functions that are re-used, but for the sake of this tutorial, I've left most things in the same file. Or, if you’d like to skip typing projector all the time: You should see your prompt change once you’ve entered the environment. Continuing with our project, we will need to activate our development environment again. Obtain a set of image thumbnails of faces to constitute “positive” training samples. Once we have the information we need, we have to reverse the height and width of our resolution. At the end of the project, you'll have learned how Optical and Dense Optical Flow work, how to use MeanShift and CamShist and how to do a Single and a Multi-Object Tracking. To establish an acceptance criteria, I’ll consider this project finished when someone can walk in front of the projector and have the computer track them, projecting a Santa hat on their head. You will be able to follow along with this tutorial without installing or using a virtualenv. Finally, we compute the edges and return our results. This artifacting is introduced by the fact that we cannot have the camera and projector directly on axis with each other. How can you use both these applications? A prime example is how we import VideoStream; we could have imported imutils directly and still accessed VideoStream like so: Another note about organization. Add it to your cart, read through the guide, and adjust the cart as necessary. If you install everything into /usr/lib/python2.7/site-packages (or whatever your platform’s standard location is), it’s easy to end up in a situation where you unintentionally upgrade an application that shouldn’t be upgraded. At this point in the code, we have our identified contours, but we need to sort them into some sort of order. You can build a project to detect certain types of shapes. As you move the head around in front of the monitor, our project should move the sprite around behind it. This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible. Here's an example of getting our faces from our image: This is the basic usage for finding faces. "Magic Numbers" are the constants that appear in the code without explanation. Interesting note here, if you are in an environment that has poor lighting and you run into issues with the camera seeing and detecting faces, you can change the black background to varying shades of gray to have the projector create its own lighting. This also has a pre-built binary available for the Raspberry Pi, which again saves us hours of time. If you are connected to a monitor, you’ll see the preview, but just not over the network. Why doesn't the code work without them? We start by gathering our arguments and starting our video stream. From their website: Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. Here we have a function that builds that white screen for us. One thing that I've noticed as I worked through this demo is the amount of "Magic Numbers" that are included in code. In this tutorial, we'll show you how to use the Flask framework for Python to send data from ESP8266 WiFi nodes to a Raspberry Pi over an internal WiFi network. What are they? The code to do this now that we have all our information is fairly straightforward. Once we've found the angle, we apply our sprite to our black image and hand it back to the calling function. OpenCV, or Open Source Computer Vision library, started out as a research project at Intel. We can install dlib in the same way. First, we will learn how to get started with OpenCV and OpenCV3's Python API, and develop a computer vision application that tracks body parts. Computer Vision Best final year projects on computer vision Meghna Adhikary. You'll remember that in our last section, we calibrated our camera and saved the important information in a JSON file. We subtract the height of our scaled sprite from our y placement to see if it's negative (off the top of the page). If you have any questions or need any clarifications, please comment, and I'll try to clarify where I can. Ok, now that we've identified our project-able region, it's time to watch for and identify faces! Open a terminal window and execute the following commands: This can take quite a while, depending on how out of date the software image you started with was. Here’s a blurb from their documentation: The basic problem being addressed is one of dependencies and versions, and indirectly permissions. In this last section we leverage OpenCV to give us the new correction information we need from our camera calibration. Next we look at y_origin, or the placement of our sprite. Later, you’ll see how to read text from license plates from real-world images using Google’s Tesseract Software. Everything else is discarded. After that, we call our function get_perspective_transform and pass it the information it needs. Here again, you'll find the the OpenCV library has made things easy for us. 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