GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. In this project readers will learn how to create a standard real-time project using OpenCV for desktopand how to perform a new method of marker-less augmented reality, using the actual environment as the input instead of printed square markers.
It covers some of the theory of marker-less AR and show how to apply it in useful projects. See the related Medium post for more information! Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Latest commit ce1 Jun 30, Marker-less Augmented Reality In this project readers will learn how to create a standard real-time project using OpenCV for desktopand how to perform a new method of marker-less augmented reality, using the actual environment as the input instead of printed square markers.
Once the target image is detected just track the keypoints using sparse optical flow calcOpticalFlowPyrLK and compute camera pose solvePnp instead of performing feature detection and matching on every frame. The feature detection will be performed again when tracking is lost on most of the keypoints.
Dimensionality reduction will be performed on key points to make pattern detector more robust. You signed in with another tab or window. Reload to refresh your session.Arkwood has a long-standing phobia of Lego policemen. Of course, it was just his drug-addled brain playing a cruel trick.
Their sticky yellow fingers are oft found in the cookie jar. Dropping into a while loop, I obtain the current frame from my webcam via the Webcam class. Next, the Detection class tries to find Lego policemen in the webcam image, using a haar cascade classifier. If a Lego policeman has been found then I use the Effects class to draw a 3D jail on the image, to incarcerate any Lego criminals.
The code from the classes, along with a link to my haar cascade classifier, is at the foot of this post. But first, a demo…. A Lego soldier walks into shot. A military man is not an officer of the law — alas — therefore our grid does not yield a virtual jail:. Sorry, my angel of the wards, but the virtual jail is not for you:. Our haar cascade classifier has detected the Lego policeman in the webcam image.
A virtual jail has been drawn on the grid. Granted, there is a bit of work still to do. For a start, the jail has no bars, so all those nasty criminals will be able to make good their escape.
And we could do with omitting the detection rectangle around the policeman, as it spoils the sense of mind-boggling augmented reality. Next, the Detection class. It uses my Lego Policeman haar cascade to attempt to find Lego policemen in the supplied image. Anuj said:. October 25, at pm.
You are commenting using your WordPress. You are commenting using your Google account.Augmented Reality tutorials with OpenCV and OpenGL
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Notify me of new posts via email. Finally, we show the augmented image in a window. But first, a demo… The scene is set: A Lego soldier walks into shot.
Sorry, my angel of the wards, but the virtual jail is not for you: But what is that I hear? Why, it is a bobby on the beat: Hurray! We can build and pan our jail at different angles: Granted, there is a bit of work still to do. VideoCapture 0 self. Share this: Twitter Facebook.
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Like this: Like Loading Hi, do I require a depth camera for this or a simple laptop webcam can work?? Leave a Reply Cancel reply Enter your comment here Fill in your details below or click an icon to log in:.The main idea is to avoid framing problems as black box problems, throw a neural network at it and hope for the best.
The main idea is rather to do the maximum amount of work with proven technologies, and let deep learning work only on a well-defined subset of the problem. This time, I was working on an augmented reality problem, where I have an image, and I want to overlay stuff on it. In OpenCV pinhole camera model, those parameters are: fx horizontal focal lengthfy vertical focal lengthcx camera center X coordcy camera center Y coord.
You want to overlay stuff on the original image. Now you have estimated the OpenCV camera parameter, you need to turn it into an OpengL projection matrix, so that you can render stuff on top of the original image using the OpenGL graphics pipeline. First of all, the OpenCV camera matrix projects vertices directly to screen coordinates. OpenGL projection matrix projects vertices to clip space. Here is a source code sample to demonstrate how you can project a point with OpenCV and OpenGL and get the same results therefore validating your matrices :.
When you have your OpenGL projection matrix, you can then render and overlay all the stuff you need on your image. I initially expected this step to take me 1 or 2 hours and it ended up taking me like 6 or 7 hours, so I thought I would share the solution.
Augmented Reality using OpenCV and Python
Thoughts and opinions from a startup CTO passionate about aerospace and computer graphics View all posts by Fruty. You are commenting using your WordPress. You are commenting using your Google account. You are commenting using your Twitter account.
Augmented Reality with OpenCV and OpenGL: the tricky projection matrix
You are commenting using your Facebook account. Notify me of new comments via email. Notify me of new posts via email. Skip to content. Home Contact Hire Me! Fruty computer graphics August 29, 2 Minutes. This is the OpenCV camera matrix: You want to overlay stuff on the original image. Share this: Twitter Facebook. Like this: Like Loading Published by Fruty. Published August 29, Leave a Reply Cancel reply Enter your comment here Fill in your details below or click an icon to log in:.
Email required Address never made public. Name required.But what about some augmented reality, where the cube is actually floating in front of us?
No problem:. As per the previous post, I am using OpenCV computer vision to obtain a snap from the webcam and inspect it for hand gestures. But this time I am also using the snap as a background for my spinning cube.
However, if the Vicky hand gesture is detected, the cube will vanish. First up, the import statements and initializer:. It also sets class instance variables for rotating the cube on its axis, managing textures for the cube and background, as well as a flag to determine if the cube should be shown. It takes care of loading and binding the texture for our cube the face of a devil! This is where all the shit happens, as the kids would say.
The cube is drawn on top of the background notice how its glTranslatef z value is We blend the cube to make it transparent. We rotate the cube. Otherwise the cube would stay in the same position each time the window is sketched. Then I use my Detection class to detect whether there are any hand gestures in the snap. Finally, I need to bind the webcam snap to my background texture. We are applying the appropriate texture webcam snap for the background, devil face for the cube and drawing the shapes.
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I am willing to overlay a 3D object on a calibrated image of a checkerboard. Any tips or guidance? The basic idea is that you have 2 cameras: one is the physical one the one where you are retriving the images with opencv and one is the opengl one. You have to align those two matrices. You need a distortion parameters because every lens more or less has some optical distortionand build with those parameters the so called intrinsic parameters.
You do this with printing a chessboard in a paper, using it for get some images and calibrate the camera. It's full of nice tutorial about that on the internet, and from your answer it seems you have them. That's nice.
You have to calibrate the position of the camera. And this is done with the so called extrinsic parameters. Those parameters encoded the position and the rotation the the 3D world of those camera.
The intrinsic parameters are needed by the OpenCV methods cv::solvePnP and cv::Rodrigues and that uses the rodrigues method to get the extrinsic parameters. This method get in input 2 set of corresponding points : some 3D knowon points and their 2D projection. That's why all augmented reality applications need some markers: usually the markers are square, so after detecting it you know the 2D projection of the point P1 0,0,0 P2 0,1,0 P3 1,1,0 P4 1,0,0 that forms a square and you can find the plane lying on them.
Once you have the extrinsic parameters all the game is easily solved: you just have to make a perspective projection in OpenGL with the FoV and the aperture angle of the camera from intrinsic parameter and put the camera in the position given by the extrinsic parameters. Of course, if you want and you should understand and handle each step of this process correctly. Hartley and A. Moreover, to handle correctly the opengl part you have to deal with the so called "Modern OpenGL" remember that glLoadMatrix is deprecated and a little bit of shader for loading the matrices of the camera position for me this was a problem because I didn't knew anything about it.
I have dealt with this some times ago and I have some code so feel free to ask any kind of problems you have. Here some links I found interested:. Please read them before anything else. As usual, once you got the concept it is an easy joke, need to crash your brain a little bit against the wall.
Just don't be scared from all those math :. Learn more. Asked 6 years, 1 month ago. Active 4 years, 2 months ago. Viewed 12k times. What do you mean by "calibrated"? Do you mean that the length of each side of each square on the checkerboard is related to some useful measurement i.The main idea is to render in the screen of a tablet, PC or smartphone a 3D model of a specific figure on top of a card according to the position and orientation of the card. Figure 1: Invizimal augmented reality cards.
Well, this past semester I took a course in Computer Vision where we studied some aspects of projective geometry and thought it would be an entertaining project to develop my own implementation of a card based augmented reality application.
To make the most out of it you should be comfortable working with different coordinate systems and transformation matrices. First, this post does not pretend to be a tutorial, a comprehensive guide or an explanation of the Computer Vision techniques involved and I will just mention the essentials required to follow the post.
However, I encourage you to dig deeper in the concepts that will appear along the way. Secondly, do not expect some professional looking results. I did this just for fun and there are plenty of decisions I made that could have been done better. The main idea is to develop a proof of concept application. With that said, here it goes my take on it.
Looking at the project as a whole may make it seem more difficult than it really is. Luckily for us, we will be able to divide it into smaller parts that, when combined one on top of another, will allow us to have our augmented reality application working. The question now is, which are these smaller chunks that we need? As stated before, we want to project in a screen a 3D model of a figure whose position and orientation matches the position and orientation of some predefined flat surface.
Furthermore, we want to do it in real time, so that if the surface changes its position or orientation the projected model does so accordingly. To achieve this we first have to be able to identify the flat surface of reference in an image or video frame.
Once identified, we can easily determine the transformation from the reference surface image 2D to the target image 2D. This transformation is called homography. However, if what we want is to project a 3D model placed on top of the reference surface to the target image we need to extend the previous transformation to handle cases were the height of the point to project in the reference surface coordinate system is different than zero.
This can be achieved with a bit of algebra. Finally, we should apply this transformation to our 3D model and draw it on the screen. Bearing the previous points in mind our project can be divided into:. Recognize the reference flat surface. Estimate the homography. Derive from the homography the transformation from the reference surface coordinate system to the target image coordinate system. Project our 3D model in the image pixel space and draw it.
Figure 2: Overview of the whole process that brings to life our augmented reality application. The main tools we will use are Python and OpenCV because they are both open source, easy to set up and use and it is fast to build prototypes with them.
For the needed algebra bit I will be using numpy. From the many possible techniques that exist to perform object recognition I decided to tackle the problem with a feature based recognition method. This kind of methods, without going into much detail, consist in three main steps: feature detection or extraction, feature description and feature matching. Roughly speaking, this step consists in first looking in both the reference and target images for features that stand out and, in some way, describe part the object to be recognized.
Augmented Reality using OpenCV and Python
This features can be later used to find the reference object in the target image. We will assume we have found the object when a certain number of positive feature matches are found between the target and reference images. For this to work it is important to have a reference image where the only thing seen is the object or surface, in this case to be found. And, although we will deal with this later, we will use the dimensions of the reference image when estimating the pose of the surface in a scene.
For a region or point of an image to be labeled as feature it should fulfill two important properties: first of all, it should present some uniqueness at least locally. Good examples of this could be corners or edges. As a rule of thumb, the more invariant the better. Figure 3: On the left, features extracted from the model of the surface I will be using.In this post, we will explain what ArUco markers are and how to use them for simple augmented reality tasks using OpenCV. ArUco markers have been used for a while in augmented reality, camera pose estimation, and camera calibration.
ArUco markers were originally developed in by S. Garrido-Jurado et al. That is where it was developed in Spain. Below are some examples of the ArUco markers. An aruco marker is a fiducial marker that is placed on the object or scene being imaged. It is a binary square with black background and boundaries and a white generated pattern within it that uniquely identifies it. The black boundary helps making their detection easier.
They can be generated in a variety of sizes. The size is chosen based on the object size and the scene, for a successful detection. If very small markers are not being detected, just increasing their size can make their detection easier. The idea is that you print these markers and put them in the real world. You can photograph the real world and detect these markers uniquely. If you are a beginner, you may be thinking how is this useful?
In the example we have shared in the post, we have put the printed and put the markers on the corners of a picture frame. When we uniquely identify the markers, we are able to replace the picture frame with an arbitrary video or image. The new picture has the correct perspective distortion when we move the camera.
In a robotics application, you can put these markers along the path of the warehouse robot equipped with a camera. When the camera mounted on the robot detects one these markers, it can know its precise location in the warehouse because each marker has a unique ID and we know where the markers were placed in the warehouse. We can generate these markers very easily using OpenCV. The aruco module in OpenCV has a total of 25 predefined dictionaries of markers.
The drawMarker function above lets us choose the marker with a given id the second parameter — 33 from the collection of markers which have ids from 0 to The third parameter to the drawMarker function decides the size of the marker generated.
The fourth parameter represents the object that would store the generated marker markerImage above. Finally, the fifth parameter is the thickness parameter and decides how many blocks should be added as boundary to the generated binary pattern.
The marker generated using the above code would look like the image below. Once the scene is imaged with the aruco markers, we need to detect them and use them for further processing. Below we show how to detect the markers. An initial set of parameters are detected using DetectorParameters::create.
OpenCV allows us to change multiple parameters in the detection process.
The list of parameters that can be adjusted including the adaptive threshold values can be found here. In most of the cases, the default parameter work well and OpenCV recommends to use those. So we will stick to the default parameters. For each successful marker detection, the four corner points of the marker are detected, in order from top left, top right, bottom right and bottom left.
In Python, they are stored as Numpy array of arrays. The detectMarkers function is used to detect and locate the corners of the markers.