The first parameter is the image of the scene with the markers. The detectMarkers function is used to detect and locate the corners of the markers. In Python, they are stored as Numpy array of arrays. In C++, these 4 detected corner points are stored as a vector of points and multiple markers in the image are together stored in a vector of vector of points. So we will stick to the default parameters.įor 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 most of the cases, the default parameter work well and OpenCV recommends to use those. The list of parameters that can be adjusted including the adaptive threshold values can be found here. OpenCV allows us to change multiple parameters in the detection process. We start by loading the same dictionary that we used to generate the markers.Īn initial set of parameters are detected using DetectorParameters::create(). MarkerCorners, markerIds, rejectedCandidates = cv.tectMarkers(frame, dictionary, parameters=parameters) Parameters = cv.aruco.DetectorParameters_create() # Initialize the detector parameters using default values #Load the dictionary that was used to generate the markers. The marker generated using the above code would look like the image below. In the above example, a boundary of 1 bit would be added around the 6×6 generated pattern, to produce an image with 7×7 bits in a 200×200 pixel image. Finally, the fifth parameter is the thickness parameter and decides how many blocks should be added as boundary to the generated binary pattern. The fourth parameter represents the object that would store the generated marker (markerImage above). In the above example, it would generate an image with 200×200 pixels. The third parameter to the drawMarker function decides the size of the marker generated. The drawMarker function above lets us choose the marker with a given id (the second parameter – 33) from the collection of 250 markers which have ids from 0 to 249. MarkerImage = cv.aruco.drawMarker(dictionary, 33, 200, markerImage, 1) The function call getPredefinedDictionary below shows how to load a dictionary of 250 markers, where each marker contains a 6×6 bit binary pattern.ĭictionary = cv.aruco.Dictionary_get(cv.aruco.DICT_6X6_250) We will need to use the aruco module in the code. Below we show how to generate and detect various kinds of aruco markers in both C++ and Python. All the markers in a dictionary contain the same number of blocks or bits(4×4, 5×5, 6×6 or 7×7), and each dictionary contains a fixed number of markers(50, 100, 250 or 1000). The aruco module in OpenCV has a total of 25 predefined dictionaries of markers. ![]() We can generate these markers very easily using OpenCV. 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. In a robotics application, you can put these markers along the path of the warehouse robot equipped with a camera. The new picture has the correct perspective distortion when we move the camera. When we uniquely identify the markers, we are able to replace the picture frame with an arbitrary video or image. 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. If you are a beginner, you may be thinking how is this useful? Let’s look at a couple of use cases. You can photograph the real world and detect these markers uniquely. ![]() The idea is that you print these markers and put them in the real world. If very small markers are not being detected, just increasing their size can make their detection easier. The size is chosen based on the object size and the scene, for a successful detection. They can be generated in a variety of sizes. The black boundary helps making their detection easier. It is a binary square with black background and boundaries and a white generated pattern within it that uniquely identifies it. An aruco marker is a fiducial marker that is placed on the object or scene being imaged.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |