![]() If you want to learn the theory and math behind this method you can visit the following post. It is a feature extraction technique used in image processing for detecting simple shapes such as circles and lines. Let’s first remind ourselves what the Hough transform method is. Detecting lines and circles using Hough transform ![]() In our code, we will use the Hough Transform line detection method. There are several functions in OpenCV we can use for this purpose. Instead of trying to find an identical match, we need to compare the corresponding properties between the original and the reference shape. That’s why we need to choose a different approach. However, this method will not work if two objects have different sizes. When the reference image overlaps the corresponding object in the input image we will get a large matching percentage and we will be able to detect the object. We will move the reference image across the input image and in each position, we will calculate an inner product. The idea of this method is to find a correlation between the object in the reference image and the object in the original image. We take another image of that object as a reference and try to identify that same shape in our original image. How can we do that? One way to do that is to apply a method called template matching (brute search method). Let’s say that we want to identify the shape of the square. Here, in the image above we can see several different shapes. Let’s take a look at the following image. Hence, to detect contours we need to apply threshold or Canny edge detection. The easiest way to do that is to use binary images (the object that we need to detect should be white and the background should be black). So, to detect shapes we first need to analyze and understand the contours of that shape. We can use various algorithms to analyze the contours of a lot of shapes that we are dealing with in the real world. In the computer vision field, contours can be a very useful tool for shape analysis and object detection and recognition. So, what exactly is an edge? If we look carefully at the image, we will notice that the edge represents a change in pixel intensity.Ĭontours can be seen as a curved line joining all the continuous points along the edge. Therefore, we can conclude from this example that sometimes simplified objects like contours (shapes) can help us to recognize the content of an image. In the process, we lost colors but we keep the most important part of an image – edges, and contours. Although the image has been altered, we can still recognize what is on it. ACM, 2003.Now, we can see the silhouette of the same objects. In ACM Transactions on Graphics (ToG), volume 22, pages 277–286. Graphcut textures: image and video synthesis using graph cuts. Vivek Kwatra, Arno Schödl, Irfan Essa, Greg Turk, and Aaron Bobick. Look into the releases section for the pre-compiled binary raspivid-inatech. Step5: Encode videoĪssemble all stitched JPEG files into a video again and encode it back to H264. Rebuild stitching_detailed with the matrices selected in the previous step as hardcoded transformation, and stitch all the video frames. ![]() Only one set of matrices is selected for the next step. Each matching frames of the stream will have different matrices. In case of success, each frame has two matrices: the camera matrix (aka “K”) which encodes the camera characteristics (e.g., focal, aspect ratio), and the rotation matrix (aka “R”) which encodes the camera rotation.Īs explained in the beginning of this article, stitching will assumes a pure rotation, which is not the case in real life. Depending of the feature algorithm (e.g., SURF, ORB, …) and the details visible in the overlapping area of the frames, matching will succeed or not. Run OpenCV stitching_detailed on some matching frames to find transformation matrices. Step2: Align framesĬopy video streams, split video in single JPEG files along with capture timing information in text file (i.e., modified raspivid PTS file).įind the matching frames. Power on, let PTP stabilize (might take a few minutes), start recording on each Raspberry Pi. While recording the video, the network is only used for PTP and no other communication is made. Even though the Raspberry Pi Ethernet lacks the dedicated hardware for high accuracy PTP clocks ( hardware timestamping), it still often achieves a clock synchronization well under 1ms using PTP in software mode. The software PLL changes the camera framerate at runtime to align frame capture time on the Linux system clock.Īll eight system clocks are synchronized over Ethernet using PTP (which stands for Precision Time Protocol). ![]()
0 Comments
Leave a Reply. |