In typical cases we will most likely have the ROI's bounding box (x,y,w,h) coordinates obtained from cv2.boundingRect() when iterating through contours cnts = cv2.findContours(grayscale_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)Ĭnts = cnts if len(cnts) = 2 else cnts Related course: Master Computer Vision with OpenCV. If we have (x1,y1) as the top-left and (x2,y2) as the bottom-right vertex of a ROI, we can use Numpy slicing to crop the image with: ROI = imageīut normally we will not have the bottom-right vertex. The program will detect regions of interest, classify them as cars and show rectangles around them. We will use the OpenCV function “drawContours()” using color as White (R,G,B=255,2555,255) and thickness as FILLED (-1) to draw all four segment contours over a black background.Here's a visualization for selecting a ROI from an image -Ĭonsider (0,0) as the top-left corner of the image with left-to-right as the x-direction and top-to-bottom as the y-direction. Python opencv region of interest code#A good knowledge of Numpy is required to write better optimized code with OpenCV. Now that we have the four segments identified, we need to build the image mask which will allow us to pull out the desired features from the original image. Set a Region of Interest (ROI) Split and merge images Almost all the operations in this section are mainly related to Numpy rather than OpenCV. Centroid detection of a segment contour requires applying the OpenCV “moments()” function on the contour and then calculating the center X, Y coordinates using the formula below: center_x, center_y = (int(M / M), int(M / M))Ĭomparing the segment centroid coordinates with the image center coordinates lets us place the four segments in their respective positions. This is also pretty straightforward and involves identifying the image center coordinates as well as the centroid of each of our detected segments. Top-Left, Top-Right, Bottom-Left and Bottom-Right. The logic is pretty straightforward hence we do not need any inbuilt OpenCV or Python functions.Īnother important logic is to identify the four segments separately i.e. we look at all overlapping contours and select the one with the maximum area as the final candidate. This problem is solved using Non Maxima Suppression, i.e. It is often the case that multiple overlapping contours are detected over a segment whereas we are interested in only one. Creating a simple Region of Interest (ROI) inside a video streaming using OpenCV in Python Regions of interests help to mix and add a window for several tasks, such as transformations, virtual pen. It may appear that our task is finished but there is a little bit of fine tuning required. For example, let's say that we have a car and there is a camera centered in the middle of the car. A region of interest is a place on an image where we want to search for something. First, let's explain the concept of a region of interest. Python opencv region of interest how to#
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