4/29/2023 0 Comments Python text cleaner![]() ![]() # (skip the background component at index 0) # get the indices/labels of the remaining components based on the area stat num_comps, labeled_pixels, comp_stats, comp_centroids = \Ĭv2.connectedComponentsWithStats(thresh_image, connectivity=4) after grayscale conversion, black hat morphing and Otsu's thesholding) - OpenCV documentation recommends working with the binarized images with the white foreground when applying morphological operations and stuff like that. This is a fully coded Python solution based on the direction provided by code assumes that you are already working with the properly binarized white-on-black image (e.g. If the area is below the threshold, color the blob pink (in this case, but usually you want black). Loop thru all the found connected components (blobs), retrive the area for the current blob via the stats matrix and compare it to the area threshold. All blobs or pixels below this area will be colored with a (ridiculous) pink. ![]() From a range 0 – 255, 3 values for each pixel: BGR.Ĭonsider that the background is colored in black, so ignore this “connected component” and its color (black). The colors are generated randomly by the rand function. Prepare a color vector of size “ numberOfcomponents”, this will help visualize the blobs that we are actually filtering. Will compute the number of connected components, matrix of labels andĪn additional matrix with statistics – including blob area. Pass a binary image to connectedComponentsWithStats. Using the area filter I get this result on your noisiest image: small regions are painted with (ridiculous) pink color do not count the original background-> label = 0:įor( int i = 1 i (i-1, cv::CC_STAT_AREA) Int numberofComponents = cv::connectedComponentsWithStats(bwImage, outputLabels, Use opencv's connectedComponentsWithStats, here's a C implementation of a very basic area filter: cv::Mat outputLabels, stats, img_color, centroids That is, filter every blob that does not exhibit a minimum area. In the case you can't use morphology or blurring to get a cleaner image, consider using an "Area Filter". MH304's answer is very nice and straightforward. ![]()
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