In the output, we can see the transparent image “Background Transparent Image1.png” in the images folder. _,alpha = cv2.threshold(tmp,0,255,cv2.THRESH_BINARY)Ĭv2.imwrite("Images/Background Transparent Image1.png", dst) Tmp = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY) Src = cv2.imread('Images/flower-black-background.jpg', 1) In this example, we will take a “flower-black-background.jpg” image as an input to remove the background. Specifies the number of input matrices when the input vector is a plain C array.ĭst: output array with the same size and the same depth as input array. all the matrices must have the same size and the same depth.Ĭount: must be greater than zero. Mv: input vector of matrices to be merged. Following is the syntax of this function – cv2.merge(mv) This function returns an array of the concatenation of the elements of the input arrays. The cv2.merge() function takes single-channel arrays and combines them to make a multi-channel array/image. Following is the syntax of this function – cv2.split(m) It will return an array with the three channels, each of which corresponds to blue, green, and red channels represented as a ndarray with two dimensions. Python OpenCV module provides a function cv2.split() to split a multi-channel/colored array into separate single-channel arrays. The mainly used functions in this article are cv2.split() and cv2.merge() functions, which are used to split and merge the channels. ApproachĬreate the alpha channels by specifying threshold values.Īnd finally save the image using the combined channels. We will follow the below steps to to remove the black background and make it transparent. Like RGB channels, the alpha channel is used to stores the transparency information. In this article, we will see how to remove the black background from an image to make it transparent using OpenCV Python. For image processing/image editing, background removing is allowing us to highlight the subject of the photo and create a transparent background to place the subject into various new designs and destinations.Ĭertain image formats do not support transparency, for example, TIFF, PNG, and WebP graphics formats support transparency, whereas JPEGs have none. In digital images, Transparency is the functionality that supports transparent areas in an image or image layer. Mask = mahotas.dilate(mask, np.ones(( 48, 24))) The following code snippet helps in finding the Wally in the crowd. Let’s see how Template Matching can be done with Mahotas for finding the wally. The most popular functions of Mahotas are It reads and writes images in NumPy array, and is implemented in C++ with a smooth python interface. Mahotas is another image processing and computer vision library that was designed for bioimage informatics. “Active contour models are defined for image segmentation based on the curve flow, curvature, and contour to obtain the exact target region or segment in the image.”įollowing code produces the above output: import numpy as npįrom gmentation import active_contourĬntr = active_contour(gaussian(img, 3),init, alpha= 0.015, beta= 10, gamma= 0.001)įig, ax = plt.subplots( 1, 2, figsize=( 7, 7))Īx.plot(init, init, '-r', lw= 3)Īx.plot(cntr, cntr, '-b', lw= 3)Īx.set_title( "Active Contour Image") In computer vision, contour models describe the boundaries of shapes in an image.
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