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boundingBoxes = (contours, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes), key=lambda x:x))
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Image generated using colaboratory Bounding Boxesīounding boxes are created for respective contours. vertical_lines = cv2.dilate(eroded_image, vertical_kernel, iterations=5)Įrosion and dilation are used to remove noise.Īfter performing erosion and dilation, the image looks like this: This makes the vertical lines more prominent. Dilation will make the pixel 1, if at least one of the pixels under the kernel is 1. import numpy vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, np.array(img).shape//150)) eroded_image = cv2.erode(binary_image, vertical_kernel, iterations=5) Dilation So in this way, the horizontal lines get eroded, as only the pixels in each column remain. The vertical kernel which moves on the image, a pixel will be considered only if all pixels are 1 under the vertical kernel. What happens when erosion is performed on this image? The vertical kernel consists of a rectangular matrix consisting of one row and columns equal to number of columns in original image pixel array divided by 150. Here we will perform erosion on the binary image with the vertical kernel. So first, the rectangular vertical kernel is defined with 1 row and columns as length of the numpy array of the image divided by 150. Np.array(image).shape is used to get the image dimensions. Kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2)) The second argument is tuple denoting the shape of the required kernel, the width and height.The first argument specifies the shape of the kernel that you want, can be rectangular, circular or even elliptical.
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This is the format of use, as this may be tricky to remember: cv2.getStructuringElement(shape,(num_of_columns,num_of_rows)) It takes the image and a structuring element or kernel.Ĭv2.getStructuringElement() : Here the shape and size of the kernel can be passed as parameters and accordingly a matrix is generated. Morphological operations are performed on images based on their shapes. thresh,img_bin = cv2.threshold(img,128,255,cv2.THRESH_BINARY) img_bin = 255-img_bin plotting = plt.imshow(img_bin,cmap='gray') plt.title("Inverted Image with global thresh holding") plt.show() Global, because the same value is applied as a threshold for all pixels. Global thresh holding : In global thresholding, an arbitrary value is used as threshold value. OpenCV provides different styles of thresholding and it is decided by the fourth parameter of the function. Third argument is the maxVal which represents the value to be given if pixel value is more than the threshold value. Second argument is the threshold value which is used to classify the pixel values. Here different parameters can be specified for different styles of thresholding.Ĭv2.threshold() : First argument is the source image, which should be a grayscale image. if the pixel value is greater than a certain value, it is assigned one value, and if it is less, then the other value. First the image has to be converted to binary, i.e.