![]() ![]() Take a second now to convince yourself that the above statement is true.īut if you’re a bit more confused and need more convincing, don’t worry! I’ll show you some code examples later in this guide to make image cropping with OpenCV more clear and concrete for you. The startY:endY slice provides our rows (since the y-axis is our number of rows) while startX:endX provides our columns (since the x-axis is the number of columns) in the image. When applying NumPy array slicing to images, we extract the ROI using the following syntax: ![]() This result provides the final two rows of the image, minus the first column. Now, let’s extract the pixels starting at x = 1, y = 3 and ending at x = 5 and y = 5: > I ![]() Notice how we have extracted three rows ( y = 3) and two columns ( x = 2). Doing so can be accomplished using the following code: > I Now, let’s suppose I want to extract the “pixels” starting at x = 0, y = 0 and ending at x = 2, y = 3. Let’s start by initializing a NumPy list with values ranging from : > import numpy as npĪrray([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,Īnd let’s now reshape this 1D list into a 2D matrix, pretending that it is an image: > I = I.reshape((5, 5)) We can accomplish image cropping by using NumPy array slicing. We commonly refer to this process as selecting our Region of Interest, or more simply, our ROI. When we crop an image, we want to remove the outer parts of the image we are not interested in. Figure 1: We accomplish image cropping by using NumPy array slicing ( image source).
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