![]() ![]() Start by using the “Downloads” section of this guide to access the source code and example images: $ tree. Gain access to Jupyter Notebooks for this tutorial and other PyImageSearch guides that are pre-configured to run on Google Colab’s ecosystem right in your web browser! No installation required.Īnd best of all, these Jupyter Notebooks will run on Windows, macOS, and Linux! Project structureīefore we can implement image cropping with OpenCV, let’s first review our project directory structure. Ready to run the code right now on your Windows, macOS, or Linux system?.Wanting to skip the hassle of fighting with the command line, package managers, and virtual environments?.Learning on your employer’s administratively locked system?.Having problems configuring your development environment?įigure 2: Having trouble configuring your development environment? Want access to pre-configured Jupyter Notebooks running on Google Colab? Be sure to join PyImageSearch Plus - you will be up and running with this tutorial in a matter of minutes. If you need help configuring your development environment for OpenCV, I highly recommend that you read my pip install OpenCV guide - it will have you up and running in a matter of minutes. Luckily, OpenCV is pip-installable: $ pip install opencv-contrib-python To follow this guide, you need to have the OpenCV library installed on your system. 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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