- #IMAGE RESIZE PYTHON HOW TO#
- #IMAGE RESIZE PYTHON CODE#
#IMAGE RESIZE PYTHON CODE#
Start by accessing the “Downloads” section of this tutorial to retrieve the source code and example image.įrom there, the project folder should look like this: $ tree. Resizing an image is relatively straightforward using OpenCV’s cv2.resize function, but before reviewing any code, let’s first review our project directory structure.
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 structure
Ready to run the code right now on your Windows, macOS, or Linux systems?. Wanting to skip the hassle of fighting with the command line, package managers, and virtual environments?. Learning on your employer’s administratively locked system?. 
#IMAGE RESIZE PYTHON HOW TO#
To learn how to resize an image using OpenCV and the cv2.resize method, just keep reading.įigure 3: 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.
Large resolution images are almost always downsampled to help image processing systems run faster and be more accurateīy the end of this tutorial, you’ll understand how to resize images with OpenCV. High-resolution images are highly detailed - but from a computer vision/image processing perspective, we’re more interested in the structural components of the images, not so much the super fine-grained details. By definition, the larger the image, the more the data, and therefore the longer it takes for algorithms to process the data. Keep in mind that while high-resolution images are visually appealing to the human eyes, they harm computer vision and image processing pipelines: By decreasing the image size, we have fewer pixels to process (not to mention less “noise” to deal with), which leads to faster and more accurate image processing algorithms. It now looks “pixelated” and “blown up.”Īs I mentioned above, you’ll generally be decreasing the size of an image rather than increasing (exceptions do apply, of course). In the middle, we have resized the image to half its size - and other than the image being resized, there is no loss in image “quality.” However, on the right, we have dramatically increased the image size. In terms of “quality” of the output image, decreasing an image’s size is always easier (and more aesthetically pleasing) than increasing it. On the other hand, if we were to increase the size of the image, the interpolation function would have to “fill in the gaps” between pixels that previously did not exist.įor example, take a look at the image in Figure 2:įigure 2: Increasing and decreasing the size of an image with OpenCV. This is because the interpolation function simply has to remove pixels from an image. In general, it’s far more beneficial (and visually appealing) to decrease the size of the image. And the goal of an interpolation function is to take these neighborhoods of pixels and use them to either increase or decrease the size of the image. In this case, the “known points” are the pixels of our original image. The formal definition of interpolation is:Ī method of constructing new data points within the range of a discrete set of known data points. 
We also need to keep in mind the interpolation method of our resizing function. As we explore machine learning/deep learning techniques, we’ll find that our internal algorithms often ignore the aspect ratio of an image but more on that once we understand the fundamentals of computer vision. Exceptions most certainly do apply, though. In general, you’ll want to preserve the images’ aspect ratio when resizing - especially if these images are presented as output to the user. They have been resized by ignoring the ratio of the width to the height of the image. And on the right, we have two images that have been distorted by not preserving the aspect ratio. Figure 1: Ignoring the aspect ratio of an image can lead to resized images that look distorted, squished, and crunched.
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