12/6/2023 0 Comments Pil image resizeImg = img.resize((basewidth, hsize), Image.ANTIALIAS) Hsize = int((float(img.size) * float(wpercent))) Here's a basic script to resize an image using the Pillow module: from PIL import Image To install Pillow, use the pip module of Python: $ python3 -m pip install Pillow Scaling by width So I looked around and found Pillow, a Python imaging library and "friendly fork" of an old library just called PIL. Some time ago, I wrote a Python script where I needed to resize a bunch of images while at the same time keeping the aspect ratio (the proportions) intact. The primary difference between reshape and resize in Numpy is that resize modifies the original array, while reshape does not.I love Python, and I've been learning it for a while now. What Is the Difference Between Reshape and Resize? You could use a Numpy-based library like OpenCV or SciKit Image to first convert the image to a Numpy array and then perform a resize operation. The easiest way is to use the PIL library, which turns an image file into a PIL Image object and exposes methods for image manipulation. There are many ways to resize an image in Python. To rescale an image with these parameters in mind, use a library built on Numpy like OpenCV or SciKit Image. However, Numpy alone will not account for interpolation and extrapolation. To resize an image using Numpy, you can simply call the resize function on a Numpy array that has been derived from image data. Working with Numpy to resize images is a common use case for people who want to use large image datasets to train AIs. Here is the basic method for resizing an image in PIL. It is possible to convert Numpy arrays to PIL Image objects and vice versa. The PIL Image object provides methods for opening and reading image data, for saving image data to a file, and for manipulating image data. Rather, it converts images into PIL Image objects. PIL does not convert an image into an ndarray. One such library is Pillow (Python Image Library fork since PIL was deprecated.) The code for resizing images using Pillow) is similar to the code for doing it in OpenCV or SciKit Image although the underlying method is different. It is possible to write your own function to resize an image, or to use a different library that isn't based on Numpy. It's not necessary to use OpenCV or SciKit Image to resize images in Python code. The resize function accepts the ndarray and a new width ratio and height for the output image. Similarly to OpenCV, SciKit Image exposes imread and imsave functions for converting image data to and from an ndarray. Here is the basic code for resizing an image with SciKit Image: SciKit Image is another Python library based on Numpy that does image resizing with interpolation and extrapolation. INTER_LANCZOS4 - Lanczos interpolation over 8x8 pixel neighborhoodįor more information on the different types of interpolation, read the docs.INTER_CUBIC - bicubic interpolation over 4x4 pixel neighborhood.INTER_AREA - resampling using pixel area relation.INTER_LINEAR - bilinear interpolation (default).INTER_NEAREST - interpolation using the nearest neighboring pixels.The interpolation parameter can be any of the following: The dimensions you provide to the dsize parameter directly translate to the new image width and height in pixels. It adjusts the size of the Numpy array based on these parameters.įinally, the imwrite function takes the resulting Numpy array, converts it back into a regular image, and writes the output image to a file with a typical image file extension. Next, the resize function accepts the image, the desired size, and an interpolation parameter. The imread function reads a standard image file into memory and converts it to a Numpy array. ![]() ![]() ![]() Res = cv2.resize(img, dsize=(54, 140), interpolation=cv2.INTER_LINEAR)
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