With reducing_gap greater or equal to 3.0, the result is indistinguishable from fair resampling in most cases. The smaller reducing_gap, the faster resizing. The bigger reducing_gap, the closer the result to the fair resampling. reducing_gap may be None (no first step is performed) or should be greater than 1.0. The last step changes size no less than by reducing_gap times. Second, resizing using regular resampling. First, reducing the image by integer times using reduce() or draft() for JPEG images. reducing_gap: Apply optimization by resizing the image in two steps.(was Resampling.NEAREST prior to version 2.5.0). If omitted, it defaults to Resampling.BICUBIC. This can be one of Resampling.NEAREST, Resampling.BOX, Resampling.BILINEAR, Resampling.HAMMING, Resampling.BICUBIC or Resampling.LANCZOS. size: The requested size in pixels, as a 2-tuple: (width, height).This method modifies the image to contain a thumbnail version of itself, no larger than the given size. Image.thumbnail(size, resample = Resampling. INTER_AREA ) # print the old and new shape print ( f "old shape: " ) Syntaxįor the syntax of cv2.resize() and (), please see the previous tutorial: Python | Resize Image | OpenCV vs Pillow. shrink by max size max_width = 256 max_height = 256 height, width = cv2_img.shape scale_ratio = min (max_width / width, max_height / height, 1 ) # use min(., 1) to disallow enlargement # reuse the code of resizing by scale ratio: new_width = int (cv2_img.shape * scale_ratio) new_height = int (cv2_img.shape * scale_ratio) new_size = (new_width, new_height) cv2_img_thumbnail = cv2.resize(cv2_img, new_size, interpolation = cv2. resize to fit a max size max_width = 256 max_height = 256 height, width = cv2_img.shape scale_ratio = min (max_width / width, max_height / height) # reuse the code of resizing by scale ratio: new_width = int (cv2_img.shape * scale_ratio) new_height = int (cv2_img.shape * scale_ratio) new_size = (new_width, new_height) cv2_img_resized_2 = cv2.resize(cv2_img, new_size, interpolation = cv2. resize by scale ratio scale_ratio = 0.6 new_width = int (cv2_img.shape * scale_ratio) new_height = int (cv2_img.shape * scale_ratio) new_size = (new_width, new_height) cv2_img_resized_1 = cv2.resize(cv2_img, new_size, interpolation = cv2. This article was updated in January 2021 by the editor.Import cv2 # read image cv2_img = cv2.imread( " test_images/test1.jpg " ) # 1. If the height is fixed and the width proportionally variable, it's pretty much the same thing, you just need to switch things around a bit: blog and republished under Creative Commons with permission. You can use the same filename to overwrite the full-size image with the resized image, if that is what you want. Also, notice I saved the resized image under a different name, resized_image.jpg, because I wanted to preserve the full-size image ( fullsized_image.jpg) as well. You can change basewidth to any other number if you need a different width for your images. The resulting height value is saved in the variable hsize. The proportional height is calculated by determining what percentage 300 pixels is of the original width ( img.size) and then multiplying the original height ( img.size) by that percentage. ![]() These few lines of Python code resize an image ( fullsized_image.jpg) using Pillow to a width of 300 pixels, which is set in the variable basewidth and a height proportional to the new width. Img = 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. I love Python, and I've been learning it for a while now.
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