![]() Instead of the 4-tuple, you could instead use a 2-tuple to add the same number of pixels on the left/right and top/bottom, or a 1-tuple to add the same number of pixels to all sides. You can try to change the algorithm or you can apply further postprocessing ('sharpening') to enrich the contrasts again. Some kind of algorithm ( interpolationcv2.INTERCUBIC, others here) tweaks the pixel values to merge/average them so you do not loose too much of information. # and bottom, making sure to account for even or odd numbersĪdd_left = add_right = (new_size - old_size) // 2Īdd_left = (new_size - old_size) // 2Īdd_right = ((new_size - old_size) // 2) + 1Īdd_top = add_bottom = (new_size - old_size) // 2Īdd_top = (new_size - old_size) // 2Īdd_bottom = ((new_size - old_size) // 2) + 1 That is exactly what happens when resizing images. # Set number of pixels to expand to the left, top, right, With Image.open('/path/to/image.gif') as im: This code accounts for odd pixel sizes: from PIL import Image Negative numbers for left and top will add black pixels to those edges, while numbers greater than the original width and height for right and bottom will add black pixels to those edges. Here's a python script that uses this function to run batch image resizing.PIL's crop method can actually handle this for you by using numbers that are outside the bounding box of the original image, though it's not explicitly stated in the documentation. Print('writing to disk'.format(out_f_path)) Img = img.resize((max_px_size, hsize), Image.ANTIALIAS) Hsize = int(float(height_0) * float(wpercent)) Out_f_path = os.path.join(output_folder, out_f_name) Not the prettiest but gets the job done and is easy to understand: def resize(img_path, max_px_size, output_folder): Return img.resize(size_new, resample=Image.LANCZOS)Ī simple method for keeping constrained ratios and passing a max width / height. If img_ratio = video_ratio: # image is not tall enough Width, height = video_size # these are the MAX dimensions So after I couldn't find an obvious way to do that here (or at some other places), I wrote this function and put it here for the ones to come: from PIL import Imageĭef get_resized_img(img_path, video_size): The Image.thumbnail method was promising, but I could not make it upscale a smaller image. I was trying to resize some images for a slideshow video and because of that, I wanted not just one max dimension, but a max width and a max height (the size of the video frame).Īnd there was always the possibility of a portrait video. To resize an image, you call the resize() method of pillows image class by giving width and height. I hope it might be helpful to someone out there! I tried to document it as much as I can, so it is clear. ![]() # Enter the name under which you would like to save the new imageĪnd, it is done. TL DR :torchvision's Resize behaves differently if the input is a PIL.Image or a torch tensor from readimage. The solution was not to use the new Tensor API and just use PIL as the image reader. # resample filter ->, (default),, etc. This transform can accept or Tensors, in short, the resizing does not produce the same image, one is way softer than the other. #new_width = round(new_height * asp_rat) # uncomment the second line (new_width) and comment the first one (new_height) # NOTE: if you want to adjust the width to the height, instead -> ![]() Img = img.resize((new_width, new_height), Image.ANTIALIAS) Img = Image.open(img_path) # puts our image to the buffer of the PIL.Image object You do not need the semicolons ( ), I keep them just to remind myself of syntax of languages I use more often. In this case, it will adjust the height to match the width of the new image, based on the initial aspect ratio, asp_rat, which is float (!).īut, to adjust the width to the height, instead, you just need to comment one line and uncomment the other in the else loop. I will also add a version of the resize that keeps the aspect ratio fixed.
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