Pil image resize aspect ratio9/10/2023 In either case, decreasing the size of an image (in terms of quality) is always an easier task than increasing the size of an image. When decreasing (downsampling) the size of an image, the OpenCV documentation suggests using cv2.INTER_AREA - although this method is very similar to nearest neighbor interpolation. The cv2.INTER_LINEAR method tends to be slightly faster than the cv2.INTER_CUBIC method, but go with whichever one gives you the best results for your images. ![]() The size of the thumbnail will be the largest size that preserves the aspect ratio such. When increasing (upsampling) the size of an image, consider using cv2.INTER_LINEAR and cv2.INTER_CUBIC . In the following example, we create a thumbnail from the image img. So which method interpolation methods should you be using? Print('The new shape is: ".format(name), resized) # multiply that same change to height and convert to intĬustom_size = cv2.resize(img, (width, int(width_ratio * h))) This can be one of (use nearest neighbour), (linear interpolation), (cubic spline interpolation), or (a high-quality downsampling filter). Width_ratio= width/w # calculate the ratio of change in width Parameters : size The requested size in pixels, as a 2-tuple: (width, height). from PIL import Image if name 'main': with Image.open ('in.png') as image: width, height image.size shrink smallest dimension to 256 and other dimension with respect to aspect ratio if width > height: width, height int (width 256 / height), 256 else: width, height 256, int (height 256 / width) resize image image image. Width = 672 # lets have custom width of 672 You can do that by specifying a custom size for either height or width and rescale the other accordingly. Note: Always remember while OpenCV deals with images as (x, y) or (cols, rows), NumPy treats them as (y, x) or (rows, cols) this is the reason when you run image.shape you will get (rows, cols) but when you specify a size to resize you will enter (cols, rows).Ī spect ratio - which is the ratio of the width of the image to the height of an image. So in order to prevent such distortions from happening you have to keep the Aspect Ratio (width/height ratio) constant. Plt.subplot(133) plt.imshow(custom_size) plt.title("Custom Size") ĭid you noticed the problem with the custom size, the image look distorted, this happens if you try to specify both custom height and weight. Plt.subplot(132) plt.imshow(halfimg) plt.title("50% Reduced Size") Plt.subplot(131) plt.imshow(img) plt.title("Original") #Specify a custom size, width first than height
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