![]() ![]() 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. When increasing (upsampling) the size of an image, consider using cv2.INTER_LINEAR and cv2.INTER_CUBIC . 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))) Width_ratio= width/w # calculate the ratio of change in width 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. resize (src, dst, dst. To perform a simple resizing task with OpenCV: Import the OpenCV library: import cv2 import matplotlib. ![]() ![]() So in order to prevent such distortions from happening you have to keep the Aspect Ratio (width/height ratio) constant. If you want to resize src so that it fits the pre-created dst, you may call the function as follows: // explicitly specify dsizedst.size () fx and fy will be computed from that. Here is the full syntax for the resize () method in OpenCV: cv2.resize (src, dsize, fx, fy, interpolation) The parameters are as follows: Note: Apply either dsize or fx and fy, or all three. 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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