Img_inter = cv2.resize(img, (600, 600), interpolation=cv2.INTER_NEAREST)Ĭv2.imshow('Nearest Interpolated Image', img_inter) We will use the nearest interpolation mode instead of the default mode. To set an interpolation mode while scaling, pass the interpolation parameter. You can see that the right side image is stretched with respect to (600, 6000) dimensions. Img_stretch = cv2.resize(img, (600, 600))Ĭv2.imshow('Stretched Image', img_stretch) To stretch the image in Python, we will pass the explicit dimensions on scaling the images. To upscale the image, we can pass the fx and fy value to 1.5. This will output in the image that is half the size in both dimensions of the original. We will pass in a value of ( img, ), and then we pass in the absolute size, or (0,0) to not set an absolute size in pixels, and then we can optionally pass in our relative factors of fx=0.5, fy=0.5.
In this example, we used a resize() image to scale half the size of our original image. To downscale the image to half size, we can pass the fx and fy value to 0.5. Resize the image to the specific width and height.Resize an image only with height (Increase or decrease the height of the image keeping width unchanged).Resize an image only with width (Increase or decrease the width of the image keeping height unchanged).Upscale (Increase the size of the image).Downscale (Decrease the size of the image).While preserving the aspect ratio (height to width ratio of the image is maintained),.
#Cv2 resize image code
The following code demonstrates this function. It should be noted that this method is a basic resizing function, independent of any libraries, and will not perform interpolation, anti-aliasing as the above methods will. We can also create our own function to achieve resizing in Python. Create a user-defined function to resize images in Python It can be substituted with any method of your preference. Note that we use the () function to read the image in the above method.
#Cv2 resize image how to
The following code shows how to use this function. It can work on a variety of channels while taking care of interpolation, anti-aliasing, etc. This module is built on the numpy library and has the resize() function, which can effectively resize images. Use the scikit-image module to resize images in Python There is no best way to select this parameter it differs from situation to situation. There are several ways to resize the image like INTER_NEAREST, INTER_LINEAR, and more. An important aspect here is the interpolation parameter, which essentially tells how to resize an image.
The imread() returns an array that stores the image. Res = cv2.resize(img, dsize=(54, 140), interpolation=cv2.INTER_CUBIC) To resize an image, we will first read the image using the imread() function and resize it using the resize() function as shown below. The OpenCV module is widely used in Python for image processing and computer vision. This is common for all the methods discussed below Use the opencv module to resize images in Python Note that after resizing, we can export this resized array and save it as an image. We cannot directly use the resize() function because it disregards the axis and does not apply interpolation or extrapolation. There is no direct functionality in the numpy module to achieve this. In this tutorial, we will discuss how to resize an image.Įssentially, we will resize the size of the numpy array, which represents an image.
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