Then multiply both the width and the height of the original image by the scaling factor.įinally, call the cv2.resize() function with downscaled width and height.įor instance, let’s scale the image down to 25% of the original size and show it using the imshow() function. To do this, specify a scaling factor that is less than 1. This means you keep the aspect ratio of the image but make the image smaller. One way to change the size of your image is by downscaling it. (new_width, new_height) is the dsize parameter from the original syntax. In other words, we are going to call the cv2.resize() function with the following syntax: cv2.resize(src, (new_width, new_height)) To keep it simple, we are only going to use these two parameters at first: interpolation flag that determines how the output pixels are arranged.fy scale factor along the vertical axis.fx scale factor along the horizontal axis.dsize is the desired size of the output image.The syntax of the cv2.resize() function is: cv2.resize(src, dsize, fx, fy, interpolation) It takes the original image, modifies it, and returns a new image. To resize images with OpenCV, use the cv2.resize() function. Resize an Image with cv2.resize() Function Now that you have read the image, let’s resize it. Padded_image = np.zeros(shape_out, dtype=np.For example, if your script is on the same folder with “image.jpeg” you can read the image into your program by: import cv2 If not DO_PADDING or np.all(hw_out = hw_wk): Image, tuple(hw_wk), interpolation=cv2.INTER_NEAREST Hw_wk = (hw_image * resize_ratio + TINY_FLOAT).astype(int) Hw_out, hw_image = ) for x in (shape_out, image.shape)] If image.ndim = 3 and len(shape_out) = 2: It should work with either black and white image or color image def resize_with_padding(image, shape_out, DO_PADDING=True, TINY_FLOAT=1e-5): # Add the resized image to the padded image, with padding on the left and right sides # Calculate the number of rows/columns to add as padding # Create a black image with the target size Resized_image = cv2.resize(image, (new_width, new_height), interpolation = cv2.INTER_NEAREST) New_height = int(new_width * aspect_ratio) New_width = int(new_height / aspect_ratio) # Calculate the new height and width after resizing to (224,224) # Calculate the aspect ratio of the image Resizes a black and white image to the specified size,Īdding padding to preserve the aspect ratio. It's the best what i could do, works only with black and white images def resize_with_padding(image, size=(224,224)): Img.thumbnail((expected_size, expected_size))ĭelta_width = expected_size - img.sizeĭelta_height = expected_size - img.size Padding = (pad_width, pad_height, delta_width - pad_width, delta_height - pad_height)ĭef resize_with_padding(img, expected_size): Try to use this function: from PIL import Image, ImageOpsĭelta_width = desired_size - img.sizeĭelta_height = desired_size - img.size Result[y_center:y_center+old_image_height, # copy img image into center of result image Y_center = (new_image_height - old_image_height) // 2 X_center = (new_image_width - old_image_width) // 2 Result = np.full((new_image_height,new_image_width, channels), color, dtype=np.uint8) # create new image of desired size and color (blue) for padding Old_image_height, old_image_width, channels = img.shape I think this is easier to do using width, height, xoffset, yoffset, rather than how much to pad on each side. Here I compute the offset to do center padding. It uses Numpy slicing to copy the input image into a new image of the desired output size and a given offset. Here is another way to do that in Python/OpenCV/Numpy.
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