11/22/2023 0 Comments Pil image resizeThe resize function accepts the ndarray and a new width ratio and height for the output image. Similarly to OpenCV, SciKit Image exposes imread and imsave functions for converting image data to and from an ndarray. Here is the basic code for resizing an image with SciKit Image: SciKit Image is another Python library based on Numpy that does image resizing with interpolation and extrapolation. INTER_LANCZOS4 - Lanczos interpolation over 8x8 pixel neighborhoodįor more information on the different types of interpolation, read the docs.INTER_CUBIC - bicubic interpolation over 4x4 pixel neighborhood.INTER_AREA - resampling using pixel area relation.INTER_LINEAR - bilinear interpolation (default).INTER_NEAREST - interpolation using the nearest neighboring pixels. The interpolation parameter can be any of the following: The dimensions you provide to the dsize parameter directly translate to the new image width and height in pixels. It adjusts the size of the Numpy array based on these parameters.įinally, the imwrite function takes the resulting Numpy array, converts it back into a regular image, and writes the output image to a file with a typical image file extension. Next, the resize function accepts the image, the desired size, and an interpolation parameter. The imread function reads a standard image file into memory and converts it to a Numpy array. Padded_image = np.zeros(shape_out, dtype=np.Res = cv2.resize(img, dsize=(54, 140), interpolation=cv2.INTER_LINEAR) 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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