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| from math import ceil, floor | |
| from PIL import Image | |
| import torchvision.transforms as transforms | |
| import numpy as np | |
| pil_to_torch = transforms.Compose([ | |
| transforms.PILToTensor() | |
| ]) | |
| from typing import Tuple | |
| def get_padding_for_aspect_ratio(img: Image.Image, target_aspect_ratio: float = 16/9) -> list[int]: | |
| aspect_ratio = img.width / img.height | |
| if aspect_ratio != target_aspect_ratio: | |
| w_target = ceil(target_aspect_ratio*img.height) # r = w /h = w_i / h_i | |
| h_target = floor(img.width * (1/target_aspect_ratio)) | |
| if w_target >= img.width: | |
| w_scale = w_target / img.width | |
| else: | |
| w_scale = np.inf | |
| if h_target >= img.height: | |
| h_scale = h_target / img.height | |
| else: | |
| h_scale = np.inf | |
| if min([h_scale, w_scale]) == h_scale: | |
| scale_axis = 1 | |
| target_size = h_target | |
| else: | |
| scale_axis = 0 | |
| target_size = w_target | |
| pad_size = [0, 0, 0, 0] | |
| img_size = img.size | |
| pad_size[2+scale_axis] = int(target_size - img_size[scale_axis]) | |
| return pad_size | |
| else: | |
| return None | |
| def get_padding_for_aspect_ratio(img: Image, target_aspect_ratio: float = 16/9): | |
| aspect_ratio = img.width / img.height | |
| if aspect_ratio != target_aspect_ratio: | |
| w_target = ceil(target_aspect_ratio*img.height) # r = w /h = w_i / h_i | |
| h_target = floor(img.width * (1/target_aspect_ratio)) | |
| if w_target >= img.width: | |
| w_scale = w_target / img.width | |
| else: | |
| w_scale = np.inf | |
| if h_target >= img.height: | |
| h_scale = h_target / img.height | |
| else: | |
| h_scale = np.inf | |
| if min([h_scale, w_scale]) == h_scale: | |
| scale_axis = 1 | |
| target_size = h_target | |
| else: | |
| scale_axis = 0 | |
| target_size = w_target | |
| pad_size = [0, 0, 0, 0] | |
| img_size = img.size | |
| pad_size[2+scale_axis] = int(target_size - img_size[scale_axis]) | |
| return pad_size | |
| else: | |
| return None | |
| def add_margin(pil_img, top, right, bottom, left, color): | |
| width, height = pil_img.size | |
| new_width = width + right + left | |
| new_height = height + top + bottom | |
| result = Image.new(pil_img.mode, (new_width, new_height), color) | |
| result.paste(pil_img, (left, top)) | |
| return result | |
| def resize_to_fit(image, size): | |
| W, H = size | |
| w, h = image.size | |
| if H / h > W / w: | |
| H_ = int(h * W / w) | |
| W_ = W | |
| else: | |
| W_ = int(w * H / h) | |
| H_ = H | |
| return image.resize((W_, H_)) | |
| def pad_to_fit(image, size): | |
| W, H = size | |
| w, h = image.size | |
| pad_h = (H - h) // 2 | |
| pad_w = (W - w) // 2 | |
| return add_margin(image, pad_h, pad_w, pad_h, pad_w, (0, 0, 0)) | |
| def resize_and_keep(pil_img): | |
| expanded_size = [pil_img.width, pil_img.height] | |
| myheight = 576 | |
| hpercent = (myheight/float(pil_img.size[1])) | |
| wsize = int((float(pil_img.size[0])*float(hpercent))) | |
| pil_img = pil_img.resize((wsize, myheight)) | |
| return pil_img, expanded_size | |
| def resize_and_crop(pil_img: Image.Image) -> Tuple[Image.Image, Tuple[int, int]]: | |
| img, expanded_size = resize_and_keep(pil_img) | |
| assert img.width >= 1024 and img.height >= 576,f"Got {img.width} and {img.height}" | |
| return img.crop((0, 0, 1024, 576)), expanded_size | |
| def center_crop(pil_img): | |
| width, height = pil_img.size | |
| new_width = 576 | |
| new_height = 576 | |
| left = (width - new_width)/2 | |
| top = (height - new_height)/2 | |
| right = (width + new_width)/2 | |
| bottom = (height + new_height)/2 | |
| # Crop the center of the image | |
| pil_img = pil_img.crop((left, top, right, bottom)) | |
| return pil_img | |