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| from __future__ import annotations | |
| from pathlib import Path | |
| from typing import Literal | |
| import numpy as np | |
| import PIL.Image | |
| from gradio import processing_utils | |
| PIL.Image.init() # fixes https://github.com/gradio-app/gradio/issues/2843 (remove when requiring Pillow 9.4+) | |
| def format_image( | |
| im: PIL.Image.Image | None, | |
| type: Literal["numpy", "pil", "filepath"], | |
| cache_dir: str, | |
| name: str = "image", | |
| format: str = "webp", | |
| ) -> np.ndarray | PIL.Image.Image | str | None: | |
| """Helper method to format an image based on self.type""" | |
| if im is None: | |
| return im | |
| if type == "pil": | |
| return im | |
| elif type == "numpy": | |
| return np.array(im) | |
| elif type == "filepath": | |
| try: | |
| path = processing_utils.save_pil_to_cache( | |
| im, cache_dir=cache_dir, name=name, format=format | |
| ) | |
| # Catch error if format is not supported by PIL | |
| except (KeyError, ValueError): | |
| path = processing_utils.save_pil_to_cache( | |
| im, | |
| cache_dir=cache_dir, | |
| name=name, | |
| format="png", # type: ignore | |
| ) | |
| return path | |
| else: | |
| raise ValueError( | |
| "Unknown type: " | |
| + str(type) | |
| + ". Please choose from: 'numpy', 'pil', 'filepath'." | |
| ) | |
| def save_image( | |
| y: np.ndarray | PIL.Image.Image | str | Path, cache_dir: str, format: str = "webp" | |
| ): | |
| if isinstance(y, np.ndarray): | |
| path = processing_utils.save_img_array_to_cache( | |
| y, cache_dir=cache_dir, format=format | |
| ) | |
| elif isinstance(y, PIL.Image.Image): | |
| try: | |
| path = processing_utils.save_pil_to_cache( | |
| y, cache_dir=cache_dir, format=format | |
| ) | |
| # Catch error if format is not supported by PIL | |
| except (KeyError, ValueError): | |
| path = processing_utils.save_pil_to_cache( | |
| y, cache_dir=cache_dir, format="png" | |
| ) | |
| elif isinstance(y, Path): | |
| path = str(y) | |
| elif isinstance(y, str): | |
| path = y | |
| else: | |
| raise ValueError( | |
| "Cannot process this value as an Image, it is of type: " + str(type(y)) | |
| ) | |
| return path | |
| def crop_scale(img: PIL.Image.Image, final_width: int, final_height: int): | |
| original_width, original_height = img.size | |
| target_aspect_ratio = final_width / final_height | |
| if original_width / original_height > target_aspect_ratio: | |
| crop_height = original_height | |
| crop_width = crop_height * target_aspect_ratio | |
| else: | |
| crop_width = original_width | |
| crop_height = crop_width / target_aspect_ratio | |
| left = (original_width - crop_width) / 2 | |
| top = (original_height - crop_height) / 2 | |
| img_cropped = img.crop( | |
| (int(left), int(top), int(left + crop_width), int(top + crop_height)) | |
| ) | |
| img_resized = img_cropped.resize((final_width, final_height)) | |
| return img_resized | |