import gradio as gr import kornia as K import numpy as np import kornia.geometry as KG import torch import cv2 def geometry_transform(images: list, translation: float, scale: float, angle: float) -> np.ndarray: file_names: list = [f.name for f in images] image_list: list = [K.io.load_image(f, K.io.ImageLoadType(0)).float().unsqueeze(0)/255 for f in file_names] image_batch: torch.Tensor = torch.cat(image_list, 0) center: torch.Tensor = torch.tensor([x.shape[1:] for x in image_batch])/2 translation = torch.tensor(translation).repeat(len(image_list), 2) scale = torch.tensor(scale).repeat(len(image_list), 2) angle = torch.tensor(angle).repeat(len(image_list)) affine_matrix: torch.Tensor = KG.get_affine_matrix2d(translation, center, scale, angle) transformed: torch.Tensor = KG.transform.warp_affine(image_batch, affine_matrix[:, :2], dsize=image_batch.shape[2:]) np_images: list = [K.tensor_to_image(f*255).astype(np.uint8) for f in transformed] final_images: np.ndarray = cv2.hconcat(np_images) return final_images def main(): title = """