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| import os | |
| import cv2 | |
| import yaml | |
| import copy | |
| import pygame | |
| import numpy as np | |
| from PIL import Image | |
| from fontTools.ttLib import TTFont | |
| import torch | |
| import torchvision.transforms as transforms | |
| def save_args_to_yaml(args, output_file): | |
| # Convert args namespace to a dictionary | |
| args_dict = vars(args) | |
| # Write the dictionary to a YAML file | |
| with open(output_file, 'w') as yaml_file: | |
| yaml.dump(args_dict, yaml_file, default_flow_style=False) | |
| def save_single_image(save_dir, image): | |
| save_path = f"{save_dir}/out_single.png" | |
| image.save(save_path) | |
| def save_image_with_content_style(save_dir, image, content_image_pil, content_image_path, style_image_path, resolution): | |
| new_image = Image.new('RGB', (resolution*3, resolution)) | |
| if content_image_pil is not None: | |
| content_image = content_image_pil | |
| else: | |
| content_image = Image.open(content_image_path).convert("RGB").resize((resolution, resolution), Image.BILINEAR) | |
| style_image = Image.open(style_image_path).convert("RGB").resize((resolution, resolution), Image.BILINEAR) | |
| new_image.paste(content_image, (0, 0)) | |
| new_image.paste(style_image, (resolution, 0)) | |
| new_image.paste(image, (resolution*2, 0)) | |
| save_path = f"{save_dir}/out_with_cs.jpg" | |
| new_image.save(save_path) | |
| def x0_from_epsilon(scheduler, noise_pred, x_t, timesteps): | |
| """Return the x_0 from epsilon | |
| """ | |
| batch_size = noise_pred.shape[0] | |
| for i in range(batch_size): | |
| noise_pred_i = noise_pred[i] | |
| noise_pred_i = noise_pred_i[None, :] | |
| t = timesteps[i] | |
| x_t_i = x_t[i] | |
| x_t_i = x_t_i[None, :] | |
| pred_original_sample_i = scheduler.step( | |
| model_output=noise_pred_i, | |
| timestep=t, | |
| sample=x_t_i, | |
| # predict_epsilon=True, | |
| generator=None, | |
| return_dict=True, | |
| ).pred_original_sample | |
| if i == 0: | |
| pred_original_sample = pred_original_sample_i | |
| else: | |
| pred_original_sample = torch.cat((pred_original_sample, pred_original_sample_i), dim=0) | |
| return pred_original_sample | |
| def reNormalize_img(pred_original_sample): | |
| pred_original_sample = (pred_original_sample / 2 + 0.5).clamp(0, 1) | |
| return pred_original_sample | |
| def normalize_mean_std(image): | |
| transforms_norm = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) | |
| image = transforms_norm(image) | |
| return image | |
| def is_char_in_font(font_path, char): | |
| TTFont_font = TTFont(font_path) | |
| cmap = TTFont_font['cmap'] | |
| for subtable in cmap.tables: | |
| if ord(char) in subtable.cmap: | |
| return True | |
| return False | |
| def load_ttf(ttf_path, fsize=128): | |
| pygame.init() | |
| font = pygame.freetype.Font(ttf_path, size=fsize) | |
| return font | |
| def ttf2im(font, char, fsize=128): | |
| try: | |
| surface, _ = font.render(char) | |
| except: | |
| print("No glyph for char {}".format(char)) | |
| return | |
| bg = np.full((fsize, fsize), 255) | |
| imo = pygame.surfarray.pixels_alpha(surface).transpose(1, 0) | |
| imo = 255 - np.array(Image.fromarray(imo)) | |
| im = copy.deepcopy(bg) | |
| h, w = imo.shape[:2] | |
| if h > fsize: | |
| h, w = fsize, round(w*fsize/h) | |
| imo = cv2.resize(imo, (w, h)) | |
| if w > fsize: | |
| h, w = round(h*fsize/w), fsize | |
| imo = cv2.resize(imo, (w, h)) | |
| x, y = round((fsize-w)/2), round((fsize-h)/2) | |
| im[y:h+y, x:x+w] = imo | |
| pil_im = Image.fromarray(im.astype('uint8')).convert('RGB') | |
| return pil_im | |