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| # ------------------------------------------ | |
| # TextDiffuser: Diffusion Models as Text Painters | |
| # Paper Link: https://arxiv.org/abs/2305.10855 | |
| # Code Link: https://github.com/microsoft/unilm/tree/master/textdiffuser | |
| # Copyright (c) Microsoft Corporation. | |
| # This file defines a set of commonly used utility functions. | |
| # ------------------------------------------ | |
| import os | |
| import re | |
| import cv2 | |
| import math | |
| import shutil | |
| import string | |
| import textwrap | |
| import numpy as np | |
| from PIL import Image, ImageFont, ImageDraw, ImageOps | |
| from typing import * | |
| # define alphabet and alphabet_dic | |
| alphabet = string.digits + string.ascii_lowercase + string.ascii_uppercase + string.punctuation + ' ' # len(aphabet) = 95 | |
| alphabet_dic = {} | |
| for index, c in enumerate(alphabet): | |
| alphabet_dic[c] = index + 1 # the index 0 stands for non-character | |
| def transform_mask_pil(mask_root, size): | |
| """ | |
| This function extracts the mask area and text area from the images. | |
| Args: | |
| mask_root (str): The path of mask image. | |
| * The white area is the unmasked area | |
| * The gray area is the masked area | |
| * The white area is the text area | |
| """ | |
| img = np.array(mask_root) | |
| img = cv2.resize(img, (size, size), interpolation=cv2.INTER_NEAREST) | |
| gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) | |
| ret, binary = cv2.threshold(gray, 250, 255, cv2.THRESH_BINARY) # pixel value is set to 0 or 255 according to the threshold | |
| return 1 - (binary.astype(np.float32) / 255) | |
| def transform_mask(mask_root, size): | |
| """ | |
| This function extracts the mask area and text area from the images. | |
| Args: | |
| mask_root (str): The path of mask image. | |
| * The white area is the unmasked area | |
| * The gray area is the masked area | |
| * The white area is the text area | |
| """ | |
| img = cv2.imread(mask_root) | |
| img = cv2.resize(img, (size, size), interpolation=cv2.INTER_NEAREST) | |
| gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) | |
| ret, binary = cv2.threshold(gray, 250, 255, cv2.THRESH_BINARY) # pixel value is set to 0 or 255 according to the threshold | |
| return 1 - (binary.astype(np.float32) / 255) | |
| def segmentation_mask_visualization(font_path: str, segmentation_mask: np.array): | |
| """ | |
| This function visualizes the segmentaiton masks with characters. | |
| Args: | |
| font_path (str): The path of font. We recommand to use Arial.ttf | |
| segmentation_mask (np.array): The character-level segmentation mask. | |
| """ | |
| segmentation_mask = cv2.resize(segmentation_mask, (64, 64), interpolation=cv2.INTER_NEAREST) | |
| font = ImageFont.truetype(font_path, 8) | |
| blank = Image.new('RGB', (512,512), (0,0,0)) | |
| d = ImageDraw.Draw(blank) | |
| for i in range(64): | |
| for j in range(64): | |
| if int(segmentation_mask[i][j]) == 0 or int(segmentation_mask[i][j])-1 >= len(alphabet): | |
| continue | |
| else: | |
| d.text((j*8, i*8), alphabet[int(segmentation_mask[i][j])-1], font=font, fill=(0, 255, 0)) | |
| return blank | |
| def make_caption_pil(font_path: str, captions: List[str]): | |
| """ | |
| This function converts captions into pil images. | |
| Args: | |
| font_path (str): The path of font. We recommand to use Arial.ttf | |
| captions (List[str]): List of captions. | |
| """ | |
| caption_pil_list = [] | |
| font = ImageFont.truetype(font_path, 18) | |
| for caption in captions: | |
| border_size = 2 | |
| img = Image.new('RGB', (512-4,48-4), (255,255,255)) | |
| img = ImageOps.expand(img, border=(border_size, border_size, border_size, border_size), fill=(127, 127, 127)) | |
| draw = ImageDraw.Draw(img) | |
| border_size = 2 | |
| text = caption | |
| lines = textwrap.wrap(text, width=40) | |
| x, y = 4, 4 | |
| line_height = font.getsize('A')[1] + 4 | |
| start = 0 | |
| for line in lines: | |
| draw.text((x, y+start), line, font=font, fill=(200, 127, 0)) | |
| y += line_height | |
| caption_pil_list.append(img) | |
| return caption_pil_list | |
| def filter_segmentation_mask(segmentation_mask: np.array): | |
| """ | |
| This function removes some noisy predictions of segmentation masks. | |
| Args: | |
| segmentation_mask (np.array): The character-level segmentation mask. | |
| """ | |
| segmentation_mask[segmentation_mask==alphabet_dic['-']] = 0 | |
| segmentation_mask[segmentation_mask==alphabet_dic[' ']] = 0 | |
| return segmentation_mask | |
| def combine_image(args, resolution, sub_output_dir: str, pred_image_list: List, image_pil: Image, character_mask_pil: Image, character_mask_highlight_pil: Image, caption_pil_list: List): | |
| """ | |
| This function combines all the outputs and useful inputs together. | |
| Args: | |
| args (argparse.ArgumentParser): The arguments. | |
| pred_image_list (List): List of predicted images. | |
| image_pil (Image): The original image. | |
| character_mask_pil (Image): The character-level segmentation mask. | |
| character_mask_highlight_pil (Image): The character-level segmentation mask highlighting character regions with green color. | |
| caption_pil_list (List): List of captions. | |
| """ | |
| size = len(pred_image_list) | |
| if size == 1: | |
| return pred_image_list[0] | |
| elif size == 2: | |
| blank = Image.new('RGB', (resolution*2, resolution), (0,0,0)) | |
| blank.paste(pred_image_list[0],(0,0)) | |
| blank.paste(pred_image_list[1],(resolution,0)) | |
| elif size == 3: | |
| blank = Image.new('RGB', (resolution*3, resolution), (0,0,0)) | |
| blank.paste(pred_image_list[0],(0,0)) | |
| blank.paste(pred_image_list[1],(resolution,0)) | |
| blank.paste(pred_image_list[2],(resolution*2,0)) | |
| elif size == 4: | |
| blank = Image.new('RGB', (resolution*2, resolution*2), (0,0,0)) | |
| blank.paste(pred_image_list[0],(0,0)) | |
| blank.paste(pred_image_list[1],(resolution,0)) | |
| blank.paste(pred_image_list[2],(0,resolution)) | |
| blank.paste(pred_image_list[3],(resolution,resolution)) | |
| return blank | |
| def combine_image_gradio(args, size, sub_output_dir: str, pred_image_list: List, image_pil: Image, character_mask_pil: Image, character_mask_highlight_pil: Image, caption_pil_list: List): | |
| """ | |
| This function combines all the outputs and useful inputs together. | |
| Args: | |
| args (argparse.ArgumentParser): The arguments. | |
| pred_image_list (List): List of predicted images. | |
| image_pil (Image): The original image. | |
| character_mask_pil (Image): The character-level segmentation mask. | |
| character_mask_highlight_pil (Image): The character-level segmentation mask highlighting character regions with green color. | |
| caption_pil_list (List): List of captions. | |
| """ | |
| size = len(pred_image_list) | |
| if size == 1: | |
| return pred_image_list[0] | |
| elif size == 2: | |
| blank = Image.new('RGB', (size*2, size), (0,0,0)) | |
| blank.paste(pred_image_list[0],(0,0)) | |
| blank.paste(pred_image_list[1],(size,0)) | |
| elif size == 3: | |
| blank = Image.new('RGB', (size*3, size), (0,0,0)) | |
| blank.paste(pred_image_list[0],(0,0)) | |
| blank.paste(pred_image_list[1],(size,0)) | |
| blank.paste(pred_image_list[2],(size*2,0)) | |
| elif size == 4: | |
| blank = Image.new('RGB', (size*2, size*2), (0,0,0)) | |
| blank.paste(pred_image_list[0],(0,0)) | |
| blank.paste(pred_image_list[1],(size,0)) | |
| blank.paste(pred_image_list[2],(0,size)) | |
| blank.paste(pred_image_list[3],(size,size)) | |
| return blank | |
| def get_width(font_path, text): | |
| """ | |
| This function calculates the width of the text. | |
| Args: | |
| font_path (str): user prompt. | |
| text (str): user prompt. | |
| """ | |
| font = ImageFont.truetype(font_path, 24) | |
| width, _ = font.getsize(text) | |
| return width | |
| def get_key_words(text: str): | |
| """ | |
| This function detect keywords (enclosed by quotes) from user prompts. The keywords are used to guide the layout generation. | |
| Args: | |
| text (str): user prompt. | |
| """ | |
| words = [] | |
| text = text | |
| matches = re.findall(r"'(.*?)'", text) # find the keywords enclosed by '' | |
| if matches: | |
| for match in matches: | |
| words.extend(match.split()) | |
| if len(words) >= 8: | |
| return [] | |
| return words | |
| def adjust_overlap_box(box_output, current_index): | |
| """ | |
| This function adjust the overlapping boxes. | |
| Args: | |
| box_output (List): List of predicted boxes. | |
| current_index (int): the index of current box. | |
| """ | |
| if current_index == 0: | |
| return box_output | |
| else: | |
| # judge whether it contains overlap with the last output | |
| last_box = box_output[0, current_index-1, :] | |
| xmin_last, ymin_last, xmax_last, ymax_last = last_box | |
| current_box = box_output[0, current_index, :] | |
| xmin, ymin, xmax, ymax = current_box | |
| if xmin_last <= xmin <= xmax_last and ymin_last <= ymin <= ymax_last: | |
| print('adjust overlapping') | |
| distance_x = xmax_last - xmin | |
| distance_y = ymax_last - ymin | |
| if distance_x <= distance_y: | |
| # avoid overlap | |
| new_x_min = xmax_last + 0.025 | |
| new_x_max = xmax - xmin + xmax_last + 0.025 | |
| box_output[0,current_index,0] = new_x_min | |
| box_output[0,current_index,2] = new_x_max | |
| else: | |
| new_y_min = ymax_last + 0.025 | |
| new_y_max = ymax - ymin + ymax_last + 0.025 | |
| box_output[0,current_index,1] = new_y_min | |
| box_output[0,current_index,3] = new_y_max | |
| elif xmin_last <= xmin <= xmax_last and ymin_last <= ymax <= ymax_last: | |
| print('adjust overlapping') | |
| new_x_min = xmax_last + 0.05 | |
| new_x_max = xmax - xmin + xmax_last + 0.05 | |
| box_output[0,current_index,0] = new_x_min | |
| box_output[0,current_index,2] = new_x_max | |
| return box_output | |
| def shrink_box(box, scale_factor = 0.9): | |
| """ | |
| This function shrinks the box. | |
| Args: | |
| box (List): List of predicted boxes. | |
| scale_factor (float): The scale factor of shrinking. | |
| """ | |
| x1, y1, x2, y2 = box | |
| x1_new = x1 + (x2 - x1) * (1 - scale_factor) / 2 | |
| y1_new = y1 + (y2 - y1) * (1 - scale_factor) / 2 | |
| x2_new = x2 - (x2 - x1) * (1 - scale_factor) / 2 | |
| y2_new = y2 - (y2 - y1) * (1 - scale_factor) / 2 | |
| return (x1_new, y1_new, x2_new, y2_new) | |
| def adjust_font_size(args, width, height, draw, text): | |
| """ | |
| This function adjusts the font size. | |
| Args: | |
| args (argparse.ArgumentParser): The arguments. | |
| width (int): The width of the text. | |
| height (int): The height of the text. | |
| draw (ImageDraw): The ImageDraw object. | |
| text (str): The text. | |
| """ | |
| size_start = height | |
| while True: | |
| font = ImageFont.truetype(args.font_path, size_start) | |
| text_width, _ = draw.textsize(text, font=font) | |
| if text_width >= width: | |
| size_start = size_start - 1 | |
| else: | |
| return size_start | |
| def inpainting_merge_image(original_image, mask_image, inpainting_image): | |
| """ | |
| This function merges the original image, mask image and inpainting image. | |
| Args: | |
| original_image (PIL.Image): The original image. | |
| mask_image (PIL.Image): The mask images. | |
| inpainting_image (PIL.Image): The inpainting images. | |
| """ | |
| original_image = original_image.resize((512, 512)) | |
| mask_image = mask_image.resize((512, 512)) | |
| inpainting_image = inpainting_image.resize((512, 512)) | |
| mask_image.convert('L') | |
| threshold = 250 | |
| table = [] | |
| for i in range(256): | |
| if i < threshold: | |
| table.append(1) | |
| else: | |
| table.append(0) | |
| mask_image = mask_image.point(table, "1") | |
| merged_image = Image.composite(inpainting_image, original_image, mask_image) | |
| return merged_image | |