app.py

import gradio as gr
from PIL import Image, ImageDraw, ImageFont
import random
import pandas as pd
import numpy as np
from datasets import concatenate_datasets
from operator import itemgetter
import collections

# download datasets
from datasets import load_dataset

dataset_small = load_dataset("pierreguillou/DocLayNet-small")
dataset_base = load_dataset("pierreguillou/DocLayNet-base")

id2label = {idx:label for idx,label in enumerate(dataset_small["train"].features["categories"].feature.names)}
labels = [label for idx, label in id2label.items()]

# need to change the coordinates format
def convert_box(box):
    x, y, w, h = tuple(box) # the row comes in (left, top, width, height) format
    actual_box = [x, y, x+w, y+h] # we turn it into (left, top, left+widght, top+height) to get the actual box 
    return actual_box

# get back original size
def original_box(box, original_width, original_height, coco_width, coco_height):
    return [
        int(original_width * (box[0] / coco_width)),
        int(original_height * (box[1] / coco_height)),
        int(original_width * (box[2] / coco_width)),
        int(original_height* (box[3] / coco_height)),
    ]

# function to sort bounding boxes
def get_sorted_boxes(bboxes):

  # sort by y from page top to bottom 
  bboxes = sorted(bboxes, key=itemgetter(1), reverse=False)
  y_list = [bbox[1] for bbox in bboxes]

  # sort by x from page left to right when boxes with same y
  if len(list(set(y_list))) != len(y_list):
    y_list_duplicates_indexes = dict()
    y_list_duplicates = [item for item, count in collections.Counter(y_list).items() if count > 1]
    for item in y_list_duplicates:
      y_list_duplicates_indexes[item] = [i for i, e in enumerate(y_list) if e == item]
      bbox_list_y_duplicates = sorted(np.array(bboxes)[y_list_duplicates_indexes[item]].tolist(), key=itemgetter(0), reverse=False)
      np_array_bboxes = np.array(bboxes)
      np_array_bboxes[y_list_duplicates_indexes[item]] = np.array(bbox_list_y_duplicates)
      bboxes = np_array_bboxes.tolist()

  return bboxes

# categories colors
label2color = {
    'Caption': 'brown',
    'Footnote': 'orange',
    'Formula': 'gray',
    'List-item': 'yellow',
    'Page-footer': 'red',
    'Page-header': 'red',
    'Picture': 'violet',
    'Section-header': 'orange',
    'Table': 'green',
    'Text': 'blue',
    'Title': 'pink'
    }

# image witout content
examples_dir = 'samples/'
images_wo_content = examples_dir + "wo_content.png"

df_paragraphs_wo_content, df_lines_wo_content = pd.DataFrame(), pd.DataFrame()

df_paragraphs_wo_content["paragraphs"] = [0]
df_paragraphs_wo_content["categories"] = ["no content"]
df_paragraphs_wo_content["texts"] = ["no content"]
df_paragraphs_wo_content["bounding boxes"] = ["no content"]

df_lines_wo_content["lines"] = [0]
df_lines_wo_content["categories"] = ["no content"]
df_lines_wo_content["texts"] = ["no content"]
df_lines_wo_content["bounding boxes"] = ["no content"]

# lists
font = ImageFont.load_default()

dataset_names = ["small", "base"]
splits = ["all", "train", "validation", "test"]
domains = ["all", "Financial Reports", "Manuals", "Scientific Articles", "Laws & Regulations", "Patents", "Government Tenders"]
domains_names = [domain_name.lower().replace(" ", "_") for domain_name in domains]
categories = labels + ["all"]

# function to get a rendom image and all data from DocLayNet
def generate_annotated_image(dataset_name, split, domain, category):

  # error message
  msg_error = ""

  # get dataset
  if dataset_name == "small": example = dataset_small
  else: example = dataset_base

  # get split
  if split == "all":
    example = concatenate_datasets([example["train"], example["validation"], example["test"]])
  else:
    example = example[split]

  # get domain
  domain_name = domains_names[domains.index(domain)]
  if domain_name != "all":
    example = example.filter(lambda example: example["doc_category"] == domain_name)
    if len(example) == 0:
      msg_error = f'There is no image with at least one annotated bounding box that matches to your parameters (domain: "{domain}" / dataset: "DocLayNet {dataset_name}" / split: "{split}").'
      example = dict()

  # get category
  idx_list = list()
  if category != "all":
    for idx, categories_list in zip(example["id"], example["categories"]):
      if category in categories_list:
        idx_list.append(idx)
    example = example.select(idx_list)
    if len(example) == 0:
      msg_error = f'There is no image with at least one annotated bounding box that matches to your parameters (category: "{category}" / domain: "{domain}" / dataset: "DocLayNet {dataset_name}" / split: "{split}").'
      example = dict()

  if len(msg_error) > 0:
    return msg_error, images_wo_content, images_wo_content, df_paragraphs_wo_content, df_lines_wo_content
  else:
    # get random image & PDF data
    image_files = example["image"]
    index = random.randint(0, len(image_files))
    image = image_files[index] # original image
    coco_width, coco_height = example[index]["coco_width"], example[index]["coco_height"]
    original_width, original_height = example[index]["original_width"], example[index]["original_height"]
    original_filename = example[index]["original_filename"]
    page_no = example[index]["page_no"]
    num_pages = example[index]["num_pages"]

    # resize image to original
    image = image.resize((original_width, original_height))

    # get corresponding annotations
    texts = example[index]["texts"]
    bboxes_block = example[index]["bboxes_block"]
    bboxes_line = example[index]["bboxes_line"]
    categories = example[index]["categories"]
    domain = example[index]["doc_category"]

    # get list of categories
    categories_unique = sorted(list(set([categories_list for categories_list in categories])))
    categories_unique = [id2label[idx] for idx in categories_unique]

    # convert boxes to original
    original_bboxes_block = [original_box(convert_box(box), original_width, original_height, coco_width, coco_height) for box in bboxes_block]
    original_bboxes_line = [original_box(convert_box(box), original_width, original_height, coco_width, coco_height) for box in bboxes_line]
    original_bboxes = [original_bboxes_block, original_bboxes_line]

    ##### block boxes #####

    # get list of unique block boxes
    original_blocks = dict()
    original_bboxes_block_list = list()
    original_bbox_block_prec = list()
    for count_block, original_bbox_block in enumerate(original_bboxes_block):
      if original_bbox_block != original_bbox_block_prec:
        original_bbox_block_indexes = [i for i, original_bbox in enumerate(original_bboxes_block) if original_bbox == original_bbox_block]
        original_blocks[count_block] = original_bbox_block_indexes
        original_bboxes_block_list.append(original_bbox_block)
      original_bbox_block_prec = original_bbox_block

    # get list of categories and texts by unique block boxes
    category_block_list, text_block_list = list(), list()
    for original_bbox_block in original_bboxes_block_list:
      count_block = original_bboxes_block.index(original_bbox_block)
      original_bbox_block_indexes = original_blocks[count_block]
      category_block = categories[original_bbox_block_indexes[0]]
      category_block_list.append(category_block)
      if id2label[category_block] == "Text" or id2label[category_block] == "Caption" or id2label[category_block] == "Footnote":
        text_block = ' '.join(np.array(texts)[original_bbox_block_indexes].tolist())
      elif id2label[category_block] == "Section-header" or id2label[category_block] == "Title" or id2label[category_block] == "Picture" or id2label[category_block] == "Formula" or id2label[category_block] == "List-item" or id2label[category_block] == "Table" or id2label[category_block] == "Page-header" or id2label[category_block] == "Page-footer":
        text_block = '\n'.join(np.array(texts)[original_bbox_block_indexes].tolist())
      text_block_list.append(text_block)

    # sort data from y = 0 to end of page (and after, x=0 to end of page when necessary)
    sorted_original_bboxes_block_list = get_sorted_boxes(original_bboxes_block_list)
    sorted_original_bboxes_block_list_indexes = [original_bboxes_block_list.index(item) for item in sorted_original_bboxes_block_list]
    sorted_category_block_list = np.array(category_block_list)[sorted_original_bboxes_block_list_indexes].tolist()
    sorted_text_block_list = np.array(text_block_list)[sorted_original_bboxes_block_list_indexes].tolist()

    ##### line boxes ####

    # sort data from y = 0 to end of page (and after, x=0 to end of page when necessary)
    original_bboxes_line_list = original_bboxes_line
    category_line_list = categories
    text_line_list = texts
    sorted_original_bboxes_line_list = get_sorted_boxes(original_bboxes_line_list)
    sorted_original_bboxes_line_list_indexes = [original_bboxes_line_list.index(item) for item in sorted_original_bboxes_line_list]
    sorted_category_line_list = np.array(category_line_list)[sorted_original_bboxes_line_list_indexes].tolist()
    sorted_text_line_list = np.array(text_line_list)[sorted_original_bboxes_line_list_indexes].tolist()
    
    # setup images & PDf data
    columns = 2
    images = [image.copy(), image.copy()]
    num_imgs = len(images)

    imgs, df_paragraphs, df_lines = dict(), pd.DataFrame(), pd.DataFrame()
    for i, img in enumerate(images):
        
        draw = ImageDraw.Draw(img)
        
        for box, label_idx, text in zip(original_bboxes[i], categories, texts):
            label = id2label[label_idx]
            color = label2color[label]
            draw.rectangle(box, outline=color)
            text = text.encode('latin-1', 'replace').decode('latin-1') # https://stackoverflow.com/questions/56761449/unicodeencodeerror-latin-1-codec-cant-encode-character-u2013-writing-to
            draw.text((box[0] + 10, box[1] - 10), text=label, fill=color, font=font)

        if i == 0: 
          imgs["paragraphs"] = img
        
          df_paragraphs["paragraphs"] = list(range(len(sorted_original_bboxes_block_list)))
          df_paragraphs["categories"] = [id2label[label_idx] for label_idx in sorted_category_block_list]
          df_paragraphs["texts"] = sorted_text_block_list
          df_paragraphs["bounding boxes"] = [str(bbox) for bbox in sorted_original_bboxes_block_list]

        else: 
          imgs["lines"] = img

          df_lines["lines"] = list(range(len(sorted_original_bboxes_line_list)))
          df_lines["categories"] = [id2label[label_idx] for label_idx in sorted_category_line_list]
          df_lines["texts"] = sorted_text_line_list
          df_lines["bounding boxes"] = [str(bbox) for bbox in sorted_original_bboxes_line_list]

    msg = f'The page {page_no} of PDF "{original_filename}" (domain "{domain}") matches your parameters.'

    return msg, imgs["paragraphs"], imgs["lines"], df_paragraphs, df_lines

# gradio APP
with gr.Blocks(title="DocLayNet image viewer", css=".gradio-container") as demo:
    gr.HTML("""
    <div style="font-family:'Times New Roman', 'Serif'; font-size:26pt; font-weight:bold; text-align:center;"><h1>DocLayNet image viewer</h1></div>
    <div style="margin-top: 20px"><p>(01/29/2023) This APP is an image viewer of the DocLayNet dataset.</p></div>
    <div><p>It uses the dataset <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://huggingface.co/datasets/pierreguillou/DocLayNet-small" target="_blank">DocLayNet small</a> (in the corresponding <a href="https://github.com/piegu/language-models/blob/master/DocLayNet_image_viewer_APP.ipynb" target="_blank">notebook</a>, the <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://huggingface.co/datasets/pierreguillou/DocLayNet-base" target="_blank">DocLayNet base</a> is used, too).</p></div>
    <div><p>Make your parameters selections and the output will show 2 images of a randomly selected PDF with annotated bounding boxes, one of paragraphs and the other of lines, and a table of texts with their labels.</p></div>
    """)
    with gr.Row():
        with gr.Column():
            dataset_name_gr = gr.Radio(["small"], value="small", label="DocLayNet dataset")
        with gr.Column():
            split_gr = gr.Dropdown(splits, value="all", label="Split")
        with gr.Column():
            domain_gr = gr.Dropdown(domains, value="all", label="Domain")
        with gr.Column():
            category_gr = gr.Dropdown(categories, value="all", label="Category")
    btn = gr.Button("Display PDF image")
    with gr.Row():
      output_msg = gr.Textbox(label="Output message")
    with gr.Row():
        # with gr.Column():
        #   json = gr.JSON(label="JSON")
        with gr.Column():
          img_paragraphs = gr.Image(type="pil", label="Bounding boxes of labeled paragraphs")
        with gr.Column():
          img_lines = gr.Image(type="pil", label="Bounding boxes of labeled lines")

    with gr.Row():
      with gr.Column():
        df_paragraphs = gr.Dataframe(
            headers=["paragraphs", "categories", "texts", "bounding boxes"],
            datatype=["number", "str", "str", "str"],
            # row_count='dynamic',
            col_count=(4, "fixed"), 
            interactive=False,
            label="Paragraphs data",
            type="pandas",
            wrap=True
          )
      with gr.Column():
          df_lines = gr.Dataframe(
              headers=["lines", "categories", "texts", "bounding boxes"],
              datatype=["number", "str", "str", "str"],
              # row_count='dynamic',
              col_count=(4, "fixed"), 
              interactive=False,
              label="Lines data",
              type="pandas",
              wrap=True
            )
    btn.click(generate_annotated_image, inputs=[dataset_name_gr, split_gr, domain_gr, category_gr], outputs=[output_msg, img_paragraphs, img_lines, df_paragraphs, df_lines])

    gr.Markdown("## Example")
    gr.Examples(
        [["small", "all", "all", "all"]],
        [dataset_name_gr, split_gr, domain_gr, category_gr],
        [output_msg, img_paragraphs, img_lines, df_paragraphs, df_lines],
        fn=generate_annotated_image,
        cache_examples=True,
    )

demo.launch()