import os
# workaround: install old version of pytorch since detectron2 hasn't released packages for pytorch 1.9 (issue: https://github.com/facebookresearch/detectron2/issues/3158)
# os.system('pip install torch==1.8.0+cu101 torchvision==0.9.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html')
os.system('pip install -q torch==1.10.0+cu111 torchvision==0.11+cu111 -f https://download.pytorch.org/whl/torch_stable.html')
# install detectron2 that matches pytorch 1.8
# See https://detectron2.readthedocs.io/tutorials/install.html for instructions
#os.system('pip install -q detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.8/index.html')
os.system('pip install git+https://github.com/facebookresearch/detectron2.git')
import detectron2
from detectron2.utils.logger import setup_logger
setup_logger()
import gradio as gr
import re
import string
import torch
from operator import itemgetter
import collections
import pypdf
from pypdf import PdfReader
from pypdf.errors import PdfReadError
import pdf2image
from pdf2image import convert_from_path
import langdetect
from langdetect import detect_langs
import pandas as pd
import numpy as np
import random
import tempfile
import itertools
from matplotlib import font_manager
from PIL import Image, ImageDraw, ImageFont
import cv2
import pathlib
from pathlib import Path
import shutil
from functools import partial
# Tesseract
print(os.popen(f'cat /etc/debian_version').read())
print(os.popen(f'cat /etc/issue').read())
print(os.popen(f'apt search tesseract').read())
import pytesseract
## Key parameters
# 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'
}
# bounding boxes start and end of a sequence
cls_box = [0, 0, 0, 0]
sep_box_lilt = cls_box
sep_box_layoutxlm = [1000, 1000, 1000, 1000]
# models
model_id_lilt = "pierreguillou/lilt-xlm-roberta-base-finetuned-with-DocLayNet-base-at-linelevel-ml384"
model_id_layoutxlm = "pierreguillou/layout-xlm-base-finetuned-with-DocLayNet-base-at-linelevel-ml384"
# tokenizer for LayoutXLM
tokenizer_id_layoutxlm = "xlm-roberta-base"
# (tokenization) The maximum length of a feature (sequence)
if str(384) in model_id_lilt:
max_length_lilt = 384
elif str(512) in model_id_lilt:
max_length_lilt = 512
else:
print("Error with max_length_lilt of chunks!")
if str(384) in model_id_layoutxlm:
max_length_layoutxlm = 384
elif str(512) in model_id_layoutxlm:
max_length_layoutxlm = 512
else:
print("Error with max_length_layoutxlm of chunks!")
# (tokenization) overlap
doc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed.
# max PDF page images that will be displayed
max_imgboxes = 1
# get files
examples_dir = 'files/'
Path(examples_dir).mkdir(parents=True, exist_ok=True)
from huggingface_hub import hf_hub_download
files = ["example.pdf", "blank.pdf", "blank.png", "languages_iso.csv", "languages_tesseract.csv", "wo_content.png"]
for file_name in files:
path_to_file = hf_hub_download(
repo_id = "pierreguillou/Inference-APP-Document-Understanding-at-linelevel-LiLT-base-LayoutXLM-base-v1",
filename = "files/" + file_name,
repo_type = "space"
)
shutil.copy(path_to_file,examples_dir)
# path to files
image_wo_content = examples_dir + "wo_content.png" # image without content
pdf_blank = examples_dir + "blank.pdf" # blank PDF
image_blank = examples_dir + "blank.png" # blank image
## get langdetect2Tesseract dictionary
t = "files/languages_tesseract.csv"
l = "files/languages_iso.csv"
df_t = pd.read_csv(t)
df_l = pd.read_csv(l)
langs_t = df_t["Language"].to_list()
langs_t = [lang_t.lower().strip().translate(str.maketrans('', '', string.punctuation)) for lang_t in langs_t]
langs_l = df_l["Language"].to_list()
langs_l = [lang_l.lower().strip().translate(str.maketrans('', '', string.punctuation)) for lang_l in langs_l]
langscode_t = df_t["LangCode"].to_list()
langscode_l = df_l["LangCode"].to_list()
Tesseract2langdetect, langdetect2Tesseract = dict(), dict()
for lang_t, langcode_t in zip(langs_t,langscode_t):
try:
if lang_t == "Chinese - Simplified".lower().strip().translate(str.maketrans('', '', string.punctuation)): lang_t = "chinese"
index = langs_l.index(lang_t)
langcode_l = langscode_l[index]
Tesseract2langdetect[langcode_t] = langcode_l
except:
continue
langdetect2Tesseract = {v:k for k,v in Tesseract2langdetect.items()}
## model / feature extractor / tokenizer
# get device
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
## model LiLT
import transformers
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer_lilt = AutoTokenizer.from_pretrained(model_id_lilt)
model_lilt = AutoModelForTokenClassification.from_pretrained(model_id_lilt);
model_lilt.to(device);
## model LayoutXLM
from transformers import LayoutLMv2ForTokenClassification # LayoutXLMTokenizerFast,
model_layoutxlm = LayoutLMv2ForTokenClassification.from_pretrained(model_id_layoutxlm);
model_layoutxlm.to(device);
# feature extractor
from transformers import LayoutLMv2FeatureExtractor
feature_extractor = LayoutLMv2FeatureExtractor(apply_ocr=False)
# tokenizer
from transformers import AutoTokenizer
tokenizer_layoutxlm = AutoTokenizer.from_pretrained(tokenizer_id_layoutxlm)
# get labels
id2label_lilt = model_lilt.config.id2label
label2id_lilt = model_lilt.config.label2id
num_labels_lilt = len(id2label_lilt)
id2label_layoutxlm = model_layoutxlm.config.id2label
label2id_layoutxlm = model_layoutxlm.config.label2id
num_labels_layoutxlm = len(id2label_layoutxlm)
## General
# get text and bounding boxes from an image
# https://stackoverflow.com/questions/61347755/how-can-i-get-line-coordinates-that-readed-by-tesseract
# https://medium.com/geekculture/tesseract-ocr-understanding-the-contents-of-documents-beyond-their-text-a98704b7c655
def get_data(results, factor, conf_min=0):
data = {}
for i in range(len(results['line_num'])):
level = results['level'][i]
block_num = results['block_num'][i]
par_num = results['par_num'][i]
line_num = results['line_num'][i]
top, left = results['top'][i], results['left'][i]
width, height = results['width'][i], results['height'][i]
conf = results['conf'][i]
text = results['text'][i]
if not (text == '' or text.isspace()):
if conf >= conf_min:
tup = (text, left, top, width, height)
if block_num in list(data.keys()):
if par_num in list(data[block_num].keys()):
if line_num in list(data[block_num][par_num].keys()):
data[block_num][par_num][line_num].append(tup)
else:
data[block_num][par_num][line_num] = [tup]
else:
data[block_num][par_num] = {}
data[block_num][par_num][line_num] = [tup]
else:
data[block_num] = {}
data[block_num][par_num] = {}
data[block_num][par_num][line_num] = [tup]
# get paragraphs dicionnary with list of lines
par_data = {}
par_idx = 1
for _, b in data.items():
for _, p in b.items():
line_data = {}
line_idx = 1
for _, l in p.items():
line_data[line_idx] = l
line_idx += 1
par_data[par_idx] = line_data
par_idx += 1
# get lines of texts, grouped by paragraph
lines = list()
row_indexes = list()
row_index = 0
for _,par in par_data.items():
count_lines = 0
for _,line in par.items():
if count_lines == 0: row_indexes.append(row_index)
line_text = ' '.join([item[0] for item in line])
lines.append(line_text)
count_lines += 1
row_index += 1
# lines.append("\n")
row_index += 1
# lines = lines[:-1]
# get paragraphes boxes (par_boxes)
# get lines boxes (line_boxes)
par_boxes = list()
par_idx = 1
line_boxes = list()
line_idx = 1
for _, par in par_data.items():
xmins, ymins, xmaxs, ymaxs = list(), list(), list(), list()
for _, line in par.items():
xmin, ymin = line[0][1], line[0][2]
xmax, ymax = (line[-1][1] + line[-1][3]), (line[-1][2] + line[-1][4])
line_boxes.append([int(xmin/factor), int(ymin/factor), int(xmax/factor), int(ymax/factor)])
xmins.append(xmin)
ymins.append(ymin)
xmaxs.append(xmax)
ymaxs.append(ymax)
line_idx += 1
xmin, ymin, xmax, ymax = min(xmins), min(ymins), max(xmaxs), max(ymaxs)
par_boxes.append([int(xmin/factor), int(ymin/factor), int(xmax/factor), int(ymax/factor)])
par_idx += 1
return lines, row_indexes, par_boxes, line_boxes #data, par_data #
# rescale image to get 300dpi
def set_image_dpi_resize(image):
"""
Rescaling image to 300dpi while resizing
:param image: An image
:return: A rescaled image
"""
length_x, width_y = image.size
factor = min(1, float(1024.0 / length_x))
size = int(factor * length_x), int(factor * width_y)
# image_resize = image.resize(size, Image.Resampling.LANCZOS)
image_resize = image.resize(size, Image.LANCZOS)
temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='1.png')
temp_filename = temp_file.name
image_resize.save(temp_filename, dpi=(300, 300))
return factor, temp_filename
# it is important that each bounding box should be in (upper left, lower right) format.
# source: https://github.com/NielsRogge/Transformers-Tutorials/issues/129
def upperleft_to_lowerright(bbox):
x0, y0, x1, y1 = tuple(bbox)
if bbox[2] < bbox[0]:
x0 = bbox[2]
x1 = bbox[0]
if bbox[3] < bbox[1]:
y0 = bbox[3]
y1 = bbox[1]
return [x0, y0, x1, y1]
# convert boundings boxes (left, top, width, height) format to (left, top, left+widght, top+height) format.
def convert_box(bbox):
x, y, w, h = tuple(bbox) # the row comes in (left, top, width, height) format
return [x, y, x+w, y+h] # we turn it into (left, top, left+widght, top+height) to get the actual box
# LiLT model gets 1000x10000 pixels images
def normalize_box(bbox, width, height):
return [
int(1000 * (bbox[0] / width)),
int(1000 * (bbox[1] / height)),
int(1000 * (bbox[2] / width)),
int(1000 * (bbox[3] / height)),
]
# LiLT model gets 1000x10000 pixels images
def denormalize_box(bbox, width, height):
return [
int(width * (bbox[0] / 1000)),
int(height * (bbox[1] / 1000)),
int(width* (bbox[2] / 1000)),
int(height * (bbox[3] / 1000)),
]
# 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)),
]
def get_blocks(bboxes_block, categories, texts):
# get list of unique block boxes
bbox_block_dict, bboxes_block_list, bbox_block_prec = dict(), list(), list()
for count_block, bbox_block in enumerate(bboxes_block):
if bbox_block != bbox_block_prec:
bbox_block_indexes = [i for i, bbox in enumerate(bboxes_block) if bbox == bbox_block]
bbox_block_dict[count_block] = bbox_block_indexes
bboxes_block_list.append(bbox_block)
bbox_block_prec = bbox_block
# get list of categories and texts by unique block boxes
category_block_list, text_block_list = list(), list()
for bbox_block in bboxes_block_list:
count_block = bboxes_block.index(bbox_block)
bbox_block_indexes = bbox_block_dict[count_block]
category_block = np.array(categories, dtype=object)[bbox_block_indexes].tolist()[0]
category_block_list.append(category_block)
text_block = np.array(texts, dtype=object)[bbox_block_indexes].tolist()
text_block = [text.replace("\n","").strip() for text in text_block]
if id2label[category_block] == "Text" or id2label[category_block] == "Caption" or id2label[category_block] == "Footnote":
text_block = ' '.join(text_block)
else:
text_block = '\n'.join(text_block)
text_block_list.append(text_block)
return bboxes_block_list, category_block_list, text_block_list
# function to sort bounding boxes
def get_sorted_boxes(bboxes):
# sort by y from page top to bottom
sorted_bboxes = sorted(bboxes, key=itemgetter(1), reverse=False)
y_list = [bbox[1] for bbox in sorted_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(sorted_bboxes, dtype=object)[y_list_duplicates_indexes[item]].tolist(), key=itemgetter(0), reverse=False)
np_array_bboxes = np.array(sorted_bboxes)
np_array_bboxes[y_list_duplicates_indexes[item]] = np.array(bbox_list_y_duplicates)
sorted_bboxes = np_array_bboxes.tolist()
return sorted_bboxes
# sort data from y = 0 to end of page (and after, x=0 to end of page when necessary)
def sort_data(bboxes, categories, texts):
sorted_bboxes = get_sorted_boxes(bboxes)
sorted_bboxes_indexes = [bboxes.index(bbox) for bbox in sorted_bboxes]
sorted_categories = np.array(categories, dtype=object)[sorted_bboxes_indexes].tolist()
sorted_texts = np.array(texts, dtype=object)[sorted_bboxes_indexes].tolist()
return sorted_bboxes, sorted_categories, sorted_texts
# sort data from y = 0 to end of page (and after, x=0 to end of page when necessary)
def sort_data_wo_labels(bboxes, texts):
sorted_bboxes = get_sorted_boxes(bboxes)
sorted_bboxes_indexes = [bboxes.index(bbox) for bbox in sorted_bboxes]
sorted_texts = np.array(texts, dtype=object)[sorted_bboxes_indexes].tolist()
return sorted_bboxes, sorted_texts
## PDF processing
# get filename and images of PDF pages
def pdf_to_images(uploaded_pdf):
# Check if None object
if uploaded_pdf is None:
path_to_file = pdf_blank
filename = path_to_file.replace(examples_dir,"")
msg = "Invalid PDF file."
images = [Image.open(image_blank)]
else:
# path to the uploaded PDF
path_to_file = uploaded_pdf.name
filename = path_to_file.replace("/tmp/","")
try:
PdfReader(path_to_file)
except PdfReadError:
path_to_file = pdf_blank
filename = path_to_file.replace(examples_dir,"")
msg = "Invalid PDF file."
images = [Image.open(image_blank)]
else:
try:
images = convert_from_path(path_to_file, last_page=max_imgboxes)
num_imgs = len(images)
msg = f'The PDF "{filename}" was converted into {num_imgs} images.'
except:
msg = f'Error with the PDF "{filename}": it was not converted into images.'
images = [Image.open(image_wo_content)]
return filename, msg, images
# Extraction of image data (text and bounding boxes)
def extraction_data_from_image(images):
num_imgs = len(images)
if num_imgs > 0:
# https://pyimagesearch.com/2021/11/15/tesseract-page-segmentation-modes-psms-explained-how-to-improve-your-ocr-accuracy/
custom_config = r'--oem 3 --psm 3 -l eng' # default config PyTesseract: --oem 3 --psm 3 -l eng+deu+fra+jpn+por+spa+rus+hin+chi_sim
results, lines, row_indexes, par_boxes, line_boxes, images_pixels = dict(), dict(), dict(), dict(), dict(), dict()
images_ids_list, lines_list, par_boxes_list, line_boxes_list, images_list, images_pixels_list, page_no_list, num_pages_list = list(), list(), list(), list(), list(), list(), list(), list()
try:
for i,image in enumerate(images):
# image preprocessing
# https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_imgproc/py_thresholding/py_thresholding.html
img = image.copy()
factor, path_to_img = set_image_dpi_resize(img) # Rescaling to 300dpi
img = Image.open(path_to_img)
img = np.array(img, dtype='uint8') # convert PIL to cv2
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # gray scale image
ret,img = cv2.threshold(img,127,255,cv2.THRESH_BINARY)
# OCR PyTesseract | get langs of page
txt = pytesseract.image_to_string(img, config=custom_config)
txt = txt.strip().lower()
txt = re.sub(r" +", " ", txt) # multiple space
txt = re.sub(r"(\n\s*)+\n+", "\n", txt) # multiple line
# txt = os.popen(f'tesseract {img_filepath} - {custom_config}').read()
try:
langs = detect_langs(txt)
langs = [langdetect2Tesseract[langs[i].lang] for i in range(len(langs))]
langs_string = '+'.join(langs)
except:
langs_string = "eng"
langs_string += '+osd'
custom_config = f'--oem 3 --psm 3 -l {langs_string}' # default config PyTesseract: --oem 3 --psm 3
# OCR PyTesseract | get data
results[i] = pytesseract.image_to_data(img, config=custom_config, output_type=pytesseract.Output.DICT)
# results[i] = os.popen(f'tesseract {img_filepath} - {custom_config}').read()
# get image pixels
images_pixels[i] = feature_extractor(images[i], return_tensors="pt").pixel_values
lines[i], row_indexes[i], par_boxes[i], line_boxes[i] = get_data(results[i], factor, conf_min=0)
lines_list.append(lines[i])
par_boxes_list.append(par_boxes[i])
line_boxes_list.append(line_boxes[i])
images_ids_list.append(i)
images_pixels_list.append(images_pixels[i])
images_list.append(images[i])
page_no_list.append(i)
num_pages_list.append(num_imgs)
except:
print(f"There was an error within the extraction of PDF text by the OCR!")
else:
from datasets import Dataset
dataset = Dataset.from_dict({"images_ids": images_ids_list, "images": images_list, "images_pixels": images_pixels_list, "page_no": page_no_list, "num_pages": num_pages_list, "texts": lines_list, "bboxes_line": line_boxes_list})
# print(f"The text data was successfully extracted by the OCR!")
return dataset, lines, row_indexes, par_boxes, line_boxes
## Inference
def prepare_inference_features(example, tokenizer, max_length, cls_box, sep_box):
images_ids_list, chunks_ids_list, input_ids_list, attention_mask_list, bb_list, images_pixels_list = list(), list(), list(), list(), list(), list()
# get batch
batch_images_ids = example["images_ids"]
batch_images = example["images"]
batch_images_pixels = example["images_pixels"]
batch_bboxes_line = example["bboxes_line"]
batch_texts = example["texts"]
batch_images_size = [image.size for image in batch_images]
batch_width, batch_height = [image_size[0] for image_size in batch_images_size], [image_size[1] for image_size in batch_images_size]
# add a dimension if not a batch but only one image
if not isinstance(batch_images_ids, list):
batch_images_ids = [batch_images_ids]
batch_images = [batch_images]
batch_images_pixels = [batch_images_pixels]
batch_bboxes_line = [batch_bboxes_line]
batch_texts = [batch_texts]
batch_width, batch_height = [batch_width], [batch_height]
# process all images of the batch
for num_batch, (image_id, image_pixels, boxes, texts, width, height) in enumerate(zip(batch_images_ids, batch_images_pixels, batch_bboxes_line, batch_texts, batch_width, batch_height)):
tokens_list = []
bboxes_list = []
# add a dimension if only on image
if not isinstance(texts, list):
texts, boxes = [texts], [boxes]
# convert boxes to original
normalize_bboxes_line = [normalize_box(upperleft_to_lowerright(box), width, height) for box in boxes]
# sort boxes with texts
# we want sorted lists from top to bottom of the image
boxes, texts = sort_data_wo_labels(normalize_bboxes_line, texts)
count = 0
for box, text in zip(boxes, texts):
tokens = tokenizer.tokenize(text)
num_tokens = len(tokens) # get number of tokens
tokens_list.extend(tokens)
bboxes_list.extend([box] * num_tokens) # number of boxes must be the same as the number of tokens
# use of return_overflowing_tokens=True / stride=doc_stride
# to get parts of image with overlap
# source: https://huggingface.co/course/chapter6/3b?fw=tf#handling-long-contexts
encodings = tokenizer(" ".join(texts),
truncation=True,
padding="max_length",
max_length=max_length,
stride=doc_stride,
return_overflowing_tokens=True,
return_offsets_mapping=True
)
otsm = encodings.pop("overflow_to_sample_mapping")
offset_mapping = encodings.pop("offset_mapping")
# Let's label those examples and get their boxes
sequence_length_prev = 0
for i, offsets in enumerate(offset_mapping):
# truncate tokens, boxes and labels based on length of chunk - 2 (special tokens and )
sequence_length = len(encodings.input_ids[i]) - 2
if i == 0: start = 0
else: start += sequence_length_prev - doc_stride
end = start + sequence_length
sequence_length_prev = sequence_length
# get tokens, boxes and labels of this image chunk
bb = [cls_box] + bboxes_list[start:end] + [sep_box]
# as the last chunk can have a length < max_length
# we must to add [tokenizer.pad_token] (tokens), [sep_box] (boxes) and [-100] (labels)
if len(bb) < max_length:
bb = bb + [sep_box] * (max_length - len(bb))
# append results
input_ids_list.append(encodings["input_ids"][i])
attention_mask_list.append(encodings["attention_mask"][i])
bb_list.append(bb)
images_ids_list.append(image_id)
chunks_ids_list.append(i)
images_pixels_list.append(image_pixels)
return {
"images_ids": images_ids_list,
"chunk_ids": chunks_ids_list,
"input_ids": input_ids_list,
"attention_mask": attention_mask_list,
"normalized_bboxes": bb_list,
"images_pixels": images_pixels_list
}
from torch.utils.data import Dataset
class CustomDataset(Dataset):
def __init__(self, dataset, tokenizer):
self.dataset = dataset
self.tokenizer = tokenizer
def __len__(self):
return len(self.dataset)
def __getitem__(self, idx):
# get item
example = self.dataset[idx]
encoding = dict()
encoding["images_ids"] = example["images_ids"]
encoding["chunk_ids"] = example["chunk_ids"]
encoding["input_ids"] = example["input_ids"]
encoding["attention_mask"] = example["attention_mask"]
encoding["bbox"] = example["normalized_bboxes"]
encoding["images_pixels"] = example["images_pixels"]
return encoding
import torch.nn.functional as F
# get predictions at token level
def predictions_token_level(images, custom_encoded_dataset, model_id, model):
num_imgs = len(images)
if num_imgs > 0:
chunk_ids, input_ids, bboxes, pixels_values, outputs, token_predictions = dict(), dict(), dict(), dict(), dict(), dict()
images_ids_list = list()
for i,encoding in enumerate(custom_encoded_dataset):
# get custom encoded data
image_id = encoding['images_ids']
chunk_id = encoding['chunk_ids']
input_id = torch.tensor(encoding['input_ids'])[None]
attention_mask = torch.tensor(encoding['attention_mask'])[None]
bbox = torch.tensor(encoding['bbox'])[None]
pixel_values = torch.tensor(encoding["images_pixels"])
# save data in dictionnaries
if image_id not in images_ids_list: images_ids_list.append(image_id)
if image_id in chunk_ids: chunk_ids[image_id].append(chunk_id)
else: chunk_ids[image_id] = [chunk_id]
if image_id in input_ids: input_ids[image_id].append(input_id)
else: input_ids[image_id] = [input_id]
if image_id in bboxes: bboxes[image_id].append(bbox)
else: bboxes[image_id] = [bbox]
if image_id in pixels_values: pixels_values[image_id].append(pixel_values)
else: pixels_values[image_id] = [pixel_values]
# get prediction with forward pass
with torch.no_grad():
if model_id == model_id_lilt:
output = model(
input_ids=input_id.to(device),
attention_mask=attention_mask.to(device),
bbox=bbox.to(device),
)
elif model_id == model_id_layoutxlm:
output = model(
input_ids=input_id.to(device),
attention_mask=attention_mask.to(device),
bbox=bbox.to(device),
image=pixel_values.to(device)
)
# save probabilities of predictions in dictionnary
if image_id in outputs: outputs[image_id].append(F.softmax(output.logits.squeeze(), dim=-1))
else: outputs[image_id] = [F.softmax(output.logits.squeeze(), dim=-1)]
return outputs, images_ids_list, chunk_ids, input_ids, bboxes
else:
print("An error occurred while getting predictions!")
from functools import reduce
# Get predictions (line level)
def predictions_line_level(max_length, tokenizer, id2label, dataset, outputs, images_ids_list, chunk_ids, input_ids, bboxes, cls_box, sep_box):
ten_probs_dict, ten_input_ids_dict, ten_bboxes_dict = dict(), dict(), dict()
bboxes_list_dict, input_ids_dict_dict, probs_dict_dict, df = dict(), dict(), dict(), dict()
if len(images_ids_list) > 0:
for i, image_id in enumerate(images_ids_list):
# get image information
images_list = dataset.filter(lambda example: example["images_ids"] == image_id)["images"]
image = images_list[0]
width, height = image.size
# get data
chunk_ids_list = chunk_ids[image_id]
outputs_list = outputs[image_id]
input_ids_list = input_ids[image_id]
bboxes_list = bboxes[image_id]
# create zeros tensors
ten_probs = torch.zeros((outputs_list[0].shape[0] - 2)*len(outputs_list), outputs_list[0].shape[1])
ten_input_ids = torch.ones(size=(1, (outputs_list[0].shape[0] - 2)*len(outputs_list)), dtype =int)
ten_bboxes = torch.zeros(size=(1, (outputs_list[0].shape[0] - 2)*len(outputs_list), 4), dtype =int)
if len(outputs_list) > 1:
for num_output, (output, input_id, bbox) in enumerate(zip(outputs_list, input_ids_list, bboxes_list)):
start = num_output*(max_length - 2) - max(0,num_output)*doc_stride
end = start + (max_length - 2)
if num_output == 0:
ten_probs[start:end,:] += output[1:-1]
ten_input_ids[:,start:end] = input_id[:,1:-1]
ten_bboxes[:,start:end,:] = bbox[:,1:-1,:]
else:
ten_probs[start:start + doc_stride,:] += output[1:1 + doc_stride]
ten_probs[start:start + doc_stride,:] = ten_probs[start:start + doc_stride,:] * 0.5
ten_probs[start + doc_stride:end,:] += output[1 + doc_stride:-1]
ten_input_ids[:,start:start + doc_stride] = input_id[:,1:1 + doc_stride]
ten_input_ids[:,start + doc_stride:end] = input_id[:,1 + doc_stride:-1]
ten_bboxes[:,start:start + doc_stride,:] = bbox[:,1:1 + doc_stride,:]
ten_bboxes[:,start + doc_stride:end,:] = bbox[:,1 + doc_stride:-1,:]
else:
ten_probs += outputs_list[0][1:-1]
ten_input_ids = input_ids_list[0][:,1:-1]
ten_bboxes = bboxes_list[0][:,1:-1]
ten_probs_list, ten_input_ids_list, ten_bboxes_list = ten_probs.tolist(), ten_input_ids.tolist()[0], ten_bboxes.tolist()[0]
bboxes_list = list()
input_ids_dict, probs_dict = dict(), dict()
bbox_prev = [-100, -100, -100, -100]
for probs, input_id, bbox in zip(ten_probs_list, ten_input_ids_list, ten_bboxes_list):
bbox = denormalize_box(bbox, width, height)
if bbox != bbox_prev and bbox != cls_box and bbox != sep_box and bbox[0] != bbox[2] and bbox[1] != bbox[3]:
bboxes_list.append(bbox)
input_ids_dict[str(bbox)] = [input_id]
probs_dict[str(bbox)] = [probs]
elif bbox != cls_box and bbox != sep_box and bbox[0] != bbox[2] and bbox[1] != bbox[3]:
input_ids_dict[str(bbox)].append(input_id)
probs_dict[str(bbox)].append(probs)
bbox_prev = bbox
probs_bbox = dict()
for i,bbox in enumerate(bboxes_list):
probs = probs_dict[str(bbox)]
probs = np.array(probs).T.tolist()
probs_label = list()
for probs_list in probs:
prob_label = reduce(lambda x, y: x*y, probs_list)
prob_label = prob_label**(1./(len(probs_list))) # normalization
probs_label.append(prob_label)
max_value = max(probs_label)
max_index = probs_label.index(max_value)
probs_bbox[str(bbox)] = max_index
bboxes_list_dict[image_id] = bboxes_list
input_ids_dict_dict[image_id] = input_ids_dict
probs_dict_dict[image_id] = probs_bbox
df[image_id] = pd.DataFrame()
df[image_id]["bboxes"] = bboxes_list
df[image_id]["texts"] = [tokenizer.decode(input_ids_dict[str(bbox)]) for bbox in bboxes_list]
df[image_id]["labels"] = [id2label[probs_bbox[str(bbox)]] for bbox in bboxes_list]
return probs_bbox, bboxes_list_dict, input_ids_dict_dict, probs_dict_dict, df
else:
print("An error occurred while getting predictions!")
# Get labeled images with lines bounding boxes
def get_labeled_images(id2label, dataset, images_ids_list, bboxes_list_dict, probs_dict_dict):
labeled_images = list()
for i, image_id in enumerate(images_ids_list):
# get image
images_list = dataset.filter(lambda example: example["images_ids"] == image_id)["images"]
image = images_list[0]
width, height = image.size
# get predicted boxes and labels
bboxes_list = bboxes_list_dict[image_id]
probs_bbox = probs_dict_dict[image_id]
draw = ImageDraw.Draw(image)
# https://stackoverflow.com/questions/66274858/choosing-a-pil-imagefont-by-font-name-rather-than-filename-and-cross-platform-f
font = font_manager.FontProperties(family='sans-serif', weight='bold')
font_file = font_manager.findfont(font)
font_size = 30
font = ImageFont.truetype(font_file, font_size)
for bbox in bboxes_list:
predicted_label = id2label[probs_bbox[str(bbox)]]
draw.rectangle(bbox, outline=label2color[predicted_label])
draw.text((bbox[0] + 10, bbox[1] - font_size), text=predicted_label, fill=label2color[predicted_label], font=font)
labeled_images.append(image)
return labeled_images
# get data of encoded chunk
def get_encoded_chunk_inference(tokenizer, dataset, encoded_dataset, index_chunk=None):
# get datasets
example = dataset
encoded_example = encoded_dataset
# get randomly a document in dataset
if index_chunk == None: index_chunk = random.randint(0, len(encoded_example)-1)
encoded_example = encoded_example[index_chunk]
encoded_image_ids = encoded_example["images_ids"]
# get the image
example = example.filter(lambda example: example["images_ids"] == encoded_image_ids)[0]
image = example["images"] # original image
width, height = image.size
page_no = example["page_no"]
num_pages = example["num_pages"]
# get boxes, texts, categories
bboxes, input_ids = encoded_example["normalized_bboxes"][1:-1], encoded_example["input_ids"][1:-1]
bboxes = [denormalize_box(bbox, width, height) for bbox in bboxes]
num_tokens = len(input_ids) + 2
# get unique bboxes and corresponding labels
bboxes_list, input_ids_list = list(), list()
input_ids_dict = dict()
bbox_prev = [-100, -100, -100, -100]
for i, (bbox, input_id) in enumerate(zip(bboxes, input_ids)):
if bbox != bbox_prev:
bboxes_list.append(bbox)
input_ids_dict[str(bbox)] = [input_id]
else:
input_ids_dict[str(bbox)].append(input_id)
# start_indexes_list.append(i)
bbox_prev = bbox
# do not keep "..."
if input_ids_dict[str(bboxes_list[-1])][0] == (tokenizer.convert_tokens_to_ids('')):
del input_ids_dict[str(bboxes_list[-1])]
bboxes_list = bboxes_list[:-1]
# get texts by line
input_ids_list = input_ids_dict.values()
texts_list = [tokenizer.decode(input_ids) for input_ids in input_ids_list]
# display DataFrame
df = pd.DataFrame({"texts": texts_list, "input_ids": input_ids_list, "bboxes": bboxes_list})
return image, df, num_tokens, page_no, num_pages
# display chunk of PDF image and its data
def display_chunk_lines_inference(dataset, encoded_dataset, index_chunk=None):
# get image and image data
image, df, num_tokens, page_no, num_pages = get_encoded_chunk_inference(dataset, encoded_dataset, index_chunk=index_chunk)
# get data from dataframe
input_ids = df["input_ids"]
texts = df["texts"]
bboxes = df["bboxes"]
print(f'Chunk ({num_tokens} tokens) of the PDF (page: {page_no+1} / {num_pages})\n')
# display image with bounding boxes
print(">> PDF image with bounding boxes of lines\n")
draw = ImageDraw.Draw(image)
labels = list()
for box, text in zip(bboxes, texts):
color = "red"
draw.rectangle(box, outline=color)
# resize image to original
width, height = image.size
image = image.resize((int(0.5*width), int(0.5*height)))
# convert to cv and display
img = np.array(image, dtype='uint8') # PIL to cv2
cv2_imshow(img)
cv2.waitKey(0)
# display image dataframe
print("\n>> Dataframe of annotated lines\n")
cols = ["texts", "bboxes"]
df = df[cols]
display(df)