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 # 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 ## model / feature extractor / tokenizer from transformers import LayoutLMv2ForTokenClassification # LayoutXLMTokenizerFast, import torch device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # model # tokenizer = LayoutXLMTokenizerFast.from_pretrained(model_id) model = LayoutLMv2ForTokenClassification.from_pretrained(model_id); model.to(device); # feature extractor from transformers import LayoutLMv2FeatureExtractor feature_extractor = LayoutLMv2FeatureExtractor(apply_ocr=False) # tokenizer from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained(tokenizer_id) ## 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 = [1000, 1000, 1000, 1000] # model model_id = "pierreguillou/layout-xlm-base-finetuned-with-DocLayNet-base-at-linelevel-ml384" # tokenizer tokenizer_id = "xlm-roberta-base" # (tokenization) The maximum length of a feature (sequence) if str(384) in model_id: max_length = 384 elif str(512) in model_id: max_length = 512 else: print("Error with max_length 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 = 2 # 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-v2", 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()} ## 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, cls_box = cls_box, sep_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): 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(): 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(dataset, outputs, images_ids_list, chunk_ids, input_ids, bboxes): 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: bboxes_list.append(bbox) input_ids_dict[str(bbox)] = [input_id] probs_dict[str(bbox)] = [probs] else: if bbox != cls_box: 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) 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(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(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(index_chunk=None): # get image and image data image, df, num_tokens, page_no, num_pages = get_encoded_chunk_inference(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)