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| import os | |
| os.system('pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu') | |
| os.system('sudo apt-get install tesseract-ocr') | |
| os.system('pip install -q pytesseract') | |
| os.environ["TOKENIZERS_PARALLELISM"] = "false" | |
| import gradio as gr | |
| import torch | |
| from datasets import load_dataset, ClassLabel | |
| from transformers import LayoutLMv3ForTokenClassification, LayoutLMv3Processor,LayoutLMv3FeatureExtractor | |
| import pytesseract | |
| import numpy as np | |
| from PIL import ImageDraw, ImageFont | |
| examples = [['example1.png'],['example2.png'],['example3.png']] | |
| dataset = load_dataset("nielsr/cord-layoutlmv3")['train'] | |
| def get_label_list(labels): | |
| unique_labels = set() | |
| for label in labels: | |
| unique_labels = unique_labels | set(label) | |
| label_list = list(unique_labels) | |
| label_list.sort() | |
| return label_list | |
| def convert_l2n_n2l(dataset): | |
| features = dataset.features | |
| label_column_name = "ner_tags" | |
| label_list = features[label_column_name].feature.names | |
| if isinstance(features[label_column_name].feature, ClassLabel): | |
| id2label = {k:v for k,v in enumerate(label_list)} | |
| label2id = {v:k for k,v in enumerate(label_list)} | |
| else: | |
| label_list = get_label_list(dataset[label_column_name]) | |
| id2label = {k:v for k,v in enumerate(label_list)} | |
| label2id = {v:k for k,v in enumerate(label_list)} | |
| return label_list, id2label, label2id, len(label_list) | |
| def label_colour(label): | |
| label2color ={'MENU.PRICE':'blue','MENU.NM':'red','other':None,'MENU.NUM':'orange','TOTAL.TOTAL_PRICE':'green'} | |
| if label in label2color: | |
| colour = label2color.get(label) | |
| else: | |
| colour = None | |
| return colour | |
| def iob_to_label(label): | |
| label = label[2:] | |
| if not label: | |
| return 'other' | |
| return label | |
| def convert_results(words,tags): | |
| ents = set() | |
| completeword = "" | |
| for word, tag in zip(words, tags): | |
| if tag != "O": | |
| ent_position, ent_type = tag.split("-") | |
| if ent_position == "S": | |
| ents.add((word,ent_type)) | |
| else: | |
| if ent_position == "B": | |
| completeword = completeword+ " "+ word | |
| elif ent_position == "I": | |
| completeword= completeword+ " " + word | |
| elif ent_position == "E": | |
| completeword =completeword+" " + word | |
| ents.add((completeword,ent_type)) | |
| completeword= "" | |
| return ents | |
| def unnormalize_box(bbox, width, height): | |
| return [ | |
| width * (bbox[0] / 1000), | |
| height * (bbox[1] / 1000), | |
| width * (bbox[2] / 1000), | |
| height * (bbox[3] / 1000), | |
| ] | |
| def predict(image): | |
| model = LayoutLMv3ForTokenClassification.from_pretrained("keldrenloy/LayoutLMv3FineTunedwithCORDandSGReceipts") #add your model directory here | |
| processor = LayoutLMv3Processor.from_pretrained("microsoft/layoutlmv3-base") | |
| label_list,id2label,label2id, num_labels = convert_l2n_n2l(dataset) | |
| width, height = image.size | |
| encoding_inputs = processor(image,return_offsets_mapping=True, return_tensors="pt",truncation = True) | |
| offset_mapping = encoding_inputs.pop('offset_mapping') | |
| with torch.no_grad(): | |
| outputs = model(**encoding_inputs) | |
| predictions = outputs.logits.argmax(-1).squeeze().tolist() | |
| token_boxes = encoding_inputs.bbox.squeeze().tolist() | |
| is_subword = np.array(offset_mapping.squeeze().tolist())[:,0] != 0 | |
| true_predictions = [id2label[pred] for idx, pred in enumerate(predictions) if not is_subword[idx]] | |
| true_boxes = [unnormalize_box(box, width, height) for idx, box in enumerate(token_boxes) if not is_subword[idx]] | |
| draw = ImageDraw.Draw(image) | |
| font = ImageFont.load_default() | |
| for prediction, box in zip(true_predictions, true_boxes): | |
| predicted_label = iob_to_label(prediction) | |
| draw.rectangle(box, outline=label_colour(predicted_label)) | |
| draw.text((box[0]+10, box[1]-10), text=predicted_label, fill=label_colour(predicted_label), font=font) | |
| words = text_extraction(image) | |
| extracted_words = convert_results(words,true_predictions) | |
| menu_list = [] | |
| price_list = [] | |
| for idx,item in enumerate(extracted_words): | |
| if item[1] == 'MENU.NM': | |
| menu_list.append(f"item {idx}.{item[0]}") | |
| if item[1] == 'MENU.PRICE': | |
| price_list.append(f"item {idx}. ${item[0]}") | |
| return image,menu_list,price_list | |
| def text_extraction(image): | |
| feature_extractor = LayoutLMv3FeatureExtractor() | |
| encoding = feature_extractor(image, return_tensors="pt") | |
| return encoding['words'][0] | |
| css = """.output_image, .input_image {height: 600px !important}""" | |
| demo = gr.Interface(fn = predict, | |
| inputs = gr.inputs.Image(type="pil"), | |
| outputs = [gr.outputs.Image(type="pil", label="annotated image"),'text','text'], | |
| css = css, | |
| examples = examples, | |
| allow_flagging=True, | |
| flagging_options=["incorrect", "correct"], | |
| flagging_callback = gr.CSVLogger(), | |
| flagging_dir = "flagged", | |
| analytics_enabled = True, enable_queue=True | |
| ) | |
| demo.launch(debug=False) |