Spaces:
Runtime error
Runtime error
| import matplotlib.pyplot as plt | |
| import matplotlib.patches as patches | |
| from matplotlib.patches import Patch | |
| import io | |
| from PIL import Image, ImageDraw | |
| import numpy as np | |
| import csv | |
| import pandas as pd | |
| from torchvision import transforms | |
| from transformers import AutoModelForObjectDetection | |
| import torch | |
| import easyocr | |
| import gradio as gr | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| class MaxResize(object): | |
| def __init__(self, max_size=800): | |
| self.max_size = max_size | |
| def __call__(self, image): | |
| width, height = image.size | |
| current_max_size = max(width, height) | |
| scale = self.max_size / current_max_size | |
| resized_image = image.resize((int(round(scale*width)), int(round(scale*height)))) | |
| return resized_image | |
| detection_transform = transforms.Compose([ | |
| MaxResize(800), | |
| transforms.ToTensor(), | |
| transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) | |
| ]) | |
| structure_transform = transforms.Compose([ | |
| MaxResize(1000), | |
| transforms.ToTensor(), | |
| transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) | |
| ]) | |
| # load table detection model | |
| # processor = TableTransformerImageProcessor(max_size=800) | |
| model = AutoModelForObjectDetection.from_pretrained("microsoft/table-transformer-detection", revision="no_timm").to(device) | |
| # load table structure recognition model | |
| # structure_processor = TableTransformerImageProcessor(max_size=1000) | |
| structure_model = AutoModelForObjectDetection.from_pretrained("microsoft/table-transformer-structure-recognition-v1.1-all").to(device) | |
| # load EasyOCR reader | |
| reader = easyocr.Reader(['en']) | |
| def outputs_to_objects(outputs, img_size, id2label): | |
| m = outputs.logits.softmax(-1).max(-1) | |
| pred_labels = list(m.indices.detach().cpu().numpy())[0] | |
| pred_scores = list(m.values.detach().cpu().numpy())[0] | |
| pred_bboxes = outputs['pred_boxes'].detach().cpu()[0] | |
| pred_bboxes = [elem.tolist() for elem in rescale_bboxes(pred_bboxes, img_size)] | |
| objects = [] | |
| for label, score, bbox in zip(pred_labels, pred_scores, pred_bboxes): | |
| class_label = id2label[int(label)] | |
| if not class_label == 'no object': | |
| objects.append({'label': class_label, 'score': float(score), | |
| 'bbox': [float(elem) for elem in bbox]}) | |
| return objects | |
| def detect_and_crop_table(image): | |
| # prepare image for the model | |
| # pixel_values = processor(image, return_tensors="pt").pixel_values | |
| pixel_values = detection_transform(image).unsqueeze(0).to(device) | |
| # forward pass | |
| with torch.no_grad(): | |
| outputs = model(pixel_values) | |
| # postprocess to get detected tables | |
| id2label = model.config.id2label | |
| id2label[len(model.config.id2label)] = "no object" | |
| detected_tables = outputs_to_objects(outputs, image.size, id2label) | |
| # visualize | |
| # fig = visualize_detected_tables(image, detected_tables) | |
| # image = fig2img(fig) | |
| # crop first detected table out of image | |
| cropped_table = image.crop(detected_tables[0]["bbox"]) | |
| return cropped_table | |
| def process_pdf(): | |
| print('process_pdf') | |
| cropped_table = detect_and_crop_table(image) | |
| # image, cells = recognize_table(cropped_table) | |
| # cell_coordinates = get_cell_coordinates_by_row(cells) | |
| # df, data = apply_ocr(cell_coordinates, image) | |
| return cropped_table | |
| # return image, df, data | |
| # return [], [], [] | |
| title = "Sheriff's Demo: Table Detection & Recognition with Table Transformer (TATR)." | |
| description = """A demo by M Sheriff for table extraction with the Table Transformer. | |
| First, table detection is performed on the input image using https://huggingface.co/microsoft/table-transformer-detection, | |
| after which the detected table is extracted and https://huggingface.co/microsoft/table-transformer-structure-recognition-v1.1-all recognizes the | |
| individual rows, columns and cells. OCR is then performed per cell, row by row.""" | |
| # examples = [['image.png'], ['mistral_paper.png']] | |
| app = gr.Interface(fn=process_pdf, | |
| inputs=gr.Image(type="pil"), | |
| outputs=[gr.Image(type="pil", label="Detected table")], | |
| # outputs=[gr.Image(type="pil", label="Detected table"), gr.Dataframe(label="Table as CSV"), gr.JSON(label="Data as JSON")], | |
| title=title, | |
| description=description, | |
| # examples=examples | |
| ) | |
| app.queue() | |
| app.launch(debug=True) | |