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import matplotlib.pyplot as plt
import matplotlib.patches as patches
from matplotlib.patches import Patch
import io
from PIL import Image, ImageDraw
from transformers import TableTransformerImageProcessor, AutoModelForObjectDetection
import torch
import gradio as gr
# load table detection model
processor = TableTransformerImageProcessor(max_size=800)
model = AutoModelForObjectDetection.from_pretrained("microsoft/table-transformer-detection", revision="no_timm")
# load table structure recognition model
structure_processor = TableTransformerImageProcessor(max_size=1000)
structure_model = AutoModelForObjectDetection.from_pretrained("microsoft/table-structure-recognition-v1.1-all")
# for output bounding box post-processing
def box_cxcywh_to_xyxy(x):
x_c, y_c, w, h = x.unbind(-1)
b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (x_c + 0.5 * w), (y_c + 0.5 * h)]
return torch.stack(b, dim=1)
def rescale_bboxes(out_bbox, size):
width, height = size
boxes = box_cxcywh_to_xyxy(out_bbox)
boxes = boxes * torch.tensor([width, height, width, height], dtype=torch.float32)
return boxes
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 fig2img(fig):
"""Convert a Matplotlib figure to a PIL Image and return it"""
buf = io.BytesIO()
fig.savefig(buf)
buf.seek(0)
image = Image.open(buf)
return image
def visualize_detected_tables(img, det_tables):
plt.imshow(img, interpolation="lanczos")
fig = plt.gcf()
fig.set_size_inches(20, 20)
ax = plt.gca()
for det_table in det_tables:
bbox = det_table['bbox']
if det_table['label'] == 'table':
facecolor = (1, 0, 0.45)
edgecolor = (1, 0, 0.45)
alpha = 0.3
linewidth = 2
hatch='//////'
elif det_table['label'] == 'table rotated':
facecolor = (0.95, 0.6, 0.1)
edgecolor = (0.95, 0.6, 0.1)
alpha = 0.3
linewidth = 2
hatch='//////'
else:
continue
rect = patches.Rectangle(bbox[:2], bbox[2]-bbox[0], bbox[3]-bbox[1], linewidth=linewidth,
edgecolor='none',facecolor=facecolor, alpha=0.1)
ax.add_patch(rect)
rect = patches.Rectangle(bbox[:2], bbox[2]-bbox[0], bbox[3]-bbox[1], linewidth=linewidth,
edgecolor=edgecolor,facecolor='none',linestyle='-', alpha=alpha)
ax.add_patch(rect)
rect = patches.Rectangle(bbox[:2], bbox[2]-bbox[0], bbox[3]-bbox[1], linewidth=0,
edgecolor=edgecolor,facecolor='none',linestyle='-', hatch=hatch, alpha=0.2)
ax.add_patch(rect)
plt.xticks([], [])
plt.yticks([], [])
legend_elements = [Patch(facecolor=(1, 0, 0.45), edgecolor=(1, 0, 0.45),
label='Table', hatch='//////', alpha=0.3),
Patch(facecolor=(0.95, 0.6, 0.1), edgecolor=(0.95, 0.6, 0.1),
label='Table (rotated)', hatch='//////', alpha=0.3)]
plt.legend(handles=legend_elements, bbox_to_anchor=(0.5, -0.02), loc='upper center', borderaxespad=0,
fontsize=10, ncol=2)
plt.gcf().set_size_inches(10, 10)
plt.axis('off')
return fig
def detect_and_crop_table(image):
# prepare image for the model
pixel_values = processor(image, return_tensors="pt").pixel_values
# 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 recognize_table(image):
# prepare image for the model
pixel_values = structure_processor(images=image, return_tensors="pt").pixel_values
# forward pass
with torch.no_grad():
outputs = structure_model(pixel_values)
# postprocess to get individual elements
id2label = structure_model.config.id2label
id2label[len(structure_model.config.id2label)] = "no object"
cells = outputs_to_objects(outputs, image.size, id2label)
# visualize cells on cropped table
draw = ImageDraw.Draw(image)
for cell in cells:
draw.rectangle(cell["bbox"], outline="red")
return image, cells
def get_cell_coordinates_by_row(table_data):
# Extract rows and columns
rows = [entry for entry in table_data if entry['label'] == 'table row']
columns = [entry for entry in table_data if entry['label'] == 'table column']
# Sort rows and columns by their Y and X coordinates, respectively
rows.sort(key=lambda x: x['bbox'][1])
columns.sort(key=lambda x: x['bbox'][0])
# Function to find cell coordinates
def find_cell_coordinates(row, column):
cell_bbox = [column['bbox'][0], row['bbox'][1], column['bbox'][2], row['bbox'][3]]
return cell_bbox
# Generate cell coordinates and count cells in each row
cell_coordinates = []
for row in rows:
row_cells = []
for column in columns:
cell_bbox = find_cell_coordinates(row, column)
row_cells.append({'column': column['bbox'], 'cell': cell_bbox})
# Sort cells in the row by X coordinate
row_cells.sort(key=lambda x: x['column'][0])
# Append row information to cell_coordinates
cell_coordinates.append({'row': row['bbox'], 'cells': row_cells, 'cell_count': len(row_cells)})
# Sort rows from top to bottom
cell_coordinates.sort(key=lambda x: x['row'][1])
return cell_coordinates
def apply_ocr(cell_coordinates):
# let's OCR row by row
data = dict()
max_num_columns = 0
for idx, row in enumerate(cell_coordinates):
row_text = []
for cell in row["cells"]:
# crop cell out of image
cell_image = np.array(cropped_table.crop(cell["cell"]))
# apply OCR
result = reader.readtext(np.array(cell_image))
if len(result) > 0:
text = " ".join([x[1] for x in result])
row_text.append(text)
if len(row_text) > max_num_columns:
max_num_columns = len(row_text)
data[idx] = row_text
# pad rows which don't have max_num_columns elements
# to make sure all rows have the same number of columns
for row, row_data in data.copy().items():
if len(row_data) != max_num_columns:
row_data = row_data + ["" for _ in range(max_num_columns - len(row_data))]
data[row] = row_data
return data
def process_pdf(image):
cropped_table = detect_and_crop_table(image)
image, cells = recognize_table(cropped_table)
cell_coordinates = get_cell_coordinates_by_row(cells)
data = apply_ocr(cell_coordinates)
return image, data
title = "Demo: table detection with Table Transformer"
description = "Demo for the Table Transformer (TATR)."
examples =[['image.png']]
app = gr.Interface(fn=process_pdf,
inputs=gr.Image(type="pil"),
outputs=[gr.Image(type="pil", label="Detected table"), "json"],
title=title,
description=description,
examples=examples)
app.queue()
app.launch(debug=True)