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import traceback
import gradio as gr
from utils.get_RGB_image import get_RGB_image, is_online_file, steam_online_file
from pdf2image import convert_from_path, convert_from_bytes
import layoutparser as lp
from PIL import Image
from utils.get_features import get_features
from imagehash import average_hash
from sklearn.metrics.pairwise import cosine_similarity
from utils.visualize_bboxes_on_image import visualize_bboxes_on_image
label_map = {0: 'Caption', 1: 'Footnote', 2: 'Formula', 3: 'List-item', 4: 'Page-footer', 5: 'Page-header', 6: 'Picture', 7: 'Section-header', 8: 'Table', 9: 'Text', 10: 'Title'}
label_names = list(label_map.values())
color_map = {'Caption': '#acc2d9', 'Footnote': '#56ae57', 'Formula': '#b2996e', 'List-item': '#a8ff04', 'Page-footer': '#69d84f', 'Page-header': '#894585', 'Picture': '#70b23f', 'Section-header': '#d4ffff', 'Table': '#65ab7c', 'Text': '#952e8f', 'Title': '#fcfc81'}
cache = {
'document_image_1_hash': None,
'document_image_2_hash': None,
'document_image_1_features': None,
'document_image_2_features': None,
}
pre_message_style = 'overflow:auto;border:2px solid pink;padding:4px;border-radius:4px;'
visualize_bboxes_on_image_kwargs = {
'label_text_color': 'white',
'label_rectangle_color': 'black',
'label_text_size': 12,
'label_text_padding': 3,
'label_rectangle_left_margin': 0,
'label_rectangle_top_margin': 0
}
vectors_types = ['vectors', 'weighted_vectors', 'reduced_vectors', 'reduced_weighted_vectors']
annotation_key = 'is_annotated_document_image'
annotation_original_image_key = 'original_image'
def annotate_document_image(document_image: Image.Image, original_document_image: Image.Image):
document_image.info.update({
annotation_key: True,
annotation_original_image_key: original_document_image
})
return document_image
def get_original_document_image(document_image: Image.Image):
if document_image.info.get(annotation_key) == True:
return document_image.info.get(annotation_original_image_key)
return document_image
def similarity_fn(model: lp.Detectron2LayoutModel, document_image_1: Image.Image, document_image_2: Image.Image, vectors_type: str):
message = None
annotations = {
'predicted_bboxes': 'predicted_bboxes' if vectors_type in ['vectors', 'weighted_vectors'] else 'reduced_predicted_bboxes',
'predicted_scores': 'predicted_scores' if vectors_type in ['vectors', 'weighted_vectors'] else 'reduced_predicted_scores',
'predicted_labels': 'predicted_labels' if vectors_type in ['vectors', 'weighted_vectors'] else 'reduced_predicted_labels',
}
show_vectors_type = False
try:
if document_image_1 is None or document_image_2 is None:
message = f'<pre style="{pre_message_style}">Please load both the documents to compare.<pre>'
else:
document_image_1 = get_original_document_image(document_image_1)
document_image_2 = get_original_document_image(document_image_2)
document_image_1_hash = str(average_hash(document_image_1))
document_image_2_hash = str(average_hash(document_image_2))
if document_image_1_hash == cache['document_image_1_hash']:
document_image_1_features = cache['document_image_1_features']
else:
document_image_1_features = get_features(document_image_1, model, label_names)
cache['document_image_1_hash'] = document_image_1_hash
cache['document_image_1_features'] = document_image_1_features
if document_image_2_hash == cache['document_image_2_hash']:
document_image_2_features = cache['document_image_2_features']
else:
document_image_2_features = get_features(document_image_2, model, label_names)
cache['document_image_2_hash'] = document_image_2_hash
cache['document_image_2_features'] = document_image_2_features
[[similarity]] = cosine_similarity(
[
cache['document_image_1_features'][vectors_type]
],
[
cache['document_image_2_features'][vectors_type]
])
message = f'<pre style="{pre_message_style}">Similarity between the two documents is: {round(similarity, 4)}<pre>'
annotated_document_image_1 = visualize_bboxes_on_image(
image = document_image_1,
bboxes = cache['document_image_1_features'][annotations['predicted_bboxes']],
titles = [f'{label}, score:{round(score, 2)}' for label, score in zip(
cache['document_image_1_features'][annotations['predicted_labels']],
cache['document_image_1_features'][annotations['predicted_scores']])],
**visualize_bboxes_on_image_kwargs)
annotated_document_image_2 = visualize_bboxes_on_image(
image = document_image_2,
bboxes = cache['document_image_2_features'][annotations['predicted_bboxes']],
titles = [f'{label}, score:{score}' for label, score in zip(
cache['document_image_2_features'][annotations['predicted_labels']],
cache['document_image_2_features'][annotations['predicted_scores']])],
**visualize_bboxes_on_image_kwargs)
show_vectors_type = True
document_image_1 = annotate_document_image(annotated_document_image_1, document_image_1)
document_image_2 = annotate_document_image(annotated_document_image_2, document_image_2)
except Exception as e:
message = f'<pre style="{pre_message_style}">{traceback.format_exc()}<pre>'
return [
gr.HTML(message, visible=True),
document_image_1,
document_image_2,
gr.Dropdown(visible=show_vectors_type)
]
def load_image(filename, page = 0):
try:
image = None
try:
if (is_online_file(filename)):
image = get_RGB_image(convert_from_bytes(steam_online_file(filename))[page])
else:
image = get_RGB_image(convert_from_path(filename)[page])
except:
image = get_RGB_image(filename)
return [
gr.Image(value=image, visible=True),
None
]
except:
error = traceback.format_exc()
return [None, gr.HTML(value=error, visible=True)]
def preview_url(url, page = 0):
[image, error] = load_image(url, page = page)
if image:
return [gr.Tabs(selected=0), image, error]
else:
return [gr.Tabs(selected=1), image, error]
def document_view(document_number: int):
gr.HTML(value=f'<h4>Load the {"first" if document_number == 1 else "second"} PDF or Document Image<h4>', elem_classes=['center'])
with gr.Tabs() as document_tabs:
with gr.Tab("From Image", id=0):
document = gr.Image(type="pil", label=f"Document {document_number}", visible=False)
document_error_message = gr.HTML(label="Error Message", visible=False)
document_preview = gr.UploadButton(
"Upload PDF or Document Image",
file_types=["image", ".pdf"],
file_count="single")
with gr.Tab("From URL", id=1):
document_url = gr.Textbox(
label=f"Document {document_number} URL",
info="Paste a Link/URL to PDF or Document Image",
placeholder="https://datasets-server.huggingface.co/.../image.jpg")
document_url_error_message = gr.HTML(label="Error Message", visible=False)
document_url_preview = gr.Button(value="Preview", variant="primary")
document_preview.upload(
fn = lambda file: load_image(file.name),
inputs = [document_preview],
outputs = [document, document_error_message])
document_url_preview.click(
fn = preview_url,
inputs = [document_url],
outputs = [document_tabs, document, document_url_error_message])
return document
def app(*, model_path, config_path, debug = False):
model: lp.Detectron2LayoutModel = lp.Detectron2LayoutModel(
config_path = config_path,
model_path = model_path,
label_map = label_map)
title = 'Document Similarity Search Using Visual Layout Features'
description = f"<h2>{title}<h2>"
css = '''
image { max-height="86vh" !important; }
.center { display: flex; flex: 1 1 auto; align-items: center; align-content: center; justify-content: center; justify-items: center; }
.hr { width: 100%; display: block; padding: 0; margin: 0; background: gray; height: 4px; border: none; }
'''
with gr.Blocks(title=title, css=css) as app:
with gr.Row():
gr.HTML(value=description, elem_classes=['center'])
with gr.Row(equal_height = False):
with gr.Column():
document_1_image = document_view(1)
with gr.Column():
document_2_image = document_view(2)
gr.HTML('<hr/>', elem_classes=['hr'])
with gr.Row(elem_classes=['center']):
with gr.Column():
submit = gr.Button(value="Get Similarity", variant="primary")
with gr.Column():
vectors_type = gr.Dropdown(
choices = vectors_types,
value = vectors_types[0],
visible = False,
label = "Vectors Type",
info = "Select the Vectors Type to use for Similarity Calculation")
similarity_output = gr.HTML(label="Similarity Score", visible=False)
reset = gr.Button(value="Reset", variant="secondary")
kwargs = {
'fn': lambda document_1_image, document_2_image, vectors_type: similarity_fn(
model,
document_1_image,
document_2_image,
vectors_type),
'inputs': [document_1_image, document_2_image, vectors_type],
'outputs': [similarity_output, document_1_image, document_2_image, vectors_type]
}
submit.click(**kwargs)
vectors_type.change(**kwargs)
return app.launch(debug=debug) |