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import os
from pathlib import Path
import pandas as pd
import gradio as gr
from collections import OrderedDict
from PIL import Image, ImageDraw, ImageFont
from io import BytesIO
import PyPDF2
import pdf2image
MAX_PAGES = 50
MAX_PDF_SIZE = 100000000 # almost 100MB
MIN_WIDTH, MIN_HEIGHT = 150, 150
def equal_image_grid(images):
def compute_grid(n, max_cols=6):
equalDivisor = int(n**0.5)
cols = min(equalDivisor, max_cols)
rows = equalDivisor
if rows * cols >= n:
return rows, cols
cols += 1
if rows * cols >= n:
return rows, cols
while rows * cols < n:
rows += 1
return rows, cols
# assert len(images) == rows*cols
rows, cols = compute_grid(len(images))
# rescaling to min width [height padding]
images = [im for im in images if (im.height > 0) and (im.width > 0)] # could be NA
min_width = min(im.width for im in images)
images = [im.resize((min_width, int(im.height * min_width / im.width)), resample=Image.BICUBIC) for im in images]
w, h = max([img.size[0] for img in images]), max([img.size[1] for img in images])
grid = Image.new("RGB", size=(cols * w, rows * h))
grid_w, grid_h = grid.size
for i, img in enumerate(images):
grid.paste(img, box=(i % cols * w, i // cols * h))
return grid
def add_pagenumbers(im_list, height_scale=40):
def add_pagenumber(image, i):
width, height = image.size
draw = ImageDraw.Draw(image)
fontsize = int((width * height) ** (0.5) / height_scale)
font = ImageFont.truetype("Arial.ttf", fontsize)
margin = int(2 * fontsize)
draw.text(
(width - margin, height - margin),
str(i + 1),
fill="#D00917",
font=font,
spacing=4,
align="right",
)
for i, image in enumerate(im_list):
add_pagenumber(image, i)
def pdf_to_grid(pdf_path):
reader = PyPDF2.PdfReader(pdf_path)
reached_page_limit = False
images = []
try:
for p, page in enumerate(reader.pages):
if reached_page_limit:
break
for image in page.images:
im = Image.open(BytesIO(image.data))
if im.width < MIN_WIDTH and im.height < MIN_HEIGHT:
continue
images.append(im)
except Exception as e:
print(f"{pdf_path} PyPDF get_images {e}")
images = pdf2image.convert_from_path(pdf_path)
# simpler but slower
# images = pdf2image.convert_from_path(pdf_path)
if len(images) == 0:
return None
add_pagenumbers(images)
return equal_image_grid(images)
def main(complexity, evidence, form, operation, type):
# need to write a query on diagnostic test and sample from it based on slider values
# then return the sample
query = " and ".join(
[
f"{cat}_{val} == {True}"
for cat, val in zip(meta_cats.keys(), [complexity, evidence, form, operation, type])
if val
]
)
results = DIAGNOSTIC_TEST.query(query)
if len(results) == 0:
return f"No results found for query {query}", "", "", "", ""
for i, sample in results.sample(frac=1).iterrows():
if not sample['nhash']:
continue
print("Sampled: ", sample["nhash"])
# first get PDF file
PDF, grid = None, None
pdf_path = PDF_PATH / "test" / (sample["nhash"] + ".pdf")
if not os.path.exists(pdf_path):
continue
PDF = pdf_path
grid = pdf_to_grid(pdf_path)
if not grid:
continue
question, answer = sample["label"] #might need to translate
return label, grid, PDF
_CLASSES = [
"letter",
"form",
"email",
"handwritten",
"advertisement",
"scientific report",
"scientific publication",
"specification",
"file folder",
"news article",
"budget",
"invoice",
"presentation",
"questionnaire",
"resume",
"memo",
]
# test
# l, im, f = main(*slider_defaults)
#load both datasets in memory? --> easier retrieval afterwards with seed index based on pressing button
DATASETS = {
'rvl_cdip': load_dataset(
"bdpc/rvl_cdip_mp",
split="test"),
'rvl_cdip_N': load_dataset(
"bdpc/rvl_cdip_mp",
split="test")
}
meta_cats = {'dataset': ['rvl_cdip', 'rvl_cdip_N'],
'label': _CLASSES
}
sliders = [gr.Dropdown(choices=choices, value=choices[-1], label=label) for label, choices in meta_cats.items()]
slider_defaults = [slider.value for slider in sliders]
outputs = [
gr.Textbox(label="label"),
gr.Image(label="image grid of PDF"),
gr.File(label="PDF"),
]
iface = gr.Interface(fn=main, inputs=sliders, outputs=outputs, description="Visualize PDF samples from multi-page (PDF) document classification datasets", title='Beyond Document Page Classification: Examples')
iface.launch(share=True) |