Spaces:
Sleeping
Sleeping
File size: 2,739 Bytes
aa8cd87 0bbf6ef e91a768 0380162 57d20a1 f496449 07f5bd9 aa8cd87 0bbf6ef 07f5bd9 43d306c e91a768 aa8cd87 43d306c 879dfbb c65777e e91a768 c65777e e91a768 b296597 3cadd69 4504622 3cadd69 c65777e 3cadd69 ec2e6e8 07f5bd9 ff2c42f 07f5bd9 ff2c42f 07f5bd9 ff2c42f 649e38b ff2c42f 07f5bd9 ff2c42f 649e38b 89415f2 e91a768 0bbf6ef e91a768 e0a154b e91a768 0bbf6ef 03c8fc6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
import gradio as gr
import numpy as np
import fitz # PyMuPDF
import spaces
from ultralytics import YOLOv10
# Load the trained model
model = YOLOv10("best.pt")
# Define the class indices for figures and tables
figure_class_index = 3 # class index for figures
table_class_index = 4 # class index for tables
# Function to perform inference on an image and return bounding boxes for figures and tables
@spaces.GPU
def infer_image_and_get_boxes(image, confidence_threshold=0.6):
results = model.predict(image)
boxes = [
(int(box.xyxy[0][0]), int(box.xyxy[0][1]), int(box.xyxy[0][2]), int(box.xyxy[0][3]))
for result in results for box in result.boxes
if int(box.cls[0]) in {figure_class_index, table_class_index} and box.conf[0] > confidence_threshold
]
return boxes
# Function to crop images from the boxes
def crop_images_from_boxes(image, boxes, scale_factor):
cropped_images = [
image[int(y1 * scale_factor):int(y2 * scale_factor), int(x1 * scale_factor):int(x2 * scale_factor)]
for (x1, y1, x2, y2) in boxes
]
return cropped_images
@spaces.GPU
def process_pdf(pdf_file):
# Open the PDF file
doc = fitz.open(pdf_file)
all_cropped_images = []
# Set the DPI for inference and high resolution for cropping
low_dpi = 50
high_dpi = 300
# Calculate the scaling factor
scale_factor = high_dpi / low_dpi
# Pre-cache all page pixmaps at low DPI
low_res_pixmaps = [page.get_pixmap(dpi=low_dpi) for page in doc]
# Loop through each page
for page_num, low_res_pix in enumerate(low_res_pixmaps):
low_res_img = np.frombuffer(low_res_pix.samples, dtype=np.uint8).reshape(low_res_pix.height, low_res_pix.width, 3)
# Get bounding boxes from low DPI image
boxes = infer_image_and_get_boxes(low_res_img)
if boxes:
# Load high DPI image for cropping only if boxes are found
high_res_pix = doc[page_num].get_pixmap(dpi=high_dpi)
high_res_img = np.frombuffer(high_res_pix.samples, dtype=np.uint8).reshape(high_res_pix.height, high_res_pix.width, 3)
# Crop images at high DPI
cropped_imgs = crop_images_from_boxes(high_res_img, boxes, scale_factor)
all_cropped_images.extend(cropped_imgs)
return all_cropped_images
# Create Gradio interface
iface = gr.Interface(
fn=process_pdf,
inputs=gr.File(label="Upload a PDF"),
outputs=gr.Gallery(label="Cropped Figures and Tables from PDF Pages"),
title="Fast document layout analysis based on YOLOv10",
description="Upload a PDF file to get cropped figures and tables from each page."
)
# Launch the app
iface.launch()
|