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Update app.py
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app.py
CHANGED
@@ -5,9 +5,8 @@ import cv2
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import numpy as np
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import fitz # PyMuPDF
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from PIL import Image
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from concurrent.futures import ThreadPoolExecutor, as_completed
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import spaces
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# Load the trained model
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model_path = 'best.pt' # Replace with the path to your trained .pt file
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model = YOLO(model_path)
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@@ -44,9 +43,23 @@ def crop_images_from_boxes(image, boxes, scale_factor):
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cropped_images.append(cropped_image)
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return cropped_images
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def
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# Perform inference at low DPI
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low_res_pix = page.get_pixmap(dpi=low_dpi)
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low_res_img = Image.frombytes("RGB", [low_res_pix.width, low_res_pix.height], low_res_pix.samples)
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@@ -62,32 +75,8 @@ def process_single_page(page, low_dpi, high_dpi, scale_factor):
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# Crop images at high DPI
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cropped_imgs = crop_images_from_boxes(high_res_img, boxes, scale_factor)
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return cropped_imgs
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except Exception as e:
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print(f"Error processing page: {e}")
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return []
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@spaces.GPU
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def process_pdf(pdf_file):
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# Open the PDF file
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doc = fitz.open(pdf_file)
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all_cropped_images = []
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# Set the DPI for inference and high resolution for cropping
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low_dpi = 50
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high_dpi = 300
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# Calculate the scaling factor
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scale_factor = high_dpi / low_dpi
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# Use ThreadPoolExecutor for batch processing
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with ThreadPoolExecutor() as executor:
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futures = [executor.submit(process_single_page, doc.load_page(page_num), low_dpi, high_dpi, scale_factor) for page_num in range(len(doc))]
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for future in as_completed(futures):
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all_cropped_images.extend(future.result())
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return all_cropped_images
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# Create Gradio interface
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@@ -101,3 +90,4 @@ iface = gr.Interface(
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# Launch the app
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iface.launch()
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import numpy as np
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import fitz # PyMuPDF
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from PIL import Image
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import spaces
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# Load the trained model
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model_path = 'best.pt' # Replace with the path to your trained .pt file
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model = YOLO(model_path)
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cropped_images.append(cropped_image)
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return cropped_images
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@spaces.GPU
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def process_pdf(pdf_file):
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# Open the PDF file
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doc = fitz.open(pdf_file)
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all_cropped_images = []
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# Set the DPI for inference and high resolution for cropping
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low_dpi = 50
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high_dpi = 300
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# Calculate the scaling factor
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scale_factor = high_dpi / low_dpi
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# Loop through each page
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for page_num in range(len(doc)):
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page = doc.load_page(page_num)
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# Perform inference at low DPI
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low_res_pix = page.get_pixmap(dpi=low_dpi)
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low_res_img = Image.frombytes("RGB", [low_res_pix.width, low_res_pix.height], low_res_pix.samples)
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# Crop images at high DPI
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cropped_imgs = crop_images_from_boxes(high_res_img, boxes, scale_factor)
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all_cropped_images.extend(cropped_imgs)
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return all_cropped_images
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# Create Gradio interface
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# Launch the app
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iface.launch()
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