Update pdfextract_fun.py

pdfextract_fun.py

@@ -1,82 +1,193 @@
-import os
-import re
 import warnings
+from concurrent.futures import ThreadPoolExecutor, as_completed
+import time
+# Filter warnings about inputs not requiring gradients
+warnings.filterwarnings("ignore", message="None of the inputs have requires_grad=True. Gradients will be None")
+warnings.filterwarnings("ignore", message="torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument.")
+
 import cv2
+import os
 import fitz  # PyMuPDF
 import numpy as np
+import re
 import pytesseract
 import torch
 from PIL import Image
-from tqdm import tqdm
+from tqdm import tqdm 
+
+from unilm.dit.object_detection.ditod import add_vit_config
+
+from detectron2.config import CfgNode as CN
 from detectron2.config import get_cfg
+from detectron2.utils.visualizer import ColorMode, Visualizer
 from detectron2.data import MetadataCatalog
 from detectron2.engine import DefaultPredictor
-from detectron2.utils.visualizer import ColorMode, Visualizer
-from unilm.dit.object_detection.ditod import add_vit_config
 
-# Filter specific warnings
-warnings.filterwarnings("ignore", message="None of the inputs have requires_grad=True. Gradients will be None")
-warnings.filterwarnings("ignore", message="torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument.")
 
-# Configuration setup
-def setup_config():
-    cfg = get_cfg()
-    add_vit_config(cfg)
-    cfg.merge_from_file("cascade_dit_base.yml")
-    cfg.MODEL.WEIGHTS = "publaynet_dit-b_cascade.pth"
-    cfg.MODEL.DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-    return cfg
-
-# Analyze image
-def analyze_image(img, cfg):
-    """Analyze an image and return the result image, output, and visualizer."""
-    md = MetadataCatalog.get(cfg.DATASETS.TEST[0])
-    thing_classes = ["table"] if cfg.DATASETS.TEST[0] == 'icdar2019_test' else ["text", "title", "list", "table", "figure"]
-    md.set(thing_classes=thing_classes)
+# Step 1: instantiate config
+cfg = get_cfg()
+add_vit_config(cfg)
+cfg.merge_from_file("cascade_dit_base.yml")
+
+# Step 2: add model weights URL to config
+cfg.MODEL.WEIGHTS = "publaynet_dit-b_cascade.pth"
+
+# Step 3: set device
+cfg.MODEL.DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+#cfg.MODEL.DEVICE =  "cuda"
 
-    output = DefaultPredictor(cfg)(img)["instances"]
-    v = Visualizer(img[:, :, ::-1], metadata=md, scale=1.0, instance_mode=ColorMode.SEGMENTATION)
+# Step 4: define model
+predictor = DefaultPredictor(cfg)
+
+def analyze_image(img):
+    
+    md = MetadataCatalog.get(cfg.DATASETS.TEST[0])
+    if cfg.DATASETS.TEST[0]=='icdar2019_test':
+        md.set(thing_classes=["table"])
+    else:
+        md.set(thing_classes=["text","title","list","table","figure"])
+    
+    output = predictor(img)["instances"]
+    v = Visualizer(img[:, :, ::-1],
+                    md,
+                    scale=1.0,
+                    instance_mode=ColorMode.SEGMENTATION)
     result = v.draw_instance_predictions(output.to("cpu"))
-    return result.get_image()[:, :, ::-1], output, v
+    result_image = result.get_image()[:, :, ::-1]
+    
+    return result_image, output, v
+
+
 
-# PDF to JPEG conversion
 def convert_pdf_to_jpg(pdf_path, output_folder, zoom_factor=2):
-    """Convert PDF file to JPEG images, saved in the specified output folder."""
     doc = fitz.open(pdf_path)
-    for page_num, page in enumerate(doc):
-        mat = fitz.Matrix(zoom_factor, zoom_factor)
-        pix = page.get_pixmap(matrix=mat)
-        output_file = os.path.join(output_folder, f"page_{page_num}.jpg")
+    for page_num in range(len(doc)):
+        page = doc.load_page(page_num)
+
+        # Adjust zoom factor for higher resolution
+        mat = fitz.Matrix(zoom_factor, zoom_factor)  # Create a Matrix with the zoom factor
+        pix = page.get_pixmap(matrix=mat)  # Render the page using the matrix
+
+        output_file = f"{output_folder}/page_{page_num}.jpg"
         pix.save(output_file)
 
-# Process JPEG images in a folder
-def process_jpeg_images(output_folder, cfg):
-    """Process each JPEG image in the output folder."""
-    for page_num in tqdm(range(len(os.listdir(output_folder))), desc="Processing the pdf"):
-        file_path = os.path.join(output_folder, f"page_{page_num}.jpg")
-        img = cv2.imread(file_path)
-        if img is None:
-            print(f"Failed to read {file_path}. Skipping.")
-            continue
-        result_image, output, v = analyze_image(img, cfg)
-        save_extracted_instances(img, output, page_num, output_folder)
-
-# Save extracted instances
+
+
+def process_jpeg_images(output_folder):
+     for page_num in tqdm(range(len(os.listdir(output_folder))), desc="Processing the pdf"):
+         file_path = f"{output_folder}/page_{page_num}.jpg"
+         img = cv2.imread(file_path)
+         if img is None:
+             print(f"Failed to read {file_path}. Skipping.")
+             continue
+         result_image, output, v = analyze_image(img)
+
+         # Saving logic
+         save_extracted_instances(img, output, page_num,output_folder)
+
+
+
 def save_extracted_instances(img, output, page_num, dest_folder, confidence_threshold=0.8):
-    """Save instances extracted from an image to the destination folder."""
-    class_names = {0: "text", 1: "title", 2: "list", 3: "table", 4: "figure"}
+    class_names = {
+        0: "text",
+        1: "title",
+        2: "list",
+        3: "table",
+        4: "figure"
+    }
+
+    threshold_value = 0  # Standard deviation threshold
+    min_height = 0  # Minimum height threshold
+
     instances = output.to("cpu")
-    for i, (box, class_id, score) in enumerate(zip(instances.pred_boxes.tensor.numpy(), instances.pred_classes.tolist(), instances.scores.tolist())):
-        if score >= confidence_threshold and class_names.get(class_id) in ["figure", "table", "text"]:
-            x1, y1, x2, y2 = map(int, box)
-            cropped_image = img[y1:y2, x1:x2]
-            if np.std(cropped_image) > 0 and (y2 - y1) > 0:  # Replace with actual thresholds if needed
-                save_path = os.path.join(dest_folder, f"page_{page_num}_{class_names[class_id]}_{i + 1}.jpg")
-                cv2.imwrite(save_path, cropped_image)
-
-# Additional functions like delete_files_in_folder, rename_files_sequentially, ocr_folder, and ocr_image can be included as is, assuming they were satisfactory before.
-
-cfg = setup_config()
-# Example usage
-convert_pdf_to_jpg("sample.pdf", "output_folder")
-process_jpeg_images("output_folder", cfg)
+    boxes = instances.pred_boxes.tensor.numpy()
+    class_ids = instances.pred_classes.tolist()
+    scores = instances.scores.tolist()  # Get prediction scores
+
+    image_counter = 1
+    for box, class_id, score in zip(boxes, class_ids, scores):
+        # Check if the prediction score meets the confidence threshold
+        if score >= confidence_threshold:
+            class_name = class_names.get(class_id, "unknown")
+
+            # Save only if class is 'figure' or 'table'
+            if class_name in ["figure", "table","text"]:
+                x1, y1, x2, y2 = map(int, box)
+                cropped_image = img[y1:y2, x1:x2]
+
+                if np.std(cropped_image) > threshold_value and (y2 - y1) > min_height:
+                    save_path = os.path.join(dest_folder, f"page_{page_num}_{class_name}_{image_counter}.jpg")
+                    cv2.imwrite(save_path, cropped_image)
+                    image_counter += 1
+
+
+def delete_files_in_folder(folder_path):
+    for filename in os.listdir(folder_path):
+        file_path = os.path.join(folder_path, filename)
+        if os.path.isfile(file_path):
+            os.remove(file_path)
+
+
+
+def rename_files_sequentially(folder_path):
+    # Regex pattern to match 'page_{page_num}_{class_name}_{image_counter}.jpg'
+    pattern = re.compile(r'page_(\d+)_(\w+)_(\d+).jpg', re.IGNORECASE)
+
+    # List files in the folder
+    files = os.listdir(folder_path)
+
+    # Filter and sort files based on the regex pattern
+    sorted_files = sorted(
+        [f for f in files if pattern.match(f)],
+        key=lambda x: (int(pattern.match(x).group(1)), pattern.match(x).group(2).lower(), int(pattern.match(x).group(3)))
+    )
+
+    # Initialize an empty dictionary for counters
+    counters = {}
+
+    for filename in sorted_files:
+        match = pattern.match(filename)
+        if match:
+            page_num, class_name, _ = match.groups()
+            class_name = class_name.lower()  # Convert class name to lowercase
+
+            # Initialize counter for this class if it doesn't exist
+            if class_name not in counters:
+                counters[class_name] = 1
+
+            # New filename format: '{class_name}_{sequential_number}.jpg'
+            new_filename = f"{class_name}_{counters[class_name]}.jpg"
+            counters[class_name] += 1
+
+            # Rename the file
+            os.rename(os.path.join(folder_path, filename), os.path.join(folder_path, new_filename))
+
+            #print(f"Renamed '{filename}' to '{new_filename}'")
+
+
+def ocr_folder(folder_path):
+    # Regex pattern to match 'text_{number}.jpg'
+    pattern = re.compile(r'text_\d+\.jpg', re.IGNORECASE)
+
+    # Create a subfolder for the OCR text files
+    ocr_text_folder = os.path.join(folder_path, "ocr_results")
+    if not os.path.exists(ocr_text_folder):
+        os.makedirs(ocr_text_folder)
+
+    for filename in os.listdir(folder_path):
+        if pattern.match(filename):
+            image_path = os.path.join(folder_path, filename)
+            text = ocr_image(image_path)
+            
+            # Save the OCR result to a text file in the subfolder
+            text_file_name = filename.replace('.jpg', '.txt')
+            text_file_path = os.path.join(ocr_text_folder, text_file_name)
+            with open(text_file_path, 'w') as file:
+                file.write(text)
+            
+            #print(f"OCR result for {filename} saved to {text_file_path}\n")
+
+def ocr_image(image_path):
+    image = Image.open(image_path)
+    text = pytesseract.image_to_string(image)
+    return text