ocr remove excess code

Layoutlmv3_inference/ocr.py

@@ -49,85 +49,6 @@ def preprocess_image(image_path, max_file_size_mb=1, target_file_size_mb=0.5):
         return None
 
 
-def enhance_txt(img, intensity_increase=20, bilateral_filter_diameter=9, bilateral_filter_sigma_color=75, bilateral_filter_sigma_space=75):
-    # Get the width and height of the image
-    w = img.shape[1]
-    h = img.shape[0]
-    w1 = int(w * 0.05)
-    w2 = int(w * 0.95)
-    h1 = int(h * 0.05)
-    h2 = int(h * 0.95)
-    ROI = img[h1:h2, w1:w2]  # 95% of the center of the image
-    threshold = np.mean(ROI) * 0.88  # % of average brightness
-
-    # Convert image to grayscale
-    grayscale_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-
-    # Apply Gaussian blur
-    blurred = cv2.GaussianBlur(grayscale_img, (1, 1), 0)
-
-    edged = 255 - cv2.Canny(blurred, 100, 150, apertureSize=7)
-
-    # Increase intensity by adding a constant value
-    img = np.clip(img + intensity_increase, 0, 255).astype(np.uint8)
-
-    # Apply bilateral filter to reduce noise
-    img = cv2.bilateralFilter(img, bilaterimport os
-import pandas as pd
-import cv2
-import numpy as np
-import json
-import requests
-import traceback
-import tempfile
-from rembg import remove
-
-
-from PIL import Image
-
-
-def preprocess_image(image_path, max_file_size_mb=1, target_file_size_mb=0.5):
-    try:
-        # Read the image
-        image = cv2.imread(image_path)
-        # Enhance text
-        enhanced = enhance_txt(image)
-
-        # Save the enhanced image to a temporary file
-        temp_file_path = tempfile.NamedTemporaryFile(suffix='.jpg').name
-        cv2.imwrite(temp_file_path, enhanced)
-
-        # Check file size of the temporary file
-        file_size_mb = os.path.getsize(
-            temp_file_path) / (1024 * 1024)  # Convert to megabytes
-
-        while file_size_mb > max_file_size_mb:
-            print(
-                f"File size ({file_size_mb} MB) exceeds the maximum allowed size ({max_file_size_mb} MB). Resizing the image.")
-            ratio = np.sqrt(target_file_size_mb / file_size_mb)
-            new_width = int(image.shape[1] * ratio)
-            new_height = int(image.shape[0] * ratio)
-
-            # Resize the image
-            enhanced = cv2.resize(enhanced, (new_width, new_height))
-
-            # Save the resized image to a temporary file
-            temp_file_path = tempfile.NamedTemporaryFile(suffix='.jpg').name
-            cv2.imwrite(temp_file_path, enhanced)
-
-            # Update file size
-            file_size_mb = os.path.getsize(temp_file_path) / (1024 * 1024)
-            print(f"New file size: ({file_size_mb} MB)")
-
-        # Return the final resized image
-        image_resized = cv2.imread(temp_file_path)
-        return image_resized
-
-    except Exception as e:
-        print(f"An error occurred in preprocess_image: {str(e)}")
-        return None
-
-
 def enhance_txt(img, intensity_increase=20, bilateral_filter_diameter=9, bilateral_filter_sigma_color=75, bilateral_filter_sigma_space=75):
     # Get the width and height of the image
     w = img.shape[1]