Update app.py

app.py

@@ -102,14 +102,14 @@ def main():
     
 
 # Function to display files in a directory
-def display_files_in_directory(path="."):
-    if os.path.exists(path):
-        files = os.listdir(path)
-        st.write(f"Files in directory: {path}")
-        for file in files:
-            st.write(file)
-    else:
-        st.write(f"Directory {path} does not exist!")   
+# def display_files_in_directory(path="."):
+#     if os.path.exists(path):
+#         files = os.listdir(path)
+#         st.write(f"Files in directory: {path}")
+#         for file in files:
+#             st.write(file)
+#     else:
+#         st.write(f"Directory {path} does not exist!")   
   
 def resize_image(image_path, size):
     """Function to resize an image"""
@@ -159,9 +159,6 @@ def process_image(selected_image):
     if gray_image.dtype != np.uint8:
         gray_image = (gray_image * 255).astype(np.uint8)
 
-    # Optionally apply Gaussian Blur
-    # blurred_img = cv2.GaussianBlur(gray_image, (3, 3), 0)
-
     # Apply adaptive thresholding
 
     # Let's assume `gray_image` is your grayscale image array
@@ -180,23 +177,6 @@ def process_image(selected_image):
 
     thresh = binary_adaptive
         
-    # Apply OTSU's thresholding
-    # _, thresh = cv2.threshold(binary_adaptive, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
-
-    # kernel = get_dynamic_kernel(thresh.shape[0])
-    # kernel = np.ones((5, 1), np.uint8)
-    # thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=2)
-
-    # # Apply median blur
-    # thresh = cv2.medianBlur(thresh, 3)
-
-    # # Apply morphological thinning
-    # thresh = cv2.ximgproc.thinning(thresh)
-
-    # # Apply morphological closing
-    # kernel = np.ones((3,3), np.uint8)
-    # thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
-
     # Convert thresholded image back to PIL format to display in Streamlit
     thresh_pil = Image.fromarray(thresh)
 
@@ -316,7 +296,7 @@ def display_detected_lines(original_path, output_path):
 
         # Create a temporary directory to store the detected lines
         with TemporaryDirectory() as temp_dir:
-            detected_line_paths = []  # For storing paths of the thresholded line images for OCR
+            # detected_line_paths = []  # For storing paths of the thresholded line images for OCR
             original_line_paths = []  # For storing paths of the original line images for display
 
             for index, box in enumerate(boxes):
@@ -332,14 +312,14 @@ def display_detected_lines(original_path, output_path):
                 original_line.save(original_line_path)
                 original_line_paths.append(original_line_path)
 
-                # Crop the THRESHOLDED image and save for OCR
-                extracted_line = thresholded_image.crop((x_min, y_min, x_max, y_max))
-                detected_line_path = os.path.join(temp_dir, f"detected_line_{index}.jpg")
-                extracted_line.save(detected_line_path)
-                detected_line_paths.append(detected_line_path)
+                # # Crop the THRESHOLDED image and save for OCR
+                # extracted_line = thresholded_image.crop((x_min, y_min, x_max, y_max))
+                # detected_line_path = os.path.join(temp_dir, f"detected_line_{index}.jpg")
+                # extracted_line.save(detected_line_path)
+                # detected_line_paths.append(detected_line_path)
 
             # Perform OCR on thresholded lines
-            recognized_texts = perform_ocr_on_detected_lines(detected_line_paths)
+            recognized_texts = perform_ocr_on_detected_lines(original_line_paths)
 
             # Display the results
             for original_img_path, text in zip(original_line_paths, recognized_texts):