Updates utlity functions

src/utils.py

@@ -1,10 +1,12 @@
 import os
+from datetime import datetime
 
 import cv2
 import matplotlib.image as mpimg
 import matplotlib.pyplot as plt
 import numpy as np
 from PIL import Image, ImageOps
+from zoneinfo import ZoneInfo
 
 
 def crop_and_pad_image(image_path, threshold=20, target_size=(512, 512)):
@@ -50,7 +52,7 @@ def crop_and_pad_image(image_path, threshold=20, target_size=(512, 512)):
     return squared_img
 
 
-def track_files(folder_path, extensions=('.jpg', '.jpeg', '.png')):
+def track_files(folder_path, extensions=(".jpg", ".jpeg", ".png")):
     """
     Track all the files in a folder and its subfolders.
 
@@ -83,7 +85,6 @@ def track_files(folder_path, extensions=('.jpg', '.jpeg', '.png')):
     return file_list
 
 
-
 def crop_circle_roi(image_path):
     """
     Crop the circular Region of Interest (ROI) from a fundus image.
@@ -104,7 +105,9 @@ def crop_circle_roi(image_path):
     _, thresholded_image = cv2.threshold(gray_image, 50, 255, cv2.THRESH_BINARY)
 
     # Find contours in the binary image
-    contours, _ = cv2.findContours(thresholded_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    contours, _ = cv2.findContours(
+        thresholded_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
+    )
 
     # Assuming the largest contour corresponds to the ROI
     contour = max(contours, key=cv2.contourArea)
@@ -113,10 +116,11 @@ def crop_circle_roi(image_path):
     x, y, w, h = cv2.boundingRect(contour)
 
     # Crop the circular ROI using the bounding rectangle
-    cropped_roi = image[y:y+h, x:x+w]
+    cropped_roi = image[y : y + h, x : x + w]
 
     return cropped_roi
 
+
 def plot_image_grid(image_paths, roi_crop=False):
     """
     Create a grid plot with a maximum of 16 images.
@@ -138,9 +142,32 @@ def plot_image_grid(image_paths, roi_crop=False):
             else:
                 img = mpimg.imread(image_paths[i])
             ax.imshow(img)
-            ax.axis('off')
+            ax.axis("off")
         else:
-            ax.axis('off')
+            ax.axis("off")
 
     plt.tight_layout()
-    plt.show()
+    plt.show()
+
+
+def generate_run_id(zone: ZoneInfo = ZoneInfo("Asia/Kathmandu")) -> str:
+    """Generate a unique run ID using current UTC date and time.
+
+    Args:
+        zone (ZoneInfo, optional): Timezone information. Defaults to Indian Standard Time.
+
+    Returns:
+        str: A unique run ID in the format 'run-YYYY-MM-DD-HH-MM-SS'.
+    """
+    try:
+        current_utc_time = datetime.utcnow().astimezone(zone)
+        formatted_time = current_utc_time.strftime("%Y-%m-%d-%H-%M-%S")
+        return f"run-{formatted_time}"
+    except Exception as e:
+        # Handle exceptions gracefully
+        print(f"Error generating run ID: {e}")
+        return None  # Or raise an exception if appropriate
+
+
+if __name__ == "__main__":
+    print(generate_run_id())