Fix Ruff format

scripts/rectify_image_from_distorted_image.py

@@ -1,69 +1,71 @@
-import numpy as np
-import cv2
 import os
 from pathlib import Path
+
+import cv2
+import numpy as np
 import yaml
 
+
 class ImageRectifier:
-    def __init__(self,calib_file_path):
+    def __init__(self, calib_file_path):
         # Load the YAML file
-        with open(calib_file_path, 'r') as file:
+        with open(calib_file_path, "r") as file:
             calib_data = yaml.safe_load(file)
 
         # Restructure the YAML data
-        camera_model = calib_data['sensor']['camera_model']
-        cameras = calib_data['sensor']['cameras']
+        camera_model = calib_data["sensor"]["camera_model"]
+        cameras = calib_data["sensor"]["cameras"]
 
         # Create the desired dictionary structure
-        self.sensor_dict = {
-            "camera_model": camera_model,
-            "cameras": {camera['label']: camera for camera in cameras}
-        }
+        self.sensor_dict = {"camera_model": camera_model, "cameras": {camera["label"]: camera for camera in cameras}}
         self._init_rectification_maps()
-        
+
     def _init_rectification_maps(self):
-        for cam_label,cam_data in self.sensor_dict['cameras'].items():
-            balance = 0.0  
-            scale_factor = 1.0 
-            new_width = int(cam_data['image_width'] * scale_factor)
-            new_height = int(cam_data['image_height'] * scale_factor)
+        for cam_label, cam_data in self.sensor_dict["cameras"].items():
+            balance = 0.0
+            scale_factor = 1.0
+            new_width = int(cam_data["image_width"] * scale_factor)
+            new_height = int(cam_data["image_height"] * scale_factor)
             new_size = (new_width, new_height)
 
             new_K = cv2.fisheye.estimateNewCameraMatrixForUndistortRectify(
-               convert_to_intrinsic_matrix( np.array(cam_data['intrinsics'])), 
-                np.array(cam_data['extra_params']),
+                convert_to_intrinsic_matrix(np.array(cam_data["intrinsics"])),
+                np.array(cam_data["extra_params"]),
                 (new_width, new_height),
                 np.eye(3),
                 balance=balance,
-                new_size=new_size
+                new_size=new_size,
             )
             print(f"{cam_label} has:")
             print(new_K)
-
-            self.sensor_dict['cameras'][cam_label]['map1'] , self.sensor_dict['cameras'][cam_label]['map2']  = cv2.fisheye.initUndistortRectifyMap(
-               convert_to_intrinsic_matrix(np.array(cam_data['intrinsics'])),
-               np.array(cam_data['extra_params']),
-                np.eye(3), 
-                new_K,
-                new_size,
-                cv2.CV_32FC1
+
+            self.sensor_dict["cameras"][cam_label]["map1"], self.sensor_dict["cameras"][cam_label]["map2"] = (
+                cv2.fisheye.initUndistortRectifyMap(
+                    convert_to_intrinsic_matrix(np.array(cam_data["intrinsics"])),
+                    np.array(cam_data["extra_params"]),
+                    np.eye(3),
+                    new_K,
+                    new_size,
+                    cv2.CV_32FC1,
+                )
             )
-            self.sensor_dict['cameras'][cam_label]['rectified_width'] = new_width
-            self.sensor_dict['cameras'][cam_label]['rectified_height'] = new_height
-
-            print(f"Camera {cam_label} rectified image size: Width={new_width}, Height={new_height}, please copy this value \n")
-            print("")
+            self.sensor_dict["cameras"][cam_label]["rectified_width"] = new_width
+            self.sensor_dict["cameras"][cam_label]["rectified_height"] = new_height
 
+            print(
+                f"Camera {cam_label} rectified image size: Width={new_width}, Height={new_height}, please copy this value \n"
+            )
+            print("")
 
     def process_image(self, image, output_path, cam_label):
         # Remap the image
         rectified = cv2.remap(
-            image, 
-            self.sensor_dict['cameras'][cam_label]['map1'],
-            self.sensor_dict['cameras'][cam_label]['map2'],
+            image,
+            self.sensor_dict["cameras"][cam_label]["map1"],
+            self.sensor_dict["cameras"][cam_label]["map2"],
             interpolation=cv2.INTER_LINEAR,
-            borderMode=cv2.BORDER_CONSTANT
-            )
+            borderMode=cv2.BORDER_CONSTANT,
+        )
 
         # Save the rectified image
         os.makedirs(os.path.dirname(output_path), exist_ok=True)
@@ -73,10 +75,10 @@ def process_directory(self, input_dir, output_dir):
         input_path = Path(input_dir)
         output_path = Path(output_dir)
         output_path.mkdir(parents=True, exist_ok=True)
-        for cam_label, cam_data in self.sensor_dict['cameras'].items():
-            path_parts = cam_data["topic"].split("/", 1) 
-            img_subfolder = path_parts[0] + path_parts[1].replace("/", "_") 
-            for img_path in input_path.rglob('*'):
+        for cam_label, cam_data in self.sensor_dict["cameras"].items():
+            path_parts = cam_data["topic"].split("/", 1)
+            img_subfolder = path_parts[0] + path_parts[1].replace("/", "_")
+            for img_path in input_path.rglob("*"):
                 if img_path.parent == img_subfolder:
                     image = cv2.imread(str(img_path))
                     if image is None:
@@ -87,30 +89,26 @@ def process_directory(self, input_dir, output_dir):
                     self.process_image(image, output_file, cam_label)
                     # print(f"Processed: {img_path.name}")
 
+
 def convert_to_intrinsic_matrix(intrinsics):
     if len(intrinsics) != 4:
         raise ValueError("Intrinsics must be a list or array with exactly 4 elements: [fx, fy, cx, cy].")
     fx, fy, cx, cy = intrinsics
-    intrinsic_matrix = np.array([
-        [fx,  0, cx],
-        [ 0, fy, cy],
-        [ 0,  0,  1]
-    ], dtype=np.float64)
+    intrinsic_matrix = np.array([[fx, 0, cx], [0, fy, cy], [0, 0, 1]], dtype=np.float64)
     return intrinsic_matrix
 
 
-
 if __name__ == "__main__":
-    '''
+    """
     Args:
     - calib_file_path: base calibration file 
     - distorted_image_input_path: distorted images folder directory 
     - rectified_image_output_path: output folder name to save undistorted rectified images
-    '''
-    
-    calib_file_path = './config/sensor.yaml'
-    distorted_image_input_path = '/data/2024-03-12-keble-college-01/images'
-    rectified_image_output_path = '/data/2024-03-12-keble-college-01/test/images_rectified'
+    """
+
+    calib_file_path = "./config/sensor.yaml"
+    distorted_image_input_path = "/data/2024-03-12-keble-college-01/images"
+    rectified_image_output_path = "/data/2024-03-12-keble-college-01/test/images_rectified"
 
     # Initialize rectifier
     rectifier = ImageRectifier(calib_file_path)