Add a script for image undistortion and rectification and save them

scripts/rectify_image_from_distorted_image.py

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+import numpy as np
+import cv2
+import os
+from pathlib import Path
+import yaml
+
+class ImageRectifier:
+    def __init__(self,calib_file_path):
+        # Load the YAML 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']
+
+        # Create the desired dictionary structure
+        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)
+            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']),
+                (new_width, new_height),
+                np.eye(3),
+                balance=balance,
+                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]['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(f"")
+
+
+    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'],
+            interpolation=cv2.INTER_LINEAR,
+            borderMode=cv2.BORDER_CONSTANT
+            )
+
+        # Save the rectified image
+        os.makedirs(os.path.dirname(output_path), exist_ok=True)
+        cv2.imwrite(str(output_path), rectified)
+
+    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('*'):
+                if img_path.parent == img_subfolder:
+                    image = cv2.imread(str(img_path))
+                    if image is None:
+                        print(f"Failed to read image: {img_path}")
+                        continue
+                    rel_path = img_path.relative_to(input_path)
+                    output_file = output_path / rel_path
+                    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)
+    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'
+
+    # Initialize rectifier
+    rectifier = ImageRectifier(calib_file_path)
+    rectifier.process_directory(distorted_image_input_path, rectified_image_output_path)
+    print("Please copy and paste output rectified intrinsics")