import os import glob import cv2 import numpy as np import yaml import json import rosbag2_py from rclpy.serialization import deserialize_message from rosidl_runtime_py.utilities import get_message import sensor_msgs_py.point_cloud2 as pc2 class BagDatasetExtractor: def __init__(self, bag_path, output_dir, lidar_transform_path, camera_transform_path, start_frame_idx=0): self.bag_path = bag_path self.output_dir = output_dir self.lidar_transform_path = lidar_transform_path self.camera_transform_path = camera_transform_path self.frame_idx = start_frame_idx self.prepare_folders() # ROS 2 Bag Reader Setup self.reader = rosbag2_py.SequentialReader() storage_options = rosbag2_py.StorageOptions(uri=self.bag_path, storage_id='sqlite3') converter_options = rosbag2_py.ConverterOptions( input_serialization_format='cdr', output_serialization_format='cdr' ) self.reader.open(storage_options, converter_options) self.topic_types = {topic.name: topic.type for topic in self.reader.get_all_topics_and_types()} self.transforms = self._load_transforms() # Dynamic Mappings ''' 11225069610@A70i:~/Desktop/BEV_EDL/data/bags$ ros2 topic echo --once /bev/semantic_labels data: '{"0":{"class":"BACKGROUND"},"1":{"class":"UNLABELLED"},"2":{"class":"leaf"},"3":{"class":"branch"},"4":{"class":"weed"},"5":{"cl...' --- 11225069610@A70i:~/Desktop/BEV_EDL/data/bags$ ros2 topic echo --once /bev/semantic_labels_bb data: '{"0":{"class":"ground"},"1":{"class":"leaf"},"2":{"class":"branch"},"3":{"class":"weed"},"4":{"class":"obstacle"},"time_stamp":{...' --- ''' self.target_classes = {"ground": 0, "leaf": 1, "branch": 2, "weed": 3, "obstacle": 4} self.semantic_mapping = {} self.bb_mapping = {} def _create_matrix_from_yaml(self, transform_data): t = transform_data['transformation']['translation'] q = transform_data['transformation']['rotation'] translation = np.array([t['x'], t['y'], t['z']]) x, y, z, w = q['x'], q['y'], q['z'], q['w'] # Normalize quaternion norm = np.sqrt(x*x + y*y + z*z + w*w) if norm == 0: # Handle zero-norm quaternion (invalid rotation) x, y, z, w = 0, 0, 0, 1 else: x /= norm y /= norm z /= norm w /= norm # Rotation matrix from quaternion rotation_matrix = np.array([ [1 - 2*y*y - 2*z*z, 2*x*y - 2*z*w, 2*x*z + 2*y*w], [2*x*y + 2*z*w, 1 - 2*x*x - 2*z*z, 2*y*z - 2*x*w], [2*x*z - 2*y*w, 2*y*z + 2*x*w, 1 - 2*x*x - 2*y*y] ]) # Create 4x4 homogeneous matrix matrix = np.eye(4) matrix[:3, :3] = rotation_matrix matrix[:3, 3] = translation return matrix def _load_transforms(self): transforms = {} try: with open(self.lidar_transform_path, 'r') as f: lidar_transform_data = yaml.safe_load(f) transforms['lidar_to_parent'] = self._create_matrix_from_yaml(lidar_transform_data) with open(self.camera_transform_path, 'r') as f: camera_transform_data = yaml.safe_load(f) transforms['camera_to_parent'] = self._create_matrix_from_yaml(camera_transform_data) except Exception as e: print(f"Warning: Transform file issue: {e}") return transforms def prepare_folders(self): for sub in ['rgb', 'depth', 'lidar', 'bev_label', 'extrinsics']: os.makedirs(os.path.join(self.output_dir, sub), exist_ok=True) def _parse_label_json(self, json_str): """Extracts the mapping from Isaac Sim JSON string to our fixed IDs.""" mapping = {} try: data = json.loads(json_str) for key, val in data.items(): if isinstance(val, dict) and "class" in val: class_name = val["class"].lower() if class_name in self.target_classes: mapping[int(key)] = self.target_classes[class_name] except json.JSONDecodeError: print(f"Warning: Failed to parse JSON label string: {json_str}") return mapping def process_bev_logic(self, semantic_msg, bbox_msg): height, width = semantic_msg.height, semantic_msg.width semantic_raw = np.frombuffer(semantic_msg.data, dtype=np.int32).reshape((height, width)) # 1. Remap Semantic Image remapped_semantic = np.full_like(semantic_raw, 3, dtype=np.uint8) # Default 3 (Other/Background) for isaac_id, target_id in self.semantic_mapping.items(): remapped_semantic[semantic_raw == isaac_id] = target_id # 2. Add bounding box centers for branches for detection in bbox_msg.detections: if detection.results: try: isaac_class_id = int(detection.results[0].hypothesis.class_id) # Use the BB mapping specifically for the bounding boxes if self.bb_mapping.get(isaac_class_id) == 2: # 2 is Branch center_x = int(detection.bbox.center.position.x) center_y = int(detection.bbox.center.position.y) cv2.circle(remapped_semantic, (center_x, center_y), radius=3, color=2, thickness=-1) except Exception as e: continue return remapped_semantic def run(self): print(f"Starting extraction for bag: {os.path.basename(self.bag_path)}") sync_data = {} # Added the label topics to the filter self.reader.set_filter(rosbag2_py.StorageFilter(topics=[ '/camera_front/rgb', '/camera_front/depth', '/lidar_front/point_cloud', '/bev/semantic_segmentation', '/bev/bbox_2d', '/bev/semantic_labels', '/bev/semantic_labels_bb' ])) while self.reader.has_next(): (topic, data, t_msg) = self.reader.read_next() msg_type = get_message(self.topic_types[topic]) msg = deserialize_message(data, msg_type) # Update Mappings dynamically if we see the label topics if topic == '/bev/semantic_labels': self.semantic_mapping = self._parse_label_json(msg.data) continue elif topic == '/bev/semantic_labels_bb': self.bb_mapping = self._parse_label_json(msg.data) continue ts = msg.header.stamp.sec * 1e9 + msg.header.stamp.nanosec if hasattr(msg, 'header') else t_msg time_key = int(ts / 100_000_000) if time_key not in sync_data: sync_data[time_key] = {} sync_data[time_key][topic] = msg # Only save the sample if we have all sensor data AND we have successfully captured our label mappings required = ['/camera_front/rgb', '/camera_front/depth', '/lidar_front/point_cloud', '/bev/semantic_segmentation', '/bev/bbox_2d'] if all(topic in sync_data[time_key] for topic in required): if not self.semantic_mapping or not self.bb_mapping: # Skip until we receive the label definitions del sync_data[time_key] continue self.save_sample(sync_data[time_key]) del sync_data[time_key] return self.frame_idx def save_sample(self, data): prefix = f"{self.frame_idx:06d}" if not self.transforms or 'lidar_to_parent' not in self.transforms: return lidar_matrix = self.transforms['lidar_to_parent'] cam_matrix = self.transforms['camera_to_parent'] np.save(f"{self.output_dir}/extrinsics/{prefix}_lidar_to_base.npy", lidar_matrix) np.save(f"{self.output_dir}/extrinsics/{prefix}_cam_to_base.npy", cam_matrix) rgb_img = np.frombuffer(data['/camera_front/rgb'].data, dtype=np.uint8).reshape((480, 640, 3)) cv2.imwrite(f"{self.output_dir}/rgb/{prefix}.jpg", cv2.cvtColor(rgb_img, cv2.COLOR_RGB2BGR)) depth_img = np.frombuffer(data['/camera_front/depth'].data, dtype=np.float32).reshape((480, 640)) np.save(f"{self.output_dir}/depth/{prefix}.npy", depth_img) points = pc2.read_points_numpy(data['/lidar_front/point_cloud'], field_names=("x", "y", "z")) points_hom = np.hstack((points, np.ones((points.shape[0], 1)))) points_transformed = (lidar_matrix @ points_hom.T).T[:, :3] np.save(f"{self.output_dir}/lidar/{prefix}.npy", points_transformed) processed_label = self.process_bev_logic(data['/bev/semantic_segmentation'], data['/bev/bbox_2d']) cv2.imwrite(f"{self.output_dir}/bev_label/{prefix}.png", processed_label) if self.frame_idx % 50 == 0: print(f" Saved {self.frame_idx} samples...") self.frame_idx += 1 if __name__ == "__main__": BAGS_DIR = "/home/11225069610/Desktop/BEV_EDL/data/bags" OUTPUT_DIR = "/home/11225069610/Desktop/bag_extract/" LIDAR_TRANSFORM_PATH = "/home/11225069610/Desktop/BEV_EDL/data/lidar.yaml" CAMERA_TRANSFORM_PATH = "/home/11225069610/Desktop/BEV_EDL/data/camera.yaml" # Find all bags in the directory bag_paths = sorted(glob.glob(os.path.join(BAGS_DIR, "*"))) if not bag_paths: print(f"No bags found in {BAGS_DIR}") bag_paths = ["/home/11225069610/Desktop/rosbag2_2026_03_27-16_05_37"] current_frame_idx = 0 for bag_path in bag_paths: extractor = BagDatasetExtractor( bag_path=bag_path, output_dir=OUTPUT_DIR, lidar_transform_path=LIDAR_TRANSFORM_PATH, camera_transform_path=CAMERA_TRANSFORM_PATH, start_frame_idx=current_frame_idx ) current_frame_idx = extractor.run() print(f"Finished extracting! Total frames processed: {current_frame_idx}")