new lint fix (#26)

* new lint fix

* Fix code style issues with clang_format

* Fix code style issues with clang_format

---------

Co-authored-by: WATonomousAdmin <admin@watonomous.ca>

Author: hassaan141

.github/workflows/linting_auto.yml

@@ -26,7 +26,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v1
         with:
-          python-version: '3.10.18'
+          python-version: '3.10.19'
 
       - name: Install Python dependencies
         run: pip install autopep8 clang-format

autonomy/behaviour/octo_map/launch/octo_map.launch.py

@@ -10,4 +10,4 @@ def generate_launch_description():
             executable='octo_map_node',
             output='screen',
             emulate_tty=True),
-    ])
+    ])

autonomy/behaviour/octo_map/octo_map/octo_map_node.py

@@ -10,10 +10,11 @@
 from std_msgs.msg import Header
 import sensor_msgs_py.point_cloud2 as pc2
 
+
 class OctoMapNode(Node):
     def __init__(self):
         super().__init__('octo_map_node')
-        
+
         # Subscribe to processed perception data
         self.rgb_sub = self.create_subscription(
             Image, '/perception/rgb_processed', self.rgb_callback, 10)
@@ -22,18 +23,19 @@ def __init__(self):
         self.info_sub = self.create_subscription(
             CameraInfo, '/perception/camera_info', self.info_callback, 10)
 
-        self.octo_pub = self.create_publisher(PointCloud2, '/behaviour/octo_map', 10)
-        
+        self.octo_pub = self.create_publisher(
+            PointCloud2, '/behaviour/octo_map', 10)
+
         self.rgb_image = None
         self.depth_image = None
         self.bridge = CvBridge()
-        
+
         # Camera intrinsics (will be updated from camera info)
         self.fx, self.fy, self.cx, self.cy = 525.0, 525.0, 320.0, 240.0
-        
-        self.voxel_size = 0.04  
-        self.max_range = 3.5    
-        
+
+        self.voxel_size = 0.04
+        self.max_range = 3.5
+
         self.get_logger().info('OctoMap Node started - subscribing to processed perception data')
 
     def info_callback(self, msg):
@@ -42,7 +44,8 @@ def info_callback(self, msg):
         self.fy = msg.k[4]
         self.cx = msg.k[2]
         self.cy = msg.k[5]
-        self.get_logger().info(f'Updated camera intrinsics: fx={self.fx}, fy={self.fy}')
+        self.get_logger().info(
+            f'Updated camera intrinsics: fx={self.fx}, fy={self.fy}')
 
     def rgb_callback(self, msg):
         """Receive processed RGB image from perception node."""
@@ -58,11 +61,11 @@ def depth_callback(self, msg):
 
     # TODO: Implement point cloud generation and octomap updating
 
-    
+
 def main(args=None):
     rclpy.init(args=args)
     node = OctoMapNode()
-    
+
     try:
         rclpy.spin(node)
     except KeyboardInterrupt:
@@ -71,5 +74,6 @@ def main(args=None):
         node.destroy_node()
         rclpy.shutdown()
 
+
 if __name__ == '__main__':
-    main()
+    main()

autonomy/behaviour/octo_map/setup.py

@@ -10,7 +10,8 @@
     packages=[package_name],
     data_files=[
         # Install marker file in the package index
-        ('share/ament_index/resource_index/packages', ['resource/' + package_name]),
+        ('share/ament_index/resource_index/packages',
+         ['resource/' + package_name]),
         # Include our package.xml file
         (os.path.join('share', package_name), ['package.xml']),
         # Include all launch files.
@@ -32,4 +33,4 @@
             'octo_map_node = octo_map.octo_map_node:main'
         ],
     },
-)
+)

autonomy/behaviour/voxel_grid/launch/voxel_grid.launch.py

@@ -10,4 +10,4 @@ def generate_launch_description():
             executable='voxel_grid_node',
             output='screen',
             emulate_tty=True),
-    ])
+    ])

autonomy/behaviour/voxel_grid/setup.py

@@ -10,7 +10,8 @@
     packages=[package_name],
     data_files=[
         # Install marker file in the package index
-        ('share/ament_index/resource_index/packages', ['resource/' + package_name]),
+        ('share/ament_index/resource_index/packages',
+         ['resource/' + package_name]),
         # Include our package.xml file
         (os.path.join('share', package_name), ['package.xml']),
         # Include all launch files.
@@ -32,4 +33,4 @@
             'voxel_grid_node = voxel_grid.voxel_grid_node:main'
         ],
     },
-)
+)

autonomy/behaviour/voxel_grid/voxel_grid/voxel_grid_node.py

@@ -23,14 +23,20 @@ def __init__(self):
         super().__init__('voxel_grid_node')
 
         self.rgb_sub = self.create_subscription(
-            Image, '/camera/color/image_raw', self.rgb_callback, qos_profile_sensor_data
-        )
+            Image,
+            '/camera/color/image_raw',
+            self.rgb_callback,
+            qos_profile_sensor_data)
         self.depth_sub = self.create_subscription(
-            Image, '/camera/depth/image_raw', self.depth_callback, qos_profile_sensor_data
-        )
+            Image,
+            '/camera/depth/image_raw',
+            self.depth_callback,
+            qos_profile_sensor_data)
         self.info_sub = self.create_subscription(
-            CameraInfo, '/camera/depth/camera_info', self.info_callback, qos_profile_sensor_data
-        )
+            CameraInfo,
+            '/camera/depth/camera_info',
+            self.info_callback,
+            qos_profile_sensor_data)
 
         self.voxel_pub = self.create_publisher(
             PointCloud2, '/behaviour/voxel_grid', qos_profile_sensor_data
@@ -42,10 +48,10 @@ def __init__(self):
         self.fx, self.fy, self.cx, self.cy = 525.0, 525.0, 320.0, 240.0
         self.have_intrinsics = False
 
-        self.voxel_size = 0.04   
-        self.max_range = 3.5    
-        self.min_range = 0.1  
-        self.stride = 4      
+        self.voxel_size = 0.04
+        self.max_range = 3.5
+        self.min_range = 0.1
+        self.stride = 4
 
         self.get_logger().info('Voxel Grid Node started (world modelling, headless)')
 
@@ -67,18 +73,34 @@ def _decode_depth(msg: Image) -> np.ndarray:
         enc = (msg.encoding or '').lower()
 
         if enc in ('mono16', '16uc1'):
-            d = np.frombuffer(msg.data, dtype=np.uint16).reshape(msg.height, msg.width)
+            d = np.frombuffer(
+                msg.data,
+                dtype=np.uint16).reshape(
+                msg.height,
+                msg.width)
             return (d.astype(np.float32) / 1000.0)  # mm -> m
 
         if enc == '32fc1':
-            d = np.frombuffer(msg.data, dtype=np.float32).reshape(msg.height, msg.width)
+            d = np.frombuffer(
+                msg.data,
+                dtype=np.float32).reshape(
+                msg.height,
+                msg.width)
             return d
 
         if enc == '16fc1':
-            d = np.frombuffer(msg.data, dtype=np.float16).reshape(msg.height, msg.width)
+            d = np.frombuffer(
+                msg.data,
+                dtype=np.float16).reshape(
+                msg.height,
+                msg.width)
             return d.astype(np.float32)
 
-        d = np.frombuffer(msg.data, dtype=np.uint16).reshape(msg.height, msg.width)
+        d = np.frombuffer(
+            msg.data,
+            dtype=np.uint16).reshape(
+            msg.height,
+            msg.width)
         return (d.astype(np.float32) / 1000.0)
 
     # ---------- callbacks ----------
@@ -89,7 +111,8 @@ def info_callback(self, msg: CameraInfo):
         self.cy = float(msg.k[5])
         self.have_intrinsics = True
 
-        self.get_logger().info(f'Intrinsics updated: fx={self.fx:.2f}, fy={self.fy:.2f}, cx={self.cx:.2f}, cy={self.cy:.2f}')
+        self.get_logger().info(
+            f'Intrinsics updated: fx={self.fx:.2f}, fy={self.fy:.2f}, cx={self.cx:.2f}, cy={self.cy:.2f}')
 
     def rgb_callback(self, msg: Image):
         self.rgb_image = self._decode_rgb_bgr8(msg)
@@ -117,7 +140,7 @@ def process_rgbd_if_ready(self):
 
         self.publish_voxel_grid(voxel_centers)
 
-        #reset
+        # reset
         self.depth_image = None
         self.rgb_image = None
 
@@ -165,14 +188,15 @@ def create_voxel_grid(self, points: np.ndarray) -> np.ndarray:
 
         voxel_gen = PointToVoxel(
             vsize_xyz=[self.voxel_size, self.voxel_size, self.voxel_size],
-            coors_range_xyz=coors_range_m.tolist(), #Convert numpy to python
+            coors_range_xyz=coors_range_m.tolist(),  # Convert numpy to python
             num_point_features=3,
             max_num_voxels=10000,
             max_num_points_per_voxel=20
         )
 
         pc_tensor = torch.from_numpy(points)  # float32
-        voxels, indices, num_points = voxel_gen(pc_tensor)  # indices: [M, 3] (z,y,x) or (x,y,z) depending on impl
+        # indices: [M, 3] (z,y,x) or (x,y,z) depending on impl
+        voxels, indices, num_points = voxel_gen(pc_tensor)
 
         if indices.numel() == 0:
             return np.zeros((0, 3), dtype=np.float32)
@@ -186,13 +210,14 @@ def create_voxel_grid(self, points: np.ndarray) -> np.ndarray:
         origin = coors_range_m[:3]  # meters
         centers = origin + (idx_xyz + 0.5) * self.voxel_size  # meters
 
-        self.get_logger().info(f'Voxels: {centers.shape[0]} from points: {points.shape[0]}')
+        self.get_logger().info(
+            f'Voxels: {centers.shape[0]} from points: {points.shape[0]}')
         return centers.astype(np.float32)
 
     def publish_voxel_grid(self, voxel_centers: np.ndarray):
         header = Header()
         header.stamp = self.get_clock().now().to_msg()
-        header.frame_id = 'camera_link'  
+        header.frame_id = 'camera_link'
 
         fields = [
             PointField(name='x', offset=0, datatype=PointField.FLOAT32, count=1),

autonomy/perception/launch/perception.launch.py

@@ -11,7 +11,7 @@ def generate_launch_description():
             default_value='info',
             description='Log level for the dummy publisher node'
         ),
-        
+
         Node(
             package='perception',
             executable='dummy_publisher_node',
@@ -24,4 +24,4 @@ def generate_launch_description():
                 # Add any topic remappings here if needed
             ]
         )
-    ])
+    ])

autonomy/perception/perception/dummy_publisher_node.py

@@ -46,27 +46,49 @@ def publish_dummy_data(self):
 
         # Create a simple scene with geometric shapes at different depths
         rgb_image = np.zeros((480, 640, 3), dtype=np.uint8)
-        depth_image = np.full((480, 640), 3000, dtype=np.uint16)  # Background at 3000mm
+        depth_image = np.full(
+            (480, 640), 3000, dtype=np.uint16)  # Background at 3000mm
 
         # Add a large rectangle (closer object) - like a wall or box
-        cv2.rectangle(rgb_image, (150, 120), (490, 360), (100, 150, 200), -1)  # Filled rectangle
-        cv2.rectangle(depth_image, (150, 120), (490, 360), 1200, -1)  # 1200mm depth
+        cv2.rectangle(rgb_image, (150, 120), (490, 360),
+                      (100, 150, 200), -1)  # Filled rectangle
+        cv2.rectangle(depth_image, (150, 120), (490, 360),
+                      1200, -1)  # 1200mm depth
 
         # Add a smaller circle (closest object) - like a ball
-        cv2.circle(rgb_image, (320, 240), 80, (50, 200, 50), -1)  # Green circle
-        cv2.circle(depth_image, (320, 240), 80, 800, -1)  # 800mm depth (closest)
+        cv2.circle(rgb_image, (320, 240), 80,
+                   (50, 200, 50), -1)  # Green circle
+        # 800mm depth (closest)
+        cv2.circle(depth_image, (320, 240), 80, 800, -1)
 
         # Add another rectangle (medium distance)
-        cv2.rectangle(rgb_image, (50, 300), (200, 450), (200, 100, 100), -1)  # Reddish rectangle
-        cv2.rectangle(depth_image, (50, 300), (200, 450), 1800, -1)  # 1800mm depth
+        cv2.rectangle(rgb_image, (50, 300), (200, 450),
+                      (200, 100, 100), -1)  # Reddish rectangle
+        cv2.rectangle(depth_image, (50, 300), (200, 450),
+                      1800, -1)  # 1800mm depth
 
         # Optional: Add some gradient/texture to make it more realistic
         noise = np.random.randint(-20, 20, rgb_image.shape, dtype=np.int16)
-        rgb_image = np.clip(rgb_image.astype(np.int16) + noise, 0, 255).astype(np.uint8)
+        rgb_image = np.clip(
+            rgb_image.astype(
+                np.int16) +
+            noise,
+            0,
+            255).astype(
+            np.uint8)
 
         # Add slight depth variation (noise)
-        depth_noise = np.random.randint(-30, 30, depth_image.shape, dtype=np.int16)
-        depth_image = np.clip(depth_image.astype(np.int32) + depth_noise, 0, 65535).astype(np.uint16)
+        depth_noise = np.random.randint(-30,
+                                        30,
+                                        depth_image.shape,
+                                        dtype=np.int16)
+        depth_image = np.clip(
+            depth_image.astype(
+                np.int32) +
+            depth_noise,
+            0,
+            65535).astype(
+            np.uint16)
 
         rgb_msg = self.bridge.cv2_to_imgmsg(
             rgb_image, encoding='bgr8'

autonomy/perception/setup.py

@@ -10,7 +10,8 @@
     packages=[package_name],
     data_files=[
         # Install marker file in the package index
-        ('share/ament_index/resource_index/packages', ['resource/' + package_name]),
+        ('share/ament_index/resource_index/packages',
+         ['resource/' + package_name]),
         # Include our package.xml file
         (os.path.join('share', package_name), ['package.xml']),
         # Include all launch files.
@@ -32,4 +33,4 @@
             'dummy_publisher_node = perception.dummy_publisher_node:main'
         ],
     },
-)
+)