diff --git a/Go2Py/assets/mujoco/go2.xml b/Go2Py/assets/mujoco/go2.xml
index c4db8fa..43fd6d7 100644
--- a/Go2Py/assets/mujoco/go2.xml
+++ b/Go2Py/assets/mujoco/go2.xml
@@ -69,6 +69,7 @@
+
diff --git a/Go2Py/sim/mujoco.py b/Go2Py/sim/mujoco.py
index c5b34fc..04c9f55 100644
--- a/Go2Py/sim/mujoco.py
+++ b/Go2Py/sim/mujoco.py
@@ -17,9 +17,232 @@ nray = vec.shape[0]
geomid = np.zeros(nray, np.int32)
dist = np.zeros(nray, np.float64)
+# Credit to: https://github.com/google-deepmind/mujoco/issues/1672
+class Camera:
+ def __init__(self, resolution, model, data, cam_name: str = "", min_depth=0.35, max_depth=3.):
+ """Initialize Camera instance.
+
+ Args:
+ - args: Arguments containing camera width and height.
+ - model: Mujoco model.
+ - data: Mujoco data.
+ - cam_name: Name of the camera.
+ - save_dir: Directory to save captured images.
+ """
+ self._min_depth = min_depth
+ self._max_depth = max_depth
+ self._cam_name = cam_name
+ self._model = model
+ self._data = data
+ self._width = resolution[0]
+ self._height = resolution[1]
+ self._cam_id = self._data.cam(self._cam_name).id
+
+ self._renderer = mujoco.Renderer(self._model, self._height, self._width)
+ self._camera = mujoco.MjvCamera()
+ self._scene = mujoco.MjvScene(self._model, maxgeom=10_000)
+
+ self._image = np.zeros((self._height, self._width, 3), dtype=np.uint8)
+ self._depth_image = np.zeros((self._height, self._width, 1), dtype=np.float32)
+ self._seg_id_image = np.zeros((self._height, self._width, 3), dtype=np.float32)
+ self._point_cloud = np.zeros((self._height, self._width, 1), dtype=np.float32)
+
+ @property
+ def height(self) -> int:
+ """
+ Get the height of the camera.
+
+ Returns:
+ int: The height of the camera.
+ """
+ return self._height
+
+ @property
+ def width(self) -> int:
+ """
+ Get the width of the camera.
+
+ Returns:
+ int: The width of the camera.
+ """
+ return self._width
+
+ @property
+ def name(self) -> str:
+ """
+ Get the name of the camera.
+
+ Returns:
+ str: The name of the camera.s
+ """
+ return self._cam_name
+
+ @property
+ def K(self) -> np.ndarray:
+ """
+ Compute the intrinsic camera matrix (K) based on the camera's field of view (fov),
+ width (_width), and height (_height) parameters, following the pinhole camera model.
+
+ Returns:
+ np.ndarray: The intrinsic camera matrix (K), a 3x3 array representing the camera's intrinsic parameters.
+ """
+ # Convert the field of view from degrees to radians
+ theta = np.deg2rad(self.fov)
+
+ # Focal length calculation (f in terms of sensor width and height)
+ f_x = (self._width / 2) / np.tan(theta / 2)
+ f_y = (self._height / 2) / np.tan(theta / 2)
+
+ # Pixel resolution (assumed to be focal length per pixel unit)
+ alpha_u = f_x # focal length in terms of pixel width
+ alpha_v = f_y # focal length in terms of pixel height
+
+ # Optical center offsets (assuming they are at the center of the sensor)
+ u_0 = (self._width - 1) / 2.0
+ v_0 = (self._height - 1) / 2.0
+
+ # Intrinsic camera matrix K
+ K = np.array([[alpha_u, 0, u_0], [0, alpha_v, v_0], [0, 0, 1]])
+
+ return K
+
+ @property
+ def T_world_cam(self) -> np.ndarray:
+ """
+ Compute the homogeneous transformation matrix for the camera.
+
+ The transformation matrix is computed from the camera's position and orientation.
+ The position and orientation are retrieved from the camera data.
+
+ Returns:
+ np.ndarray: The 4x4 homogeneous transformation matrix representing the camera's pose.
+ """
+ pos = self._data.cam(self._cam_id).xpos
+ rot = self._data.cam(self._cam_id).xmat.reshape(3, 3).T
+ T = np.hstack([rot, pos.reshape(3,1)])
+ T = np.vstack([T, np.array([0., 0., 0., 1.])])
+ return T
+
+ @property
+ def P(self) -> np.ndarray:
+ """
+ Compute the projection matrix for the camera.
+
+ The projection matrix is computed as the product of the camera's intrinsic matrix (K)
+ and the homogeneous transformation matrix (T_world_cam).
+
+ Returns:
+ np.ndarray: The 3x4 projection matrix.
+ """
+ return self.K @ self.T_world_cam
+
+ @property
+ def image(self) -> np.ndarray:
+ """Return the captured RGB image."""
+ self._renderer.update_scene(self._data, camera=self.name)
+ self._image = self._renderer.render()
+ return self._image
+
+ @property
+ def depth_image(self) -> np.ndarray:
+ """Return the captured depth image."""
+ self._renderer.update_scene(self._data, camera=self.name)
+ self._renderer.enable_depth_rendering()
+ self._depth_image = self._renderer.render()
+ self._renderer.disable_depth_rendering()
+ return np.clip(self._depth_image, self._min_depth, self._max_depth)
+
+ @property
+ def seg_image(self) -> np.ndarray:
+ """Return the captured segmentation image based on object's id."""
+ self._renderer.update_scene(self._data, camera=self.name)
+ self._renderer.enable_segmentation_rendering()
+
+ self._seg_id_image = self._renderer.render()[:, :, 0].reshape(
+ (self.height, self.width)
+ )
+ self._renderer.disable_segmentation_rendering()
+ return self._seg_id_image
+
+ @property
+ def point_cloud(self) -> np.ndarray:
+ """Return the captured point cloud."""
+ self._point_cloud = self._depth_to_point_cloud(self.depth_image)
+ return self._point_cloud
+
+ @property
+ def fov(self) -> float:
+ """Get the field of view (FOV) of the camera.
+
+ Returns:
+ - float: The field of view angle in degrees.
+ """
+ return self._model.cam(self._cam_id).fovy[0]
+
+ @property
+ def id(self) -> int:
+ """Get the identifier of the camera.
+
+ Returns:
+ - int: The identifier of the camera.
+ """
+ return self._cam_id
+
+ def _depth_to_point_cloud(self, depth_image: np.ndarray) -> np.ndarray:
+ """
+ Method to convert depth image to a point cloud in camera coordinates.
+
+ Args:
+ - depth_image: The depth image we want to convert to a point cloud.
+
+ Returns:
+ - np.ndarray: 3D points in camera coordinates.
+ """
+ # Get image dimensions
+ dimg_shape = depth_image.shape
+ height = dimg_shape[0]
+ width = dimg_shape[1]
+
+ # Create pixel grid
+ y, x = np.meshgrid(np.arange(height), np.arange(width), indexing="ij")
+
+ # Flatten arrays for vectorized computation
+ x_flat = x.flatten()
+ y_flat = y.flatten()
+ depth_flat = depth_image.flatten()
+
+ # Negate depth values because z-axis goes into the camera
+ depth_flat = -depth_flat
+
+ # Stack flattened arrays to form homogeneous coordinates
+ homogeneous_coords = np.vstack((x_flat, y_flat, np.ones_like(x_flat)))
+
+ # Compute inverse of the intrinsic matrix K
+ K_inv = np.linalg.inv(self.K)
+
+ # Calculate 3D points in camera coordinates
+ points_camera = np.dot(K_inv, homogeneous_coords) * depth_flat
+
+ # Homogeneous coordinates to 3D points
+ points_camera = np.vstack((points_camera, np.ones_like(x_flat)))
+
+ points_camera = points_camera.T
+
+ # dehomogenize
+ points_camera = points_camera[:, :3] / points_camera[:, 3][:, np.newaxis]
+
+ return points_camera
class Go2Sim:
- def __init__(self, mode='lowlevel', render=True, dt=0.002, height_map = None, xml_path=None):
+ def __init__(self,
+ mode='lowlevel',
+ render=True,
+ dt=0.002,
+ height_map = None,
+ xml_path=None,
+ camera_name = "front_camera",
+ camera_resolution = (640, 480),
+ camera_depth_range = (0.35, 3.0)):
if xml_path is None:
self.model = mujoco.MjModel.from_xml_path(
@@ -109,6 +332,10 @@ class Go2Sim:
self.e_omega_sum=0
else:
self.step = self.stepLowlevel
+ self.camera_name = camera_name
+ self.camera_resolution = camera_resolution
+ self.camera_depth_range = camera_depth_range
+ self.camera = Camera(self.camera_resolution, self.model, self.data, self.camera_name, min_depth=self.camera_depth_range[0] ,max_depth=self.camera_depth_range[1])
def updateHeightMap(self, height_map, hfield_size = (300,300), raw_deoth_to_height_ratio = 255.):
try:
@@ -254,6 +481,26 @@ class Go2Sim:
idx = np.where(np.logical_and(dist!=-1, dist"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "