Source code for morse.sensors.semantic_camera

import logging; logger = logging.getLogger("morse." + __name__)
from morse.core import blenderapi


from morse.helpers import passive_objects
from morse.helpers.components import add_data, add_property
from morse.helpers.transformation import Transformation3d

[docs]class SemanticCamera( """ This sensor emulates a high level *abstract* camera that outputs the name and 6D pose of visible objects (*i.e.* objects in the field of view of the camera). It also outputs the *type* of the object if the ``Type`` property is set (:python:`"Bottle")` for instance). General usage ------------- You need to *tag* the objects you want your camera to track by either adding a boolean property ``Object`` to your object: :python:``, or by setting a *type* and using this type as the value of the ``tag`` property of the camera: .. code-block:: python object_to_track = PassiveObject(...)"Bottle") ... semcam = SemanticCamera()"Bottle") ... See the *Examples* section below for a complete working example. If the ``Label`` property is defined, it is used as exported name. Otherwise, the Blender object name is used. By default, the pose of the objects is provided in the **world** frame. When setting the ``relative`` property to ``True`` (:python:``), the pose is computed in the **camera** frame instead. Details of implementation ------------------------- A test is made to identify which of these objects are inside of the view frustum of the camera. Finally, a single visibility test is performed by casting a ray from the center of the camera to the center of the object. If anything other than the test object is found first by the ray, the object is considered to be occluded by something else, even if it is only the center that is being blocked. This occulsion check can be deactivated (for slightly improved performances) by setting the sensor property ``noocclusion`` to ``True``. See also :doc:`../sensors/camera` for generic informations about MORSE cameras. .. example:: from morse.builder import * # add a 'passive' object visible to the semantic cameras table = PassiveObject('props/objects','SmallTable') table.translate(x=3.5, y=-3, z=0) table.rotate(z=0.2) # by setting the 'Object' property to true, this object becomes # visible to the semantic cameras present in the simulation. # Note that you can set this property on any object (other robots, humans,...). = "table", Label = "MY_FAVORITE_TABLE") # then, create a robot robot = Morsy() # creates a new instance of the sensor, that tracks all tables. # If you do not specify a particular 'tag', the camera tracks by default # all object with the properties 'type="Object"' or 'Object=True'. semcam = SemanticCamera() = "table") # place the camera at the correct location semcam.translate(<x>, <y>, <z>) semcam.rotate(<rx>, <ry>, <rz>) robot.append(semcam) # define one or several communication interface, like 'socket' semcam.add_interface(<interface>) env = Environment('empty') :noautoexample: """ _name = "Semantic camera" _short_desc = "A smart camera allowing to retrieve objects in its \ field of view" add_data('visible_objects', [], 'list<objects>', "A list containing the different objects visible by the camera. \ Each object is represented by a dictionary composed of: \n\ - **name** (String): the name of the object \n\ - **type** (String): the type of the object \n\ - **position** (vec3<float>): the position of the \ object, in meter, in the blender frame \n\ - **orientation** (quaternion): the orientation of the \ object, in the blender frame") add_property('relative', False, 'relative', 'bool', 'Return object position' ' relatively to the sensor frame.') add_property('noocclusion', False, 'noocclusion', 'bool', 'Do not check for' ' objects possibly hiding each others (faster but less ' 'realistic behaviour)') add_property('tag', 'Object', 'tag', "string", "The type of " "detected objects. This type is looked for as a game property of scene " "objects or as their 'Type' property. You must then add fix this " "property to the objects you want to be detected by the semantic " "camera.") def __init__(self, obj, parent=None): """ Constructor method. Receives the reference to the Blender object. The second parameter should be the name of the object's parent. """'%s initialization' % # Call the constructor of the parent class, obj, parent) # Locate the Blender camera object associated with this sensor main_obj = self.bge_object for obj in main_obj.children: if hasattr(obj, 'lens'): self.blender_cam = obj"Camera object: {0}".format(self.blender_cam)) break if not self.blender_cam: logger.error("no camera object associated to the semantic camera. \ The semantic camera requires a standard Blender \ camera in its children.") # TrackedObject is a dictionary containing the list of tracked objects # (->meshes with a class property set up) as keys # and the bounding boxes of these objects as value. self.trackedObjects = {} for o in blenderapi.scene().objects: tagged = ('Type' in o and o['Type'] == self.tag) or (self.tag in o and bool(o[self.tag])) if tagged: self.trackedObjects[o] = blenderapi.objectdata( logger.warning(' - %s' % if self.noocclusion:"Semantic camera running in 'no occlusion' mode (fast mode).")"Component initialized, runs at %.2f Hz ", self.frequency)
[docs] def default_action(self): """ Do the actual semantic 'grab'. Iterate over all the tracked objects, and check if they are visible for the robot. Visible objects must have a bounding box and be active for physical simulation (have the 'Actor' checkbox selected) """ # Call the action of the parent class # Create dictionaries self.local_data['visible_objects'] = [] for obj, bb in self.trackedObjects.items(): if self._check_visible(obj, bb): # Create dictionary to contain object name, type, # description, position and orientation if self.relative: t3d = Transformation3d(obj) logger.debug("t3d(obj) = {t}".format(t=t3d)) logger.debug("t3d(cam) = {t}".format(t=self.position_3d)) transformation = self.position_3d.transformation3d_with(t3d) logger.debug("transform = {t}".format(t=transformation)) else: transformation = Transformation3d(obj) obj_dict = {'name': obj.get('Label',, 'description': obj.get('Description', ''), 'type': obj.get('Type', ''), 'position': transformation.translation, 'orientation': transformation.rotation} self.local_data['visible_objects'].append(obj_dict) logger.debug("Visible objects: %s" % self.local_data['visible_objects'])
def _check_visible(self, obj, bb): """ Check if an object lies inside of the camera frustum. The behaviour of this method is impacted by the sensor's property 'noocclusion': if true, only checks the object is in the frustum. Does not check it is actually visible (ie, not hidden away by another object). """ # TrackedObjects was filled at initialization # with the object's bounding boxes pos = obj.position bbox = [[bb_corner[i] + pos[i] for i in range(3)] for bb_corner in bb] if logger.isEnabledFor(logging.DEBUG): logger.debug("\n--- NEW TEST ---") logger.debug("OBJECT '{0}' AT {1}".format(obj, pos)) logger.debug("CAMERA '{0}' AT {1}".format( self.blender_cam, self.blender_cam.position)) logger.debug("BBOX: >{0}<".format(bbox)) logger.debug("BBOX: {0}".format(bb)) # Translate the bounding box to the current object position # and check if it is in the frustum if self.blender_cam.boxInsideFrustum(bbox) != self.blender_cam.OUTSIDE: if not self.noocclusion: # Check that there are no other objects between the camera # and the selected object # NOTE: This is a very simple test. Hiding only the 'center' # of an object will make it invisible, even if the rest is # still seen from the camera closest_obj = self.bge_object.rayCastTo(obj) if closest_obj in [obj] + list(obj.children): return True else: return True return False