Create artificial information for laptop imaginative and prescient pipelines on AWS

mkdir nugget_images
ffmpeg -i VIDEO.mov ffmpeg nugget_images/nugget_percent06d.jpg

class Object3D:
	'''
	object container to retailer mesh details about the
	given object

	Returns
	the Object3D object
	'''
	def __init__(self, object: Union[bpy.types.Object, str]):
		"""Creates a Object3D object.

		Args:
		obj (Union[bpy.types.Object, str]): Scene object (or it is title)
		"""
		self.object = object
		self.obj_poly = None
		self.mat = None
		self.vert = None
		self.poly = None
		self.bvht = None
		self.calc_mat()
		self.calc_world_vert()
		self.calc_poly()
		self.calc_bvht()

	def calc_mat(self) -> None:
		"""retailer an occasion of the article's matrix_world"""
		self.mat = self.object.matrix_world

	def calc_world_vert(self) -> None:
		"""calculate the verticies from object's matrix_world perspective"""
		self.vert = [self.mat @ v.co for v in self.object.data.vertices]
		self.obj_poly = np.array(self.vert)

	def calc_poly(self) -> None:
		"""retailer an occasion of the article's polygons"""
		self.poly = [p.vertices for p in self.object.data.polygons]

	def calc_bvht(self) -> None:
		"""create a BVHTree from the article's polygon"""
		self.bvht = BVHTree.FromPolygons( self.vert, self.poly )

	def regenerate(self) -> None:
		"""reinstantiate the article's variables;
		used when the article is manipulated after it is creation"""
		self.calc_mat()
		self.calc_world_vert()
		self.calc_poly()
		self.calc_bvht()

	def __repr__(self):
		return "Object3D: " + self.object.__repr__()

class DensityController:
    """Container that controlls the spacial relationship between 3D objects

    Returns:
        DensityController: The DensityController object.
    """
    def __init__(self):
        self.bvhtrees = None
        self.overlaps = None
        self.occupied = None
        self.unoccupied = None
        self.objects3d = []

    def auto_generate_kdtree_cubes(
        self,
        num_objects: int = 100, # max dimension of nuggets
    ) -> None:
        """
        operate to generate bodily kdtree cubes given a aircraft of -resize- dimension
        this enables us to entry every dice's overlap/occupancy standing at any given
        time
        
        creates a KDTree assortment, a dice, a set of particular person cubes, and the 
        BVHTree object for every particular person dice

        Args:
            resize (Tuple[float]): the dimensions of a dice to create XYZ.
            cuts (int): what number of cuts are made to the dice face
                12 cuts == 13 Rows x 13 Columns  
        """

        # learn the nugget,
        # see how massive the dice must be to embody a single nugget
        # then contact a parameter to permit it to be smaller or bigger (eg extra touching)
        bpy.context.view_layer.objects.energetic = bpy.context.scene.objects.get('nugget_base')
        bpy.ops.object.origin_set(sort="ORIGIN_GEOMETRY", heart="BOUNDS")
        #create a dice for the bounding field
        bpy.ops.mesh.primitive_cube_add(location=Vector((0,0,0))) 
        #our new dice is now the energetic object, so we are able to maintain monitor of it in a variable:
        bound_box = bpy.context.active_object
        bound_box.title="CUBE1"
        bpy.context.view_layer.replace()
        #copy transforms
        nug_dims = bpy.information.objects["nugget_base"].dimensions
        bpy.information.objects["CUBE1"].dimensions = nug_dims
        bpy.context.view_layer.replace()
        bpy.information.objects["CUBE1"].location = bpy.information.objects["nugget_base"].location
        bpy.context.view_layer.replace()
        bpy.information.objects["CUBE1"].rotation_euler = bpy.information.objects["nugget_base"].rotation_euler
        bpy.context.view_layer.replace()
        print("bound_box.dimensions: ", bound_box.dimensions)
        print("bound_box.location:", bound_box.location)

        # this dice created is not at all times sq., however we will make it sq.
        # to suit into our 
        x, y, z = bound_box.dimensions
        v = max(x, y)
        if np.spherical(v) < v:
            v = np.spherical(v)+1
        bb_x, bb_y = v, v
        bound_box.dimensions = Vector((v, v, z))
        bpy.context.view_layer.replace()
        print("bound_box.dimensions up to date: ", bound_box.dimensions)
        # now we generate a aircraft
        # calc the dimensions of the aircraft given a max variety of bins.

        x, y, z = bound_box.dimensions
        bb_loc = bound_box.location
        bb_rot_eu = bound_box.rotation_euler
        min_area = (x*y)*num_objects
        min_length = min_area / num_objects
        print(min_length)
        # now we generate a aircraft
        # calc the dimensions of the aircraft given a max variety of bins.
        bpy.ops.mesh.primitive_plane_add(location=Vector((0,0,0)), dimension = min_length)
        aircraft = bpy.context.selected_objects[0]
        aircraft.title="PLANE"
        # transfer our aircraft to our background assortment
        # current_collection = aircraft.users_collection
        link_object('PLANE', 'BACKGROUND')
        bpy.context.view_layer.replace()

        # New Assortment
        my_coll = bpy.information.collections.new("KDTREE")
        # Add assortment to scene assortment
        bpy.context.scene.assortment.kids.hyperlink(my_coll)
        # now we generate cubes primarily based on the dimensions of the aircraft.
        bpy.ops.mesh.primitive_cube_add(location=Vector((0,0,0)), dimension = min_length)
        bpy.context.view_layer.replace()
        dice = bpy.context.selected_objects[0]
        cube_dimensions = dice.dimensions
        bpy.context.view_layer.replace()
        dice.dimensions = Vector((cube_dimensions[0], cube_dimensions[1], z))
        bpy.context.view_layer.replace()
        dice.location = bb_loc
        bpy.context.view_layer.replace()
        dice.rotation_euler = bb_rot_eu
        bpy.context.view_layer.replace()
        dice.title="dice"
        bpy.context.view_layer.replace()
        current_collection = dice.users_collection
        link_object('dice', 'KDTREE')
        bpy.context.view_layer.replace()

        # get the bb quantity and make the right cuts to the article 
        bb_vol = x*y*z
        cube_vol = cube_dimensions[0]*cube_dimensions[1]*cube_dimensions[2]
        n_cubes = cube_vol / bb_vol
        cuts = n_cubes / ((x+y) / 2)
        cuts = int(np.spherical(cuts)) - 1 # 
        # choose the dice
        for object in bpy.information.objects:
            object.select_set(False)
        bpy.context.view_layer.replace()
        for object in bpy.information.objects:
            object.select_set(False)
        bpy.information.objects['cube'].select_set(True) # Blender 2.8x
        bpy.context.view_layer.objects.energetic = bpy.context.scene.objects.get('dice')
        # set to edit mode
        bpy.ops.object.mode_set(mode="EDIT", toggle=False)
        print('edit mode success')
        # get face_data
        context = bpy.context
        obj = context.edit_object
        me = obj.information
        mat = obj.matrix_world
        bm = bmesh.from_edit_mesh(me)
        up_face = None
        # choose upwards dealing with cube-face
        # https://blender.stackexchange.com/questions/43067/get-a-face-selected-pointing-upwards
        for face in bm.faces:
            if (face.normal-UP_VECTOR).size < EPSILON:
                up_face = face
                break
        assert(up_face)
        # subdivide the perimeters to get the right kdtree cubes
        bmesh.ops.subdivide_edges(bm,
                edges=up_face.edges,
                use_grid_fill=True,
                cuts=cuts)
        bpy.context.view_layer.replace()
        # get the middle level of every face

        face_data = {}
        sizes = []
        for f, face in enumerate(bm.faces): 
            face_data[f] = {}
            face_data[f]['calc_center_bounds'] = face.calc_center_bounds()
            loc = mat @ face_data[f]['calc_center_bounds']
            face_data[f]['loc'] = loc
            sizes.append(loc[-1])
        # get the commonest cube-z; we use this to find out the proper loc
        counter = Counter()
        counter.replace(sizes)
        most_common = counter.most_common()[0][0]
        cube_loc = mat @ dice.location
        # get out of edit mode
        bpy.ops.object.mode_set(mode="OBJECT", toggle=False)
        # go to new colection
        bvhtrees = {}
        for f in face_data:
            loc = face_data[f]['loc']
            loc = mat @ face_data[f]['calc_center_bounds']
            print(loc)
            if loc[-1] == most_common:
                # set it again all the way down to the ground as a result of the face is elevated to the
                # prime floor of the dice
                loc[-1] = cube_loc[-1]
                bpy.ops.mesh.primitive_cube_add(location=loc, dimension = x)
                dice = bpy.context.selected_objects[0]
                dice.dimensions = Vector((x, y, z))
                # bpy.context.view_layer.replace()
                dice.title = "cube_{}".format(f)
                #my_coll.objects.hyperlink(dice)
                link_object("cube_{}".format(f), 'KDTREE')
                #bpy.context.view_layer.replace()
                bvhtrees[f] = {
                    'occupied' : 0,
                    'object' : Object3D(dice)
                }
        for object in bpy.information.objects:
            object.select_set(False)
        bpy.information.objects['CUBE1'].select_set(True) # Blender 2.8x
        bpy.ops.object.delete()
        return bvhtrees

    def find_occupied_space(
        self, 
        objects3d: Record[Object3D],
    ) -> None:
        """
        uncover which dice's bvhtree is occupied in our kdtree area

        Args:
            checklist of Object3D objects

        """
        rely = 0
        occupied = []
        for i in self.bvhtrees:
            bvhtree = self.bvhtrees[i]['object']
            for object3d in objects3d:
                if object3d.bvht.overlap(bvhtree.bvht):
                    self.bvhtrees[i]['occupied'] = 1

    def find_overlapping_objects(
        self, 
        objects3d: Record[Object3D],
    ) -> Record[Tuple[int]]:
        """
        returns which Object3D objects are overlapping

        Args:
            checklist of Object3D objects
        
        Returns:
            Record of indicies from objects3d which can be overlap
        """
        rely = 0
        overlaps = []
        for i, x_object3d in enumerate(objects3d):
            for ii, y_object3d in enumerate(objects3d[i+1:]):
                if x_object3d.bvht.overlap(y_object3d.bvht):
                    overlaps.append((i, ii))
        return overlaps

    def calc_most_overlapped(
        self,
        overlaps: Record[Tuple[int]]
    ) -> Record[Tuple[int]]:
        """
        Algorithm to rely the variety of edges every index has
        and return a sorted checklist from most->least with the quantity
        of edges every index has. 

        Args:
            checklist of indicies which can be overlapping
        
        Returns:
            checklist of indicies with the overall variety of overlapps they've 
            [index, count]
        """
        keys = {}
        for x,y in overlaps:
            if x not in keys:
                keys[x] = 0
            if y not in keys:
                keys[y] = 0
            keys[x]+=1
            keys[y]+=1
        # kind by most edges first
        index_counts = sorted(keys.gadgets(), key=lambda x: x[1])[::-1]
        return index_counts
    
    def get_random_unoccupied(
        self
    ) -> Union[int,None]:
        """
        returns a randomly chosen unoccuped kdtree dice

        Return
            both the kdtree dice's key or None (that means all areas are
            at the moment occupied)
            Union[int,None]
        """
        unoccupied = []
        for i in self.bvhtrees:
            if not self.bvhtrees[i]['occupied']:
                unoccupied.append(i)
        if unoccupied:
            random.shuffle(unoccupied)
            return unoccupied[0]
        else:
            return None

    def regenerate(
        self,
        iterable: Union[None, List[Object3D]] = None
    ) -> None:
        """
        this operate recalculates every objects world-view info
        we default to None, which suggests we're recalculating the self.bvhtree cubes

        Args:
            iterable (None or Record of Object3D objects). if None, we default to
            recalculating the kdtree
        """
        if isinstance(iterable, checklist):
            for object in iterable:
                object.regenerate()
        else:
            for idx in self.bvhtrees:
                self.bvhtrees[idx]['object'].regenerate()
                self.update_tree(idx, occupied=0)       

    def process_trees_and_objects(
        self,
        objects3d: Record[Object3D],
    ) -> Record[Tuple[int]]:
        """
        This operate finds all overlapping objects inside objects3d,
        calculates the objects with essentially the most overlaps, searches inside
        the kdtree dice area to see which cubes are occupied. It then returns 
        the edge-counts from essentially the most overlapping objects

        Args:
            checklist of Object3D objects
        Returns
            this returns the output of most_overlapped
        """
        overlaps = self.find_overlapping_objects(objects3d)
        most_overlapped = self.calc_most_overlapped(overlaps)
        self.find_occupied_space(objects3d)
        return most_overlapped

    def move_objects(
        self, 
        objects3d: Record[Object3D],
        most_overlapped: Record[Tuple[int]],
        z_increase_offset: float = 2.,
    ) -> None:
        """
        This operate iterates via most-overlapped, and makes use of 
        the index to extract the matching object from object3d - it then
        finds a random unoccupied kdtree dice and strikes the given overlapping
        object to that area. It does this for every index from the most-overlapped
        operate

        Args:
            objects3d: checklist of Object3D objects
            most_overlapped: an inventory of tuples (index, rely) - the place index pertains to
                the place it is present in objects3d and rely - what number of occasions it overlaps 
                with different objects
            z_increase_offset: this worth will increase the Z worth of the article so as to
                make it seem as if it is off the ground. Should you do not increase this worth
                the article appears prefer it's 'inside' the bottom aircraft
        """
        for idx, cnt in most_overlapped:
            object3d = objects3d[idx]
            unoccupied_idx = self.get_random_unoccupied()
            if unoccupied_idx:
                object3d.object.location =  self.bvhtrees[unoccupied_idx]['object'].object.location
                # make sure the nuggest is above the groundplane
                object3d.object.location[-1] = z_increase_offset
                self.update_tree(unoccupied_idx, occupied=1)
    
    def dynamic_movement(
        self, 
        objects3d: Record[Object3D],
        tries: int = 100,
        z_offset: float = 2.,
    ) -> None:
        """
        This operate resets all objects to get their present positioning
        and randomly strikes objects round in an try and keep away from any object
        overlaps (we do not need two objects to be spawned in the identical place)

        Args:
            objects3d: checklist of Object3D objects
            tries: int the variety of occasions we need to transfer objects to random areas
                to make sure no overlaps are current.
            z_offset: this worth will increase the Z worth of the article so as to
                make it seem as if it is off the ground. Should you do not increase this worth
                the article appears prefer it's 'inside' the bottom aircraft (see `move_objects`)
        """
    
        # reset all objects
        self.regenerate(objects3d)
        # regenerate bvhtrees
        self.regenerate(None)

        most_overlapped = self.process_trees_and_objects(objects3d)
        makes an attempt = 0
        whereas most_overlapped:
            if makes an attempt>=tries:
                break
            self.move_objects(objects3d, most_overlapped, z_offset)
            makes an attempt+=1
            # recalc objects
            self.regenerate(objects3d)
            # regenerate bvhtrees
            self.regenerate(None)
            # recalculate overlaps
            most_overlapped = self.process_trees_and_objects(objects3d)

    def generate_spawn_point(
        self,
    ) -> Vector:
        """
        this operate generates a random spawn level by discovering which
        of the kdtree-cubes are unoccupied, and returns a type of

        Returns
            the Vector location of the kdtree-cube that is unoccupied
        """
        idx = self.get_random_unoccupied()
        print(idx)
        self.update_tree(idx, occupied=1)
        return self.bvhtrees[idx]['object'].object.location

    def update_tree(
        self,
        idx: int,
        occupied: int,
    ) -> None:
        """
        this operate updates the given state (occupied vs. unoccupied) of the
        kdtree given the idx

        Args:
            idx: int
            occupied: int
        """
        self.bvhtrees[idx]['occupied'] = occupied

@gin.configurable("run")
@zpy.blender.save_and_revert
def run(
    max_num_nuggets: int = 100,
    jitter_mesh: bool = True,
    jitter_nugget_scale: bool = True,
    jitter_material: bool = True,
    jitter_nugget_material: bool = False,
    number_of_random_materials: int = 50,
    nugget_texture_path: str = os.getcwd()+"/nugget_textures",
    annotations_path = os.getcwd()+'/nugget_data',
):
    """
    Essential run operate.
    """
    density_controller = DensityController()
    # Random seed leads to distinctive conduct
    zpy.blender.set_seed(random.randint(0,1000000000))

    # Create the saver object
    saver = zpy.saver_image.ImageSaver(
        description="Picture of the randomized Amazon nuggets",
        output_dir=annotations_path,
    )
    saver.add_category(title="nugget")

    # Make an inventory of supply nugget objects
    source_nugget_objects = []
    for obj in zpy.objects.for_obj_in_collections(
        [
            bpy.data.collections["NUGGET"],
        ]
    ):
        assert(obj!=None)

        # go on the whole lot not named nugget
        if 'nugget_base' not in obj.title:
            print('passing on {}'.format(obj.title))
            proceed
        zpy.objects.phase(obj, title="nugget", as_category=True) #shade=nugget_seg_color
        print("zpy.objects.phase: test {}".format(obj.title))
        source_nugget_objects.append(obj.title)

    # transfer nugget level up 10 z's so it will not collide with base-cube
    bpy.information.objects["nugget_base"].location[-1] = 10

    # Save the place of the digital camera and light-weight
    # create gentle and digital camera
    zpy.objects.save_pose("Digicam")
    zpy.objects.save_pose("Solar")
    zpy.objects.save_pose("Airplane")
    zpy.objects.save_pose("Essential Axis")
    axis = bpy.information.objects['Main Axis']
    print('saving poses')
    # add some parameters to this 

    # get the plane-3d object
    plane3d = Object3D(bpy.information.objects['Plane'])

    # generate kdtree cubes
    density_controller.generate_kdtree_cubes()

    # Pre-create a bunch of random textures
    #random_materials = [
    #    zpy.material.random_texture_mat() for _ in range(number_of_random_materials)
    #]
    p = os.path.abspath(os.getcwd()+'/random_textures')
    print(p)
    random_materials = []
    for x in os.listdir(p):
        texture_path = Path(os.path.be a part of(p,x))
        y = zpy.materials.make_mat_from_texture(texture_path, title=texture_path.stem)
        random_materials.append(y)
    #print(random_materials[0])

    # Pre-create a bunch of random textures
    random_nugget_materials = [
        random_nugget_texture_mat(Path(nugget_texture_path)) for _ in range(number_of_random_materials)
    ]

    # Run the sim.
    for step_idx in zpy.blender.step():
        density_controller.generate_kdtree_cubes()

        objects3d = []
        num_nuggets = random.randint(40, max_num_nuggets)
        log.information(f"Spawning {num_nuggets} nuggets.")
        spawned_nugget_objects = []
        for _ in vary(num_nuggets):

            # Select location to spawn nuggets
            spawn_point = density_controller.generate_spawn_point()
            # manually spawn above the ground
            # spawn_point[-1] = 1.8 #2.0

            # Choose a random object to spawn
            _name = random.alternative(source_nugget_objects)
            log.information(f"Spawning a duplicate of supply nugget {_name} at {spawn_point}")
            obj = zpy.objects.copy(
                bpy.information.objects[_name],
                assortment=bpy.information.collections["SPAWNED"],
                is_copy=True,
            )

            obj.location = spawn_point
            obj.matrix_world = mathutils.Matrix.Translation(spawn_point)
            spawned_nugget_objects.append(obj)

            # Section the newly spawned nugget as an example
            zpy.objects.phase(obj)

            # Jitter remaining pose of the nugget a bit
            zpy.objects.jitter(
                obj,
                rotate_range=(
                    (0.0, 0.0),
                    (0.0, 0.0),
                    (-math.pi * 2, math.pi * 2),
                ),
            )

            if jitter_nugget_scale:
                # Jitter the size of every nugget
                zpy.objects.jitter(
                    obj,
                    scale_range=(
                        (0.8, 2.0), #1.2
                        (0.8, 2.0), #1.2
                        (0.8, 2.0), #1.2
                    ),
                )

            if jitter_mesh:
                # Jitter (deform) the mesh of every nugget
                zpy.objects.jitter_mesh(
                    obj=obj,
                    scale=(
                        random.uniform(0.01, 0.03),
                        random.uniform(0.01, 0.03),
                        random.uniform(0.01, 0.03),
                    ),
                )

            if jitter_nugget_material:
                # Jitter the fabric (apperance) of every nugget
                for i in vary(len(obj.material_slots)):
                    obj.material_slots[i].materials = random.alternative(random_nugget_materials)
                    zpy.materials.jitter(obj.material_slots[i].materials)          

            # create 3d obj for motion
            nugget3d = Object3D(obj)

            # ensure the underside most a part of the nugget is NOT
            # contained in the plane-object       
            plane_overlap(plane3d, nugget3d)

            objects3d.append(nugget3d)

        # guarantee objects aren't on prime of one another
        density_controller.dynamic_movement(objects3d)

        # Return digital camera to authentic place
        zpy.objects.restore_pose("Digicam")
        zpy.objects.restore_pose("Essential Axis")
        zpy.objects.restore_pose("Digicam")
        zpy.objects.restore_pose("Essential Axis")

        # assert these are the proper variations...
        assert(bpy.information.objects["Camera"].location == Vector((0,0,100)))
        assert(bpy.information.objects["Main Axis"].location == Vector((0,0,0)))
        assert(bpy.information.objects["Main Axis"].rotation_euler == Euler((0,0,0)))

        # alter the Z ditance with the digital camera
        bpy.information.objects["Camera"].location = (0, 0, random.uniform(0.75, 3.5)*100)

        # alter the main-axis beta/gamma params
        top_down_mostly = False 
        if top_down_mostly:
            zpy.objects.rotate(
                bpy.information.objects["Main Axis"],
                rotation=(
                    random.uniform(0.05, 0.05),
                    random.uniform(0.05, 0.05),
                    random.uniform(0.05, 0.05),
                ),
            )
        else:
            zpy.objects.rotate(
                bpy.information.objects["Main Axis"],
                rotation=(
                    random.uniform(-1., 1.),
                    random.uniform(-1., 1.),
                    random.uniform(-1., 1.),
                ),
            )

        print(bpy.information.objects["Main Axis"].rotation_euler)
        print(bpy.information.objects["Camera"].location)

        # change the background materials
        # Randomize texture of shelf, flooring and partitions
        for obj in bpy.information.collections["BACKGROUND"].all_objects:
            for i in vary(len(obj.material_slots)):
                # TODO
                # Choose one of many random supplies
                obj.material_slots[i].materials = random.alternative(random_materials)
                if jitter_material:
                    zpy.materials.jitter(obj.material_slots[i].materials)
                # Units the fabric relative to the article
                obj.material_slots[i].hyperlink = "OBJECT"
        # Choose a random hdri (from the native textures folder for background background)
        zpy.hdris.random_hdri()
        # Return gentle to authentic place
        zpy.objects.restore_pose("Solar")

        # Jitter the sunshine place
        zpy.objects.jitter(
            "Solar",
            translate_range=(
                (-5, 5),
                (-5, 5),
                (-5, 5),
            ),
        )
        bpy.information.objects["Sun"].information.power = random.uniform(0.5, 7)

# we conceal the dice objects<br />for obj in         # we conceal the dice objects
        for obj in bpy.information.objects:
            if 'dice' in obj.title:
                obj.hide_render = True
                attempt:
                    zpy.objects.toggle_hidden(obj, hidden=True)
                besides:
                    # cope with this exception right here...
                    go
        # we conceal our base nugget object
        bpy.information.objects["nugget_base"].hide_render = True
        zpy.objects.toggle_hidden(bpy.information.objects["nugget_base"], hidden=True)

        # create the picture title
        image_uuid = str(uuid.uuid4())

        # Identify for every of the output photos
        rgb_image_name = format_image_string(image_uuid, 'rgb')
        iseg_image_name = format_image_string(image_uuid, 'iseg')
        depth_image_name = format_image_string(image_uuid, 'depth')

        zpy.render.render(
            rgb_path=saver.output_dir / rgb_image_name,
            iseg_path=saver.output_dir / iseg_image_name,
            depth_path=saver.output_dir / depth_image_name,
        )

        # Add photos to saver
        saver.add_image(
            title=rgb_image_name,
            model="default",
            output_path=saver.output_dir / rgb_image_name,
            body=step_idx,
        )
    
        saver.add_image(
            title=iseg_image_name,
            model="segmentation",
            output_path=saver.output_dir / iseg_image_name,
            body=step_idx,
        )
        saver.add_image(
            title=depth_image_name,
            model="depth",
            output_path=saver.output_dir / depth_image_name,
            body=step_idx,
        )

        # ideally on this thread, we'll open the anno file
        # and write to it straight, saving it after every era
        for obj in spawned_nugget_objects:
            # Add annotation to segmentation picture
            saver.add_annotation(
                picture=rgb_image_name,
                class="nugget",
                seg_image=iseg_image_name,
                seg_color=tuple(obj.seg.instance_color),
            )

        # Delete the spawned nuggets
        zpy.objects.empty_collection(bpy.information.collections["SPAWNED"])

        # Write out annotations
        saver.output_annotated_images()
        saver.output_meta_analysis()

        # # ZUMO Annotations
        _output_zumo = _OutputZUMO(saver=saver, annotation_filename = Path(image_uuid + ".zumo.json"))
        _output_zumo.output_annotations()
        # change the title right here..
        saver.output_annotated_images()
        saver.output_meta_analysis()

        # take away the reminiscence of the annotation to free RAM
        saver.annotations = []
        saver.photos = {}
        saver.image_name_to_id = {}
        saver.seg_annotations_color_to_id = {}

    log.information("Simulation full.")

if __name__ == "__main__":

    # Set the logger ranges
    zpy.logging.set_log_levels("information")

    # Parse the gin-config textual content block
    # hack to learn a selected gin config
    parse_config_from_file('nugget_config.gin')

    # Run the sim
    run()

tar cvf working_blender_dir.tar.gz working_blender_dir
scp -i "your.pem" working_blender_dir.tar.gz [email protected]:/residence/ubuntu/working_blender_dir.tar.gz

tar xvf working_blender_dir.tar.gz

blender working_blender_dir/nugget.mix --background --python working_blender_dir/create_synthetic_nuggets.py

{
    "metadata": {
        "description": "3D information of a nugget!",
        "contributor": "Matt Krzus",
        "url": "[email protected]",
        "12 months": "2021",
        "date_created": "20210924_000000",
        "save_path": "/residence/ubuntu/working_blender_dir/nugget_data"
    },
    "classes": {
        "0": {
            "title": "nugget",
            "supercategories": [],
            "subcategories": [],
            "shade": [
                0.0,
                0.0,
                0.0
            ],
            "rely": 6700,
            "subcategory_count": [],
            "id": 0
        }
    },
    "photos": {
        "0": {
            "title": "a0bb1fd3-c2ec-403c-aacf-07e0c07f4fdd.rgb.png",
            "model": "default",
            "output_path": "/residence/ubuntu/working_blender_dir/nugget_data/a0bb1fd3-c2ec-403c-aacf-07e0c07f4fdd.rgb.png",
            "relative_path": "a0bb1fd3-c2ec-403c-aacf-07e0c07f4fdd.rgb.png",
            "body": 97,
            "width": 640,
            "top": 480,
            "id": 0
        },
        "1": {
            "title": "a0bb1fd3-c2ec-403c-aacf-07e0c07f4fdd.iseg.png",
            "model": "segmentation",
            "output_path": "/residence/ubuntu/working_blender_dir/nugget_data/a0bb1fd3-c2ec-403c-aacf-07e0c07f4fdd.iseg.png",
            "relative_path": "a0bb1fd3-c2ec-403c-aacf-07e0c07f4fdd.iseg.png",
            "body": 97,
            "width": 640,
            "top": 480,
            "id": 1
        },
        "2": {
            "title": "a0bb1fd3-c2ec-403c-aacf-07e0c07f4fdd.depth.png",
            "model": "depth",
            "output_path": "/residence/ubuntu/working_blender_dir/nugget_data/a0bb1fd3-c2ec-403c-aacf-07e0c07f4fdd.depth.png",
            "relative_path": "a0bb1fd3-c2ec-403c-aacf-07e0c07f4fdd.depth.png",
            "body": 97,
            "width": 640,
            "top": 480,
            "id": 2
        }
    },
    "annotations": [
        {
            "image_id": 0,
            "category_id": 0,
            "id": 0,
            "seg_color": [
                1.0,
                0.6000000238418579,
                0.9333333373069763
            ],
            "shade": [
                1.0,
                0.6,
                0.9333333333333333
            ],
            "segmentation": [
                [
                    299.0,
                    308.99,
                    292.0,
                    308.99,
                    283.01,
                    301.0,
                    286.01,
                    297.0,
                    285.01,
                    294.0,
                    288.01,
                    285.0,
                    283.01,
                    275.0,
                    287.0,
                    271.01,
                    294.0,
                    271.01,
                    302.99,
                    280.0,
                    305.99,
                    286.0,
                    305.99,
                    303.0,
                    302.0,
                    307.99,
                    299.0,
                    308.99
                ]
            ],
            "bbox": [
                283.01,
                271.01,
                22.980000000000018,
                37.98000000000002
            ],
            "space": 667.0802000000008,
            "bboxes": [
                [
                    283.01,
                    271.01,
                    22.980000000000018,
                    37.98000000000002
                ]
            ],
            "areas": [
                667.0802000000008
            ]
        },
        {
            "image_id": 0,
            "category_id": 0,
            "id": 1,
            "seg_color": [
                1.0,
                0.4000000059604645,
                1.0
            ],
            "shade": [
                1.0,
                0.4,
                1.0
            ],
            "segmentation": [
                [
                    241.0,
                    273.99,
                    236.0,
                    271.99,
                    234.0,
                    273.99,
                    230.01,
                    270.0,
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                    268.0,
                    231.01,
                    263.0,
                    233.01,
                    261.0,
                    229.0,
                    257.99,
                    225.0,
                    257.99,
                    223.01,
                    255.0,
                    225.01,
                    253.0,
                    227.01,
                    246.0,
                    235.0,
                    239.01,
                    238.0,
                    239.01,
                    240.0,
                    237.01,
                    247.0,
                    237.01,
                    252.99,
                    245.0,
                    253.99,
                    252.0,
                    246.99,
                    269.0,
                    241.0,
                    273.99
                ]
            ],
            "bbox": [
                223.01,
                237.01,
                30.980000000000018,
                36.98000000000002
            ],
            "space": 743.5502000000008,
            "bboxes": [
                [
                    223.01,
                    237.01,
                    30.980000000000018,
                    36.98000000000002
                ]
            ],
            "areas": [
                743.5502000000008
            ]
        },
...
...
...