find_box_3d — Find boxes in 3D data.
find_box_3d( : : ObjectModel3DScene, SideLen1, SideLen2, SideLen3, MinScore, GenParam : GrippingPose, Score, ObjectModel3DBox, BoxInformation)
find_box_3d finds boxes in the 3D object model
ObjectModel3DScene and returns the pose of a gripping point
GrippingPose, a 3D object model ObjectModel3DBox a score
value Score and a dictionary BoxInformation, containing
further information about the found boxes, for each found box.
The side lengths of the boxes are passed
in SideLen1, SideLen2, and SideLen3.
Each length consists of a tuple of two values,
indicating the minimum and maximum length of that side.
If only a single face of the box is expected to be visible, or no restriction
should be applied to the remaining box length,
SideLen3 can be set to -1.
The parameter MinScore sets the minimum score for boxes to be
returned. Boxes with a score smaller than this value will not be returned.
ObjectModel3DScene must contain a XYZ-mapping, like it is, e.g., the
case, when creating it with xyz_to_object_model_3d.
A typical workflow for detecting 3D boxes in 3D data looks as follows:
Obtain the 3D data either as XYZ-images, or directly as a 3D object model with XYZ-mapping.
Remove as much background and clutter that is not part of any box
from the scene as possible, in order to increase robustness and speed.
Therefore, e.g., use threshold and reduce_domain
on the XYZ-images before calling xyz_to_object_model_3d.
Further options are described in the section “Troubleshooting” below.
If the 3D data exists in the form of XYZ-images, convert them to
a 3D object model using xyz_to_object_model_3d.
Obtain the approximate box edge lengths that should be found.
Note that changing those lengths later on might make it necessary
to also change other parameters, such as MinScore.
Call find_box_3d, passing the 3D object model with the
scene and the approximate box edge lengths.
Use the procedure visualize_object_model_3d to visualize the
results, if necessary.
The boxes are returned in several ways.
First, the pose of a gripping point is returned in GrippingPose.
The used side of the box and the z-axis of the gripping pose are set
according to the XYZ-mapping. If only a single side of the box is visible,
the center of the gripping pose is in the center of that side, and its
z-axis is oriented away from the viewing point of the XYZ-mapping.
If multiple sides of the box are visible, the gripping pose lies in the
center of the side that is most parallel to the viewing point of the
XYZ-mapping. The z-axis is again oriented away from the viewing point
of the XYZ-mapping.
The x-axis of the gripping pose is set to the box axis that is roughly
the most aligned with the column direction of the XYZ-mapping. The
y-axis is computed based on the x- and z-axis.
The box is also returned in triangulated form in ObjectModel3DBox.
This allows a quick visualization of the results.
For each found box, a score between 0 and 1 is returned
in Score.
The score indicates how well the box and its edges are visible, and how
well the found box matches the specified dimensions.
Finally, additional information about the results is returned in the
dictionary BoxInformation.
get_dict_param and get_dict_tuple can be used to obtain
further information about the results.
Also, the HDevelop handle inspect window can be used to inspect
the returned dictionary.
The dictionary BoxInformation contains the following keys:
results:
This key references a dictionary containing the found boxes. They are
sorted according to their score in descending order with ascending integer
keys starting at 0.
Each box result is a dictionary with the following keys:
box_pose:This is the box's pose in the coordinate system of the scene. This pose is used for visualizing the found box.
box_length_x,
box_length_y,
box_length_z:
The side lengths of the found box corresponding to box_pose.
box_length_x and box_length_y will always
contain a positive number. If only a single side of the box is visible,
box_length_z will be set to 0.
gripping_pose:
The same pose as returned in GrippingPose.
gripping_length_x,
gripping_length_y,
gripping_length_z:
The side lengths of the found box corresponding to GrippingPose.
gripping_length_x and gripping_length_y will always
contain a positive number. If only a single side of the box is visible,
gripping_length_z will be set to 0.
score:
The same score as returned in Score.
one_side_only:Boolean indicating whether only one side of the box is visible ('true') or not ('false').
gen_param:
This is a dictionary with the parameters passed to find_box_3d.
SideLen1, SideLen2, and SideLen3 are pooled in
a tuple with key lengths. The key min_score
references MinScore. The other keys are denoted analogously to the
generic parameters of the dictionary GenParam.
sampled_edges:This is the 3D object model with sampled edges. It contains the viewing direction of the edge points as normal vectors.
sampled_edges_direction:
This is the 3D object model with sampled edges (same as for key
sampled_edges. It contains the edge directions of the edge
points as normal vectors.
sampled_scene:This is the sampled scene in which the boxes are looked for. It can be used for visualization or debugging the sampling distance.
sampled_reference_points:This is a 3D object model with all points from the 3D scene that were used as reference points in the matching process. For each reference point, the optimum pose of the box is computed under the assumption that the reference point lies on the surface of the box.
Additional parameters can be passed as key/tuple pairs in the dictionary
GenParam in order to improve the matching process.
The following parameter names serve as keys to their
corresponding tuples (see create_dict and set_dict_tuple).
3d_edges:
Allows to manually set the 3D scene edges.
The parameter must be a 3D object model handle.
The edges are usually a result of the operator
edges_object_model_3d but can further be filtered in order
to remove outliers.
If this parameter is not given, find_box_3d will
internally extract the 3D edges similar to the operator
edges_object_model_3d.
3d_edge_min_amplitude:
Sets the minimum amplitude of a discontinuity in order for it to be
classified as an edge.
Note that if edges were passed manually with the generic parameter
3d_edges, this parameter is ignored.
Otherwise, it behaves similar to the parameter MinAmplitude of
the operator edges_object_model_3d.
Restriction:
3d_edge_min_amplitude >= 0
Default: 10% of the smallest box diagonal.
max_gap:
If no edges are passed with 3d_edges, the operator will extract
3D edges internally. The parameter can be used to control
the edge extraction.
max_gap has the same meaning as in
edges_object_model_3d.
remove_outer_edges:Removes the outermost edges when set to 'true'. This is for example helpful for bin picking applications in order to remove the bin.
List of values: 'false', 'true'
Default: 'false'
max_num_boxes:
Limits the number of returned boxes.
By default, find_box_3d will return all detected boxes with a
score larger than MinScore.
This parameter can be used to limit the number of boxes respectively.
Default: 0 (return all boxes)
box_type:Sets the type of boxes to search for. For 'full_box_visible' only boxes with more than one side visible are returned. If 'single_side_visible' is set, boxes with only one visible side are searched for. If further box sides are visible nonetheless, they are ignored. For 'all' both types are returned.
List of values: 'all', 'single_side_visible', 'full_box_visible'
Default: 'all'
To debug the box detector, some of the internally used data can be
visualized by obtaining it from the returned dictionary
BoxInformation, using get_dict_tuple.
The sampled 3D scene can be extracted with the key
sampled_scene. Finding smaller boxes requires a denser
sampling and subsequently slows down the box detection.
The sampled 3D edges can be extracted with the key
sampled_edges and sampled_edges_directions.
Both 3D object models contain the same points, however,
sampled_edges contains the viewing direction of the edge
points as normal vectors, while sampled_edges_directions
contains the edge directions of the edge points as normal vectors.
Note that the edge directions should be perpendicular to the edges,
pointing outwards of the boxes.
If find_box_3d is taking too long, the following steps
might help to increase its performance.
Remove more background and clutter: A significant improvement in runtime and detection accuracy can usually be achieved by removing as much of the background and clutter from the 3D scene as possible.
The most common approaches for removing unwanted data are:
Thresholding the X-, Y- and Z-coordinates, either by
using threshold and reduce_domain
on the XYZ-images before calling xyz_to_object_model_3d,
or by using select_points_object_model_3d directly
on the 3D object model that contains the scene.
Some sensors return an intensity image along with the 3D data. Filters on the intensity image can be used to remove parts of the image that contain background.
Use background subtraction. If the scene is static, for example,
if the sensor is mounted in a fixed position over a conveyor belt,
the XYZ-images of the background can be acquired once without
any boxes in it. Afterwards, sub_image and threshold
can be used on the Z-images to select parts of the 3D data
that are not part of the background.
Increase minimum score:
An increased minimum score MinScore might lead to more boxes
being removed earlier in the detection pipeline.
Increase the smallest possible box:
The smaller the smallest possible box side is, the slower
find_box_3d runs.
For example, if all boxes are usually seen from a single side,
it might make sense to set SideLen3 to -1.
Additionally, box_type can be set to limit the
type of boxes that are searched.
Manually computing and filtering edges:
The edges of the scene can be extracted manually, using
edges_object_model_3d, and passed to find_box_3d
using the generic parameter 3d_edges (see above).
Thus, the manual extraction can be used as a further way of
filtering the edges.
This operator supports canceling timeouts and interrupts.
ObjectModel3DScene (input_control) object_model_3d → (handle)
Handle of 3D object model where to search the box.
SideLen1 (input_control) real-array → (real)
Length of the first box side.
SideLen2 (input_control) real-array → (real)
Length of the second box side.
SideLen3 (input_control) real-array → (real)
Length of the third box side.
Default: -1
MinScore (input_control) real → (real / integer)
Minimum score of the returned boxes.
Default: 0.6
Restriction:
0 <= MinScore <= 1
GenParam (input_control) dict → (handle)
Dictionary for generic parameters.
Default: []
GrippingPose (output_control) pose(-array) → (real / integer)
Gripping poses of the detected boxes.
Score (output_control) real-array → (real)
Scores of the detected boxes.
ObjectModel3DBox (output_control) object_model_3d-array → (handle)
Detected boxes as triangulated 3D object models.
BoxInformation (output_control) dict → (handle)
Additional debug information as dictionary.
If all parameters are valid and no error occurs,
find_box_3d returns 2 (
H_MSG_TRUE)
. If necessary, an exception is raised.
read_object_model_3d,
xyz_to_object_model_3d
gen_box_object_model_3d,
get_dict_tuple
3D Metrology