fcn_object_segmentation.py

What is this?

../../../../_images/fcn_object_segmentation1.png

Segment object in pixel wise with Fully Convolutional Networks.

Subscribing Topic

Default

  • ~input (sensor_msgs/Image)

    Raw image.

Optional

  • ~input/mask (sensor_msgs/Image)

    Mask whose black region must be the background label: 0. This topic is subscribed only when param ~use_mask is true.

Publishing Topic

  • ~output (sensor_msgs/Image)

    Label image each object in param ~target_names is segmented.

Parameters

Default

  • ~gpu (Int, Default: -1)

    GPU id. -1 represents CPU mode.

  • ~target_names (List of String, Required)

    Target names for classification.

  • ~model_name (String, Required)

    Currently fcn8s, fcn16s or fcn32s is only supported. See models in https://github.com/wkentaro/fcn/tree/master/fcn/models.

  • ~model_h5 (String, Required)

    Saved h5 file for trained model.

  • ~bg_label (Int, default: 0)

    Label value for background. This is used with rosparam ~proba_threshold

  • ~proba_threshold (Float, default: 0.0)

    Threshold for labeling pixels as uncertain, and the uncertain region will be labeled as background with rosparam ~bg_label.

Optional

  • ~queue_size (Int, default: 10)

    How many messages you allow about the subscriber to keep in the queue. This should be big when there is much difference about delay between two topics. This is used only when param ~use_mask is true.

  • ~approximate_sync (Bool, default: False)

    Whether to use approximate for input topics. This is used only when param ~use_mask is true.

  • ~slop (Float, default: 0.1)

    How many seconds you allow about the difference of timestamp. This is used only when param ~use_mask and ~approximate_sync are true.

Sample

Sample

roslaunch jsk_perception sample_fcn_object_segmentation.launch
../../../../_images/sample_fcn_object_segmentation.png

Sample with mask image

roslaunch jsk_perception sample_fcn_object_segmentation.launch use_mask:=true
../../../../_images/sample_fcn_object_segmentation_with_mask.png