fast_rcnn.py¶
What is this?¶

Publish an image with object bounding boxes, scores and labels.
CLASSES = ('__background__',
'aeroplane', 'bicycle', 'bird', 'boat',
'bottle', 'bus', 'car', 'cat', 'chair',
'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant',
'sheep', 'sofa', 'train', 'tvmonitor')
Subscribing Topic¶
~input
(sensor_msgs/Image
)Raw image.
~input/rect_array
(jsk_recognition_msgs/RectArray
)Object location proposals.
Publishing Topic¶
~output/class
(jsk_recognition_msgs/ClassificationResult
)Detected object class labels and probabilities.
~output/rect_array
(jsk_recognition_msgs/RectArray
)Rects of detected objects.
Parameters¶
~model
(String, required)Network model name. (
vgg_cnn_m_1024
orvgg16
)vgg_cnn_m_1024
is small network and requires ~2GB GPU memory.vgg16
is large network and requires ~5GB GPU memory.~gpu
(Int, default:-1
)GPU ID. Negative value means CPU mode.
~classifier_name
(String, default:rospy.get_name()
)Classifier name written to
classifier
field of~output/class
.~approximate_sync
(Bool, default:False
)Whether to use approximate for input topics.
~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.
~slop
(Float, default:0.1
)How many seconds you allow about the difference of timestamp when you specify
~approximate_sync
.
Example¶

roslaunch jsk_perception sample_fast_rcnn.launch