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dnn_detect

dnn_detect uses the OpenCV Deep Neural Network module to find objects in an image stream and provide their bounding boxes.

Subscribed Topics

/camera (sensor_msgs/Image)

• The images to be processed.

Published Topics

/dnn_objects (dnn_detect/DetectedObjectArray)

• Details of detected objects

/dnn_images (sensor_msgs/Image)

• Input image with detected objects outlined

Services

~detect (dnn_detect/Detect)

• Trigger a detection when in single-shot mode

Parameters

~single_shot (bool, default: false)

• If set, detection is only performed when the detect service is called

~publish_images (bool, default: true)

• If set, images with objects outlined will be published

~data_dir (string)

• Path to directory containing data

~protonet_file (string)

• Name of protonet file, relative to data_dir

~caffe_model_file (string)

• Name of caffe model file, relative to data_dir

~min_confidence (float, default: 0.2)

• Minimum confidence (0..1) for an object to be included in the results

~im_size (int, default: 300)

• size of image (pixels) to operate on

~scale_factor (float, default: 0.007843)

• Scale factor for model - the intensity values in the image are multiplied by this

~mean_val (float, default: 127.5)

• Mean value for model - this is subtracted from the intensity values in the image

~class_names (string, default: background, aeroplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, diningtable, dog, horse, motorbike, person, pottedplant, sheep, sofa, train, tvmonitor)

• Comma delimited list of class names

Installation

sudo apt install ros-kinetic-dnn-detect

Sample Usage

The sample launch file uses chuanqi305's MobileNet-SSD

A camera node, such as usb_cam needs to be running

For example:

rosrun usb_cam usb_cam_node

Then dnn_detect can be launched:

roslaunch dnn_detect dnn_detect.launch camera:=/usb_cam image:=image_raw

and the results viewed with: {{ rqt_image_view /dnn_images or:

rostopic echo /dnn_objects