Darknet

Installation

Run the install_darknet function in configure. BerryNet dev team is making this to be easier and clearer.

Test Darknet Engine Manually

Use Darknet detector service and tinyyolovoc model package as example.

1. Start detector service

   $ bn_darknet \
       -p tinyyolovoc-20170816 \
       --service_name darknet \
       --num_threads 4 \
       --draw \
       --debug
   [D 191206 21:20:42 darknet_service:109] model filepath: /usr/share/dlmodels/tinyyolovoc-20170816/tiny-yolo-voc.weights
   [D 191206 21:20:42 darknet_service:110] label filepath: /usr/share/dlmodels/tinyyolovoc-20170816/voc.names
   layer     filters    size              input                output
       0 conv     16  3 x 3 / 1   416 x 416 x   3   ->   416 x 416 x  16
       1 max          2 x 2 / 2   416 x 416 x  16   ->   208 x 208 x  16
       2 conv     32  3 x 3 / 1   208 x 208 x  16   ->   208 x 208 x  32
       3 max          2 x 2 / 2   208 x 208 x  32   ->   104 x 104 x  32
       4 conv     64  3 x 3 / 1   104 x 104 x  32   ->   104 x 104 x  64
       5 max          2 x 2 / 2   104 x 104 x  64   ->    52 x  52 x  64
       6 conv    128  3 x 3 / 1    52 x  52 x  64   ->    52 x  52 x 128
       7 max          2 x 2 / 2    52 x  52 x 128   ->    26 x  26 x 128
       8 conv    256  3 x 3 / 1    26 x  26 x 128   ->    26 x  26 x 256
       9 max          2 x 2 / 2    26 x  26 x 256   ->    13 x  13 x 256
      10 conv    512  3 x 3 / 1    13 x  13 x 256   ->    13 x  13 x 512
      11 max          2 x 2 / 1    13 x  13 x 512   ->    13 x  13 x 512
      12 conv   1024  3 x 3 / 1    13 x  13 x 512   ->    13 x  13 x1024
      13 conv   1024  3 x 3 / 1    13 x  13 x1024   ->    13 x  13 x1024
      14 conv    125  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 125
      15 detection
   mask_scale: Using default '1.000000'
   Loading weights from /usr/share/dlmodels/tinyyolovoc-20170816/tiny-yolo-voc.weights...Done!
   [D 191206 21:20:45 darknet_engine:150] inference time: 1.9036920070648193 s
   [D 191206 21:20:45 __init__:11] Connected with result code 0
   [D 191206 21:20:45 __init__:13] Subscribe topic berrynet/data/rgbimage
   

2. Send image to service by camera client

   $ bn_camera --mode file --filepath <image-filepath>
   

3. You should see the inference results on detector's terminal

   [D 191206 21:21:45 __init__:20] Receive message from topic berrynet/data/rgbimage
   [D 191206 21:21:47 darknet_engine:150] inference time: 1.8199963569641113 s
   [D 191206 21:21:47 darknet_service:66] result_hook, annotations: [{'bottom': 342.45084381103516, 'confidence': 0.48404842615127563, 'left': 56.02063751220703, 'label': 'dog', 'right': 198.14881134033203, 'id': -1, 'top': 265.13106536865234, 'type': 'detection'}, {'bottom': 350.23523712158203, 'confidence': 0.32306814193725586, 'left': 107.26636505126953, 'label': 'dog', 'right': 225.97444915771484, 'id': -1, 'top': 275.51012420654297, 'type': 'detection'}, {'bottom': 360.5461120605469, 'confidence': 0.7337859272956848, 'left': 169.8051300048828, 'label': 'person', 'right': 280.67076110839844, 'id': -1, 'top': 94.009033203125, 'type': 'detection'}, {'bottom': 346.7984390258789, 'confidence': 0.6094380021095276, 'left': 412.9965133666992, 'label': 'sheep', 'right': 541.2455520629883, 'id': -1, 'top': 156.6410140991211, 'type': 'detection'}, {'bottom': 359.8292579650879, 'confidence': 0.39942821860313416, 'left': 69.68604278564453, 'label': 'sheep', 'right': 191.7149887084961, 'id': -1, 'top': 254.3123435974121, 'type': 'detection'}]
   [D 191206 21:21:47 __init__:50] Send message to topic berrynet/engine/darknet/result
   

4. To visualize the received inference result

   $ bn_dashboard --no-decoration --no-full-screen --topic berrynet/engine/darknet/result --debug