“Video-stream stopped! when try to run Darknet Object Detection”

i am using a darknet for object detection. We have been trying to apply the weight file that we have trained in YOLOv4 to the camera for a while now.
However, when I try to run the darknet demo, I get the following error.

" Video-stream stopped!"

Our development environment is Linux, and we are trying to run the following code.

cd ~/darknet

i an using the
$./darknet detector demo data/obj.data cfg/yolov4-obj.cfg weights/yolov4-obj_3000.weights nvarguscamerasrc sensor_id=0 ! ‘video/x-raw(memory:NVMM),width=1920, height=1080, framerate=30/1’ ! nvvidconv flip-method=4 ! ‘video/x-raw,width=960, height=540’ ! nvvidconv ! nvegltransform ! nveglglessink -e

i got this error

./darknet detector demo data/obj.data cfg/yolov4-obj.cfg weights/yolov4-obj_3000.weights nvarguscamerasrc sensor_id=0 !    'video/x-raw(memory:NVMM),width=1920, height=1080, framerate=30/1' !    nvvidconv flip-method=4 ! 'video/x-raw,width=960, height=540' !    nvvidconv ! nvegltransform ! nveglglessink -e
 GPU isn't used 
 OpenCV version: 4.1.1
Demo
mini_batch = 1, batch = 1, time_steps = 1, train = 0 
   layer   filters  size/strd(dil)      input                output
   0 conv     32       3 x 3/ 1    320 x 320 x   3 ->  320 x 320 x  32 0.177 BF
   1 conv     64       3 x 3/ 2    320 x 320 x  32 ->  160 x 160 x  64 0.944 BF
   2 conv     64       1 x 1/ 1    160 x 160 x  64 ->  160 x 160 x  64 0.210 BF
   3 route  1 		                           ->  160 x 160 x  64 
   4 conv     64       1 x 1/ 1    160 x 160 x  64 ->  160 x 160 x  64 0.210 BF
   5 conv     32       1 x 1/ 1    160 x 160 x  64 ->  160 x 160 x  32 0.105 BF
   6 conv     64       3 x 3/ 1    160 x 160 x  32 ->  160 x 160 x  64 0.944 BF
   7 Shortcut Layer: 4,  wt = 0, wn = 0, outputs: 160 x 160 x  64 0.002 BF
   8 conv     64       1 x 1/ 1    160 x 160 x  64 ->  160 x 160 x  64 0.210 BF
   9 route  8 2 	                           ->  160 x 160 x 128 
  10 conv     64       1 x 1/ 1    160 x 160 x 128 ->  160 x 160 x  64 0.419 BF
  11 conv    128       3 x 3/ 2    160 x 160 x  64 ->   80 x  80 x 128 0.944 BF
  12 conv     64       1 x 1/ 1     80 x  80 x 128 ->   80 x  80 x  64 0.105 BF
  13 route  11 		                           ->   80 x  80 x 128 
  14 conv     64       1 x 1/ 1     80 x  80 x 128 ->   80 x  80 x  64 0.105 BF
  15 conv     64       1 x 1/ 1     80 x  80 x  64 ->   80 x  80 x  64 0.052 BF
  16 conv     64       3 x 3/ 1     80 x  80 x  64 ->   80 x  80 x  64 0.472 BF
  17 Shortcut Layer: 14,  wt = 0, wn = 0, outputs:  80 x  80 x  64 0.000 BF
  18 conv     64       1 x 1/ 1     80 x  80 x  64 ->   80 x  80 x  64 0.052 BF
  19 conv     64       3 x 3/ 1     80 x  80 x  64 ->   80 x  80 x  64 0.472 BF
  20 Shortcut Layer: 17,  wt = 0, wn = 0, outputs:  80 x  80 x  64 0.000 BF
  21 conv     64       1 x 1/ 1     80 x  80 x  64 ->   80 x  80 x  64 0.052 BF
  22 route  21 12 	                           ->   80 x  80 x 128 
  23 conv    128       1 x 1/ 1     80 x  80 x 128 ->   80 x  80 x 128 0.210 BF
  24 conv    256       3 x 3/ 2     80 x  80 x 128 ->   40 x  40 x 256 0.944 BF
  25 conv    128       1 x 1/ 1     40 x  40 x 256 ->   40 x  40 x 128 0.105 BF
  26 route  24 		                           ->   40 x  40 x 256 
  27 conv    128       1 x 1/ 1     40 x  40 x 256 ->   40 x  40 x 128 0.105 BF
  28 conv    128       1 x 1/ 1     40 x  40 x 128 ->   40 x  40 x 128 0.052 BF
  29 conv    128       3 x 3/ 1     40 x  40 x 128 ->   40 x  40 x 128 0.472 BF
  30 Shortcut Layer: 27,  wt = 0, wn = 0, outputs:  40 x  40 x 128 0.000 BF
  31 conv    128       1 x 1/ 1     40 x  40 x 128 ->   40 x  40 x 128 0.052 BF
  32 conv    128       3 x 3/ 1     40 x  40 x 128 ->   40 x  40 x 128 0.472 BF
  33 Shortcut Layer: 30,  wt = 0, wn = 0, outputs:  40 x  40 x 128 0.000 BF
  34 conv    128       1 x 1/ 1     40 x  40 x 128 ->   40 x  40 x 128 0.052 BF
  35 conv    128       3 x 3/ 1     40 x  40 x 128 ->   40 x  40 x 128 0.472 BF
  36 Shortcut Layer: 33,  wt = 0, wn = 0, outputs:  40 x  40 x 128 0.000 BF
  37 conv    128       1 x 1/ 1     40 x  40 x 128 ->   40 x  40 x 128 0.052 BF
  38 conv    128       3 x 3/ 1     40 x  40 x 128 ->   40 x  40 x 128 0.472 BF
  39 Shortcut Layer: 36,  wt = 0, wn = 0, outputs:  40 x  40 x 128 0.000 BF
  40 conv    128       1 x 1/ 1     40 x  40 x 128 ->   40 x  40 x 128 0.052 BF
  41 conv    128       3 x 3/ 1     40 x  40 x 128 ->   40 x  40 x 128 0.472 BF
  42 Shortcut Layer: 39,  wt = 0, wn = 0, outputs:  40 x  40 x 128 0.000 BF
  43 conv    128       1 x 1/ 1     40 x  40 x 128 ->   40 x  40 x 128 0.052 BF
  44 conv    128       3 x 3/ 1     40 x  40 x 128 ->   40 x  40 x 128 0.472 BF
  45 Shortcut Layer: 42,  wt = 0, wn = 0, outputs:  40 x  40 x 128 0.000 BF
  46 conv    128       1 x 1/ 1     40 x  40 x 128 ->   40 x  40 x 128 0.052 BF
  47 conv    128       3 x 3/ 1     40 x  40 x 128 ->   40 x  40 x 128 0.472 BF
  48 Shortcut Layer: 45,  wt = 0, wn = 0, outputs:  40 x  40 x 128 0.000 BF
  49 conv    128       1 x 1/ 1     40 x  40 x 128 ->   40 x  40 x 128 0.052 BF
  50 conv    128       3 x 3/ 1     40 x  40 x 128 ->   40 x  40 x 128 0.472 BF
  51 Shortcut Layer: 48,  wt = 0, wn = 0, outputs:  40 x  40 x 128 0.000 BF
  52 conv    128       1 x 1/ 1     40 x  40 x 128 ->   40 x  40 x 128 0.052 BF
  53 route  52 25 	                           ->   40 x  40 x 256 
  54 conv    256       1 x 1/ 1     40 x  40 x 256 ->   40 x  40 x 256 0.210 BF
  55 conv    512       3 x 3/ 2     40 x  40 x 256 ->   20 x  20 x 512 0.944 BF
  56 conv    256       1 x 1/ 1     20 x  20 x 512 ->   20 x  20 x 256 0.105 BF
  57 route  55 		                           ->   20 x  20 x 512 
  58 conv    256       1 x 1/ 1     20 x  20 x 512 ->   20 x  20 x 256 0.105 BF
  59 conv    256       1 x 1/ 1     20 x  20 x 256 ->   20 x  20 x 256 0.052 BF
  60 conv    256       3 x 3/ 1     20 x  20 x 256 ->   20 x  20 x 256 0.472 BF
  61 Shortcut Layer: 58,  wt = 0, wn = 0, outputs:  20 x  20 x 256 0.000 BF
  62 conv    256       1 x 1/ 1     20 x  20 x 256 ->   20 x  20 x 256 0.052 BF
  63 conv    256       3 x 3/ 1     20 x  20 x 256 ->   20 x  20 x 256 0.472 BF
  64 Shortcut Layer: 61,  wt = 0, wn = 0, outputs:  20 x  20 x 256 0.000 BF
  65 conv    256       1 x 1/ 1     20 x  20 x 256 ->   20 x  20 x 256 0.052 BF
  66 conv    256       3 x 3/ 1     20 x  20 x 256 ->   20 x  20 x 256 0.472 BF
  67 Shortcut Layer: 64,  wt = 0, wn = 0, outputs:  20 x  20 x 256 0.000 BF
  68 conv    256       1 x 1/ 1     20 x  20 x 256 ->   20 x  20 x 256 0.052 BF
  69 conv    256       3 x 3/ 1     20 x  20 x 256 ->   20 x  20 x 256 0.472 BF
  70 Shortcut Layer: 67,  wt = 0, wn = 0, outputs:  20 x  20 x 256 0.000 BF
  71 conv    256       1 x 1/ 1     20 x  20 x 256 ->   20 x  20 x 256 0.052 BF
  72 conv    256       3 x 3/ 1     20 x  20 x 256 ->   20 x  20 x 256 0.472 BF
  73 Shortcut Layer: 70,  wt = 0, wn = 0, outputs:  20 x  20 x 256 0.000 BF
  74 conv    256       1 x 1/ 1     20 x  20 x 256 ->   20 x  20 x 256 0.052 BF
  75 conv    256       3 x 3/ 1     20 x  20 x 256 ->   20 x  20 x 256 0.472 BF
  76 Shortcut Layer: 73,  wt = 0, wn = 0, outputs:  20 x  20 x 256 0.000 BF
  77 conv    256       1 x 1/ 1     20 x  20 x 256 ->   20 x  20 x 256 0.052 BF
  78 conv    256       3 x 3/ 1     20 x  20 x 256 ->   20 x  20 x 256 0.472 BF
  79 Shortcut Layer: 76,  wt = 0, wn = 0, outputs:  20 x  20 x 256 0.000 BF
  80 conv    256       1 x 1/ 1     20 x  20 x 256 ->   20 x  20 x 256 0.052 BF
  81 conv    256       3 x 3/ 1     20 x  20 x 256 ->   20 x  20 x 256 0.472 BF
  82 Shortcut Layer: 79,  wt = 0, wn = 0, outputs:  20 x  20 x 256 0.000 BF
  83 conv    256       1 x 1/ 1     20 x  20 x 256 ->   20 x  20 x 256 0.052 BF
  84 route  83 56 	                           ->   20 x  20 x 512 
  85 conv    512       1 x 1/ 1     20 x  20 x 512 ->   20 x  20 x 512 0.210 BF
  86 conv   1024       3 x 3/ 2     20 x  20 x 512 ->   10 x  10 x1024 0.944 BF
  87 conv    512       1 x 1/ 1     10 x  10 x1024 ->   10 x  10 x 512 0.105 BF
  88 route  86 		                           ->   10 x  10 x1024 
  89 conv    512       1 x 1/ 1     10 x  10 x1024 ->   10 x  10 x 512 0.105 BF
  90 conv    512       1 x 1/ 1     10 x  10 x 512 ->   10 x  10 x 512 0.052 BF
  91 conv    512       3 x 3/ 1     10 x  10 x 512 ->   10 x  10 x 512 0.472 BF
  92 Shortcut Layer: 89,  wt = 0, wn = 0, outputs:  10 x  10 x 512 0.000 BF
  93 conv    512       1 x 1/ 1     10 x  10 x 512 ->   10 x  10 x 512 0.052 BF
  94 conv    512       3 x 3/ 1     10 x  10 x 512 ->   10 x  10 x 512 0.472 BF
  95 Shortcut Layer: 92,  wt = 0, wn = 0, outputs:  10 x  10 x 512 0.000 BF
  96 conv    512       1 x 1/ 1     10 x  10 x 512 ->   10 x  10 x 512 0.052 BF
  97 conv    512       3 x 3/ 1     10 x  10 x 512 ->   10 x  10 x 512 0.472 BF
  98 Shortcut Layer: 95,  wt = 0, wn = 0, outputs:  10 x  10 x 512 0.000 BF
  99 conv    512       1 x 1/ 1     10 x  10 x 512 ->   10 x  10 x 512 0.052 BF
 100 conv    512       3 x 3/ 1     10 x  10 x 512 ->   10 x  10 x 512 0.472 BF
 101 Shortcut Layer: 98,  wt = 0, wn = 0, outputs:  10 x  10 x 512 0.000 BF
 102 conv    512       1 x 1/ 1     10 x  10 x 512 ->   10 x  10 x 512 0.052 BF
 103 route  102 87 	                           ->   10 x  10 x1024 
 104 conv   1024       1 x 1/ 1     10 x  10 x1024 ->   10 x  10 x1024 0.210 BF
 105 conv    512       1 x 1/ 1     10 x  10 x1024 ->   10 x  10 x 512 0.105 BF
 106 conv   1024       3 x 3/ 1     10 x  10 x 512 ->   10 x  10 x1024 0.944 BF
 107 conv    512       1 x 1/ 1     10 x  10 x1024 ->   10 x  10 x 512 0.105 BF
 108 max                5x 5/ 1     10 x  10 x 512 ->   10 x  10 x 512 0.001 BF
 109 route  107 		                           ->   10 x  10 x 512 
 110 max                9x 9/ 1     10 x  10 x 512 ->   10 x  10 x 512 0.004 BF
 111 route  107 		                           ->   10 x  10 x 512 
 112 max               13x13/ 1     10 x  10 x 512 ->   10 x  10 x 512 0.009 BF
 113 route  112 110 108 107 	                   ->   10 x  10 x2048 
 114 conv    512       1 x 1/ 1     10 x  10 x2048 ->   10 x  10 x 512 0.210 BF
 115 conv   1024       3 x 3/ 1     10 x  10 x 512 ->   10 x  10 x1024 0.944 BF
 116 conv    512       1 x 1/ 1     10 x  10 x1024 ->   10 x  10 x 512 0.105 BF
 117 conv    256       1 x 1/ 1     10 x  10 x 512 ->   10 x  10 x 256 0.026 BF
 118 upsample                 2x    10 x  10 x 256 ->   20 x  20 x 256
 119 route  85 		                           ->   20 x  20 x 512 
 120 conv    256       1 x 1/ 1     20 x  20 x 512 ->   20 x  20 x 256 0.105 BF
 121 route  120 118 	                           ->   20 x  20 x 512 
 122 conv    256       1 x 1/ 1     20 x  20 x 512 ->   20 x  20 x 256 0.105 BF
 123 conv    512       3 x 3/ 1     20 x  20 x 256 ->   20 x  20 x 512 0.944 BF
 124 conv    256       1 x 1/ 1     20 x  20 x 512 ->   20 x  20 x 256 0.105 BF
 125 conv    512       3 x 3/ 1     20 x  20 x 256 ->   20 x  20 x 512 0.944 BF
 126 conv    256       1 x 1/ 1     20 x  20 x 512 ->   20 x  20 x 256 0.105 BF
 127 conv    128       1 x 1/ 1     20 x  20 x 256 ->   20 x  20 x 128 0.026 BF
 128 upsample                 2x    20 x  20 x 128 ->   40 x  40 x 128
 129 route  54 		                           ->   40 x  40 x 256 
 130 conv    128       1 x 1/ 1     40 x  40 x 256 ->   40 x  40 x 128 0.105 BF
 131 route  130 128 	                           ->   40 x  40 x 256 
 132 conv    128       1 x 1/ 1     40 x  40 x 256 ->   40 x  40 x 128 0.105 BF
 133 conv    256       3 x 3/ 1     40 x  40 x 128 ->   40 x  40 x 256 0.944 BF
 134 conv    128       1 x 1/ 1     40 x  40 x 256 ->   40 x  40 x 128 0.105 BF
 135 conv    256       3 x 3/ 1     40 x  40 x 128 ->   40 x  40 x 256 0.944 BF
 136 conv    128       1 x 1/ 1     40 x  40 x 256 ->   40 x  40 x 128 0.105 BF
 137 conv     18       3 x 3/ 1     40 x  40 x 128 ->   40 x  40 x  18 0.066 BF
 138 conv     18       1 x 1/ 1     40 x  40 x  18 ->   40 x  40 x  18 0.001 BF
 139 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.20
nms_kind: greedynms (1), beta = 0.600000 
 140 route  136 		                           ->   40 x  40 x 128 
 141 conv    256       3 x 3/ 2     40 x  40 x 128 ->   20 x  20 x 256 0.236 BF
 142 route  141 126 	                           ->   20 x  20 x 512 
 143 conv    256       1 x 1/ 1     20 x  20 x 512 ->   20 x  20 x 256 0.105 BF
 144 conv    512       3 x 3/ 1     20 x  20 x 256 ->   20 x  20 x 512 0.944 BF
 145 conv    256       1 x 1/ 1     20 x  20 x 512 ->   20 x  20 x 256 0.105 BF
 146 conv    512       3 x 3/ 1     20 x  20 x 256 ->   20 x  20 x 512 0.944 BF
 147 conv    256       1 x 1/ 1     20 x  20 x 512 ->   20 x  20 x 256 0.105 BF
 148 conv    512       3 x 3/ 1     20 x  20 x 256 ->   20 x  20 x 512 0.944 BF
 149 conv     18       1 x 1/ 1     20 x  20 x 512 ->   20 x  20 x  18 0.007 BF
 150 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.10
nms_kind: greedynms (1), beta = 0.600000 
 151 route  147 		                           ->   20 x  20 x 256 
 152 conv    512       3 x 3/ 2     20 x  20 x 256 ->   10 x  10 x 512 0.236 BF
 153 route  152 116 	                           ->   10 x  10 x1024 
 154 conv    512       1 x 1/ 1     10 x  10 x1024 ->   10 x  10 x 512 0.105 BF
 155 conv   1024       3 x 3/ 1     10 x  10 x 512 ->   10 x  10 x1024 0.944 BF
 156 conv    512       1 x 1/ 1     10 x  10 x1024 ->   10 x  10 x 512 0.105 BF
 157 conv   1024       3 x 3/ 1     10 x  10 x 512 ->   10 x  10 x1024 0.944 BF
 158 conv    512       1 x 1/ 1     10 x  10 x1024 ->   10 x  10 x 512 0.105 BF
 159 conv   1024       3 x 3/ 1     10 x  10 x 512 ->   10 x  10 x1024 0.944 BF
 160 conv     18       1 x 1/ 1     10 x  10 x1024 ->   10 x  10 x  18 0.004 BF
 161 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05
nms_kind: greedynms (1), beta = 0.600000 
Total BFLOPS 34.353 
avg_outputs = 287459 
Loading weights from weights/yolov4-obj_3000.weights...
 seen 64, trained: 192 K-images (3 Kilo-batches_64) 
Done! Loaded 162 layers from weights-file 
video file: nvarguscamerasrc

(darknet:13879): GLib-GObject-WARNING **: 10:39:51.351: invalid cast from 'GstNvArgusCameraSrc' to 'GstBin'

(darknet:13879): GStreamer-CRITICAL **: 10:39:51.352: gst_bin_iterate_elements: assertion 'GST_IS_BIN (bin)' failed

(darknet:13879): GStreamer-CRITICAL **: 10:39:51.352: gst_iterator_next: assertion 'it != NULL' failed

(darknet:13879): GStreamer-CRITICAL **: 10:39:51.352: gst_iterator_free: assertion 'it != NULL' failed
[ WARN:0] global /home/nvidia/host/build_opencv/nv_opencv/modules/videoio/src/cap_gstreamer.cpp (801) open OpenCV | GStreamer warning: cannot find appsink in manual pipeline
[ WARN:0] global /home/nvidia/host/build_opencv/nv_opencv/modules/videoio/src/cap_gstreamer.cpp (480) isPipelinePlaying OpenCV | GStreamer warning: GStreamer: pipeline have not been created
[ERROR:0] global /home/nvidia/host/build_opencv/nv_opencv/modules/videoio/src/cap.cpp (116) open VIDEOIO(CV_IMAGES): raised OpenCV exception:

OpenCV(4.1.1) /home/nvidia/host/build_opencv/nv_opencv/modules/videoio/src/cap_images.cpp:253: error: (-5:Bad argument) CAP_IMAGES: can't find starting number (in the name of file): nvarguscamerasrc in function 'icvExtractPattern'


 Video-stream stopped! 
 Video-stream stopped! 
 Video-stream stopped! 
 Video-stream stopped! 

I would like to know if there is any effective way to deal with this.

by the way, i am not using the GPU, solely on Jetson Nano.

Hi,

It seems that you try to open the camera with nvarguscamerasrc.

Please noted that the nvarguscamerasrc is used for the CSI camera.
Do you use a CSI camera?

Thanks.

i am using Raspberry Pi Camera Module 2
sony IMX219 8MP

when i test this command line

gst-launch-1.0 nvarguscamerasrc sensor_id=0 !    'video/x-raw(memory:NVMM),width=1920, height=1080, framerate=30/1' !    nvvidconv flip-method=4 ! 'video/x-raw,width=960, height=540' !    nvvidconv ! nvegltransform ! nveglglessink -e
                                         or
gst-launch-1.0 nvarguscamerasrc ! nvoverlaysink

the video input from the camera was displayed on the screen.

thanks.

based on your experience, what do you think is the problem?

Hi,

Since darknet use OpenCV to read the camera, could you try to open it with OpenCV API?
Below is an example from the community for your reference:

Thanks.

i have tried the example that you gave and it works fine. is there any other solution?

Hi,

Since the sample and darknet are all using OpenCV for the camera.
Would you mind to check is there any difference?

Thanks.

how should i do that? i am sorry if my question sound offended, but i can be considered as a beginner in this field.

Hi,

Is this duplicate to topic 216214 below?
If yes, please check the latest command to see if it works.

Thanks.