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

user140308 · May 30, 2022, 9:00am

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.

AastaLLL · May 31, 2022, 2:36am

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.

user140308 · May 31, 2022, 7:20am

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.

user140308 · June 2, 2022, 11:46am

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

AastaLLL · June 6, 2022, 5:51am

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:

github.com

JetsonHacksNano/CSI-Camera/blob/master/simple_camera.py

# MIT License
# Copyright (c) 2019-2022 JetsonHacks

# Using a CSI camera (such as the Raspberry Pi Version 2) connected to a
# NVIDIA Jetson Nano Developer Kit using OpenCV
# Drivers for the camera and OpenCV are included in the base image

import cv2

""" 
gstreamer_pipeline returns a GStreamer pipeline for capturing from the CSI camera
Flip the image by setting the flip_method (most common values: 0 and 2)
display_width and display_height determine the size of each camera pane in the window on the screen
Default 1920x1080 displayd in a 1/4 size window
"""

def gstreamer_pipeline(
    sensor_id=0,
    capture_width=1920,
    capture_height=1080,

This file has been truncated. show original

Thanks.

user140308 · June 6, 2022, 6:13am

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

AastaLLL · June 8, 2022, 6:24am

Hi,

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

Thanks.

user140308 · June 8, 2022, 6:38am

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

AastaLLL · June 16, 2022, 5:53am

Hi,

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

Thanks.

system · July 6, 2022, 7:38am

This topic was automatically closed 14 days after the last reply. New replies are no longer allowed.

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

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