"Video-stream stopped!"

ee181163 · October 4, 2021, 6:56am

We are using a darknet for object detection. We have been trying to apply the weight file that we have trained in YOLOv3 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

$./darknet detector demo datasets.data yolov3-voc.cfg yolov3-voc_4000.weights “gst-launch-1.0 nvarguscamerasrc sensor_mode=0 ! ‘video/x-raw(memory:NVMM),width=3820, height=2464, framerate=21/1, format=NV12’ ! nvvidconv flip-method=0 ! ‘video/x-raw,width=1280, height=760’ ! nvvidconv ! nvegltransform ! nveglglessink -e”

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

AastaLLL · October 4, 2021, 7:29am

Hi,

Do you run the command on Nano or a Linux-based desktop?
If Nano is used, would you mind sharing the whole output log with us?

Thanks.

ee181163 · October 4, 2021, 7:44am

Hello, thanks for the reply.

We use the command on Nano.
The whole output log is following.

CUDA-version: 10020 (10020), cuDNN: 8.2.1, GPU count: 1  
 OpenCV version: 4.1.1
Demo
 0 : compute_capability = 530, cudnn_half = 0, GPU: NVIDIA Tegra X1 
net.optimized_memory = 0 
mini_batch = 1, batch = 8, time_steps = 1, train = 0 
   layer   filters  size/strd(dil)      input                output
   0 Create CUDA-stream - 0 
 Create cudnn-handle 0 
conv     32       3 x 3/ 1    416 x 416 x   3 ->  416 x 416 x  32 0.299 BF
   1 conv     64       3 x 3/ 2    416 x 416 x  32 ->  208 x 208 x  64 1.595 BF
   2 conv     32       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  32 0.177 BF
   3 conv     64       3 x 3/ 1    208 x 208 x  32 ->  208 x 208 x  64 1.595 BF
   4 Shortcut Layer: 1,  wt = 0, wn = 0, outputs: 208 x 208 x  64 0.003 BF
   5 conv    128       3 x 3/ 2    208 x 208 x  64 ->  104 x 104 x 128 1.595 BF
   6 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF
   7 conv    128       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x 128 1.595 BF
   8 Shortcut Layer: 5,  wt = 0, wn = 0, outputs: 104 x 104 x 128 0.001 BF
   9 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF
  10 conv    128       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x 128 1.595 BF
  11 Shortcut Layer: 8,  wt = 0, wn = 0, outputs: 104 x 104 x 128 0.001 BF
  12 conv    256       3 x 3/ 2    104 x 104 x 128 ->   52 x  52 x 256 1.595 BF
  13 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  14 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  15 Shortcut Layer: 12,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  16 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  17 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  18 Shortcut Layer: 15,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  19 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  20 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  21 Shortcut Layer: 18,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  22 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  23 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  24 Shortcut Layer: 21,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  25 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  26 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  27 Shortcut Layer: 24,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  28 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  29 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  30 Shortcut Layer: 27,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  31 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  32 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  33 Shortcut Layer: 30,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  34 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  35 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  36 Shortcut Layer: 33,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  37 conv    512       3 x 3/ 2     52 x  52 x 256 ->   26 x  26 x 512 1.595 BF
  38 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  39 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  40 Shortcut Layer: 37,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  41 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  42 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  43 Shortcut Layer: 40,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  44 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  45 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  46 Shortcut Layer: 43,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  47 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  48 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  49 Shortcut Layer: 46,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  50 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  51 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  52 Shortcut Layer: 49,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  53 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  54 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  55 Shortcut Layer: 52,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  56 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  57 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  58 Shortcut Layer: 55,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  59 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  60 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  61 Shortcut Layer: 58,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  62 conv   1024       3 x 3/ 2     26 x  26 x 512 ->   13 x  13 x1024 1.595 BF
  63 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  64 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  65 Shortcut Layer: 62,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  66 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  67 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  68 Shortcut Layer: 65,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  69 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  70 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  71 Shortcut Layer: 68,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  72 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  73 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  74 Shortcut Layer: 71,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  75 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  76 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  77 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  78 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  79 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  80 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  81 conv     18       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x  18 0.006 BF
  82 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
  83 route  79 		                           ->   13 x  13 x 512 
  84 conv    256       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 256 0.044 BF
  85 upsample                 2x    13 x  13 x 256 ->   26 x  26 x 256
  86 route  85 61 	                           ->   26 x  26 x 768 
  87 conv    256       1 x 1/ 1     26 x  26 x 768 ->   26 x  26 x 256 0.266 BF
  88 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  89 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  90 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  91 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  92 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  93 conv     18       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x  18 0.012 BF
  94 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
  95 route  91 		                           ->   26 x  26 x 256 
  96 conv    128       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 128 0.044 BF
  97 upsample                 2x    26 x  26 x 128 ->   52 x  52 x 128
  98 route  97 36 	                           ->   52 x  52 x 384 
  99 conv    128       1 x 1/ 1     52 x  52 x 384 ->   52 x  52 x 128 0.266 BF
 100 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
 101 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
 102 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
 103 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
 104 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
 105 conv     18       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x  18 0.025 BF
 106 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
Total BFLOPS 65.304 
avg_outputs = 516723 
 Allocate additional workspace_size = 52.44 MB 
Loading weights from yolov3-voc_4000.weights...
 seen 64, trained: 32 K-images (0 Kilo-batches_64) 
Done! Loaded 107 layers from weights-file 
video file: gst-launch-1.0 nvarguscamerasrc sensor_mode=0 ! 'video/x-raw(memory:NVMM),width=3820, height=2464, framerate=21/1, format=NV12' ! nvvidconv flip-method=0 ! 'video/x-raw,width=1280, height=760' ! nvvidconv ! nvegltransform ! nveglglessink -e

(darknet:18757): GStreamer-CRITICAL **: 16:37:44.227: gst_element_make_from_uri: assertion 'gst_uri_is_valid (uri)' failed

(darknet:18757): GStreamer-CRITICAL **: 16:37:44.229: gst_element_make_from_uri: assertion 'gst_uri_is_valid (uri)' failed
[ WARN:0] global /home/nvidia/host/build_opencv/nv_opencv/modules/videoio/src/cap_gstreamer.cpp (711) open OpenCV | GStreamer warning: Error opening bin: unexpected reference "gst-launch-1" - ignoring
[ 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
 Video-stream stopped! 
 Video-stream stopped! 
 Video-stream stopped! 
 Video-stream stopped! 
 Video-stream stopped! 
 Video-stream stopped!

AastaLLL · October 14, 2021, 6:23am

Hi,

gst-launch-1.0 should not be the part of the pipeline string.
Could you try if the following command works?

$./darknet detector demo datasets.data yolov3-voc.cfg yolov3-voc_4000.weights “nvarguscamerasrc sensor_mode=0 ! ‘video/x-raw(memory:NVMM),width=3820, height=2464, framerate=21/1, format=NV12’ ! nvvidconv flip-method=0 ! ‘video/x-raw,width=1280, height=760’ ! nvvidconv ! nvegltransform ! nveglglessink -e”

Thanks.

ee181163 · October 14, 2021, 6:57am

Hi!

I tried to run the code you suggested, using “coco.data” file because "datasets.data"file did not open.
However, I got the error again.

How can I solve this problem?

./darknet detector demo cfg/coco.data cfg/yolov3.cfg yolov3-tiny.weights "nvarguscamerasrc sensor_mode=0 ! 'video/x-raw(memory:NVMM),width=3820, height=2464, framerate=21/1, format=NV12' ! nvvidconv flip-method=0 ! 'video/x-raw,width=1280, height=760' ! nvvidconv ! nvegltransform ! nveglglessink -e"
 CUDA-version: 10020 (10020), cuDNN: 8.2.1, GPU count: 1  
 OpenCV version: 4.1.1
Demo
 0 : compute_capability = 530, cudnn_half = 0, GPU: NVIDIA Tegra X1 
net.optimized_memory = 0 
mini_batch = 1, batch = 1, time_steps = 1, train = 0 
   layer   filters  size/strd(dil)      input                output
   0 Create CUDA-stream - 0 
 Create cudnn-handle 0 
conv     32       3 x 3/ 1    416 x 416 x   3 ->  416 x 416 x  32 0.299 BF
   1 conv     64       3 x 3/ 2    416 x 416 x  32 ->  208 x 208 x  64 1.595 BF
   2 conv     32       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  32 0.177 BF
   3 conv     64       3 x 3/ 1    208 x 208 x  32 ->  208 x 208 x  64 1.595 BF
   4 Shortcut Layer: 1,  wt = 0, wn = 0, outputs: 208 x 208 x  64 0.003 BF
   5 conv    128       3 x 3/ 2    208 x 208 x  64 ->  104 x 104 x 128 1.595 BF
   6 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF
   7 conv    128       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x 128 1.595 BF
   8 Shortcut Layer: 5,  wt = 0, wn = 0, outputs: 104 x 104 x 128 0.001 BF
   9 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF
  10 conv    128       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x 128 1.595 BF
  11 Shortcut Layer: 8,  wt = 0, wn = 0, outputs: 104 x 104 x 128 0.001 BF
  12 conv    256       3 x 3/ 2    104 x 104 x 128 ->   52 x  52 x 256 1.595 BF
  13 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  14 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  15 Shortcut Layer: 12,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  16 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  17 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  18 Shortcut Layer: 15,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  19 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  20 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  21 Shortcut Layer: 18,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  22 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  23 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  24 Shortcut Layer: 21,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  25 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  26 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  27 Shortcut Layer: 24,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  28 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  29 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  30 Shortcut Layer: 27,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  31 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  32 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  33 Shortcut Layer: 30,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  34 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  35 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  36 Shortcut Layer: 33,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  37 conv    512       3 x 3/ 2     52 x  52 x 256 ->   26 x  26 x 512 1.595 BF
  38 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  39 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  40 Shortcut Layer: 37,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  41 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  42 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  43 Shortcut Layer: 40,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  44 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  45 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  46 Shortcut Layer: 43,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  47 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  48 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  49 Shortcut Layer: 46,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  50 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  51 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  52 Shortcut Layer: 49,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  53 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  54 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  55 Shortcut Layer: 52,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  56 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  57 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  58 Shortcut Layer: 55,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  59 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  60 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  61 Shortcut Layer: 58,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  62 conv   1024       3 x 3/ 2     26 x  26 x 512 ->   13 x  13 x1024 1.595 BF
  63 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  64 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  65 Shortcut Layer: 62,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  66 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  67 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  68 Shortcut Layer: 65,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  69 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  70 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  71 Shortcut Layer: 68,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  72 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  73 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  74 Shortcut Layer: 71,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  75 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  76 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  77 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  78 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  79 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  80 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  81 conv    255       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 255 0.088 BF
  82 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
  83 route  79 		                           ->   13 x  13 x 512 
  84 conv    256       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 256 0.044 BF
  85 upsample                 2x    13 x  13 x 256 ->   26 x  26 x 256
  86 route  85 61 	                           ->   26 x  26 x 768 
  87 conv    256       1 x 1/ 1     26 x  26 x 768 ->   26 x  26 x 256 0.266 BF
  88 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  89 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  90 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  91 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  92 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  93 conv    255       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 255 0.177 BF
  94 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
  95 route  91 		                           ->   26 x  26 x 256 
  96 conv    128       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 128 0.044 BF
  97 upsample                 2x    26 x  26 x 128 ->   52 x  52 x 128
  98 route  97 36 	                           ->   52 x  52 x 384 
  99 conv    128       1 x 1/ 1     52 x  52 x 384 ->   52 x  52 x 128 0.266 BF
 100 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
 101 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
 102 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
 103 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
 104 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
 105 conv    255       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 255 0.353 BF
 106 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
Total BFLOPS 65.879 
avg_outputs = 532444 
 Allocate additional workspace_size = 52.44 MB 
Loading weights from yolov3-tiny.weights...
 seen 64, trained: 32013 K-images (500 Kilo-batches_64) 
Done! Loaded 48 layers from weights-file 
video file: nvarguscamerasrc sensor_mode=0 ! 'video/x-raw(memory:NVMM),width=3820, height=2464, framerate=21/1, format=NV12' ! nvvidconv flip-method=0 ! 'video/x-raw,width=1280, height=760' ! nvvidconv ! nvegltransform ! nveglglessink -e

(darknet:17691): GStreamer-CRITICAL **: 15:30:24.455: gst_element_make_from_uri: assertion 'gst_uri_is_valid (uri)' failed

(darknet:17691): GStreamer-CRITICAL **: 15:30:24.503: gst_element_make_from_uri: assertion 'gst_uri_is_valid (uri)' failed
[ WARN:0] global /home/nvidia/host/build_opencv/nv_opencv/modules/videoio/src/cap_gstreamer.cpp (711) open OpenCV | GStreamer warning: Error opening bin: syntax error
[ 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
 Video-stream stopped! 
 Video-stream stopped! 
 Video-stream stopped! 
 Video-stream stopped! 
 Video-stream stopped! 
 Video-stream stopped! 
 Video-stream stopped! 
 Video-stream stopped! 
 Video-stream stopped! 
 Video-stream stopped! 
 Video-stream stopped!

ee181163 · October 14, 2021, 9:26am

I tried various things.
I have checked the connected device by entering the following command.

$ gst-device-monitor-1.0

Probing devices...


Device found:

	name  : Monitor of Built-in Audio Digital Stereo (HDMI)
	class : Audio/Source
	caps  : audio/x-raw, format=(string){ S16LE, S16BE, F32LE, F32BE, S32LE, S32BE, S24LE, S24BE, S24_32LE, S24_32BE, U8 }, layout=(string)interleaved, rate=(int)[ 1, 2147483647 ], channels=(int)[ 1, 32 ];
	        audio/x-alaw, rate=(int)[ 1, 2147483647 ], channels=(int)[ 1, 32 ];
	        audio/x-mulaw, rate=(int)[ 1, 2147483647 ], channels=(int)[ 1, 32 ];
	properties:
		device.description = "Monitor\ of\ Built-in\ Audio\ Digital\ Stereo\ \(HDMI\)"
		device.class = monitor
		alsa.card = 0
		alsa.card_name = tegra-hda
		alsa.long_card_name = "tegra-hda\ at\ 0x70038000\ irq\ 83"
		device.bus_path = platform-70030000.hda
		sysfs.path = /devices/70030000.hda/sound/card0
		device.form_factor = internal
		device.string = 0
		module-udev-detect.discovered = 1
		device.icon_name = audio-card
	gst-launch-1.0 pulsesrc device=alsa_output.platform-70030000.hda.hdmi-stereo.monitor ! ...


Device found:

	name  : Monitor of Built-in Audio Analog Stereo
	class : Audio/Source
	caps  : audio/x-raw, format=(string){ S16LE, S16BE, F32LE, F32BE, S32LE, S32BE, S24LE, S24BE, S24_32LE, S24_32BE, U8 }, layout=(string)interleaved, rate=(int)[ 1, 2147483647 ], channels=(int)[ 1, 32 ];
	        audio/x-alaw, rate=(int)[ 1, 2147483647 ], channels=(int)[ 1, 32 ];
	        audio/x-mulaw, rate=(int)[ 1, 2147483647 ], channels=(int)[ 1, 32 ];
	properties:
		device.description = "Monitor\ of\ Built-in\ Audio\ Analog\ Stereo"
		device.class = monitor
		alsa.card = 1
		alsa.card_name = tegra-snd-t210ref-mobile-rt565x
		alsa.long_card_name = tegra-snd-t210ref-mobile-rt565x
		device.bus_path = platform-sound
		sysfs.path = /devices/sound/sound/card1
		device.form_factor = internal
		device.string = 1
		module-udev-detect.discovered = 1
		device.icon_name = audio-card
	gst-launch-1.0 pulsesrc device=alsa_output.platform-sound.analog-stereo.monitor ! ...


Device found:

	name  : Built-in Audio Analog Stereo
	class : Audio/Source
	caps  : audio/x-raw, format=(string){ S16LE, S16BE, F32LE, F32BE, S32LE, S32BE, S24LE, S24BE, S24_32LE, S24_32BE, U8 }, layout=(string)interleaved, rate=(int)[ 1, 2147483647 ], channels=(int)[ 1, 32 ];
	        audio/x-alaw, rate=(int)[ 1, 2147483647 ], channels=(int)[ 1, 32 ];
	        audio/x-mulaw, rate=(int)[ 1, 2147483647 ], channels=(int)[ 1, 32 ];
	properties:
		alsa.resolution_bits = 16
		device.api = alsa
		device.class = sound
		alsa.class = generic
		alsa.subclass = generic-mix
		alsa.name = ""
		alsa.id = "ADMAIF1\ CIF\ ADMAIF1-0"
		alsa.subdevice = 0
		alsa.subdevice_name = "subdevice\ \#0"
		alsa.device = 0
		alsa.card = 1
		alsa.card_name = tegra-snd-t210ref-mobile-rt565x
		alsa.long_card_name = tegra-snd-t210ref-mobile-rt565x
		device.bus_path = platform-sound
		sysfs.path = /devices/sound/sound/card1
		device.form_factor = internal
		device.string = front:1
		device.buffering.buffer_size = 32768
		device.buffering.fragment_size = 16384
		device.access_mode = mmap+timer
		device.profile.name = analog-stereo
		device.profile.description = "Analog\ Stereo"
		device.description = "Built-in\ Audio\ Analog\ Stereo"
		module-udev-detect.discovered = 1
		device.icon_name = audio-card
	gst-launch-1.0 pulsesrc device=alsa_input.platform-sound.analog-stereo ! ...


Device found:

	name  : Built-in Audio Digital Stereo (HDMI)
	class : Audio/Sink
	caps  : audio/x-raw, format=(string){ S16LE, S16BE, F32LE, F32BE, S32LE, S32BE, S24LE, S24BE, S24_32LE, S24_32BE, U8 }, layout=(string)interleaved, rate=(int)[ 1, 2147483647 ], channels=(int)[ 1, 32 ];
	        audio/x-alaw, rate=(int)[ 1, 2147483647 ], channels=(int)[ 1, 32 ];
	        audio/x-mulaw, rate=(int)[ 1, 2147483647 ], channels=(int)[ 1, 32 ];
	properties:
		alsa.resolution_bits = 16
		device.api = alsa
		device.class = sound
		alsa.class = generic
		alsa.subclass = generic-mix
		alsa.name = "HDMI\ 0"
		alsa.id = "HDMI\ 0"
		alsa.subdevice = 0
		alsa.subdevice_name = "subdevice\ \#0"
		alsa.device = 3
		alsa.card = 0
		alsa.card_name = tegra-hda
		alsa.long_card_name = "tegra-hda\ at\ 0x70038000\ irq\ 83"
		device.bus_path = platform-70030000.hda
		sysfs.path = /devices/70030000.hda/sound/card0
		device.form_factor = internal
		device.string = hdmi:0
		device.buffering.buffer_size = 65536
		device.buffering.fragment_size = 32768
		device.access_mode = mmap+timer
		device.profile.name = hdmi-stereo
		device.profile.description = "Digital\ Stereo\ \(HDMI\)"
		device.description = "Built-in\ Audio\ Digital\ Stereo\ \(HDMI\)"
		alsa.mixer_name = "Nvidia\ Tegra210\ HDMI/DP"
		alsa.components = "HDA:10de0029\,10de0101\,00100100"
		module-udev-detect.discovered = 1
		device.icon_name = audio-card
	gst-launch-1.0 ... ! pulsesink device=alsa_output.platform-70030000.hda.hdmi-stereo


Device found:

	name  : Built-in Audio Analog Stereo
	class : Audio/Sink
	caps  : audio/x-raw, format=(string){ S16LE, S16BE, F32LE, F32BE, S32LE, S32BE, S24LE, S24BE, S24_32LE, S24_32BE, U8 }, layout=(string)interleaved, rate=(int)[ 1, 2147483647 ], channels=(int)[ 1, 32 ];
	        audio/x-alaw, rate=(int)[ 1, 2147483647 ], channels=(int)[ 1, 32 ];
	        audio/x-mulaw, rate=(int)[ 1, 2147483647 ], channels=(int)[ 1, 32 ];
	properties:
		alsa.resolution_bits = 16
		device.api = alsa
		device.class = sound
		alsa.class = generic
		alsa.subclass = generic-mix
		alsa.name = ""
		alsa.id = "ADMAIF1\ CIF\ ADMAIF1-0"
		alsa.subdevice = 0
		alsa.subdevice_name = "subdevice\ \#0"
		alsa.device = 0
		alsa.card = 1
		alsa.card_name = tegra-snd-t210ref-mobile-rt565x
		alsa.long_card_name = tegra-snd-t210ref-mobile-rt565x
		device.bus_path = platform-sound
		sysfs.path = /devices/sound/sound/card1
		device.form_factor = internal
		device.string = front:1
		device.buffering.buffer_size = 32768
		device.buffering.fragment_size = 16384
		device.access_mode = mmap+timer
		device.profile.name = analog-stereo
		device.profile.description = "Analog\ Stereo"
		device.description = "Built-in\ Audio\ Analog\ Stereo"
		module-udev-detect.discovered = 1
		device.icon_name = audio-card
	gst-launch-1.0 ... ! pulsesink device=alsa_output.platform-sound.analog-stereo

The results showed that the camera device was not connected.
However, when I entered

gst-launch-1.0 nvarguscamerasrc ! nvoverlaysink

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

By the way, the camera I am using is
“SainSmart IMX219 camera module, 8MP sensor for NVIDIA Jetson Nano 160 degree FoV 3280x2464 resolution, RoHS certified”.

thanks.

AastaLLL · October 26, 2021, 6:42am

Hi,

Could you check if the below comment also works for your use case?

Thanks.

system · November 17, 2021, 4:53am

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

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"Video-stream stopped!"

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