Green Screen and Object Detection

user140308 · June 1, 2022, 1:11pm

Hi, i am using a darknet for object detection. I 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 a green screen when i run the command below.

./darknet detector demo data/obj.data cfg/yolov4-obj.cfg weights/yolov4-obj_3000.weights -c 0

this is the output

./darknet detector demo data/obj.data cfg/yolov4-obj.cfg weights/yolov4-obj_3000.weights -c 0
 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     32 x  32 x   3 ->   32 x  32 x  32 0.002 BF
   1 conv     64       3 x 3/ 2     32 x  32 x  32 ->   16 x  16 x  64 0.009 BF
   2 conv     64       1 x 1/ 1     16 x  16 x  64 ->   16 x  16 x  64 0.002 BF
   3 route  1 		                           ->   16 x  16 x  64 
   4 conv     64       1 x 1/ 1     16 x  16 x  64 ->   16 x  16 x  64 0.002 BF
   5 conv     32       1 x 1/ 1     16 x  16 x  64 ->   16 x  16 x  32 0.001 BF
   6 conv     64       3 x 3/ 1     16 x  16 x  32 ->   16 x  16 x  64 0.009 BF
   7 Shortcut Layer: 4,  wt = 0, wn = 0, outputs:  16 x  16 x  64 0.000 BF
   8 conv     64       1 x 1/ 1     16 x  16 x  64 ->   16 x  16 x  64 0.002 BF
   9 route  8 2 	                           ->   16 x  16 x 128 
  10 conv     64       1 x 1/ 1     16 x  16 x 128 ->   16 x  16 x  64 0.004 BF
  11 conv    128       3 x 3/ 2     16 x  16 x  64 ->    8 x   8 x 128 0.009 BF
  12 conv     64       1 x 1/ 1      8 x   8 x 128 ->    8 x   8 x  64 0.001 BF
  13 route  11 		                           ->    8 x   8 x 128 
  14 conv     64       1 x 1/ 1      8 x   8 x 128 ->    8 x   8 x  64 0.001 BF
  15 conv     64       1 x 1/ 1      8 x   8 x  64 ->    8 x   8 x  64 0.001 BF
  16 conv     64       3 x 3/ 1      8 x   8 x  64 ->    8 x   8 x  64 0.005 BF
  17 Shortcut Layer: 14,  wt = 0, wn = 0, outputs:   8 x   8 x  64 0.000 BF
  18 conv     64       1 x 1/ 1      8 x   8 x  64 ->    8 x   8 x  64 0.001 BF
  19 conv     64       3 x 3/ 1      8 x   8 x  64 ->    8 x   8 x  64 0.005 BF
  20 Shortcut Layer: 17,  wt = 0, wn = 0, outputs:   8 x   8 x  64 0.000 BF
  21 conv     64       1 x 1/ 1      8 x   8 x  64 ->    8 x   8 x  64 0.001 BF
  22 route  21 12 	                           ->    8 x   8 x 128 
  23 conv    128       1 x 1/ 1      8 x   8 x 128 ->    8 x   8 x 128 0.002 BF
  24 conv    256       3 x 3/ 2      8 x   8 x 128 ->    4 x   4 x 256 0.009 BF
  25 conv    128       1 x 1/ 1      4 x   4 x 256 ->    4 x   4 x 128 0.001 BF
  26 route  24 		                           ->    4 x   4 x 256 
  27 conv    128       1 x 1/ 1      4 x   4 x 256 ->    4 x   4 x 128 0.001 BF
  28 conv    128       1 x 1/ 1      4 x   4 x 128 ->    4 x   4 x 128 0.001 BF
  29 conv    128       3 x 3/ 1      4 x   4 x 128 ->    4 x   4 x 128 0.005 BF
  30 Shortcut Layer: 27,  wt = 0, wn = 0, outputs:   4 x   4 x 128 0.000 BF
  31 conv    128       1 x 1/ 1      4 x   4 x 128 ->    4 x   4 x 128 0.001 BF
  32 conv    128       3 x 3/ 1      4 x   4 x 128 ->    4 x   4 x 128 0.005 BF
  33 Shortcut Layer: 30,  wt = 0, wn = 0, outputs:   4 x   4 x 128 0.000 BF
  34 conv    128       1 x 1/ 1      4 x   4 x 128 ->    4 x   4 x 128 0.001 BF
  35 conv    128       3 x 3/ 1      4 x   4 x 128 ->    4 x   4 x 128 0.005 BF
  36 Shortcut Layer: 33,  wt = 0, wn = 0, outputs:   4 x   4 x 128 0.000 BF
  37 conv    128       1 x 1/ 1      4 x   4 x 128 ->    4 x   4 x 128 0.001 BF
  38 conv    128       3 x 3/ 1      4 x   4 x 128 ->    4 x   4 x 128 0.005 BF
  39 Shortcut Layer: 36,  wt = 0, wn = 0, outputs:   4 x   4 x 128 0.000 BF
  40 conv    128       1 x 1/ 1      4 x   4 x 128 ->    4 x   4 x 128 0.001 BF
  41 conv    128       3 x 3/ 1      4 x   4 x 128 ->    4 x   4 x 128 0.005 BF
  42 Shortcut Layer: 39,  wt = 0, wn = 0, outputs:   4 x   4 x 128 0.000 BF
  43 conv    128       1 x 1/ 1      4 x   4 x 128 ->    4 x   4 x 128 0.001 BF
  44 conv    128       3 x 3/ 1      4 x   4 x 128 ->    4 x   4 x 128 0.005 BF
  45 Shortcut Layer: 42,  wt = 0, wn = 0, outputs:   4 x   4 x 128 0.000 BF
  46 conv    128       1 x 1/ 1      4 x   4 x 128 ->    4 x   4 x 128 0.001 BF
  47 conv    128       3 x 3/ 1      4 x   4 x 128 ->    4 x   4 x 128 0.005 BF
  48 Shortcut Layer: 45,  wt = 0, wn = 0, outputs:   4 x   4 x 128 0.000 BF
  49 conv    128       1 x 1/ 1      4 x   4 x 128 ->    4 x   4 x 128 0.001 BF
  50 conv    128       3 x 3/ 1      4 x   4 x 128 ->    4 x   4 x 128 0.005 BF
  51 Shortcut Layer: 48,  wt = 0, wn = 0, outputs:   4 x   4 x 128 0.000 BF
  52 conv    128       1 x 1/ 1      4 x   4 x 128 ->    4 x   4 x 128 0.001 BF
  53 route  52 25 	                           ->    4 x   4 x 256 
  54 conv    256       1 x 1/ 1      4 x   4 x 256 ->    4 x   4 x 256 0.002 BF
  55 conv    512       3 x 3/ 2      4 x   4 x 256 ->    2 x   2 x 512 0.009 BF
  56 conv    256       1 x 1/ 1      2 x   2 x 512 ->    2 x   2 x 256 0.001 BF
  57 route  55 		                           ->    2 x   2 x 512 
  58 conv    256       1 x 1/ 1      2 x   2 x 512 ->    2 x   2 x 256 0.001 BF
  59 conv    256       1 x 1/ 1      2 x   2 x 256 ->    2 x   2 x 256 0.001 BF
  60 conv    256       3 x 3/ 1      2 x   2 x 256 ->    2 x   2 x 256 0.005 BF
  61 Shortcut Layer: 58,  wt = 0, wn = 0, outputs:   2 x   2 x 256 0.000 BF
  62 conv    256       1 x 1/ 1      2 x   2 x 256 ->    2 x   2 x 256 0.001 BF
  63 conv    256       3 x 3/ 1      2 x   2 x 256 ->    2 x   2 x 256 0.005 BF
  64 Shortcut Layer: 61,  wt = 0, wn = 0, outputs:   2 x   2 x 256 0.000 BF
  65 conv    256       1 x 1/ 1      2 x   2 x 256 ->    2 x   2 x 256 0.001 BF
  66 conv    256       3 x 3/ 1      2 x   2 x 256 ->    2 x   2 x 256 0.005 BF
  67 Shortcut Layer: 64,  wt = 0, wn = 0, outputs:   2 x   2 x 256 0.000 BF
  68 conv    256       1 x 1/ 1      2 x   2 x 256 ->    2 x   2 x 256 0.001 BF
  69 conv    256       3 x 3/ 1      2 x   2 x 256 ->    2 x   2 x 256 0.005 BF
  70 Shortcut Layer: 67,  wt = 0, wn = 0, outputs:   2 x   2 x 256 0.000 BF
  71 conv    256       1 x 1/ 1      2 x   2 x 256 ->    2 x   2 x 256 0.001 BF
  72 conv    256       3 x 3/ 1      2 x   2 x 256 ->    2 x   2 x 256 0.005 BF
  73 Shortcut Layer: 70,  wt = 0, wn = 0, outputs:   2 x   2 x 256 0.000 BF
  74 conv    256       1 x 1/ 1      2 x   2 x 256 ->    2 x   2 x 256 0.001 BF
  75 conv    256       3 x 3/ 1      2 x   2 x 256 ->    2 x   2 x 256 0.005 BF
  76 Shortcut Layer: 73,  wt = 0, wn = 0, outputs:   2 x   2 x 256 0.000 BF
  77 conv    256       1 x 1/ 1      2 x   2 x 256 ->    2 x   2 x 256 0.001 BF
  78 conv    256       3 x 3/ 1      2 x   2 x 256 ->    2 x   2 x 256 0.005 BF
  79 Shortcut Layer: 76,  wt = 0, wn = 0, outputs:   2 x   2 x 256 0.000 BF
  80 conv    256       1 x 1/ 1      2 x   2 x 256 ->    2 x   2 x 256 0.001 BF
  81 conv    256       3 x 3/ 1      2 x   2 x 256 ->    2 x   2 x 256 0.005 BF
  82 Shortcut Layer: 79,  wt = 0, wn = 0, outputs:   2 x   2 x 256 0.000 BF
  83 conv    256       1 x 1/ 1      2 x   2 x 256 ->    2 x   2 x 256 0.001 BF
  84 route  83 56 	                           ->    2 x   2 x 512 
  85 conv    512       1 x 1/ 1      2 x   2 x 512 ->    2 x   2 x 512 0.002 BF
  86 conv   1024       3 x 3/ 2      2 x   2 x 512 ->    1 x   1 x1024 0.009 BF
  87 conv    512       1 x 1/ 1      1 x   1 x1024 ->    1 x   1 x 512 0.001 BF
  88 route  86 		                           ->    1 x   1 x1024 
  89 conv    512       1 x 1/ 1      1 x   1 x1024 ->    1 x   1 x 512 0.001 BF
  90 conv    512       1 x 1/ 1      1 x   1 x 512 ->    1 x   1 x 512 0.001 BF
  91 conv    512       3 x 3/ 1      1 x   1 x 512 ->    1 x   1 x 512 0.005 BF
  92 Shortcut Layer: 89,  wt = 0, wn = 0, outputs:   1 x   1 x 512 0.000 BF
  93 conv    512       1 x 1/ 1      1 x   1 x 512 ->    1 x   1 x 512 0.001 BF
  94 conv    512       3 x 3/ 1      1 x   1 x 512 ->    1 x   1 x 512 0.005 BF
  95 Shortcut Layer: 92,  wt = 0, wn = 0, outputs:   1 x   1 x 512 0.000 BF
  96 conv    512       1 x 1/ 1      1 x   1 x 512 ->    1 x   1 x 512 0.001 BF
  97 conv    512       3 x 3/ 1      1 x   1 x 512 ->    1 x   1 x 512 0.005 BF
  98 Shortcut Layer: 95,  wt = 0, wn = 0, outputs:   1 x   1 x 512 0.000 BF
  99 conv    512       1 x 1/ 1      1 x   1 x 512 ->    1 x   1 x 512 0.001 BF
 100 conv    512       3 x 3/ 1      1 x   1 x 512 ->    1 x   1 x 512 0.005 BF
 101 Shortcut Layer: 98,  wt = 0, wn = 0, outputs:   1 x   1 x 512 0.000 BF
 102 conv    512       1 x 1/ 1      1 x   1 x 512 ->    1 x   1 x 512 0.001 BF
 103 route  102 87 	                           ->    1 x   1 x1024 
 104 conv   1024       1 x 1/ 1      1 x   1 x1024 ->    1 x   1 x1024 0.002 BF
 105 conv    512       1 x 1/ 1      1 x   1 x1024 ->    1 x   1 x 512 0.001 BF
 106 conv   1024       3 x 3/ 1      1 x   1 x 512 ->    1 x   1 x1024 0.009 BF
 107 conv    512       1 x 1/ 1      1 x   1 x1024 ->    1 x   1 x 512 0.001 BF
 108 max                5x 5/ 1      1 x   1 x 512 ->    1 x   1 x 512 0.000 BF
 109 route  107 		                           ->    1 x   1 x 512 
 110 max                9x 9/ 1      1 x   1 x 512 ->    1 x   1 x 512 0.000 BF
 111 route  107 		                           ->    1 x   1 x 512 
 112 max               13x13/ 1      1 x   1 x 512 ->    1 x   1 x 512 0.000 BF
 113 route  112 110 108 107 	                   ->    1 x   1 x2048 
 114 conv    512       1 x 1/ 1      1 x   1 x2048 ->    1 x   1 x 512 0.002 BF
 115 conv   1024       3 x 3/ 1      1 x   1 x 512 ->    1 x   1 x1024 0.009 BF
 116 conv    512       1 x 1/ 1      1 x   1 x1024 ->    1 x   1 x 512 0.001 BF
 117 conv    256       1 x 1/ 1      1 x   1 x 512 ->    1 x   1 x 256 0.000 BF
 118 upsample                 2x     1 x   1 x 256 ->    2 x   2 x 256
 119 route  85 		                           ->    2 x   2 x 512 
 120 conv    256       1 x 1/ 1      2 x   2 x 512 ->    2 x   2 x 256 0.001 BF
 121 route  120 118 	                           ->    2 x   2 x 512 
 122 conv    256       1 x 1/ 1      2 x   2 x 512 ->    2 x   2 x 256 0.001 BF
 123 conv    512       3 x 3/ 1      2 x   2 x 256 ->    2 x   2 x 512 0.009 BF
 124 conv    256       1 x 1/ 1      2 x   2 x 512 ->    2 x   2 x 256 0.001 BF
 125 conv    512       3 x 3/ 1      2 x   2 x 256 ->    2 x   2 x 512 0.009 BF
 126 conv    256       1 x 1/ 1      2 x   2 x 512 ->    2 x   2 x 256 0.001 BF
 127 conv    128       1 x 1/ 1      2 x   2 x 256 ->    2 x   2 x 128 0.000 BF
 128 upsample                 2x     2 x   2 x 128 ->    4 x   4 x 128
 129 route  54 		                           ->    4 x   4 x 256 
 130 conv    128       1 x 1/ 1      4 x   4 x 256 ->    4 x   4 x 128 0.001 BF
 131 route  130 128 	                           ->    4 x   4 x 256 
 132 conv    128       1 x 1/ 1      4 x   4 x 256 ->    4 x   4 x 128 0.001 BF
 133 conv    256       3 x 3/ 1      4 x   4 x 128 ->    4 x   4 x 256 0.009 BF
 134 conv    128       1 x 1/ 1      4 x   4 x 256 ->    4 x   4 x 128 0.001 BF
 135 conv    256       3 x 3/ 1      4 x   4 x 128 ->    4 x   4 x 256 0.009 BF
 136 conv    128       1 x 1/ 1      4 x   4 x 256 ->    4 x   4 x 128 0.001 BF
 137 conv     18       3 x 3/ 1      4 x   4 x 128 ->    4 x   4 x  18 0.001 BF
 138 conv     18       1 x 1/ 1      4 x   4 x  18 ->    4 x   4 x  18 0.000 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 		                           ->    4 x   4 x 128 
 141 conv    256       3 x 3/ 2      4 x   4 x 128 ->    2 x   2 x 256 0.002 BF
 142 route  141 126 	                           ->    2 x   2 x 512 
 143 conv    256       1 x 1/ 1      2 x   2 x 512 ->    2 x   2 x 256 0.001 BF
 144 conv    512       3 x 3/ 1      2 x   2 x 256 ->    2 x   2 x 512 0.009 BF
 145 conv    256       1 x 1/ 1      2 x   2 x 512 ->    2 x   2 x 256 0.001 BF
 146 conv    512       3 x 3/ 1      2 x   2 x 256 ->    2 x   2 x 512 0.009 BF
 147 conv    256       1 x 1/ 1      2 x   2 x 512 ->    2 x   2 x 256 0.001 BF
 148 conv    512       3 x 3/ 1      2 x   2 x 256 ->    2 x   2 x 512 0.009 BF
 149 conv     18       1 x 1/ 1      2 x   2 x 512 ->    2 x   2 x  18 0.000 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 		                           ->    2 x   2 x 256 
 152 conv    512       3 x 3/ 2      2 x   2 x 256 ->    1 x   1 x 512 0.002 BF
 153 route  152 116 	                           ->    1 x   1 x1024 
 154 conv    512       1 x 1/ 1      1 x   1 x1024 ->    1 x   1 x 512 0.001 BF
 155 conv   1024       3 x 3/ 1      1 x   1 x 512 ->    1 x   1 x1024 0.009 BF
 156 conv    512       1 x 1/ 1      1 x   1 x1024 ->    1 x   1 x 512 0.001 BF
 157 conv   1024       3 x 3/ 1      1 x   1 x 512 ->    1 x   1 x1024 0.009 BF
 158 conv    512       1 x 1/ 1      1 x   1 x1024 ->    1 x   1 x 512 0.001 BF
 159 conv   1024       3 x 3/ 1      1 x   1 x 512 ->    1 x   1 x1024 0.009 BF
 160 conv     18       1 x 1/ 1      1 x   1 x1024 ->    1 x   1 x  18 0.000 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 0.344 
avg_outputs = 3588 
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 
Webcam index: 0
[ WARN:0] global /home/nvidia/host/build_opencv/nv_opencv/modules/videoio/src/cap_gstreamer.cpp (1757) handleMessage OpenCV | GStreamer warning: Embedded video playback halted; module v4l2src0 reported: Internal data stream error.
[ WARN:0] global /home/nvidia/host/build_opencv/nv_opencv/modules/videoio/src/cap_gstreamer.cpp (886) open OpenCV | GStreamer warning: unable to start 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
Video stream: 3264 x 2464 
Gtk-Message: 15:06:14.421: Failed to load module "canberra-gtk-module"
Objects:


FPS:0.0 	 AVG_FPS:0.0
Objects:


FPS:0.1 	 AVG_FPS:1.2
Objects:

im solely run on jetson nano and for camera, im using raspberry pi camera module 2, Sony IMX219 8MP and its capable of 3280 x 2464 px static images. how to solve this issues? Thanks

AastaLLL · June 2, 2022, 2:58am

Hi,

Since you are using a CSI camera, could you try if nvarguscamerasrc works?

For example:

$ ./darknet detector demo data/obj.data cfg/yolov4-obj.cfg weights/yolov4-obj_3000.weights "nvarguscamerasrc ! video/x-raw(memory:NVMM), width=1280, height=720, format=NV12, framerate=30/1 ! nvvidconv  ! video/x-raw, width=1280, height=720, format=BGRx ! videoconvert ! video/x-raw, format=BGR ! appsink"

Thanks.

user140308 · June 2, 2022, 7:12am

whe i try the command below,

./darknet detector demo data/obj.data cfg/yolov4-obj.cfg weights/yolov4-obj_3000.weights nvarguscamerasrc ! 'video/x-raw(memory:NVMM), width=960, height=540, format=NV12, framerate=30/1' ! nvoverlaysink
                        
                                                     or

./darknet detector demo data/obj.data cfg/yolov4-obj.cfg weights/yolov4-obj_3000.weights nvarguscamerasrc ! 'video/x-raw(memory:NVMM), width=1280, height=720, format=NV12, framerate=30/1' ! nvvidconv  ! video/x-raw, width=1280, height=720, format=BGRx ! videoconvert ! video/x-raw, format=BGR ! appsink

it gives an output

~/darknet$ ./darknet detector demo data/obj.data cfg/yolov4-obj.cfg weights/yolov4-obj_3000.weights nvarguscamerasrc ! 'video/x-raw(memory:NVMM), width=1280, height=720, format=NV12, framerate=30/1' ! nvvidconv  ! video/x-raw, width=1280, height=720, format=BGRx ! videoconvert ! video/x-raw, format=BGR ! appsink
 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:11797): GLib-GObject-WARNING **: 09:16:54.570: invalid cast from 'GstNvArgusCameraSrc' to 'GstBin'

(darknet:11797): GStreamer-CRITICAL **: 09:16:54.570: gst_bin_iterate_elements: assertion 'GST_IS_BIN (bin)' failed

(darknet:11797): GStreamer-CRITICAL **: 09:16:54.570: gst_iterator_next: assertion 'it != NULL' failed

(darknet:11797): GStreamer-CRITICAL **: 09:16:54.570: 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!

but when i test the camera using the command below, it works fine.

~/darknet$ gst-launch-1.0 nvarguscamerasrc ! 'video/x-raw(memory:NVMM), width=1280, height=720, format=NV12, framerate=30/1' ! nvoverlaysink

how to overcome this issues? does it related with OpenCV version?

user140308 · June 2, 2022, 7:50am

when i rerun te command, it does not give a green screem but an error output.

~/darknet$ ./darknet detector demo data/obj.data cfg/yolov4-obj.cfg weights/yolov4-obj_3000.weights -c 0

~/darknet$ ./darknet detector demo data/obj.data cfg/yolov4-obj.cfg weights/yolov4-obj_3000.weights -c 0
 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 
Webcam index: 0
[ WARN:0] global /home/nvidia/host/build_opencv/nv_opencv/modules/videoio/src/cap_gstreamer.cpp (1757) handleMessage OpenCV | GStreamer warning: Embedded video playback halted; module v4l2src0 reported: Internal data stream error.
[ WARN:0] global /home/nvidia/host/build_opencv/nv_opencv/modules/videoio/src/cap_gstreamer.cpp (886) open OpenCV | GStreamer warning: unable to start 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
VIDIOC_STREAMON: Remote I/O error
VIDIOC_STREAMON: Remote I/O error
VIDIOC_STREAMON: Remote I/O error
VIDIOC_STREAMON: Remote I/O error
VIDIOC_STREAMON: Remote I/O error

it says “Remote I/O error”

AastaLLL · June 16, 2022, 5:51am

Hi,

Could you try if the following command works?
(It uses ’ … ’ to wrap the pipeline command)

$ ./darknet detector demo data/obj.data cfg/yolov4-obj.cfg weights/yolov4-obj_3000.weights 'nvarguscamerasrc ! video/x-raw(memory:NVMM), width=(int)1280, height=(int)720,format=(string)NV12, framerate=(fraction)30/1 ! nvvidconv flip-method=2 ! video/x-raw, format=(string)BGRx ! videoconvert ! video/x-raw, format=(string)BGR ! appsink'

Thanks.

user140308 · June 20, 2022, 7:05am

at first, it doesn’t work. After I deleted the darknet folder and rebuilt it again and it works! but sometimes it stopped in the middle without any further output. I don’t know why that is happening. but thank you for helping me solve the issue!

system · July 13, 2022, 2:17am

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