yoloxtensordec
Tensor decoder element for YOLOX-based object detection.
gst-launch-1.0 souphttpsrc location=https://raw.githubusercontent.com/tracel-ai/models/ab8c64bd7e1f45e99cc321ce900a5b5e6b97910c/yolox-burn/samples/dog_bike_man.jpg \
! jpegdec ! videoconvertscale ! "video/x-raw,width=800,height=640" \
! burn-yoloxinference ! yoloxtensordec label-file=COCO_classes.txt \
! videoconvertscale ! objectdetectionoverlay \
! videoconvertscale ! imagefreeze ! autovideosink -v
|] This takes a JPEG, performs object detection via `burn-yoloxinference` on it, decodes the
inferred tensors with `yoloxtensordec` and then overlays the detected objects on the frame via
`objectdetectionoverlay`.
Hierarchy
GObject ╰──GInitiallyUnowned ╰──GstObject ╰──GstElement ╰──GstBaseTransform ╰──yoloxtensordec
Factory details
Authors: – Sebastian Dröge
Classification: – Tensordecoder/Video
Rank – primary
Plugin – rsanalytics
Package – gst-plugin-analytics
Pad Templates
sink
video/x-raw:
format: { A444_16LE, A444_16BE, Y416_LE, AYUV64, RGBA64_LE, ARGB64, ARGB64_LE, BGRA64_LE, ABGR64_LE, Y416_BE, RGBA64_BE, ARGB64_BE, BGRA64_BE, ABGR64_BE, A422_16LE, A422_16BE, A420_16LE, A420_16BE, A444_12LE, GBRA_12LE, A444_12BE, GBRA_12BE, Y412_LE, Y412_BE, A422_12LE, A422_12BE, A420_12LE, A420_12BE, A444_10LE, GBRA_10LE, A444_10BE, GBRA_10BE, A422_10LE, A422_10BE, A420_10LE, A420_10BE, BGR10A2_LE, RGB10A2_LE, Y410, A444, GBRA, AYUV, VUYA, RGBA, RBGA, ARGB, BGRA, ABGR, A422, A420, AV12, Y444_16LE, GBR_16LE, Y444_16BE, GBR_16BE, Y216_LE, Y216_BE, v216, P016_LE, P016_BE, Y444_12LE, GBR_12LE, Y444_12BE, GBR_12BE, I422_12LE, I422_12BE, Y212_LE, Y212_BE, I420_12LE, I420_12BE, P012_LE, P012_BE, Y444_10LE, GBR_10LE, Y444_10BE, GBR_10BE, BGR10x2_LE, RGB10x2_LE, r210, I422_10LE, I422_10BE, NV16_10LE40, NV16_10LE32, Y210, UYVP, v210, I420_10LE, I420_10BE, P010_10LE, NV12_10LE40, NV12_10LE32, P010_10BE, MT2110R, MT2110T, NV12_10BE_8L128, NV12_10LE40_4L4, Y444, BGRP, GBR, RGBP, NV24, v308, IYU2, RGBx, xRGB, BGRx, xBGR, RGB, BGR, Y42B, NV16, NV61, YUY2, YVYU, UYVY, VYUY, I420, YV12, NV12, NV21, NV12_16L32S, NV12_32L32, NV12_4L4, NV12_64Z32, NV12_8L128, Y41B, IYU1, YUV9, YVU9, BGR16, RGB16, BGR15, RGB15, RGB8P, GRAY16_LE, GRAY16_BE, GRAY10_LE16, GRAY10_LE32, GRAY8 }
width: [ 1, 2147483647 ]
height: [ 1, 2147483647 ]
framerate: [ 0/1, 2147483647/1 ]
tensors: "tensorgroups\,\ yolox-out\=\(/uniquelist\)\{\ \(caps\)\"tensor/strided\\\,\\\ dims\\\=\\\(int\\\)\\\<\\\ 1\\\,\\\ 0\\\,\\\ \\\[\\\ 5\\\,\\\ 2147483647\\\ \\\]\\\ \\\>\\\,\\\ dims-order\\\=\\\(string\\\)row-major\\\,\\\ type\\\=\\\(string\\\)float32\"\ \}\;"
src
video/x-raw:
format: { A444_16LE, A444_16BE, Y416_LE, AYUV64, RGBA64_LE, ARGB64, ARGB64_LE, BGRA64_LE, ABGR64_LE, Y416_BE, RGBA64_BE, ARGB64_BE, BGRA64_BE, ABGR64_BE, A422_16LE, A422_16BE, A420_16LE, A420_16BE, A444_12LE, GBRA_12LE, A444_12BE, GBRA_12BE, Y412_LE, Y412_BE, A422_12LE, A422_12BE, A420_12LE, A420_12BE, A444_10LE, GBRA_10LE, A444_10BE, GBRA_10BE, A422_10LE, A422_10BE, A420_10LE, A420_10BE, BGR10A2_LE, RGB10A2_LE, Y410, A444, GBRA, AYUV, VUYA, RGBA, RBGA, ARGB, BGRA, ABGR, A422, A420, AV12, Y444_16LE, GBR_16LE, Y444_16BE, GBR_16BE, Y216_LE, Y216_BE, v216, P016_LE, P016_BE, Y444_12LE, GBR_12LE, Y444_12BE, GBR_12BE, I422_12LE, I422_12BE, Y212_LE, Y212_BE, I420_12LE, I420_12BE, P012_LE, P012_BE, Y444_10LE, GBR_10LE, Y444_10BE, GBR_10BE, BGR10x2_LE, RGB10x2_LE, r210, I422_10LE, I422_10BE, NV16_10LE40, NV16_10LE32, Y210, UYVP, v210, I420_10LE, I420_10BE, P010_10LE, NV12_10LE40, NV12_10LE32, P010_10BE, MT2110R, MT2110T, NV12_10BE_8L128, NV12_10LE40_4L4, Y444, BGRP, GBR, RGBP, NV24, v308, IYU2, RGBx, xRGB, BGRx, xBGR, RGB, BGR, Y42B, NV16, NV61, YUY2, YVYU, UYVY, VYUY, I420, YV12, NV12, NV21, NV12_16L32S, NV12_32L32, NV12_4L4, NV12_64Z32, NV12_8L128, Y41B, IYU1, YUV9, YVU9, BGR16, RGB16, BGR15, RGB15, RGB8P, GRAY16_LE, GRAY16_BE, GRAY10_LE16, GRAY10_LE32, GRAY8 }
width: [ 1, 2147483647 ]
height: [ 1, 2147483647 ]
framerate: [ 0/1, 2147483647/1 ]
Properties
box-confidence-threshold
“box-confidence-threshold” gfloat
Boxes with a location confidence level inferior to this threshold will be excluded
Flags : Read / Write
Default value : 0.4
class-confidence-threshold
“class-confidence-threshold” gfloat
Boxes with a confidence level inferior to this threshold will be excluded
Flags : Read / Write
Default value : 0.4
iou-threshold
“iou-threshold” gfloat
Maximum intersection-over-union between bounding boxes to consider them distinct
Flags : Read / Write
Default value : 0.7
label-file
“label-file” gchararray
Label file with one label per line
Flags : Read / Write
Default value : NULL
max-detections
“max-detections” guint
Maximum number of detections
Flags : Read / Write
Default value : 100
The results of the search are