Real-Time Object Detection in 10 Lines of Python code on Jetson Nano(youtube)

forumuser · December 24, 2020, 8:31am

In Real-Time Object Detection in 10 Lines of Python code on Jetson Nano(youtube).

when I executing my-detection.py, I got an error below.

jetson7@jetson7-desktop:~$ v4l2-ctl --list-devices
vi-output, imx219 8-0010 (platform:54080000.vi:4):
/dev/video0

jetson7@jetson7-desktop:~$ ls /dev/video0
/dev/video0

but I met
[gstreamer] gstCamera – didn’t discover any v4l2 devices
[gstreamer] gstCamera – device discovery failed, but /dev/video0 exists
[gstreamer] support for compressed formats is disabled

========== log =========================
python my-detection.py
jetson.inference – detectNet loading build-in network ‘ssd-mobilenet-v2’

detectNet – loading detection network model from:
– model networks/SSD-Mobilenet-v2/ssd_mobilenet_v2_coco.uff
– input_blob ‘Input’
– output_blob ‘NMS’
– output_count ‘NMS_1’
– class_labels networks/SSD-Mobilenet-v2/ssd_coco_labels.txt
– threshold 0.500000
– batch_size 1

[TRT] TensorRT version 7.1.3
[TRT] loading NVIDIA plugins…
[TRT] Registered plugin creator - ::GridAnchor_TRT version 1
[TRT] Registered plugin creator - ::NMS_TRT version 1
[TRT] Registered plugin creator - ::Reorg_TRT version 1
[TRT] Registered plugin creator - ::Region_TRT version 1
[TRT] Registered plugin creator - ::Clip_TRT version 1
[TRT] Registered plugin creator - ::LReLU_TRT version 1
[TRT] Registered plugin creator - ::PriorBox_TRT version 1
[TRT] Registered plugin creator - ::Normalize_TRT version 1
[TRT] Registered plugin creator - ::RPROI_TRT version 1
[TRT] Registered plugin creator - ::BatchedNMS_TRT version 1
[TRT] Could not register plugin creator - ::FlattenConcat_TRT version 1
[TRT] Registered plugin creator - ::CropAndResize version 1
[TRT] Registered plugin creator - ::DetectionLayer_TRT version 1
[TRT] Registered plugin creator - ::Proposal version 1
[TRT] Registered plugin creator - ::ProposalLayer_TRT version 1
[TRT] Registered plugin creator - ::PyramidROIAlign_TRT version 1
[TRT] Registered plugin creator - ::ResizeNearest_TRT version 1
[TRT] Registered plugin creator - ::Split version 1
[TRT] Registered plugin creator - ::SpecialSlice_TRT version 1
[TRT] Registered plugin creator - ::InstanceNormalization_TRT version 1
[TRT] detected model format - UFF (extension ‘.uff’)
[TRT] desired precision specified for GPU: FASTEST
[TRT] requested fasted precision for device GPU without providing valid calibrator, disabling INT8
[TRT] native precisions detected for GPU: FP32, FP16
[TRT] selecting fastest native precision for GPU: FP16
[TRT] attempting to open engine cache file /usr/local/bin/networks/SSD-Mobilenet-v2/ssd_mobilenet_v2_coco.uff.1.1.7103.GPU.FP16.engine
[TRT] loading network plan from engine cache… /usr/local/bin/networks/SSD-Mobilenet-v2/ssd_mobilenet_v2_coco.uff.1.1.7103.GPU.FP16.engine
[TRT] device GPU, loaded /usr/local/bin/networks/SSD-Mobilenet-v2/ssd_mobilenet_v2_coco.uff
[TRT] Using an engine plan file across different models of devices is not recommended and is likely to affect performance or even cause errors.
[TRT] Deserialize required 5604255 microseconds.
[TRT]
[TRT] CUDA engine context initialized on device GPU:
[TRT] – layers 116
[TRT] – maxBatchSize 1
[TRT] – workspace 0
[TRT] – deviceMemory 35449856
[TRT] – bindings 3
[TRT] binding 0
– index 0
– name ‘Input’
– type FP32
– in/out INPUT
– # dims 3
– dim #0 3 (SPATIAL)
– dim #1 300 (SPATIAL)
– dim #2 300 (SPATIAL)
[TRT] binding 1
– index 1
– name ‘NMS’
– type FP32
– in/out OUTPUT
– # dims 3
– dim #0 1 (SPATIAL)
– dim #1 100 (SPATIAL)
– dim #2 7 (SPATIAL)
[TRT] binding 2
– index 2
– name ‘NMS_1’
– type FP32
– in/out OUTPUT
– # dims 3
– dim #0 1 (SPATIAL)
– dim #1 1 (SPATIAL)
– dim #2 1 (SPATIAL)
[TRT]
[TRT] binding to input 0 Input binding index: 0
[TRT] binding to input 0 Input dims (b=1 c=3 h=300 w=300) size=1080000
[TRT] binding to output 0 NMS binding index: 1
[TRT] binding to output 0 NMS dims (b=1 c=1 h=100 w=7) size=2800
[TRT] binding to output 1 NMS_1 binding index: 2
[TRT] binding to output 1 NMS_1 dims (b=1 c=1 h=1 w=1) size=4
[TRT]
[TRT] device GPU, /usr/local/bin/networks/SSD-Mobilenet-v2/ssd_mobilenet_v2_coco.uff initialized.
[TRT] W = 7 H = 100 C = 1
[TRT] detectNet – maximum bounding boxes: 100
[TRT] detectNet – loaded 91 class info entries
[TRT] detectNet – number of object classes: 91
[gstreamer] initialized gstreamer, version 1.14.5.0
[gstreamer] gstCamera – attempting to create device v4l2:///dev/video0

(python:7447): GStreamer-CRITICAL **: 17:02:40.127: gst_element_message_full_with_details: assertion ‘GST_IS_ELEMENT (element)’ failed

(python:7447): GStreamer-CRITICAL **: 17:02:40.128: gst_element_message_full_with_details: assertion ‘GST_IS_ELEMENT (element)’ failed

(python:7447): GStreamer-CRITICAL **: 17:02:40.128: gst_element_message_full_with_details: assertion ‘GST_IS_ELEMENT (element)’ failed
[gstreamer] gstCamera – didn’t discover any v4l2 devices
[gstreamer] gstCamera – device discovery failed, but /dev/video0 exists
[gstreamer] support for compressed formats is disabled
[gstreamer] gstCamera pipeline string:
[gstreamer] v4l2src device=/dev/video0 ! appsink name=mysink
[gstreamer] gstCamera successfully created device v4l2:///dev/video0
[OpenGL] glDisplay – X screen 0 resolution: 1920x1080
[OpenGL] glDisplay – X window resolution: 1920x1080
[OpenGL] glDisplay – display device initialized (1920x1080)
[gstreamer] opening gstCamera for streaming, transitioning pipeline to GST_STATE_PLAYING
[gstreamer] gstreamer changed state from NULL to READY ==> mysink
[gstreamer] gstreamer changed state from NULL to READY ==> v4l2src0
[gstreamer] gstreamer changed state from NULL to READY ==> pipeline0
[gstreamer] gstreamer stream status CREATE ==> src
[gstreamer] gstreamer changed state from READY to PAUSED ==> v4l2src0
[gstreamer] gstreamer changed state from READY to PAUSED ==> pipeline0
[gstreamer] gstreamer message new-clock ==> pipeline0
[gstreamer] gstreamer changed state from PAUSED to PLAYING ==> v4l2src0
[gstreamer] gstCamera – end of stream (EOS)
[gstreamer] gstreamer stream status ENTER ==> src
[gstreamer] gstreamer message stream-start ==> pipeline0
[gstreamer] gstreamer v4l2src0 ERROR Internal data stream error.
[gstreamer] gstreamer Debugging info: gstbasesrc.c(3055): gst_base_src_loop (): /GstPipeline:pipeline0/GstV4l2Src:v4l2src0:
streaming stopped, reason not-negotiated (-4)
[gstreamer] gstreamer changed state from READY to PAUSED ==> mysink
^C^Z
[1]+ Stopped python my-detection.py

What am I supposed to do?

Thank you.

kayccc · December 27, 2020, 6:00am

What’s the camera connected with your device?

forumuser · December 27, 2020, 11:21pm

Hello,

raspberrypi camera module v2
and
veye327 mipi yuv camera
product url: http://www.veye.cc/en/product/mipi-camera/veye-mipi-327e/

Thank you.

dusty_nv · December 28, 2020, 7:24pm

Hi @forumuser, to run it on MIPI CSI cameras, you should set the script to use "csi://0" instead of "/dev/video0"

You can also check that you camera is working first by running the nvgstcapture-1.0 program.

forumuser · December 28, 2020, 10:00pm

Hello,

Your Github also has information on this.

github.com

dusty-nv/jetson-inference/blob/master/docs/detectnet-example-2.md

<img src="https://github.com/dusty-nv/jetson-inference/raw/master/docs/images/deep-vision-header.jpg" width="100%">
<p align="right"><sup><a href="detectnet-camera-2.md">Back</a> | <a href="segnet-console-2.md">Next</a> | </sup><a href="../README.md#hello-ai-world"><sup>Contents</sup></a>
<br/>
<sup>Object Detection</sup></p>

# Coding Your Own Object Detection Program

In this step of the tutorial, we'll walk through the creation of the previous example for realtime object detection on a live camera feed in only 10 lines of Python code.  The program will load the detection network with the [`detectNet`](https://rawgit.com/dusty-nv/jetson-inference/dev/docs/html/python/jetson.inference.html#detectNet) object, capture video frames and process them, and then render the detected objects to the display.

For your convenience and reference, the completed source is available in the [`python/examples/my-detection.py`](../python/examples/my-detection.py) file of the repo, but the guide below will act like they reside in the user's home directory or in an arbitrary directory of your choosing.  

Here's a quick preview of the Python code we'll be walking through:

``` python
import jetson.inference
import jetson.utils

net = jetson.inference.detectNet("ssd-mobilenet-v2", threshold=0.5)
camera = jetson.utils.videoSource("csi://0")      # '/dev/video0' for V4L2
display = jetson.utils.videoOutput("display://0") # 'my_video.mp4' for file

This file has been truncated. show original

Thank you.

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Real-Time Object Detection in 10 Lines of Python code on Jetson Nano(youtube)

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