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--- projects:farmrobot:nvidia-jetson-tx1 [2020/12/07 19:53]
jason
+++ projects:farmrobot:nvidia-jetson-tx1 [2021/01/10 19:28] (current)
jason
@@ Line 4: / Line 4: @@
 ^ User    ^ Password  ^
-| jetson  | jetson    |
+| ''jetson''  | ''jetson''    |
+===== Running YOLOv5 with on-board CSI camera or external USB camera (Motion JPEG) =====
+Arguments for NVIDIA's GStreamer camera pipeline depend on supported camera formats.
+To start YOLOv5's detection, run the following command and provide the GStreamer camera pipeline as the source:
+<file shell>
+# On-board CSI camera
+python3.8 detect.py --source "nvarguscamerasrc ! video/x-raw(memory:NVMM), width=(int)2592, height=(int)1458, framerate=(fraction)30/1, format=(string)NV12 ! nvvidconv flip-method=0 ! video/x-raw, format=(string)BGRx ! videoconvert ! video/x-raw, format=(string)BGR ! appsink"
+# External USB camera (Motion JPEG)
+python3.8 detect.py --source "v4l2src device=/dev/video1 ! image/jpeg, width=(int)1920, height=(int)1080, framerate=30/1 ! nvv4l2decoder mjpeg=1 ! nvvidconv ! video/x-raw,format=BGRx ! videoconvert ! video/x-raw, format=BGR ! appsink"
+</file>
+<WRAP important>
+In the example above, the on-board CSI camera is represented by ''/dev/video0'' and the external USB camera by ''/dev/video1''. Depending on the configuration of connected cameras, those paths might differ.
+</WRAP>
+<WRAP info>
+See this [[https://stackoverflow.com/questions/65638140/create-pipeline-for-gstreamer-for-usb-camera-mjpg-format|Stack Overflow post]].
+</WRAP>
+===== Current data =====
+==== On-board camera ====
+<file shell>
+sudo apt install -y v4l-utils
+</file>
+<file shell>
+v4l2-ctl -d /dev/video0 --list-formats-ext
+ioctl: VIDIOC_ENUM_FMT
+        Index       : 0
+        Type        : Video Capture
+        Pixel Format: 'BG10'
+        Name        : 10-bit Bayer BGBG/GRGR
+                Size: Discrete 2592x1944
+                        Interval: Discrete 0.033s (30.000 fps)
+                Size: Discrete 2592x1458
+                        Interval: Discrete 0.033s (30.000 fps)
+                Size: Discrete 1280x720
+                        Interval: Discrete 0.008s (120.000 fps)
+</file>
+{{:projects:farmrobot:img_20210106_183252.jpg?400|}}
+{{:projects:farmrobot:img_20210106_183318.jpg?400|}}
+{{:projects:farmrobot:img_20210106_183359.jpg?400|}}
+{{:projects:farmrobot:img_20210106_183426.jpg?400|}}
+{{:projects:farmrobot:img_20210107_175211.jpg?400|}}
+==== Measurements ====
+YOLOv5 (street.mp4): 11/12 fps (~0.086s +- 0.005s per frame)
+{{:projects:farmrobot:img_20201228_205724.jpg?600|}}
 ===== Setup =====
@@ Line 13: / Line 71: @@
 To install the JetPack SDK on the Jetson TX1 you need to download and install [[https://developer.nvidia.com/nvsdk-manager|NVIDIA's SDK Manager]] onto a separate host machine with a 16.04 or 18.04 Ubuntu operating system. You will need to get an NVIDIA Developer Program Membership to download the SDK Manager.
-After downloading the .deb file, you need to install it with apt:
+After downloading the ''.deb'' file, you need to install it with ''apt'':
 <file shell>
@@ Line 20: / Line 78: @@
 <WRAP important>
-If the installation fails because some dependencies couldn't be resolved, try enabling all common apt repositories:
+If the installation fails because some dependencies couldn't be resolved, try enabling all common ''apt'' repositories:
 <file shell>
@@ Line 51: / Line 109: @@
 After the installation finished, disconnect the Jetson TX1 from the host machine.
+==== Mounting external storage to /home ====
+Mount the partition on the SD-card to ''/mnt'':
+<file shell>
+sudo mount /dev/mmcblk2p1 /mnt
+</file>
+Change into the ''/home'' directory and copy all contents to ''/mnt'':
+<file shell>
+cd /home
+cp -adpR * /mnt
+</file>
+Unmount the partition from ''/mnt'':
+<file shell>
+sudo umount /mnt
+</file>
+Then open the disk utility and go to the mounting settings of the partition on the SD-card.
+Set it to automatically mount on ''/home'' on boot, save and reboot the Jetson TX1.
 ==== Updating apt packages ====
@@ Line 60: / Line 142: @@
 </file>
-==== Python 3.8 ====
+==== Installing Python 3.8 ====
-To install Python 3.8 you first need to make sure that the software-properties-common package is installed:
+To install Python 3.8 you first need to make sure that the ''software-properties-common'' package is installed:
 <file shell>
@@ Line 68: / Line 150: @@
 </file>
-Then add the deadsnakes PPA to apt, like so:
+Then add the ''deadsnakes'' PPA to ''apt'', like so:
 <file shell>
@@ Line 74: / Line 156: @@
 </file>
-Afterwards, you can install Python 3.8 via apt:
+Afterwards, you can install Python 3.8 via ''apt'':
 <file shell>
@@ Line 80: / Line 162: @@
 </file>
-Also make sure to have pip for Python 3.X installed:
+Also make sure to have ''pip'' for Python 3.X installed:
 <file shell>
 sudo apt install -y python3-pip
 </file>
+==== Adding temporary swap space ====
+The next few operations can be very CPU and RAM intensive.
+It's possible that the installed 4GB of RAM and pre-configured 2GB of swap space are not enough to handle these operations. That's why you may need to configure additional swap space.
+First, check if any additional swap space is already configured by running:
+<file shell>
+sudo swapon --show
+</file>
+If there's no additional swap space configured apart from the default 2GB, proceed with the following steps.
+Create a swap file that allocates enough space (Recommended are an additional 4 - 16GB):
+<file shell>
+sudo fallocate -l 8G ~/swapfile
+</file>
+Make sure that only superusers are able to read and write the swap file:
+<file shell>
+sudo chmod 600 ~/swapfile
+</file>
+Then set up a swap area on the swap file and activate it:
+<file shell>
+sudo mkswap ~/swapfile
+sudo swapon ~/swapfile
+</file>
+==== Building OpenCV from source (with GStreamer support) ====
+Because [[https://opencv.org/|OpenCV]] does not support NVIDIA's GStreamer software by default, it has to be built from source with GStreamer support enabled.
+<WRAP info>
+[[https://developer.download.nvidia.com/embedded/L4T/r32_Release_v1.0/Docs/Accelerated_GStreamer_User_Guide.pdf?9N7jupgUlde8JwGYrJQO434MhxI7ZlJf8jfNYE-eI9uMBECrQldp3897ijsNYjnxBWWiY8OtUVfEKr4cLQNUyoevaUfjyXksRnT__8EmmhKVHWYbRK7-6NdQOQ8jJtev1-0IhZKyQ1rxjQh2_35qOofKmyWN8kH58nZHAQyVRax9kaD5tXY|Accelerated GStreamer User Guide]]
+</WRAP>
+To get started, install the packages that OpenCV with GStreamer support depends on:
+<file shell>
+sudo apt install -y libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev
+</file>
+Then, install [[https://numpy.org/|NumPy]] as OpenCV also depends on it:
+<file shell>
+python3.8 -m pip install --user numpy
+</file>
+After all dependencies are installed, go ahead and clone the [[https://github.com/opencv/opencv|OpenCV GitHub repository]], change into its directory and checkout the latest version branch:
+<file shell>
+git clone https://github.com/opencv/opencv.git
+cd opencv
+# General
+git checkout <version>
+# OpenCV 4.5.0
+git checkout 4.5.0
+</file>
+Before you can start building OpenCV, create a ''/build`'' directory and change into it:
+<file shell>
+mkdir build
+cd build
+</file>
+Then, use ''cmake'' to prepare the build with the correct settings:
+<file shell>
+cmake -D CMAKE_BUILD_TYPE=RELEASE \
+-D INSTALL_PYTHON_EXAMPLES=ON \
+-D INSTALL_C_EXAMPLES=OFF \
+-D PYTHON_EXECUTABLE=$(which python3.8) \
+-D BUILD_opencv_python2=OFF \
+-D CMAKE_INSTALL_PREFIX=$(python3.8 -c “import sys; print(sys.prefix)”) \
+-D PYTHON3_EXECUTABLE=$(which python3.8) \
+-D PYTHON3_INCLUDE_DIR=$(python3.8 -c “from distutils.sysconfig import get_python_inc; print(get_python_inc())”) \
+-D PYTHON3_PACKAGES_PATH=$(python3.8 -c “from site import getsitepackages; print(getsitepackages())”) \
+-D WITH_GSTREAMER=ON \
+-D BUILD_EXAMPLES=ON ..
+</file>
+It is important to check the resulting output. Check if the output contains the following important bits:
+<file shell>
+...
+GStreamer:         YES (<version>)
+...
+Python3:
+  Interpreter:     <path>
+  Libraries:       <path>
+  numpy:           <path>
+  install path:    <path>
+...
+</file>
+If everything went well, you can go ahead and build it:
+<file shell>
+sudo make -j$(nproc)
+</file>
+When the build is finished, install it:
+<file shell>
+sudo make install
+</file>
+==== Building PyTorch and Torchvision from source ====
+At the time of writing this documentation there are no pre-built wheels of [[https://pytorch.org/|PyTorch]] 1.7.0 or later and the associated version of [[https://pytorch.org/docs/stable/torchvision/index.html|Torchvision]] that run on ARM64-based systems with Python 3.8 or later.
+<WRAP info>
+Both Python 3.8 or later and [[https://pytorch.org/|PyTorch]] 1.7.0 or later are required for [[https://github.com/ultralytics/yolov5|YOLOv5]] to run properly.
+</WRAP>
+<WRAP important>
+You should first check the [[https://forums.developer.nvidia.com/|NVIDIA Developer Forums]] for new releases of PyTorch 1.7.0 or later that run on ARM64-based systems with Python 3.8 or later. You don't have to build PyTorch and Torchvision from source if that is the case.
+[[https://forums.developer.nvidia.com/t/pytorch-for-jetson-version-1-7-0-now-available/72048|This]] is the most up-to-date forum post at the time of writing.
+</WRAP>
+First, make sure that you have ''python3.8-dev'' installed:
+<file shell>
+sudo apt install -y python3.8-dev
+</file>
+Then clone the official PyTorch repository on GitHub with the branch of the version you want to build and change into the created directory:
+<file shell>
+# General
+git clone --recursive --branch <version> https://github.com/pytorch/pytorch
+cd pytorch
+# PyTorch 1.7.0
+git clone --recursive --branch 1.7 https://github.com/pytorch/pytorch
+</file>
+Before you can build PyTorch, you first need to install all its dependencies:
+<file shell>
+python3.8 -m pip install --user -r requirements.txt
+</file>
+If all dependencies are installed, you can go ahead and build PyTorch:
+<file shell>
+python3.8 setup.py install --user
+</file>
+Once PyTorch itself is installed, you can build Torchvision from source.
+First, install all libraries that Torchvision needs:
+<file shell>
+sudo apt install -y libjpeg-dev zlib1g-dev libpython3-dev libavcodec-dev libavformat-dev libswscale-dev
+</file>
+The version of Torchvision that is needed depends on the version of PyTorch that is installed. At the time of writing Torchvision 0.8.1 is needed for PyTorch 1.7.0.
+Then clone the official Torchvision repository on GitHub with the branch of the version you want to build and change into the created directory:
+<file shell>
+# General
+git clone --branch <version> https://github.com/pytorch/vision torchvision
+cd torchvision
+# Torchvision 0.8.1
+git clone --branch v0.8.1 https://github.com/pytorch/vision torchvision
+</file>
+To build Torchvision, export Torchvision's version with the environment variable ''BUILD_VERSION'' first:
+<file shell>
+# General
+export BUILD_VERSION=<version>
+# Torchvision 0.8.1
+export BUILD_VERSION=0.8.1
+</file>
+Then build Torchvision:
+<file shell>
+python3.8 setup.py install --user
+</file>
+==== Installing YOLOv5 ====
+Clone our implementation of YOLOv5 from the GitLab repository:
+<file shell>
+git clone https://gitlab.hsrw.eu/jason.theiler/farm-robot-yolov5.git
+</file>
+Then change the directory into the local repository and clone the official [[https://github.com/ultralytics/yolov5|YOLOv5 GitHub repository]]:
+<file shell>
+cd farm-robot-yolov5
+git clone https://github.com/ultralytics/yolov5.git
+</file>
+To get ready, install all the dependencies one by one:
+<file shell>
+python3.8 -m pip install --user <package>
+</file>
+<WRAP important>
+Installing all dependencies at once using
+<file shell>
+python3.8 -m pip install --user -r requirements.txt
+</file>
+can result in errors due to how Python dependencies are installed using the ''--user'' option and the very limited amount of disk space left on the ''/tmp'' directory.
+</WRAP>

FabLab Kamp-Lintfort

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