Embedded Files
sudo apt-get -o Dpkg::Options::="--force-overwrite" install --fix-broken
========================
sudo nano ~/.bashsrc
export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda/lib64\
${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
===============
https://www.youtube.com/watch?v=lKXMyln_5q4
nvcc -std=c++17 -arch=sm_60 test.cu
cvtest: Computer Vision Test
cvtest: Computer Vision Test
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning
Do you want to test your output of computer vision application which is video or images?
Standard test for computer vision application
Standard test for computer vision application
There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware.
Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image?
https://github.com/pirahansiah/cvtest/blob/main/README.md
check S3 bucket in AWS for image and video files and versioning
Check Docker load balancer, memory usage, ...
GPU
Video Tracking on Mac
create conda based on python 3.6
conda create env_full -y --name farshid python=3.6
conda activate farshid
install OpenVino from Intel for converting deep learning model based on intel chips
conda install -y openvino-ie4py -c intel
install video library
conda install -y -c conda-forge ffmpeg
install pytorch and torchvision
conda install -y pytorch torchvision -c pytorch
conda install -y -c conda-forge matplotlib
conda install -y pandas scikit-learn plotly
conda install -y -c conda-forge opencv seaborn
conda install -y -c conda-forge tensorflow
pip install torch torchvision torchaudio
pip install matplotlib pandas scikit-learn plotly opencv seaborn tensorflow
Test for 2021
Test for 2021
3D Multi-Object Tracking: A Baseline and New Evaluation Metrics (IROS 2020, ECCVW 2020) https://github.com/xinshuoweng/AB3DMOT
Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild https://github.com/elliottwu/unsup3d
This repository contains the public release of the Python implementation of our Aggregate View Object Detection (AVOD) network for 3D object detection. https://github.com/kujason/avod
𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚔3𝚍
Run on Ubuntu PC + eGPU
Run on Ubuntu PC + eGPU
apt search nvidia-driver
apt-cache search nvidia-driver
sudo apt update
sudo apt upgrade
sudo apt install nvidia-driver-455
sudo reboot
nvidia-smi
Download cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.0
tar -xzvf cudnn-10.0-linux-x64-v7.6.5.32.tgz
sudo cp cuda/include/cudnn*.h /usr/local/cuda/include
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*
sudo dpkg -i libcudnn7_7.6.5.32-1+cuda10.0_amd64.deb
sudo dpkg -i libcudnn7-dev_7.6.5.32-1+cuda10.0_amd64.deb
sudo dpkg -i libcudnn7-doc_7.6.5.32-1+cuda10.0_amd64.deb
sudo apt-get install \
apt-transport-https \
ca-certificates \
curl \
gnupg-agent \
software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo apt-key fingerprint 0EBFCD88
sudo add-apt-repository \
"deb [arch=amd64] https://download.docker.com/linux/ubuntu \
$(lsb_release -cs) \
stable"
sudo apt-get update
sudo apt-get install docker-ce docker-ce-cli containerd.io
Make sure you have installed the NVIDIA driver and Docker engine for your Linux distribution Note that you do not need to install the CUDA Toolkit on the host system, but the NVIDIA driver needs to be installed
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
&& curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \
&& curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
curl -s -L https://nvidia.github.io/nvidia-container-runtime/experimental/$distribution/nvidia-container-runtime.list | sudo tee /etc/apt/sources.list.d/nvidia-container-runtime.list
sudo apt-get install -y nvidia-docker2
sudo systemctl restart docker
sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi
pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
CenterTrack_ROOT=/home/farshid/code/CenterTrack
git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT
cd CenterTrack_ROOT
pip install -r requirements.txt
cd $CenterTrack_ROOT/src/lib/model/networks/
git clone https://github.com/CharlesShang/DCNv2/
cd DCNv2
./make.sh
https://github.com/xingyizhou/CenterTrack/blob/master/readme/MODEL_ZOO.md
cvat
cvat
sudo groupadd docker
sudo usermod -aG docker $USER
sudo apt-get --no-install-recommends install -y python3-pip python3-setuptools
sudo python3 -m pip install setuptools docker-compose
sudo apt-get --no-install-recommends install -y git
git clone https://github.com/opencv/cvat
cd cvat
sudo docker-compose build
sudo docker-compose up -d
sudo docker exec -it cvat bash -ic 'python3 ~/manage.py createsuperuser'
Towards-Realtime-MOT
Towards-Realtime-MOT
conda activate cuda100
pip install motmetrics
pip install cython_bbox
conda install -c conda-forge ffmpeg
https://gitlab.inria.fr/yixu/deepmot
git clone https://gitlab.inria.fr/yixu/deepmot.git
sudo apt-get install libpng-dev
sudo apt install libfreetype6-dev
pip install -r requirements.txt
ImportError: torch.utils.ffi is deprecated. Please use cpp extensions instead.
conda create -y --name cuda92 python=3.6
conda activate cuda92
source activate cuda92
conda install pytorch==0.4.1 torchvision==0.2.0 cudatoolkit=9.2 -c pytorch
conda install -c conda-forge ffmpeg
conda create -n cuda100
conda activate cuda100
conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
AWS
AWS
conda create -n FairMOT
conda activate FairMOT
conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch
cd ${FAIRMOT_ROOT}
pip install -r requirements.txt
conda install -c conda-forge ffmpeg
MOTS: Multi-Object Tracking and Segmentation
Dataset: https://motchallenge.net/data/MOTS/
This benchmark extends the traditional Multi-Object Tracking benchmark to a new benchmark defined on a pixel-level with precise segmentation masks. We annotated 8 challenging video sequences (4 training, 4 test) in unconstrained environments filmed with both static and moving cameras. Tracking, segmentation and evaluation are done in image coordinates. All sequences have been annotated with high accuracy on a pixel level, strictly following a well-defined protocol.
Setup:
cd /media/farshid/exfat128/code
Conda
conda create --name CenterTrack36cuda10 python=3.6
conda activate CenterTrack
conda install pytorch torchvision -c pytorch
pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
CenterTrack_ROOT=/media/farshid/exfat128/code/CenterTrack
git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT
pip install -r requirements.txt
cd $CenterTrack_ROOT/src/lib/model/networks/
git clone https://github.com/CharlesShang/DCNv2/
cd DCNv2
./make.sh
Download pertained models for monocular 3D tracking, 80-category tracking, or pose tracking and move them to $CenterTrack_ROOT/models/. More models can be found in Model zoo.
AWS (11 December 2020)
AWS (11 December 2020)
https://github.com/xingyizhou/CenterTrack
Conda
conda create --name CenterTrack36cuda10 python=3.6
conda activate CenterTrack36cuda10
conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
conda install -c conda-forge ffmpeg
pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
CenterTrack_ROOT=/
git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT
pip install -r requirements.txt
cd $CenterTrack_ROOT/src/lib/model/networks/
git clone https://github.com/CharlesShang/DCNv2/
cd DCNv2
./make.sh
Download pertained models for monocular 3D tracking, 80-category tracking, or pose tracking and move them to $CenterTrack_ROOT/models/. More models can be found in Model zoo.
- Training
cd $CenterTrack_ROOT/src/tools/
bash get_mot_17.sh
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