Pictures of optical flow by Matlab: History: https://vision.in.tum.de/research/optical_flow_estimation optical flow: Lucas-Kanade LK法 Gunnar Farneback Horn-Schunck HS法 Thomas Brox's Algorithm as shown in the folder "eccv2004Matlab" thomas brox: https://lmb.informatik.uni-freiburg.de/Publications/2004/Bro04a/brox_eccv04_of.pdf a robust data term with a brightness constancy and a gradient constancy assumption, combined with a discontinuitypreserving spatio-temporal TV regulariser. smaller angular error https://www.mathworks.com/matlabcentral/fileexchange/17500-high-accuracy-optical-flow use of optical flow: Object Tracking video compression motion based segmentation structure from motion(3D shape and motion) alignment Alignment(global motion compensation) camcorder video stabilization UAV video analysis FlowNet SpyNet FlowNet2.0 docker run -it -v /home/frank/1T/back0529/Downloads/Mathworks_Matl
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Preliminary training: deepracer-github-simapp.tar.gz Reward function: ./opt/install/sagemaker_rl_agent/lib/python3.5/site-packages/markov/environments/deepracer_env.py action = [steering_angle, throttle] TRAINING_IMAGE_SIZE = (160, 120) Plotted waypoints in vertices array of hard track Parameters: on_track, x, y, distance_from_center, car_orientation, progress, steps, throttle, steering, track_width, waypoints, closest_waypoints Note: Above picture is from https://yanpanlau.github.io/2016/10/11/Torcs-Keras.html
For https://github.com/matrixBT/DQN-Atari-Enduro Dockerfile: FROM tensorflow/tensorflow:1.12.0-gpu-py3 #https://github.com/yanpanlau/DDPG-Keras-Torcs WORKDIR /home/frank/rl/DQN-Atari-Enduro ADD . /home/DQN-Atari-Enduro RUN apt-get update RUN apt-get install -y vim xautomation torcs RUN apt-get install -y libjpeg-dev cmake swig python-pyglet python3-opengl libboost-all-dev \ libsdl2-2.0.0 libsdl2-dev libglu1-mesa libglu1-mesa-dev libgles2-mesa-dev \ freeglut3 xvfb libav-tools #optional gtags RUN apt-get install -y exuberant-ctags libncurses-dev RUN pip install jupyter scipy gym RUN pip install "gym[atari]" RUN pip install keras-rl RUN pip install https://pypi.python.org/packages/68/c3/300c6f92b21886b0fe42c13f3a39a06c6cb90c9fbb1b71da85fe59091a7d/pyglet-1.2.4-py3-none-any.whl#md5=08e6404a678f91b4eee85eb33b028d88 ENV PATH="/usr/games:${PATH}" CMD ["/bin/bash"] launch jupyter notebook within docker container: 1.
TensorFlow Docker requirements Install Docker on your local host machine. For GPU support on Linux, install nvidia-docker . Docker is the easiest way to enable TensorFlow GPU support on Linux since only the NVIDIA® GPU driver is required on the host machine (the NVIDIA® CUDA® Toolkit does not need to be installed). docker run [-it] [--rm] [-p hostPort:containerPort] tensorflow/tensorflow[:tag] [command] $ docker run -it --rm tensorflow/tensorflow python -c "import tensorflow as tf; print(tf.__version__)" Note: nvidia-docker v1 uses the nvidia-docker alias, where v2 uses docker --runtime=nvidia . CUDA 9.0 for TensorFlow < 1.13.0 nvidia-docker2 intsall prerequisites: NVIDIA driver and Docker . If you have a custom /etc/docker/daemon.json , the nvidia-docker2 package might override it. Ubuntu 14.04/16.04/18.04, Ubuntu will install docker.io by default which isn't the latest version of D
Purpose: To check if screw exists in each screw hole on D piece of notebook by Machine Learning. Field test of MI4FA project : labeling 3 vacant holes and 1 hole with screw accurately; right pane indicated results of 4 holes with probabilities Pictures of Field Test Training: Step: Construct framework to take picture of piece D with conditioned ambient light spotted on 1.Collect pictures of screw hole with and without screw on notebook D piece in every project as many as possible. 2.Auto crop circular region of screw hole in pictures by OpenCV program 3.Train CNN model training dataset: Inference : 1.Crop loosely 2.Inverse 3.Threshold 4.pyr pyrUp pyrDown 5.can algorithm 6.hough 7.square cropping 8.circle cropping with black padding 9.predict and labeling on picture d