deep learning Archives - Robotics with ROS

Human detection and Pose Estimation with Deep Learning for Sport Analysis

1 Comment / C++, Computer Vision, Image Processing, Machine Learning, Tutorials / By admin

Pose estimation and tracking human is one the key step in sports analysis. Here is in this work I used openpose for analysis of player in a Bundesliga game HSV Hamburg vs Bayer München. Warning: the video might be disturbing for HSV fans 🙂 Original Video Analyzed Video Original Video Analyzed Video Original Video Analyzed Video Original Video …

Human detection and Pose Estimation with Deep Learning for Sport Analysis Read More »

Installing Caffe on Ubuntu 16.04

2 Comments / Computer Vision, Machine Learning, Python, Tutorials / By admin

CUDA Toolkit 9.1 visit https://developer.nvidia.com/cuda-downloads and download the correct deb file then:

sudo dpkg -i cuda-repo-ubuntu1604_9.1.85-1_amd64.deb
sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
sudo apt-get update
sudo apt-get install cuda

sudo dpkg -i cuda-repo-ubuntu1604_9.1.85-1_amd64.deb

sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub

sudo apt-get update

sudo apt-get install cuda

Basic Linear Algebra Subprograms (BLAS)

sudo apt-get install libatlas-base-dev libatlas-dev

1	sudo apt-get install libatlas-base-dev libatlas-dev

Protocol Buffers

sudo apt-get install protobuf-compiler libprotobuf-dev libprotoc-dev

1	sudo apt-get install protobuf-compiler libprotobuf-dev libprotoc-dev

or you can install protobuf v3 it from source:

export PROTOBUF_ROOT=~/.softwares/protobuf
git clone https://github.com/google/protobuf.git $PROTOBUF_ROOT -b '3.2.x'
cd $PROTOBUF_ROOT
./autogen.sh
./configure
make "-j$(nproc)"
make"-j$(nproc)"  install DESTDIR=~
cd python
python setup.py install --cpp_implementation --user

export PROTOBUF_ROOT=~/.softwares/protobuf

git clone https://github.com/google/protobuf.git $PROTOBUF_ROOT -b '3.2.x'

cd $PROTOBUF_ROOT

./autogen.sh

./configure

make "-j$(nproc)"

make"-j$(nproc)" install DESTDIR=~

cd python

python setup.py install --cpp_implementation --user

Lightning Memory-Mapped Database

sudo apt-get install liblmdb-dev

1	sudo apt-get install liblmdb-dev

LevelDB

sudo apt-get install libleveldb-dev

1	sudo apt-get install libleveldb-dev

Hdf5

sudo apt-get install libhdf5-dev libhdf5-cpp-11 libhdf5-openmpi-dev libhdf5-mpi-dev libhdf5-10

1	sudo apt-get install libhdf5-dev libhdf5-cpp-11 libhdf5-openmpi-dev libhdf5-mpi-dev libhdf5-10

gflags

git clone https://github.com/gflags/gflags
cd gflags && mkdir build && cd build
cmake -DCMAKE_BUILD_TYPE=Release -DBUILD_SHARED_LIBS=ON -DCMAKE_INSTALL_PREFIX:PATH=~/usr .. && make -j8 all install

git clone https://github.com/gflags/gflags

cd gflags && mkdir build && cd build

cmake -DCMAKE_BUILD_TYPE=Release -DBUILD_SHARED_LIBS=ON -DCMAKE_INSTALL_PREFIX:PATH=~/usr .. && make -j8 all install

glog

git clone https://github.com/google/glog
cd glog && mkdir build && cd build
cmake -DCMAKE_BUILD_TYPE=Release -DBUILD_SHARED_LIBS=ON -DCMAKE_INSTALL_PREFIX:PATH=~/usr .. && make -j8 all install

git clone https://github.com/google/glog

cd glog && mkdir build && cd build

cmake -DCMAKE_BUILD_TYPE=Release -DBUILD_SHARED_LIBS=ON -DCMAKE_INSTALL_PREFIX:PATH=~/usr .. && make -j8 all install

Snappy

git clone https://github.com/google/snappy
cd snappy && mkdir build && cd build
cmake -DCMAKE_BUILD_TYPE=Release -DBUILD_SHARED_LIBS=ON -DCMAKE_INSTALL_PREFIX:PATH=~/usr .. && make -j8 all install

git clone https://github.com/google/snappy

cd snappy && mkdir build && cd build

cmake -DCMAKE_BUILD_TYPE=Release -DBUILD_SHARED_LIBS=ON -DCMAKE_INSTALL_PREFIX:PATH=~/usr .. && make -j8 all install

Caffe

git clone https://github.com/BVLC/caffe
cd caffe && mkdir build && cd build
cmake -DCMAKE_BUILD_TYPE=Release -DBUILD_SHARED_LIBS=ON -DCMAKE_INSTALL_PREFIX:PATH=~/usr .. && make -j8 all install

git clone https://github.com/BVLC/caffe

cd caffe && mkdir build && cd build

cmake -DCMAKE_BUILD_TYPE=Release -DBUILD_SHARED_LIBS=ON -DCMAKE_INSTALL_PREFIX:PATH=~/usr .. && make -j8 all install

Car Detection Using Single Shot MultiBox Detector (SSD Convolutional Neural Network) in ROS Using Caffe

Leave a Comment / Computer Vision, Image Processing, Machine Learning, ROS, Tutorials / By admin

This work is similar to the previous work here, but this time I used Single Shot MultiBox Detector (SSD) for car detection. Installation is similar, clone the SSD Caffe:

git clone https://github.com/weiliu89/caffe.git ssdcaffe
cd ssdcaffe
git checkout 5365d0dccacd18e65f10e840eab28eb65ce0cda

git clone https://github.com/weiliu89/caffe.git ssdcaffe

cd ssdcaffe

git checkout 5365d0dccacd18e65f10e840eab28eb65ce0cda

add the following lines to your Makefile.config

INCLUDE_DIRS += /usr/include/hdf5/serial
LIBRARY_DIRS += /usr/lib/x86_64-linux-gnu/hdf5/serial

1 2	INCLUDE_DIRS += /usr/include/hdf5/serial LIBRARY_DIRS += /usr/lib/x86_64-linux-gnu/hdf5/serial

and build it:

make && make distribute

1	make && make distribute

used video_stream_opencv to stream your video:

<!--
Example of run:
roslaunch video_file.launch video_file:=/home/behnam/Desktop/dash_cam.mp4
-->
<launch>
	<!-- launch video stream -->
		<!--remaping the topic -->
		<remap from="/videofile/image_raw" to="/image_raw"/>
		<include file="$(find video_stream_opencv)/launch/camera.launch" >
	   		<!-- node name and ros graph name -->
		  	<arg name="camera_name" value="videofile" />
		  	<!-- full path to the video file -->
		  	<arg name="video_stream_provider" value="$(arg video_file)" />
		  	<!-- throttling the querying of frames to -->
		  	<arg name="fps" value="30" />
		  	<!-- setting frame_id -->
		  	<arg name="frame_id" value="videofile_frame" />
	</include>
</launch>

<!--

Example of run:

roslaunch video_file.launch video_file:=/home/behnam/Desktop/dash_cam.mp4

-->

</include>

</launch>

download the trained model from here and put them in the model …

Car Detection Using Single Shot MultiBox Detector (SSD Convolutional Neural Network) in ROS Using Caffe Read More »

Car Detection Using Fast Region-based Convolutional Networks (R-CNN) in ROS with Caffe

1 Comment / Computer Vision, Image Processing, Machine Learning, ROS, Tutorials / By admin

To run this, you need to install Fast-RCNN and Autoware. Just in case you got error regarding hd5f when making Fast-RCNN, add the following lines to your Makefile.config

INCLUDE_DIRS += /usr/include/hdf5/serial
LIBRARY_DIRS += /usr/lib/x86_64-linux-gnu/hdf5/serial

1 2	INCLUDE_DIRS += /usr/include/hdf5/serial LIBRARY_DIRS += /usr/lib/x86_64-linux-gnu/hdf5/serial

Now run the following command to start:

source /opt/ros/kinetic/setup.bash
source /home/<user_name>/Autoware/ros/devel/setup.sh
export ROS_PACKAGE_PATH=/home/<user_name>/Autoware/:$ROS_PACKAGE_PATH
roslaunch cv_tracker rcnn.launch

source /opt/ros/kinetic/setup.bash

source /home/<user_name>/Autoware/ros/devel/setup.sh

export ROS_PACKAGE_PATH=/home/<user_name>/Autoware/:$ROS_PACKAGE_PATH

roslaunch cv_tracker rcnn.launch

if you got an error like :

Check failed: error == cudaSuccess (30 vs. 0) unknown error

1	Check failed: error == cudaSuccess (30 vs. 0) unknown error

That means your graphics card is not ready or accessible, in my everytime I suspend my notebook I get that …

Car Detection Using Fast Region-based Convolutional Networks (R-CNN) in ROS with Caffe Read More »

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