Source code for DIAS, P. A.; TABB, A.; MEDEIROS, H. “Multispecies Fruit Flower Detection Using a Refined Semantic Segmentation Network” IEEE Robotics and Automation Letters, vol. 3, no. 4, 2018.

• Published in https://ieeexplore.ieee.org/document/8392727

• Open access: http://coviss.org/wp-content/uploads/2018/07/DiasTabbMedeiros2018.pdf

Requirements:

• MATLAB, OpenCV (required by Caffe)
• RGR: to clone already with this submodule, please use the following command

git clone --recurse-submodules https://bitbucket.org/phil_dias/flowernet_iros18.git

• Custom Caffe implementation tailored for DeeplabV2. Available at: https://bitbucket.org/phil_dias/caffe_flowerdeeplab2.git

Option 1: Configure using Docker (recommended): ###

• If you haven't configured docker before, please refer to the official instructions at https://docs.docker.com/install/linux/docker-ce/ubuntu/

• To add GPU support to Docker, install nvidia-docker https://github.com/NVIDIA/nvidia-docker

• Navigate to "./docker"

• To configure Docker container, run the following command (it does take some minutes): sudo docker build -t caffeflower-docker .

"caffeflower-docker" will be the container name, used in docker_getFeats.sh . It can be replaced by any desired name, as long as the .sh is also updated accordingly.

Option 2: Compile Caffe from source:

• Below is a summary of instructions need for installation. Please refer to official Caffe documentation for more details: https://caffe.berkeleyvision.org/installation.html

• Requires cuda-8.0 and the following packages

  build-essential cmake git libatlas-base-dev libprotobuf-dev libleveldb-dev libsnappy-dev libhdf5-serial-dev protobuf-compiler libboost-all-dev libgflags-dev libgoogle-glog-dev liblmdb-dev python-setuptools python-dev python-pip libopencv-dev

• git clone https://bitbucket.org/phil_dias/caffe_flowerdeeplab2.git

• Configure matio: cd matio && ./configure && make && make install
• Compile Caffe using cmake:

  cd .. && mkdir build && cd build
  cmake -DBUILD_python=OFF -DBUILD_python_layer=OFF ..
  make -j`nproc`
  

• Set getFeats.sh accordingly with the paths where Caffe was installed

We adapted it from: https://github.com/xmyqsh/deeplab-v2.git, which on its turn adapted from https://bitbucket.org/aquariusjay/deeplab-public-ver2

Deploy example

• Run pred_example.m in MATLAB. Code will ask for "sudo" password to deploy Caffe model
• Calls to Docker container take about 30-40sec for initialization, plus ~60sec for processing all portraits generate for example images of 2704x1520 pixels (286 portraits)
• outputs are saved in "output" folder. Subfolders contain
  • "./Blend": heatmap of flower likelihood estimated by the model
  • "./Binary": segmentation mask with flowers boundaries highlighted in blue

Example of expected outputs:

Installing Docker