Autoware 笔记No.7, CNN障碍物检测（CNN LiDAR Baidu Object Segmenter）

前言：测试一下Autoware 下的 CNN LiDAR Baidu Object Segmenter。我的环境是Ubuntu18.04+CUDA10.0+cudnn+ros melodic+opencv4

因为遇到了太多的坑，分享个大家，顺便也留一个记录。

安装最坑的是这个错误：undefined reference to google::FlagRegisterer::FlagRegisterer，找了很多资料也没有解决，所以我就搜索了所有的gflags和glog相关的文件，并将他们删除，然后重新安装，问题得到解决。

CUDA10.0 与 cudnn的安装参见：https://www.cnblogs.com/hgl0417/p/11844135.html

1. 安装opencv

（1）安装依赖：

$ sudo apt install build-essential cmake git pkg-config libgtk-3-dev
$ sudo apt install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev libxvidcore-dev libx264-dev
$ sudo apt install libjpeg-dev libpng-dev libtiff-dev gfortran openexr libatlas-base-dev
$ sudo apt install python3-dev python3-numpy libtbb2 libtbb-dev libdc1394-22-dev

（2）下载源码

$ mkdir ~/opencv_build && cd ~/opencv_build
$ wget https://github.com/opencv/opencv/archive/3.4.0.zip -O opencv-3.4.0.zip
$ wget https://github.com/opencv/opencv_contrib/archive/3.4.0.zip -O opencv_contrib-3.4.0.zip
$ unzip opencv-3.4.0.zip
$ unzip opencv_contrib-3.4.0.zip

用CUDA 10.0以上版本编译opencv3.0以上版本，会报错：找不到dynlink_nvcuvid.h

需要下载：https://developer.nvidia.com/designworks/video_codec_sdk/downloads/v8.2-ga2，解压Video_Codec_SDK_8.2.16.zip

unzip Video_Codec_SDK_8.2.16.zip

在~/Downloads/Video_Codec_SDK_8.2.16/Samples/NvCodec/NvDecoder/找到cuviddec.h，在~/Downloads/Video_Codec_SDK_8.2.16/Samples/NvCodec/NvDecoder/找到nvcuvid.h，讲这两个文件拷贝到/usr/local/cuda/include/。

$ sudo cp ~/Downloads/Video_Codec_SDK_8.2.16/Samples/NvCodec/NvDecoder/cuviddec.h /usr/local/cuda/include/
$ sudo cp ~/Downloads/Video_Codec_SDK_8.2.16/Samples/NvCodec/NvDecoder/nvcuvid.h /usr/local/cuda/include/

修改opencv-3.4.0下的modules下的一些头文件：

modules/cudacodec/src/precomp.hpp
modules/cudacodec/src/frame_queue.hpp
modules/cudacodec/src/cuvid_video_source.hpp
modules/cudacodec/src/video_decoder.hpp
modules/cudacodec/src/video_parser.hpp

将这些文件的

#if CUDA_VERSION >= 9000
    #include <dynlink_nvcuvid.h>
#else
    #include <nvcuvid.h>

改为：

#if CUDA_VERSION >= 9000 && CUDA_VERSION < 10000
    #include <dynlink_nvcuvid.h>
#else
    #include <nvcuvid.h>

编译，安装

$ cd ~/opencv_build/opencv-3.4.0 && mkdir build && cd build
$ cmake -DCMAKE_BUILD_TYPE=RELEASE \
-DCMAKE_INSTALL_PREFIX=/usr/local \
-DINSTALL_PYTHON_EXAMPLES=ON \
-DINSTALL_C_EXAMPLES=OFF \
-DOPENCV_EXTRA_MODULES_PATH=/home/leon/opencv_build/opencv_contrib-3.4.0/modules \
-DPYTHON_EXCUTABLE=/usr/bin/python2.7 \
-DWITH_CUDA=ON \
-DWITH_CUBLAS=ON \
-DDCUDA_NVCC_FLAGS="-D_FORCE_INLINES" \
-DCUDA_ARCH_BIN="6.1" \
-DCUDA_ARCH_PTX="" \
-DCUDA_FAST_MATH=ON \
-DWITH_TBB=ON \
-DWITH_V4L=ON \
-DWITH_GTK=ON \
-DWITH_OPENGL=ON \
-DCMAKE_C_COMPILER=/usr/bin/gcc-7 \
-DCUDA_HOST_COMPILER=/usr/bin/g++-7 \
-DCUDA_PROPAGATE_HOST_FLAGS=oFF \
-DCMAKE_CXX_FLAGS="-std=c++11" \
-DBUILD_TIFF=ON \
-DBUILD_EXAMPLES=ON ..
$ make -j$nproc
$ sudo make install

查看opencv版本

$ pkg-config opencv --modversion

2. 安装hdf5

如果没有安装hdf5，可以从 https://support.hdfgroup.org/ftp/HDF5/releases/hdf5-1.8/hdf5-1.8.21/src/ 下载hdf5-1.8.21.tar.gz，默认下载到Downloads文件夹。

解压hdf5-1.8.21.tar.gz

$ cd ~/Downloads
$ tar -xvf hdf5-1.8.21.tar.gz
$ sudo mv -f hdf5-1.8.21/ /opt
$ cd /opt/hdf5-1.8.21/

编译安装hdf5

$ sudo ./configure --prefix=/usr/local/hdf5
$ sudo make
$ sudo make check
$ sudo make install

安装成功后，在安装目录/usr/local下出现hdf5文件夹，打开后有/bin，/include，/lib，/share四个文件夹

安装成功后，测试

$ cd /usr/local/hdf5/share/hdf5_examples/c
$ sudo ./run-c-ex.sh

执行命令

$ sudo h5cc -o h5_extend h5_extend.c

如果出现sudo: h5cc: command not found，那么执行

$ sudo apt install hdf5-helpers

再次执行

$ sudo h5cc -o h5_extend h5_extend.c

如果出现hdf5.h not found，执行

sudo apt-get install libhdf5-serial-dev

这是应该没有错误提示。

在执行

$ sudo ./h5_extend

安装完毕，显示

2. 安装caffe相关的package

$ sudo apt install -y libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
$ sudo apt install -y --no-install-recommends libboost-all-dev
$ sudo apt install -y libopenblas-dev #libatlas-base-dev
$ sudo apt install -y libgflags-dev libgoogle-glog-dev liblmdb-dev

3. 下载/安装caffe

下载：

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

修改Makefile.config

$ cd /caffe
$ cp Makefile.config.example Makefile.config

编辑Makefile.config，这里我参考了网上很多的资料，所以我直接贴上我自己的Makefile.config。

## Refer to http://caffe.berkeleyvision.org/installation.html
# Contributions simplifying and improving our build system are welcome!

# cuDNN acceleration switch (uncomment to build with cuDNN).
USE_CUDNN := 1

# CPU-only switch (uncomment to build without GPU support).
# CPU_ONLY := 1

# uncomment to disable IO dependencies and corresponding data layers
USE_OPENCV := 1
# USE_LEVELDB := 0
# USE_LMDB := 0
# This code is taken from https://github.com/sh1r0/caffe-android-lib
# USE_HDF5 := 0

# uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary)
#    You should not set this flag if you will be reading LMDBs with any
#    possibility of simultaneous read and write
# ALLOW_LMDB_NOLOCK := 1

# Uncomment if you're using OpenCV 3
OPENCV_VERSION := 3.4.0

# To customize your choice of compiler, uncomment and set the following.
# N.B. the default for Linux is g++ and the default for OSX is clang++
CUSTOM_CXX := g++

# CUDA directory contains bin/ and lib/ directories that we need.
CUDA_DIR := /usr/local/cuda
# On Ubuntu 14.04, if cuda tools are installed via
# "sudo apt-get install nvidia-cuda-toolkit" then use this instead:
# CUDA_DIR := /usr

# CUDA architecture setting: going with all of them.
# For CUDA < 6.0, comment the *_50 through *_61 lines for compatibility.
# For CUDA < 8.0, comment the *_60 and *_61 lines for compatibility.
# For CUDA >= 9.0, comment the *_20 and *_21 lines for compatibility.
CUDA_ARCH := -gencode arch=compute_30,code=sm_30 \
        -gencode arch=compute_35,code=sm_35 \
        -gencode arch=compute_50,code=sm_50 \
        -gencode arch=compute_52,code=sm_52 \
        -gencode arch=compute_60,code=sm_60 \
        -gencode arch=compute_61,code=sm_61 \
        -gencode arch=compute_61,code=compute_61

# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
BLAS := atlas
# Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
# Leave commented to accept the defaults for your choice of BLAS
# (which should work)!
# BLAS_INCLUDE := /path/to/your/blas
# BLAS_LIB := /path/to/your/blas

# Homebrew puts openblas in a directory that is not on the standard search path
# BLAS_INCLUDE := $(shell brew --prefix openblas)/include
# BLAS_LIB := $(shell brew --prefix openblas)/lib

# This is required only if you will compile the matlab interface.
# MATLAB directory should contain the mex binary in /bin.
# MATLAB_DIR := /usr/local
# MATLAB_DIR := /Applications/MATLAB_R2012b.app

# NOTE: this is required only if you will compile the python interface.
# We need to be able to find Python.h and numpy/arrayobject.h.
PYTHON_INCLUDE := /usr/include/python2.7 \
        /usr/lib/python2.7/dist-packages/numpy/core/include
# Anaconda Python distribution is quite popular. Include path:
# Verify anaconda location, sometimes it's in root.
# ANACONDA_HOME := $(HOME)/anaconda
# PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
        # $(ANACONDA_HOME)/include/python2.7 \
        # $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include

# Uncomment to use Python 3 (default is Python 2)
# PYTHON_LIBRARIES := boost_python3 python3.5m
# PYTHON_INCLUDE := /usr/include/python3.5m \
#                 /usr/lib/python3.5/dist-packages/numpy/core/include

# We need to be able to find libpythonX.X.so or .dylib.
PYTHON_LIB := /usr/lib
# PYTHON_LIB := $(ANACONDA_HOME)/lib

# Homebrew installs numpy in a non standard path (keg only)
# PYTHON_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.__file__)'))/include
# PYTHON_LIB += $(shell brew --prefix numpy)/lib

# Uncomment to support layers written in Python (will link against Python libs)
WITH_PYTHON_LAYER := 1

# Whatever else you find you need goes here.
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial/ /usr/local/hdf5/include/
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/local/hdf5/lib/

# If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies
# INCLUDE_DIRS += $(shell brew --prefix)/include
# LIBRARY_DIRS += $(shell brew --prefix)/lib

# NCCL acceleration switch (uncomment to build with NCCL)
# https://github.com/NVIDIA/nccl (last tested version: v1.2.3-1+cuda8.0)
# USE_NCCL := 1

# Uncomment to use `pkg-config` to specify OpenCV library paths.
# (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
# USE_PKG_CONFIG := 1

# N.B. both build and distribute dirs are cleared on `make clean`
BUILD_DIR := build
DISTRIBUTE_DIR := distribute

# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
# DEBUG := 1

# The ID of the GPU that 'make runtest' will use to run unit tests.
TEST_GPUID := 0

# enable pretty build (comment to see full commands)
Q ?= @

Makefile文件

PROJECT := caffe

CONFIG_FILE := Makefile.config
# Explicitly check for the config file, otherwise make -k will proceed anyway.
ifeq ($(wildcard $(CONFIG_FILE)),)
$(error $(CONFIG_FILE) not found. See $(CONFIG_FILE).example.)
endif
include $(CONFIG_FILE)

BUILD_DIR_LINK := $(BUILD_DIR)
ifeq ($(RELEASE_BUILD_DIR),)
    RELEASE_BUILD_DIR := .$(BUILD_DIR)_release
endif
ifeq ($(DEBUG_BUILD_DIR),)
    DEBUG_BUILD_DIR := .$(BUILD_DIR)_debug
endif

DEBUG ?= 0
ifeq ($(DEBUG), 1)
    BUILD_DIR := $(DEBUG_BUILD_DIR)
    OTHER_BUILD_DIR := $(RELEASE_BUILD_DIR)
else
    BUILD_DIR := $(RELEASE_BUILD_DIR)
    OTHER_BUILD_DIR := $(DEBUG_BUILD_DIR)
endif

# All of the directories containing code.
SRC_DIRS := $(shell find * -type d -exec bash -c "find {} -maxdepth 1 \
    \( -name '*.cpp' -o -name '*.proto' \) | grep -q ." \; -print)

# The target shared library name
LIBRARY_NAME := $(PROJECT)
LIB_BUILD_DIR := $(BUILD_DIR)/lib
STATIC_NAME := $(LIB_BUILD_DIR)/lib$(LIBRARY_NAME).a
DYNAMIC_VERSION_MAJOR         := 1
DYNAMIC_VERSION_MINOR         := 0
DYNAMIC_VERSION_REVISION     := 0
DYNAMIC_NAME_SHORT := lib$(LIBRARY_NAME).so
#DYNAMIC_SONAME_SHORT := $(DYNAMIC_NAME_SHORT).$(DYNAMIC_VERSION_MAJOR)
DYNAMIC_VERSIONED_NAME_SHORT := $(DYNAMIC_NAME_SHORT).$(DYNAMIC_VERSION_MAJOR).$(DYNAMIC_VERSION_MINOR).$(DYNAMIC_VERSION_REVISION)
DYNAMIC_NAME := $(LIB_BUILD_DIR)/$(DYNAMIC_VERSIONED_NAME_SHORT)
COMMON_FLAGS += -DCAFFE_VERSION=$(DYNAMIC_VERSION_MAJOR).$(DYNAMIC_VERSION_MINOR).$(DYNAMIC_VERSION_REVISION)

##############################
# Get all source files
##############################
# CXX_SRCS are the source files excluding the test ones.
CXX_SRCS := $(shell find src/$(PROJECT) ! -name "test_*.cpp" -name "*.cpp")
# CU_SRCS are the cuda source files
CU_SRCS := $(shell find src/$(PROJECT) ! -name "test_*.cu" -name "*.cu")
# TEST_SRCS are the test source files
TEST_MAIN_SRC := src/$(PROJECT)/test/test_caffe_main.cpp
TEST_SRCS := $(shell find src/$(PROJECT) -name "test_*.cpp")
TEST_SRCS := $(filter-out $(TEST_MAIN_SRC), $(TEST_SRCS))
TEST_CU_SRCS := $(shell find src/$(PROJECT) -name "test_*.cu")
GTEST_SRC := src/gtest/gtest-all.cpp
# TOOL_SRCS are the source files for the tool binaries
TOOL_SRCS := $(shell find tools -name "*.cpp")
# EXAMPLE_SRCS are the source files for the example binaries
EXAMPLE_SRCS := $(shell find examples -name "*.cpp")
# BUILD_INCLUDE_DIR contains any generated header files we want to include.
BUILD_INCLUDE_DIR := $(BUILD_DIR)/src
# PROTO_SRCS are the protocol buffer definitions
PROTO_SRC_DIR := src/$(PROJECT)/proto
PROTO_SRCS := $(wildcard $(PROTO_SRC_DIR)/*.proto)
# PROTO_BUILD_DIR will contain the .cc and obj files generated from
# PROTO_SRCS; PROTO_BUILD_INCLUDE_DIR will contain the .h header files
PROTO_BUILD_DIR := $(BUILD_DIR)/$(PROTO_SRC_DIR)
PROTO_BUILD_INCLUDE_DIR := $(BUILD_INCLUDE_DIR)/$(PROJECT)/proto
# NONGEN_CXX_SRCS includes all source/header files except those generated
# automatically (e.g., by proto).
NONGEN_CXX_SRCS := $(shell find \
    src/$(PROJECT) \
    include/$(PROJECT) \
    python/$(PROJECT) \
    matlab/+$(PROJECT)/private \
    examples \
    tools \
    -name "*.cpp" -or -name "*.hpp" -or -name "*.cu" -or -name "*.cuh")
LINT_SCRIPT := scripts/cpp_lint.py
LINT_OUTPUT_DIR := $(BUILD_DIR)/.lint
LINT_EXT := lint.txt
LINT_OUTPUTS := $(addsuffix .$(LINT_EXT), $(addprefix $(LINT_OUTPUT_DIR)/, $(NONGEN_CXX_SRCS)))
EMPTY_LINT_REPORT := $(BUILD_DIR)/.$(LINT_EXT)
NONEMPTY_LINT_REPORT := $(BUILD_DIR)/$(LINT_EXT)
# PY$(PROJECT)_SRC is the python wrapper for $(PROJECT)
PY$(PROJECT)_SRC := python/$(PROJECT)/_$(PROJECT).cpp
PY$(PROJECT)_SO := python/$(PROJECT)/_$(PROJECT).so
PY$(PROJECT)_HXX := include/$(PROJECT)/layers/python_layer.hpp
# MAT$(PROJECT)_SRC is the mex entrance point of matlab package for $(PROJECT)
MAT$(PROJECT)_SRC := matlab/+$(PROJECT)/private/$(PROJECT)_.cpp
ifneq ($(MATLAB_DIR),)
    MAT_SO_EXT := $(shell $(MATLAB_DIR)/bin/mexext)
endif
MAT$(PROJECT)_SO := matlab/+$(PROJECT)/private/$(PROJECT)_.$(MAT_SO_EXT)

##############################
# Derive generated files
##############################
# The generated files for protocol buffers
PROTO_GEN_HEADER_SRCS := $(addprefix $(PROTO_BUILD_DIR)/, \
        $(notdir ${PROTO_SRCS:.proto=.pb.h}))
PROTO_GEN_HEADER := $(addprefix $(PROTO_BUILD_INCLUDE_DIR)/, \
        $(notdir ${PROTO_SRCS:.proto=.pb.h}))
PROTO_GEN_CC := $(addprefix $(BUILD_DIR)/, ${PROTO_SRCS:.proto=.pb.cc})
PY_PROTO_BUILD_DIR := python/$(PROJECT)/proto
PY_PROTO_INIT := python/$(PROJECT)/proto/__init__.py
PROTO_GEN_PY := $(foreach file,${PROTO_SRCS:.proto=_pb2.py}, \
        $(PY_PROTO_BUILD_DIR)/$(notdir $(file)))
# The objects corresponding to the source files
# These objects will be linked into the final shared library, so we
# exclude the tool, example, and test objects.
CXX_OBJS := $(addprefix $(BUILD_DIR)/, ${CXX_SRCS:.cpp=.o})
CU_OBJS := $(addprefix $(BUILD_DIR)/cuda/, ${CU_SRCS:.cu=.o})
PROTO_OBJS := ${PROTO_GEN_CC:.cc=.o}
OBJS := $(PROTO_OBJS) $(CXX_OBJS) $(CU_OBJS)
# tool, example, and test objects
TOOL_OBJS := $(addprefix $(BUILD_DIR)/, ${TOOL_SRCS:.cpp=.o})
TOOL_BUILD_DIR := $(BUILD_DIR)/tools
TEST_CXX_BUILD_DIR := $(BUILD_DIR)/src/$(PROJECT)/test
TEST_CU_BUILD_DIR := $(BUILD_DIR)/cuda/src/$(PROJECT)/test
TEST_CXX_OBJS := $(addprefix $(BUILD_DIR)/, ${TEST_SRCS:.cpp=.o})
TEST_CU_OBJS := $(addprefix $(BUILD_DIR)/cuda/, ${TEST_CU_SRCS:.cu=.o})
TEST_OBJS := $(TEST_CXX_OBJS) $(TEST_CU_OBJS)
GTEST_OBJ := $(addprefix $(BUILD_DIR)/, ${GTEST_SRC:.cpp=.o})
EXAMPLE_OBJS := $(addprefix $(BUILD_DIR)/, ${EXAMPLE_SRCS:.cpp=.o})
# Output files for automatic dependency generation
DEPS := ${CXX_OBJS:.o=.d} ${CU_OBJS:.o=.d} ${TEST_CXX_OBJS:.o=.d} \
    ${TEST_CU_OBJS:.o=.d} $(BUILD_DIR)/${MAT$(PROJECT)_SO:.$(MAT_SO_EXT)=.d}
# tool, example, and test bins
TOOL_BINS := ${TOOL_OBJS:.o=.bin}
EXAMPLE_BINS := ${EXAMPLE_OBJS:.o=.bin}
# symlinks to tool bins without the ".bin" extension
TOOL_BIN_LINKS := ${TOOL_BINS:.bin=}
# Put the test binaries in build/test for convenience.
TEST_BIN_DIR := $(BUILD_DIR)/test
TEST_CU_BINS := $(addsuffix .testbin,$(addprefix $(TEST_BIN_DIR)/, \
        $(foreach obj,$(TEST_CU_OBJS),$(basename $(notdir $(obj))))))
TEST_CXX_BINS := $(addsuffix .testbin,$(addprefix $(TEST_BIN_DIR)/, \
        $(foreach obj,$(TEST_CXX_OBJS),$(basename $(notdir $(obj))))))
TEST_BINS := $(TEST_CXX_BINS) $(TEST_CU_BINS)
# TEST_ALL_BIN is the test binary that links caffe dynamically.
TEST_ALL_BIN := $(TEST_BIN_DIR)/test_all.testbin

##############################
# Derive compiler warning dump locations
##############################
WARNS_EXT := warnings.txt
CXX_WARNS := $(addprefix $(BUILD_DIR)/, ${CXX_SRCS:.cpp=.o.$(WARNS_EXT)})
CU_WARNS := $(addprefix $(BUILD_DIR)/cuda/, ${CU_SRCS:.cu=.o.$(WARNS_EXT)})
TOOL_WARNS := $(addprefix $(BUILD_DIR)/, ${TOOL_SRCS:.cpp=.o.$(WARNS_EXT)})
EXAMPLE_WARNS := $(addprefix $(BUILD_DIR)/, ${EXAMPLE_SRCS:.cpp=.o.$(WARNS_EXT)})
TEST_WARNS := $(addprefix $(BUILD_DIR)/, ${TEST_SRCS:.cpp=.o.$(WARNS_EXT)})
TEST_CU_WARNS := $(addprefix $(BUILD_DIR)/cuda/, ${TEST_CU_SRCS:.cu=.o.$(WARNS_EXT)})
ALL_CXX_WARNS := $(CXX_WARNS) $(TOOL_WARNS) $(EXAMPLE_WARNS) $(TEST_WARNS)
ALL_CU_WARNS := $(CU_WARNS) $(TEST_CU_WARNS)
ALL_WARNS := $(ALL_CXX_WARNS) $(ALL_CU_WARNS)

EMPTY_WARN_REPORT := $(BUILD_DIR)/.$(WARNS_EXT)
NONEMPTY_WARN_REPORT := $(BUILD_DIR)/$(WARNS_EXT)

##############################
# Derive include and lib directories
##############################
CUDA_INCLUDE_DIR := $(CUDA_DIR)/include

CUDA_LIB_DIR :=
# add <cuda>/lib64 only if it exists
ifneq ("$(wildcard $(CUDA_DIR)/lib64)","")
    CUDA_LIB_DIR += $(CUDA_DIR)/lib64
endif
CUDA_LIB_DIR += $(CUDA_DIR)/lib

INCLUDE_DIRS += $(BUILD_INCLUDE_DIR) ./src ./include
ifneq ($(CPU_ONLY), 1)
    INCLUDE_DIRS += $(CUDA_INCLUDE_DIR)
    LIBRARY_DIRS += $(CUDA_LIB_DIR)
    LIBRARIES := cudart cublas curand
endif

LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_serial_hl hdf5_serial

# handle IO dependencies
USE_LEVELDB ?= 1
USE_LMDB ?= 1
# This code is taken from https://github.com/sh1r0/caffe-android-lib
USE_HDF5 ?= 1
USE_OPENCV ?= 1

ifeq ($(USE_LEVELDB), 1)
    LIBRARIES += leveldb snappy
endif
ifeq ($(USE_LMDB), 1)
    LIBRARIES += lmdb
endif
# This code is taken from https://github.com/sh1r0/caffe-android-lib
ifeq ($(USE_HDF5), 1)
    LIBRARIES += hdf5_hl hdf5
endif
ifeq ($(USE_OPENCV), 1)
    LIBRARIES += opencv_core opencv_highgui opencv_imgproc

    ifeq ($(OPENCV_VERSION), 3)
        LIBRARIES += opencv_imgcodecs
    endif

endif
PYTHON_LIBRARIES ?= boost_python python2.7
WARNINGS := -Wall -Wno-sign-compare

##############################
# Set build directories
##############################

DISTRIBUTE_DIR ?= distribute
DISTRIBUTE_SUBDIRS := $(DISTRIBUTE_DIR)/bin $(DISTRIBUTE_DIR)/lib
DIST_ALIASES := dist
ifneq ($(strip $(DISTRIBUTE_DIR)),distribute)
        DIST_ALIASES += distribute
endif

ALL_BUILD_DIRS := $(sort $(BUILD_DIR) $(addprefix $(BUILD_DIR)/, $(SRC_DIRS)) \
    $(addprefix $(BUILD_DIR)/cuda/, $(SRC_DIRS)) \
    $(LIB_BUILD_DIR) $(TEST_BIN_DIR) $(PY_PROTO_BUILD_DIR) $(LINT_OUTPUT_DIR) \
    $(DISTRIBUTE_SUBDIRS) $(PROTO_BUILD_INCLUDE_DIR))

##############################
# Set directory for Doxygen-generated documentation
##############################
DOXYGEN_CONFIG_FILE ?= ./.Doxyfile
# should be the same as OUTPUT_DIRECTORY in the .Doxyfile
DOXYGEN_OUTPUT_DIR ?= ./doxygen
DOXYGEN_COMMAND ?= doxygen
# All the files that might have Doxygen documentation.
DOXYGEN_SOURCES := $(shell find \
    src/$(PROJECT) \
    include/$(PROJECT) \
    python/ \
    matlab/ \
    examples \
    tools \
    -name "*.cpp" -or -name "*.hpp" -or -name "*.cu" -or -name "*.cuh" -or \
        -name "*.py" -or -name "*.m")
DOXYGEN_SOURCES += $(DOXYGEN_CONFIG_FILE)


##############################
# Configure build
##############################

# Determine platform
UNAME := $(shell uname -s)
ifeq ($(UNAME), Linux)
    LINUX := 1
else ifeq ($(UNAME), Darwin)
    OSX := 1
    OSX_MAJOR_VERSION := $(shell sw_vers -productVersion | cut -f 1 -d .)
    OSX_MINOR_VERSION := $(shell sw_vers -productVersion | cut -f 2 -d .)
endif

# Linux
ifeq ($(LINUX), 1)
    CXX ?= /usr/bin/g++
    GCCVERSION := $(shell $(CXX) -dumpversion | cut -f1,2 -d.)
    # older versions of gcc are too dumb to build boost with -Wuninitalized
    ifeq ($(shell echo | awk '{exit $(GCCVERSION) < 4.6;}'), 1)
        WARNINGS += -Wno-uninitialized
    endif
    # boost::thread is reasonably called boost_thread (compare OS X)
    # We will also explicitly add stdc++ to the link target.
    LIBRARIES += boost_thread stdc++
    VERSIONFLAGS += -Wl,-soname,$(DYNAMIC_VERSIONED_NAME_SHORT) -Wl,-rpath,$(ORIGIN)/../lib
endif

# OS X:
# clang++ instead of g++
# libstdc++ for NVCC compatibility on OS X >= 10.9 with CUDA < 7.0
ifeq ($(OSX), 1)
    CXX := /usr/bin/clang++
    ifneq ($(CPU_ONLY), 1)
        CUDA_VERSION := $(shell $(CUDA_DIR)/bin/nvcc -V | grep -o 'release [0-9.]*' | tr -d '[a-z ]')
        ifeq ($(shell echo | awk '{exit $(CUDA_VERSION) < 7.0;}'), 1)
            CXXFLAGS += -stdlib=libstdc++
            LINKFLAGS += -stdlib=libstdc++
        endif
        # clang throws this warning for cuda headers
        WARNINGS += -Wno-unneeded-internal-declaration
        # 10.11 strips DYLD_* env vars so link CUDA (rpath is available on 10.5+)
        OSX_10_OR_LATER   := $(shell [ $(OSX_MAJOR_VERSION) -ge 10 ] && echo true)
        OSX_10_5_OR_LATER := $(shell [ $(OSX_MINOR_VERSION) -ge 5 ] && echo true)
        ifeq ($(OSX_10_OR_LATER),true)
            ifeq ($(OSX_10_5_OR_LATER),true)
                LDFLAGS += -Wl,-rpath,$(CUDA_LIB_DIR)
            endif
        endif
    endif
    # gtest needs to use its own tuple to not conflict with clang
    COMMON_FLAGS += -DGTEST_USE_OWN_TR1_TUPLE=1
    # boost::thread is called boost_thread-mt to mark multithreading on OS X
    LIBRARIES += boost_thread-mt
    # we need to explicitly ask for the rpath to be obeyed
    ORIGIN := @loader_path
    VERSIONFLAGS += -Wl,-install_name,@rpath/$(DYNAMIC_VERSIONED_NAME_SHORT) -Wl,-rpath,$(ORIGIN)/../../build/lib
else
    ORIGIN := \$$ORIGIN
endif

# Custom compiler
ifdef CUSTOM_CXX
    CXX := $(CUSTOM_CXX)
endif

# Static linking
ifneq (,$(findstring clang++,$(CXX)))
    STATIC_LINK_COMMAND := -Wl,-force_load $(STATIC_NAME)
else ifneq (,$(findstring g++,$(CXX)))
    STATIC_LINK_COMMAND := -Wl,--whole-archive $(STATIC_NAME) -Wl,--no-whole-archive
else
  # The following line must not be indented with a tab, since we are not inside a target
  $(error Cannot static link with the $(CXX) compiler)
endif

# Debugging
ifeq ($(DEBUG), 1)
    COMMON_FLAGS += -DDEBUG -g -O0
    NVCCFLAGS += -G
else
    COMMON_FLAGS += -DNDEBUG -O2
endif

# cuDNN acceleration configuration.
ifeq ($(USE_CUDNN), 1)
    LIBRARIES += cudnn
    COMMON_FLAGS += -DUSE_CUDNN
endif

# NCCL acceleration configuration
ifeq ($(USE_NCCL), 1)
    LIBRARIES += nccl
    COMMON_FLAGS += -DUSE_NCCL
endif

# configure IO libraries
ifeq ($(USE_OPENCV), 1)
    COMMON_FLAGS += -DUSE_OPENCV
endif
ifeq ($(USE_LEVELDB), 1)
    COMMON_FLAGS += -DUSE_LEVELDB
endif
ifeq ($(USE_LMDB), 1)
    COMMON_FLAGS += -DUSE_LMDB
ifeq ($(ALLOW_LMDB_NOLOCK), 1)
    COMMON_FLAGS += -DALLOW_LMDB_NOLOCK
endif
endif
# This code is taken from https://github.com/sh1r0/caffe-android-lib
ifeq ($(USE_HDF5), 1)
    COMMON_FLAGS += -DUSE_HDF5
endif

# CPU-only configuration
ifeq ($(CPU_ONLY), 1)
    OBJS := $(PROTO_OBJS) $(CXX_OBJS)
    TEST_OBJS := $(TEST_CXX_OBJS)
    TEST_BINS := $(TEST_CXX_BINS)
    ALL_WARNS := $(ALL_CXX_WARNS)
    TEST_FILTER := --gtest_filter="-*GPU*"
    COMMON_FLAGS += -DCPU_ONLY
endif

# Python layer support
ifeq ($(WITH_PYTHON_LAYER), 1)
    COMMON_FLAGS += -DWITH_PYTHON_LAYER
    LIBRARIES += $(PYTHON_LIBRARIES)
endif

# BLAS configuration (default = ATLAS)
BLAS ?= atlas
ifeq ($(BLAS), mkl)
    # MKL
    LIBRARIES += mkl_rt
    COMMON_FLAGS += -DUSE_MKL
    MKLROOT ?= /opt/intel/mkl
    BLAS_INCLUDE ?= $(MKLROOT)/include
    BLAS_LIB ?= $(MKLROOT)/lib $(MKLROOT)/lib/intel64
else ifeq ($(BLAS), open)
    # OpenBLAS
    LIBRARIES += openblas
else
    # ATLAS
    ifeq ($(LINUX), 1)
        ifeq ($(BLAS), atlas)
            # Linux simply has cblas and atlas
            LIBRARIES += cblas atlas
        endif
    else ifeq ($(OSX), 1)
        # OS X packages atlas as the vecLib framework
        LIBRARIES += cblas
        # 10.10 has accelerate while 10.9 has veclib
        XCODE_CLT_VER := $(shell pkgutil --pkg-info=com.apple.pkg.CLTools_Executables | grep 'version' | sed 's/[^0-9]*\([0-9]\).*/\1/')
        XCODE_CLT_GEQ_7 := $(shell [ $(XCODE_CLT_VER) -gt 6 ] && echo 1)
        XCODE_CLT_GEQ_6 := $(shell [ $(XCODE_CLT_VER) -gt 5 ] && echo 1)
        ifeq ($(XCODE_CLT_GEQ_7), 1)
            BLAS_INCLUDE ?= /Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/$(shell ls /Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/ | sort | tail -1)/System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/Headers
        else ifeq ($(XCODE_CLT_GEQ_6), 1)
            BLAS_INCLUDE ?= /System/Library/Frameworks/Accelerate.framework/Versions/Current/Frameworks/vecLib.framework/Headers/
            LDFLAGS += -framework Accelerate
        else
            BLAS_INCLUDE ?= /System/Library/Frameworks/vecLib.framework/Versions/Current/Headers/
            LDFLAGS += -framework vecLib
        endif
    endif
endif
INCLUDE_DIRS += $(BLAS_INCLUDE)
LIBRARY_DIRS += $(BLAS_LIB)

LIBRARY_DIRS += $(LIB_BUILD_DIR)

# Automatic dependency generation (nvcc is handled separately)
CXXFLAGS += -MMD -MP

# Complete build flags.
COMMON_FLAGS += $(foreach includedir,$(INCLUDE_DIRS),-I$(includedir))
CXXFLAGS += -pthread -fPIC $(COMMON_FLAGS) $(WARNINGS)
NVCCFLAGS += -ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)
# mex may invoke an older gcc that is too liberal with -Wuninitalized
MATLAB_CXXFLAGS := $(CXXFLAGS) -Wno-uninitialized
LINKFLAGS += -pthread -fPIC $(COMMON_FLAGS) $(WARNINGS)

USE_PKG_CONFIG ?= 0
ifeq ($(USE_PKG_CONFIG), 1)
    PKG_CONFIG := $(shell pkg-config opencv --libs)
else
    PKG_CONFIG :=
endif
LDFLAGS += $(foreach librarydir,$(LIBRARY_DIRS),-L$(librarydir)) $(PKG_CONFIG) \
        $(foreach library,$(LIBRARIES),-l$(library))
PYTHON_LDFLAGS := $(LDFLAGS) $(foreach library,$(PYTHON_LIBRARIES),-l$(library))

# 'superclean' target recursively* deletes all files ending with an extension
# in $(SUPERCLEAN_EXTS) below.  This may be useful if you've built older
# versions of Caffe that do not place all generated files in a location known
# to the 'clean' target.
#
# 'supercleanlist' will list the files to be deleted by make superclean.
#
# * Recursive with the exception that symbolic links are never followed, per the
# default behavior of 'find'.
SUPERCLEAN_EXTS := .so .a .o .bin .testbin .pb.cc .pb.h _pb2.py .cuo

# Set the sub-targets of the 'everything' target.
EVERYTHING_TARGETS := all py$(PROJECT) test warn lint
# Only build matcaffe as part of "everything" if MATLAB_DIR is specified.
ifneq ($(MATLAB_DIR),)
    EVERYTHING_TARGETS += mat$(PROJECT)
endif

##############################
# Define build targets
##############################
.PHONY: all lib test clean docs linecount lint lintclean tools examples $(DIST_ALIASES) \
    py mat py$(PROJECT) mat$(PROJECT) proto runtest \
    superclean supercleanlist supercleanfiles warn everything

all: lib tools examples

lib: $(STATIC_NAME) $(DYNAMIC_NAME)

everything: $(EVERYTHING_TARGETS)

linecount:
    cloc --read-lang-def=$(PROJECT).cloc \
        src/$(PROJECT) include/$(PROJECT) tools examples \
        python matlab

lint: $(EMPTY_LINT_REPORT)

lintclean:
    @ $(RM) -r $(LINT_OUTPUT_DIR) $(EMPTY_LINT_REPORT) $(NONEMPTY_LINT_REPORT)

docs: $(DOXYGEN_OUTPUT_DIR)
    @ cd ./docs ; ln -sfn ../$(DOXYGEN_OUTPUT_DIR)/html doxygen

$(DOXYGEN_OUTPUT_DIR): $(DOXYGEN_CONFIG_FILE) $(DOXYGEN_SOURCES)
    $(DOXYGEN_COMMAND) $(DOXYGEN_CONFIG_FILE)

$(EMPTY_LINT_REPORT): $(LINT_OUTPUTS) | $(BUILD_DIR)
    @ cat $(LINT_OUTPUTS) > $@
    @ if [ -s "$@" ]; then \
        cat $@; \
        mv $@ $(NONEMPTY_LINT_REPORT); \
        echo "Found one or more lint errors."; \
        exit 1; \
      fi; \
      $(RM) $(NONEMPTY_LINT_REPORT); \
      echo "No lint errors!";

$(LINT_OUTPUTS): $(LINT_OUTPUT_DIR)/%.lint.txt : % $(LINT_SCRIPT) | $(LINT_OUTPUT_DIR)
    @ mkdir -p $(dir $@)
    @ python $(LINT_SCRIPT) $< 2>&1 \
        | grep -v "^Done processing " \
        | grep -v "^Total errors found: 0" \
        > $@ \
        || true

test: $(TEST_ALL_BIN) $(TEST_ALL_DYNLINK_BIN) $(TEST_BINS)

tools: $(TOOL_BINS) $(TOOL_BIN_LINKS)

examples: $(EXAMPLE_BINS)

py$(PROJECT): py

py: $(PY$(PROJECT)_SO) $(PROTO_GEN_PY)

$(PY$(PROJECT)_SO): $(PY$(PROJECT)_SRC) $(PY$(PROJECT)_HXX) | $(DYNAMIC_NAME)
    @ echo CXX/LD -o $@ $<
    $(Q)$(CXX) -shared -o $@ $(PY$(PROJECT)_SRC) \
        -o $@ $(LINKFLAGS) -l$(LIBRARY_NAME) $(PYTHON_LDFLAGS) \
        -Wl,-rpath,$(ORIGIN)/../../build/lib

mat$(PROJECT): mat

mat: $(MAT$(PROJECT)_SO)

$(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(STATIC_NAME)
    @ if [ -z "$(MATLAB_DIR)" ]; then \
        echo "MATLAB_DIR must be specified in $(CONFIG_FILE)" \
            "to build mat$(PROJECT)."; \
        exit 1; \
    fi
    @ echo MEX $<
    $(Q)$(MATLAB_DIR)/bin/mex $(MAT$(PROJECT)_SRC) \
            CXX="$(CXX)" \
            CXXFLAGS="\$$CXXFLAGS $(MATLAB_CXXFLAGS)" \
            CXXLIBS="\$$CXXLIBS $(STATIC_LINK_COMMAND) $(LDFLAGS)" -output $@
    @ if [ -f "$(PROJECT)_.d" ]; then \
        mv -f $(PROJECT)_.d $(BUILD_DIR)/${MAT$(PROJECT)_SO:.$(MAT_SO_EXT)=.d}; \
    fi

runtest: $(TEST_ALL_BIN)
    $(TOOL_BUILD_DIR)/caffe
    $(TEST_ALL_BIN) $(TEST_GPUID) --gtest_shuffle $(TEST_FILTER)

pytest: py
    cd python; python -m unittest discover -s caffe/test

mattest: mat
    cd matlab; $(MATLAB_DIR)/bin/matlab -nodisplay -r 'caffe.run_tests(), exit()'

warn: $(EMPTY_WARN_REPORT)

$(EMPTY_WARN_REPORT): $(ALL_WARNS) | $(BUILD_DIR)
    @ cat $(ALL_WARNS) > $@
    @ if [ -s "$@" ]; then \
        cat $@; \
        mv $@ $(NONEMPTY_WARN_REPORT); \
        echo "Compiler produced one or more warnings."; \
        exit 1; \
      fi; \
      $(RM) $(NONEMPTY_WARN_REPORT); \
      echo "No compiler warnings!";

$(ALL_WARNS): %.o.$(WARNS_EXT) : %.o

$(BUILD_DIR_LINK): $(BUILD_DIR)/.linked

# Create a target ".linked" in this BUILD_DIR to tell Make that the "build" link
# is currently correct, then delete the one in the OTHER_BUILD_DIR in case it
# exists and $(DEBUG) is toggled later.
$(BUILD_DIR)/.linked:
    @ mkdir -p $(BUILD_DIR)
    @ $(RM) $(OTHER_BUILD_DIR)/.linked
    @ $(RM) -r $(BUILD_DIR_LINK)
    @ ln -s $(BUILD_DIR) $(BUILD_DIR_LINK)
    @ touch $@

$(ALL_BUILD_DIRS): | $(BUILD_DIR_LINK)
    @ mkdir -p $@

$(DYNAMIC_NAME): $(OBJS) | $(LIB_BUILD_DIR)
    @ echo LD -o $@
    $(Q)$(CXX) -shared -o $@ $(OBJS) $(VERSIONFLAGS) $(LINKFLAGS) $(LDFLAGS)
    @ cd $(BUILD_DIR)/lib; rm -f $(DYNAMIC_NAME_SHORT);   ln -s $(DYNAMIC_VERSIONED_NAME_SHORT) $(DYNAMIC_NAME_SHORT)

$(STATIC_NAME): $(OBJS) | $(LIB_BUILD_DIR)
    @ echo AR -o $@
    $(Q)ar rcs $@ $(OBJS)

$(BUILD_DIR)/%.o: %.cpp $(PROTO_GEN_HEADER) | $(ALL_BUILD_DIRS)
    @ echo CXX $<
    $(Q)$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
        || (cat $@.$(WARNS_EXT); exit 1)
    @ cat $@.$(WARNS_EXT)

$(PROTO_BUILD_DIR)/%.pb.o: $(PROTO_BUILD_DIR)/%.pb.cc $(PROTO_GEN_HEADER) \
        | $(PROTO_BUILD_DIR)
    @ echo CXX $<
    $(Q)$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
        || (cat $@.$(WARNS_EXT); exit 1)
    @ cat $@.$(WARNS_EXT)

$(BUILD_DIR)/cuda/%.o: %.cu | $(ALL_BUILD_DIRS)
    @ echo NVCC $<
    $(Q)$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -M $< -o ${@:.o=.d} \
        -odir $(@D)
    $(Q)$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.$(WARNS_EXT) \
        || (cat $@.$(WARNS_EXT); exit 1)
    @ cat $@.$(WARNS_EXT)

$(TEST_ALL_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) \
        | $(DYNAMIC_NAME) $(TEST_BIN_DIR)
    @ echo CXX/LD -o $@ $<
    $(Q)$(CXX) $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) \
        -o $@ $(LINKFLAGS) $(LDFLAGS) -l$(LIBRARY_NAME) -Wl,-rpath,$(ORIGIN)/../lib

$(TEST_CU_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_CU_BUILD_DIR)/%.o \
    $(GTEST_OBJ) | $(DYNAMIC_NAME) $(TEST_BIN_DIR)
    @ echo LD $<
    $(Q)$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) \
        -o $@ $(LINKFLAGS) $(LDFLAGS) -l$(LIBRARY_NAME) -Wl,-rpath,$(ORIGIN)/../lib

$(TEST_CXX_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_CXX_BUILD_DIR)/%.o \
    $(GTEST_OBJ) | $(DYNAMIC_NAME) $(TEST_BIN_DIR)
    @ echo LD $<
    $(Q)$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) \
        -o $@ $(LINKFLAGS) $(LDFLAGS) -l$(LIBRARY_NAME) -Wl,-rpath,$(ORIGIN)/../lib

# Target for extension-less symlinks to tool binaries with extension '*.bin'.
$(TOOL_BUILD_DIR)/%: $(TOOL_BUILD_DIR)/%.bin | $(TOOL_BUILD_DIR)
    @ $(RM) $@
    @ ln -s $(notdir $<) $@

$(TOOL_BINS): %.bin : %.o | $(DYNAMIC_NAME)
    @ echo CXX/LD -o $@
    $(Q)$(CXX) $< -o $@ $(LINKFLAGS) -l$(LIBRARY_NAME) $(LDFLAGS) \
        -Wl,-rpath,$(ORIGIN)/../lib

$(EXAMPLE_BINS): %.bin : %.o | $(DYNAMIC_NAME)
    @ echo CXX/LD -o $@
    $(Q)$(CXX) $< -o $@ $(LINKFLAGS) -l$(LIBRARY_NAME) $(LDFLAGS) \
        -Wl,-rpath,$(ORIGIN)/../../lib

proto: $(PROTO_GEN_CC) $(PROTO_GEN_HEADER)

$(PROTO_BUILD_DIR)/%.pb.cc $(PROTO_BUILD_DIR)/%.pb.h : \
        $(PROTO_SRC_DIR)/%.proto | $(PROTO_BUILD_DIR)
    @ echo PROTOC $<
    $(Q)protoc --proto_path=$(PROTO_SRC_DIR) --cpp_out=$(PROTO_BUILD_DIR) $<

$(PY_PROTO_BUILD_DIR)/%_pb2.py : $(PROTO_SRC_DIR)/%.proto \
        $(PY_PROTO_INIT) | $(PY_PROTO_BUILD_DIR)
    @ echo PROTOC \(python\) $<
    $(Q)protoc --proto_path=src --python_out=python $<

$(PY_PROTO_INIT): | $(PY_PROTO_BUILD_DIR)
    touch $(PY_PROTO_INIT)

clean:
    @- $(RM) -rf $(ALL_BUILD_DIRS)
    @- $(RM) -rf $(OTHER_BUILD_DIR)
    @- $(RM) -rf $(BUILD_DIR_LINK)
    @- $(RM) -rf $(DISTRIBUTE_DIR)
    @- $(RM) $(PY$(PROJECT)_SO)
    @- $(RM) $(MAT$(PROJECT)_SO)

supercleanfiles:
    $(eval SUPERCLEAN_FILES := $(strip \
            $(foreach ext,$(SUPERCLEAN_EXTS), $(shell find . -name '*$(ext)' \
            -not -path './data/*'))))

supercleanlist: supercleanfiles
    @ \
    if [ -z "$(SUPERCLEAN_FILES)" ]; then \
        echo "No generated files found."; \
    else \
        echo $(SUPERCLEAN_FILES) | tr ' ' '\n'; \
    fi

superclean: clean supercleanfiles
    @ \
    if [ -z "$(SUPERCLEAN_FILES)" ]; then \
        echo "No generated files found."; \
    else \
        echo "Deleting the following generated files:"; \
        echo $(SUPERCLEAN_FILES) | tr ' ' '\n'; \
        $(RM) $(SUPERCLEAN_FILES); \
    fi

$(DIST_ALIASES): $(DISTRIBUTE_DIR)

$(DISTRIBUTE_DIR): all py | $(DISTRIBUTE_SUBDIRS)
    # add proto
    cp -r src/caffe/proto $(DISTRIBUTE_DIR)/
    # add include
    cp -r include $(DISTRIBUTE_DIR)/
    mkdir -p $(DISTRIBUTE_DIR)/include/caffe/proto
    cp $(PROTO_GEN_HEADER_SRCS) $(DISTRIBUTE_DIR)/include/caffe/proto
    # add tool and example binaries
    cp $(TOOL_BINS) $(DISTRIBUTE_DIR)/bin
    cp $(EXAMPLE_BINS) $(DISTRIBUTE_DIR)/bin
    # add libraries
    cp $(STATIC_NAME) $(DISTRIBUTE_DIR)/lib
    install -m 644 $(DYNAMIC_NAME) $(DISTRIBUTE_DIR)/lib
    cd $(DISTRIBUTE_DIR)/lib; rm -f $(DYNAMIC_NAME_SHORT);   ln -s $(DYNAMIC_VERSIONED_NAME_SHORT) $(DYNAMIC_NAME_SHORT)
    # add python - it's not the standard way, indeed...
    cp -r python $(DISTRIBUTE_DIR)/

-include $(DEPS)

安装caffe

$ make
$ make distribute

4. 运行节点

roslaunch lidar_apollo_cnn_seg_detect lidar_apollo_cnn_seg_detect.launch network_definition_file:=/PATH/TO/FILE.prototxt pretrained_model_file:=/PATH/TO/WEIGHTS.caffemodel points_src:=/points_raw

需要从apollo的git上下载两个文件(https://github.com/ApolloAuto/apollo/tree/master/modules/perception/production/data/perception/lidar/models/cnnseg)，分别为：deploy.prototxt及deploy.caffemodel。找到文件后填写修改上面文件的路径，可以运行。注意输入默认的电云topic为：/points_raw。

运行时可能会出现一些错误(如下)，改正后即可运行。

5. 错误修改

这里会对opencv的几个错误，需要改一些文件

错误1：

CMake Error at /opt/ros/melodic/share/image_geometry/cmake/image_geometryConfig.cmake:113 (message):

CMake Error at /opt/ros/melodic/share/grid_map_cv/cmake/grid_map_cvConfig.cmake:113 (message):

这对这类错误，可以修改相应的Config.cmake的113行的相关文件

if(NOT "include;/usr/include;/usr/include/opencv " STREQUAL " ")
  set(grid_map_cv_INCLUDE_DIRS "")
  set(_include_dirs "include;/usr/include;/usr/include/opencv")

去/usr/include/去找opencv，发现找不到，而opencv文件夹在/usr/local/include/中，所以修改为：

if(NOT "include;/usr/include;/usr/local/include/opencv " STREQUAL " ")
  set(grid_map_cv_INCLUDE_DIRS "")
  set(_include_dirs "include;/usr/include;/usr/local/include;/usr/local/include/opencv")

错误2：

calibration_publisher.cpp:(.text.startup+0xb8e): undefined reference to `cv::read(cv::FileNode const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)'

需要修改calibration_publisher.cpp

static cv::Mat CameraExtrinsicMat;
static cv::Mat CameraMat;
static cv::Mat DistCoeff;
static cv::Size ImageSize;
static std::string DistModel;
static cv::String DistModel_cv;  // add code

修改215行代码

fs["CameraExtrinsicMat"] >> CameraExtrinsicMat;
fs["CameraMat"] >> CameraMat;
fs["DistCoeff"] >> DistCoeff;
fs["ImageSize"] >> ImageSize;
// fs["DistModel"] >> DistModel;   // block code
fs["DistModel"] >> DistModel_cv;  // add code
DistModel = DistModel_cv.operator std::string();  // add code

错误3:

在运行

 roslaunch lidar_apollo_cnn_seg_detect lidar_apollo_cnn_seg_detect.launch network_definition_file:=/home/leon/autoware.ai/C16_MODEL/deploy.prototxt pretrained_model_file:=/home/leon/autoware.ai/C16_MODEL/deploy.caffemodel points_src:=/points_raw

命令时如果提示：

/home/leon/autoware.ai/install/lidar_apollo_cnn_seg_detect/lib/lidar_apollo_cnn_seg_detect/lidar_apollo_cnn_seg_detect: error while loading shared libraries: libcaffe.so.1.0.0: cannot open shared object file: No such file or directory

需要在.bashrc中添加：

# caffe
export LD_LIBRARY_PATH=/home/usr_name/caffe/.build_release/lib:$LD_LIBRARY_PATH

错误4(重点错误)：

在运行roslaunch时，会出现错误：

Check failed: bottom[0]->shape(channel_axis_) == channels_ (8 vs. 6) Input size incompatible with convolution kernel.

需要修改代码：

（1）修改autoware.ai/src/autoware/core_perception/lidar_apollo_cnn_seg_detect/include/cnn_segmentation.h文件

double range_, score_threshold_;
int width_;
int height_;
bool use_constant_feature_;  // add code
std_msgs::Header message_header_;
std::string topic_src_;

（2）修改autoware.ai/src/autoware/core_perception/lidar_apollo_cnn_seg_detect/include/feature_generator.h：

FeatureGenerator(){}
~FeatureGenerator(){}

// bool init(caffe::Blob<float>* out_blob);  // block code
bool init(caffe::Blob<float>* out_blob, bool use_constant_feature);  // add code

（3）修改autoware.ai/src/autoware/core_perception/lidar_apollo_cnn_seg_detect/launch/lidar_apollo_cnn_seg_detect.launch

<!-- -->
<launch>
  <arg name="network_definition_file" />
  <arg name="pretrained_model_file" />
  <arg name="points_src" default="/points_raw" />
  <arg name="score_threshold" default="0.6" />
  <arg name="use_gpu" default="true" />
  <arg name="gpu_device_id" default="0" />

  <arg name="width" default="512" />   <!-- add code -->
  <arg name="height" default="512" />  <!-- add code -->
  <arg name="range" default="60" />  <!-- add code -->
  <arg name="use_constant_feature" default="false"/>  <!-- add code -->

  <node pkg="lidar_apollo_cnn_seg_detect" type="lidar_apollo_cnn_seg_detect" name="lidar_apollo_cnn_seg_detect_01" output="screen">
    <param name="network_definition_file" value="$(arg network_definition_file)" />
    <param name="pretrained_model_file" value="$(arg pretrained_model_file)" />
    <param name="points_src" value="$(arg points_src)" />
    <param name="score_threshold" value="$(arg score_threshold)" />
    <param name="use_gpu" value="$(arg use_gpu)" />
    <param name="gpu_device_id" value="$(arg gpu_device_id)" />

    <param name="height" value="$(arg height)" />  <!-- add code -->
    <param name="width" value="$(arg width)" />  <!-- add code -->
    <param name="range" value="$(arg range)" />  <!-- add code -->
    <param name="use_constant_feature" value="$(arg use_constant_feature)" />  <!-- add code -->
  </node>

  <node pkg="detected_objects_visualizer" type="visualize_detected_objects" name="cluster_detect_visualization_01"
          output="screen" ns="/detection/lidar_detector" />

</launch>

（4）修改autoware.ai/src/autoware/core_perception/lidar_apollo_cnn_seg_detect/nodes/cnn_segmentation.cpp

private_node_handle.param<double>("range", range_, 60.);
ROS_INFO("[%s] range: %.2f", __APP_NAME__, range_); // add code
// ROS_INFO("[%s] Pretrained Model File: %.2f", __APP_NAME__, range_); //block code

private_node_handle.param<int>("height", height_, 512);
ROS_INFO("[%s] height: %d", __APP_NAME__, height_);

private_node_handle.param<bool>("use_constant_feature", use_constant_feature_, false); // add code
ROS_INFO("[%s] whether to use constant features: %d", __APP_NAME__, use_constant_feature_); // add code

feature_generator_.reset(new FeatureGenerator());
// if (!feature_generator_->init(feature_blob_.get()))  // block code 
if (!feature_generator_->init(feature_blob_.get(), use_constant_feature_))  // add code
{
  ROS_ERROR("[%s] Fail to Initialize feature generator for CNNSegmentation", __APP_NAME__);
  return false;
}

void CNNSegmentation::run()
{
  // init(); // block code 

  if(this->init()){  // add code
    ROS_INFO("The network init successfully!");  // add code
  }else{  // add code
    ROS_ERROR("The network init fail!!!");  // add code
  }  // add code

  points_sub_ = nh_.subscribe(topic_src_, 1, &CNNSegmentation::pointsCallback, this);
  points_pub_ = nh_.advertise<sensor_msgs::PointCloud2>("/detection/lidar_detector/points_cluster", 1);
  objects_pub_ = nh_.advertise<autoware_msgs::DetectedObjectArray>("/detection/lidar_detector/objects", 1);

  ROS_INFO("[%s] Ready. Waiting for data...", __APP_NAME__);
}

修改

// bool FeatureGenerator::init(caffe::Blob<float>* out_blob)  // block code
bool FeatureGenerator::init(caffe::Blob<float>* out_blob, bool use_constant_feature) // add code
{
  out_blob_ = out_blob;

  // raw feature parameters
  range_ = 60;
  width_ = 512;
  height_ = 512;
  min_height_ = -5.0;
  max_height_ = 5.0;
  CHECK_EQ(width_, height_)
      << "Current implementation version requires input_width == input_height.";

  // set output blob and log lookup table
  // out_blob_->Reshape(1, 8, height_, width_);  // clock code  /********* add code *********/  if(use_constant_feature){ // add code 
    out_blob_->Reshape(1, 8, height_, width_);
  }else{
    out_blob_->Reshape(1, 6, height_, width_);
  }  /********* add code *********/
  log_table_.resize(256);
  for (size_t i = 0; i < log_table_.size(); ++i) {
    log_table_[i] = std::log1p(static_cast<float>(i));
  }

  float* out_blob_data = nullptr;
  out_blob_data = out_blob_->mutable_cpu_data();

  // the pretrained model inside apollo project don't use the constant feature like direction_data_ and distance_data_  // add explaination
  int channel_index = 0;
  max_height_data_ = out_blob_data + out_blob_->offset(0, channel_index++);
  mean_height_data_ = out_blob_data + out_blob_->offset(0, channel_index++);
  count_data_ = out_blob_data + out_blob_->offset(0, channel_index++);
  // direction_data_ = out_blob_data + out_blob_->offset(0, channel_index++); // block data
　  /*********** add code ***********/
  if(use_constant_feature){
    direction_data_ = out_blob_data + out_blob_->offset(0, channel_index++);
  }
  /********** add code ************/
    top_intensity_data_ = out_blob_data + out_blob_->offset(0, channel_index++);
  mean_intensity_data_ = out_blob_data + out_blob_->offset(0, channel_index++);
  // distance_data_ = out_blob_data + out_blob_->offset(0, channel_index++);  // block data
    /********** add code ************/
  if(use_constant_feature){
    distance_data_ = out_blob_data + out_blob_->offset(0, channel_index++);
  }  /********** add code ************/

  nonempty_data_ = out_blob_data + out_blob_->offset(0, channel_index++);
  CHECK_EQ(out_blob_->offset(0, channel_index), out_blob_->count());
    /***********block code **********/
  // // compute direction and distance features
  // int siz = height_ * width_;
  // std::vector<float> direction_data(siz);
  // std::vector<float> distance_data(siz);

  // for (int row = 0; row < height_; ++row) {
  //   for (int col = 0; col < width_; ++col) {
  //     int idx = row * width_ + col;
  //     // * row <-> x, column <-> y
  //     float center_x = Pixel2Pc(row, height_, range_);
  //     float center_y = Pixel2Pc(col, width_, range_);
  //     constexpr double K_CV_PI = 3.1415926535897932384626433832795;
  //     direction_data[idx] =
  //         static_cast<float>(std::atan2(center_y, center_x) / (2.0 * K_CV_PI));
  //     distance_data[idx] =
  //         static_cast<float>(std::hypot(center_x, center_y) / 60.0 - 0.5);
  //   }
  // }
  // caffe::caffe_copy(siz, direction_data.data(), direction_data_);
  // caffe::caffe_copy(siz, distance_data.data(), distance_data_);  /************** block code ******************/
  /************** add code **************/
  if(use_constant_feature){
    // compute direction and distance features
    int siz = height_ * width_;
    std::vector<float> direction_data(siz);
    std::vector<float> distance_data(siz);

    for (int row = 0; row < height_; ++row) {
      for (int col = 0; col < width_; ++col) {
        int idx = row * width_ + col;
        // * row <-> x, column <-> y
        float center_x = Pixel2Pc(row, height_, range_);
        float center_y = Pixel2Pc(col, width_, range_);
        constexpr double K_CV_PI = 3.1415926535897932384626433832795;
        direction_data[idx] =
            static_cast<float>(std::atan2(center_y, center_x) / (2.0 * K_CV_PI));
        distance_data[idx] =
            static_cast<float>(std::hypot(center_x, center_y) / 60.0 - 0.5);
      }
    }
    caffe::caffe_copy(siz, direction_data.data(), direction_data_);
    caffe::caffe_copy(siz, distance_data.data(), distance_data_);
  }
  /****************** add code ******************/  
  return true;
}

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实测 ubuntu 20.04 使用 lidar_imu_calib 功能包进行激光雷达与imu标定并通过lio-sam建图

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