動機：想要安裝 Caffe 於 MacOS 中，方便研究深度學習程式 for C++
準備環境：
1.macOS Mojave v.10.14.5
2.Python 3.7.3
3.Caffe 1.0.0
4.OpenCV 4.1.0
5.Homebrew 2.1.4-35
6.Protocol Buffers v3.8.0
實作步驟：
1.基本上按照 [參攷.2] 步驟去執行安裝，即可成功!!
以下提供我try了很久的 Makefile.config
2.[參攷.1]也有許多可卓參的內容...我是使用 Compilation with Make 及 CMake Build 兩種皆可成功...其中，有許多需要修改的地方，因沒擷圖就不寫了~~~
準備環境：
1.macOS Mojave v.10.14.5
2.Python 3.7.3
3.Caffe 1.0.0
4.OpenCV 4.1.0
5.Homebrew 2.1.4-35
6.Protocol Buffers v3.8.0
實作步驟：
1.基本上按照 [參攷.2] 步驟去執行安裝，即可成功!!
以下提供我try了很久的 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 := 1
# 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
# 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_20,code=sm_20 \
# -gencode arch=compute_20,code=sm_21 \
# -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 := open
# 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_python37 python3.7m
PYTHON_INCLUDE := /usr/local/Cellar/python/3.7.3/Frameworks/Python.framework/Versions/3.7/include/python3.7m \
/usr/local/lib/python3.7/site-packages/numpy/core/include
# We need to be able to find libpythonX.X.so or .dylib.
PYTHON_LIB := /usr/local/Cellar/python/3.7.3/Frameworks/Python.framework/Versions/3.7/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
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/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 ?= @
2.[參攷.1]也有許多可卓參的內容...我是使用 Compilation with Make 及 CMake Build 兩種皆可成功...其中，有許多需要修改的地方，因沒擷圖就不寫了~~~
心得：想要安裝 Caffe 於 Mac，真不是普通的容易，不知是年代久遠加上大家不太使用的緣故，竟會在安裝過程中吃盡滿坑的地雷，為此特別記錄下我失去的二個禮拜的爆老肝時光...另外，別安裝 Anaconda(https://www.anaconda.com/)，它會讓 Caffe、OpenCV4 等安裝編譯失敗，也讓我白白浪費了許多熬夜的時數!!
參攷：
1.官網 OS X Installation https://caffe.berkeleyvision.org/install_osx.html
2.Mac下基于python3.7，Opencv4.01进行caffe编译安装 https://blog.csdn.net/weixin_38665284/article/details/88313937
1.官網 OS X Installation https://caffe.berkeleyvision.org/install_osx.html
2.Mac下基于python3.7，Opencv4.01进行caffe编译安装 https://blog.csdn.net/weixin_38665284/article/details/88313937
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