build failed in "make -j8 && make pycaffe" step

[Geo-fortune]

I have build Caffe successfully before building the fast-rcnn. My opencv version is 3.0.0 . the Terminal shows the error below :

Makefile:545: recipe for target '.build_release/tools/upgrade_net_proto_text.bin' failed make: *** [.build_release/tools/upgrade_net_proto_text.bin] Error 1 make: *** 正在等待未完成的任务.... Makefile:545: recipe for target '.build_release/tools/upgrade_net_proto_binary.bin' failed make: *** [.build_release/tools/upgrade_net_proto_binary.bin] Error 1 .build_release/lib/libcaffe.so：对‘cv::imread(cv::String const&, int)’未定义的引用 .build_release/lib/libcaffe.so：对‘cv::imencode(cv::String const&, cv::_InputArray const&, std::vector<unsigned char, std::allocator >&, std::vector<int, std::allocator > const&)’未定义的引用 .build_release/lib/libcaffe.so：对‘cv::imdecode(cv::_InputArray const&, int)’未定义的引用 collect2: 错误： ld 返回 1 Makefile:545: recipe for target '.build_release/tools/extract_features.bin' failed make: *** [.build_release/tools/extract_features.bin] Error 1 .build_release/lib/libcaffe.so：对‘cv::imread(cv::String const&, int)’未定义的引用 .build_release/lib/libcaffe.so：对‘cv::imencode(cv::String const&, cv::_InputArray const&, std::vector<unsigned char, std::allocator >&, std::vector<int, std::allocator > const&)’未定义的引用 .build_release/lib/libcaffe.so：对‘cv::imdecode(cv::_InputArray const&, int)’未定义的引用 collect2: 错误： ld 返回 1 Makefile:545: recipe for target '.build_release/tools/compute_image_mean.bin' failed make: *** [.build_release/tools/compute_image_mean.bin] Error 1

My Makefile.config is below:

# 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_LEVELDB := 0

# USE_LMDB := 0

# USE_OPENCV := 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 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_50,code=compute_50

# BLAS choice:

# atlas for ATLAS (default)

# mkl for MKL

# open for OpenBlas

BLAS := mkl

# 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/R2014a

# 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 \

# We need to be able to find libpythonX.X.so or .dylib.

PYTHON_LIB := /usr/local/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/lib/x86_64-linux-gnu/hdf5/serial/include LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu/hdf5/serial

# 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

# 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

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 ?= @

[bruceko]

Add opencv_imgproc in your Makefile might help.

LIBRARIES += glog gflags protobuf leveldb snappy
lmdb boost_system hdf5_hl hdf5 m
opencv_core opencv_highgui opencv_imgproc

[ajdroid]

Try it without the -j8 flag and see if you still get the error.