$ export DAEMON="/daemon"
OPENCV AND OPENCV_CONTRIB BUILD INSTRUCTIONS
OPENCV VERSION 4.1.1 OPENCV_CONTRIB VERSION 4.1.1
For Android: 1) Download and install Android NDK 2) Compile the dependencies
export ANDROID_NDK=<NDK>
export ANDROID_ABI=arm64-v8a
export ANDROID_API=29
export TOOLCHAIN=$ANDROID_NDK/toolchains/llvm/prebuilt/linux-x86_64
export TARGET=aarch64-linux-android
export CC=$TOOLCHAIN/bin/$TARGET$ANDROID_API-clang
export CXX=$TOOLCHAIN/bin/$TARGET$ANDROID_API-clang++
export AR=$TOOLCHAIN/bin/$TARGET-ar
export LD=$TOOLCHAIN/bin/$TARGET-ld
export RANLIB=$TOOLCHAIN/bin/$TARGET-ranlib
export STRIP=$TOOLCHAIN/bin/$TARGET-strip
export PATH=$PATH:$TOOLCHAIN/bin
cd contrib
mkdir native
cd native
../bootstrap --build=x86_64-pc-linux-gnu --host=$TARGET$ANDROID_API --enable-opencv --enable-opencv_contrib
make
For Linux: $ cd ${DAEMON}/contrib/native/ $ ./../bootstrap --enable-opencv --enable-opencv_contrib $ make install
TENSORFLOW BUILD INSTRUCTIONS
TF VERSION 2.1.0
Dependencies: 1 - python 3 2 - bazel 0.29.1
$ git clone https://github.com/tensorflow/tensorflow.git $ cd tensorflow $ git checkout -b v2.1.0
For Android: (Tensorflow Lite) Dependencies: 1 - Android NDK 18r
$ ./configure
>> Do you wish to build TensorFlow with XLA JIT support? [Y/n]: n
>> Do you wish to download a fresh release of clang? (Experimental) [y/N]: y
>> Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: y
>> Please specify the home path of the Android NDK to use. [Default is /home/pfreitas/Android/Sdk/ndk-bundle]: put the right path to ndk 18r
$ bazel build //tensorflow/lite:libtensorflowlite.so --crosstool_top=//external:android/crosstool --cpu=armeabi-v7a --host_crosstool_top=@bazel_tools//tools/cpp:toolchain --cxxopt="-std=c++11"
$ bazel build //tensorflow/lite:libtensorflowlite.so --crosstool_top=//external:android/crosstool --cpu=arm64-v8a --host_crosstool_top=@bazel_tools//tools/cpp:toolchain --cxxopt="-std=c++11"
$ bazel build //tensorflow/lite:libtensorflowlite.so --crosstool_top=//external:android/crosstool --cpu=x86_64 --host_crosstool_top=@bazel_tools//tools/cpp:toolchain --cxxopt="-std=c++11"
For Linux:
$ ./configure
For TFLite:
$ bazel build //tensorflow/lite:libtensorflowlite.so
or
For Tensorflow C++ API:
$ bazel build --config=v1 --define framework_shared_object=false //tensorflow:libtensorflow_cc.so
OBS.: If you want to build Tensorflow C++ API with GPU suport, be sure to have a CUDA capable GPU and that you have
followed all installation steps for the Nvidia drivers, CUDA Toolkit, CUDNN, Tensor RT, that their versions
matches and that they are correct for the Tensorflow version you want to build. The following links may be very helpfull:
- https://www.tensorflow.org/install/source
- https://developer.nvidia.com/cuda-gpus
- https://developer.nvidia.com/cuda-toolkit-archive
- https://developer.nvidia.com/cudnn
TENSORFLOWLITE INCLUDES ASSEMBLE INSTRUCTIONS
Keep in mind that after each of bazel build instructions above listed, there will be a "libtensorflowlite.so" created at:
"<tensorflow>/bazel-genfiles/tensorflow/lite/"
or at:
"<tensorflow>/bazel-out/<cpu>-opt/bin/tensorflow/lite/"
(cpu may be "armeabi-v7a", "arm64-v8a", "x86_64", "x86" or "k8" depending on the build realized)
The lib in the first folder is overwritten after each build.
The lib in the second folder is not.
create folders and copy files to have the following path struture:
~home/Libs/
_tensorflow_distribuiton/
lib/
arm64-v8a/
libtensorflowlite.so
armeabi-v7a/
libtensorflowlite.so
x86_64/
libtensorflowlite.so
x86_64-linux-gnu/
libtensorflowlite.so
...
include/
tensorflow/
lite/
c/
buitin_op_data.h
c_api_internal.h
core/
api/
error_reporter.h
op_resolver.h
profiler.h
subgraph.h
delegates/
gpu/
delegate.h
nnapi/
nnapi_delegate.h
experimental/
resource_variable/
resource_variable.h
kernels/
register.h
nnapi/
NeuralNetworksShim.h
NeuralNetworksTypes.h
nnapi_implementation.h
nnapi_util.h
schema/
schema_generated.h
tools/
evaluation/
utils.h
allocation.h
builtin_op_data.h
context.h
external_cpu_backend_context.h
interpreter.h
memory_planner.h
model.h
mutable_op_resolver.h
optinal_debug_tools.h
simple_memory_arena.h
stderr_reporter.h
string_type.h
util.h
flatbuffers/
base.h
code_generators.h
flatbuffers.h
flatc.h
flexbuffers.h
grpc.h
hash.h
idl.h
minireflect.h
reflection.h
reflection_generated.h
registry.h
stl_emulation.h
util.h
TENSORFLOW C++ API INCLUDES ASSEMBLE INSTRUCTIONS
Keep in mind that after the bazel build listed, there will be a "libtensorflow_cc.so.__version__" created at:
"<tensorflow>/bazel-genfiles/tensorflow/"
create folders and copy files to have the following path struture:
After copying libtensorflow_cc.so.__version__, rename it to libtensorflow_cc.so
~home/Libs/
_tensorflow_cc/
lib/
x86_64-linux-gnu/
libtensorflow_cc.so
...
include/
tensorflow/
core/
-> keep folder structure and copy all header files from "<tensorflow>/
tensorflow/core"
-> copy all proto header files (.pb.h) from
"<tensorflow>/bazel-genfiles/tensorflow/core/"
absl/
-> keep folder structure and copy all header and .inc files from "<tensorflow>/
bazel-tensorflow/external/com_google_absl/absl/"
google/
-> keep folder structure and copy all header, proto headers, and .inc files from "<tensorflow>/bazel-tensorflow/external/com_google_protobuf/src/google/"
third_party/
eigen3/
Eigen/
-> keep folder structure and copy all files from
"<tensorflow>/bazel-tensorflow/external/eigen_archive/Eigen/"
unsupported/
Eigen/
-> keep folder structure and copy all files from "<tensorflow>/bazel-tensorflow/external/eigen_archive/unsupported/Eigen/"
CXX11/
-> copy "<tensorflow>/third_party/eigen3/unsupported/Eigen/CXX11/FixedPoint"
-> copy "<tensorflow>/third_party/eigen3/unsupported/Eigen/CXX11/src/"
--> Be aware to apply any needed changes to the build.sh file so that the plugin can be build against the standart tensorflow_cc library.