System Environment:

System: Ubuntu 18.04

Opencv: opencv 3.2

Instructions:

1. Run demo.cpp on x86 unbuntu, make sure opencv and bazel are installed.

   1. Build libtensorflowlite.so, under the tensorflow directory.

               bazel build -c opt //tensorflow/lite:libtensorflowlite.so --fat_apk_cpu=arm64-v8a
   

   2. Move .so to tensorflow_object_detection_tflite/lib
   3. Change find_library(TFLITE_LIBRARY tensorflow-lite "lib") to find_library(TFLITE_LIBRARY tensorflowlite "lib") in CMakeLists.txt.
   4. Build cmake

           mkdir build
           cd build
           cmake ..
           make -j
           ./demo
   

2. Run demo.cpp on arm64-v8a ubuntu.

   1. Intall opencv on your arm64 motherboard.
   2. Build libtensorflow-lite.a, followed by the tensorflow tutorial https://www.tensorflow.org/lite/guide/build_arm64. Careful about the arm version, v7 or v8.
   3. Move .a to tensorflow_object_detection_tflite/lib
   4. keep find_library(TFLITE_LIBRARY tensorflow-lite "lib") unchanged.
   5. Build cmake

           mkdir build
           cd build
           cmake ..
           make -j
           ./demo
   

3. If there is a flatbuffers error, you should build flatbuffers on your desktop, and use its header files and .a lib file, put and replace them into tensorflow_object_detection_tflite/include and tensorflow_object_detection_tflite/lib, respectively. You can check here to know how to build. google/flatbuffers#5569 (comment)

4. Result image