macOS M1平台下使用NCNN

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安装

下载Vulkan Sdk，双击并且安装。

卸载方法：sudo path_to_vulkan_sdk/uninstall.sh。

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brew install ncnn

Cmake测试程序

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# #OpenMP flags for MACOS
if (APPLE)
    if (CMAKE_C_COMPILER_ID MATCHES "Clang")
        set(OpenMP_C "${CMAKE_C_COMPILER}")
        set(OpenMP_C_FLAGS "-Xpreprocessor -fopenmp -Wno-unused-command-line-argument -I/opt/homebrew/opt/libomp/include")
        set(OpenMP_C_LIB_NAMES "libomp")   
        set(OpenMP_libomp_LIBRARY "/opt/homebrew/opt/libomp/lib/libomp.dylib")
    endif ()
    if (CMAKE_CXX_COMPILER_ID MATCHES "Clang")
        set(OpenMP_CXX "${CMAKE_CXX_COMPILER}")
        set(OpenMP_CXX_FLAGS "-Xpreprocessor -fopenmp -Wno-unused-command-line-argument -I/opt/homebrew/opt/libomp/include")
        set(OpenMP_CXX_LIB_NAMES "libomp")
        set(OpenMP_libomp_LIBRARY "/opt/homebrew/opt/libomp/lib/libomp.dylib")
    endif ()
endif ()

set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")

find_package(ncnn REQUIRED)
find_package(OpenCV REQUIRED)

include_directories(${ncnn_INCLUDE} ${OpenCV_LIBRARY_DIRS}) 

add_executable(test_ncnn test_ncnn.cpp)
target_link_libraries(test_ncnn ncnn ${OpenCV_LIBS})

测试网络为midas_v21_small-int8：

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#include "net.h"
#include "mat.h"
#include "cpu.h"
#include <opencv2/opencv.hpp>
#include <sys/time.h>

int main(int argc, char* argv[]) {
    std::string img_file = "/Users/luohanjie/Workspace/Vision/depth_estimation/MiDaS/input/squirrel_iphone_sample3.png";
    std::string param_file = "/Users/luohanjie/Workspace/Vision/my_slam/data/models/midas_v21_small-int8.param";
    std::string model_file = "/Users/luohanjie/Workspace/Vision/my_slam/data/models/midas_v21_small-int8.bin";
    int target_size = 256;
    float scale = 0.33333f;

    cv::Mat img = cv::imread(img_file);
    cv::resize(img, img, cv::Size(), scale, scale);

    int img_width = img.cols;
    int img_height = img.rows;

    ncnn::Net net;
    ncnn::set_cpu_powersave(0); // 0 = all cores enabled(default)
    ncnn::set_omp_num_threads(ncnn::get_cpu_count());
    net.opt = ncnn::Option();
    net.opt.use_vulkan_compute = false;
    net.opt.num_threads = ncnn::get_cpu_count();

    net.load_param(param_file.c_str());
    net.load_model(model_file.c_str());

    // https://github.com/Tencent/ncnn/blob/master/docs/how-to-use-and-FAQ/use-ncnn-with-opencv.md
    // cv::Mat CV_8UC3 -> ncnn::Mat 3 channel + swap RGB/BGR
    ncnn::Mat img_in = ncnn::Mat::from_pixels_resize(img.data, ncnn::Mat::PIXEL_BGR2RGB, img_width, img_height, target_size, target_size);
    
    // substract_mean_normalize(const float* mean_vals, const float* norm_vals): substract channel-wise mean values, then multiply by normalize values, pass 0 to skip in ncnn.
    const float mean_vals[3] = {123.675f, 116.28f, 103.53f};
    const float norm_vals[3] = {0.01712475383f, 0.0175070028f, 0.01742919389f};
    img_in.substract_mean_normalize(mean_vals, norm_vals);

    ncnn::Extractor ex = net.create_extractor();
    ex.set_light_mode(true);
    
    ncnn::Mat img_out;
        
    ex.input("input.1", img_in);
    ex.extract("649", img_out);
    ncnn::resize_bilinear(img_out, img_out, img_width, img_height);

    cv::Mat cv_out(img_out.h, img_out.w, CV_8UC1);
    img_out.to_pixels(cv_out.data, ncnn::Mat::PIXEL_GRAY);

    cv::imshow("cv_out", cv_out);
    cv::waitKey(0);
    return 0;
}