ncnn 測試mobilessd模型

實驗平台

Ubuntu 16.04 docker

安裝opencv

build

cd  <ncnn-root-dir>
mkdir -p build
cd build
cmake ../
make -j32
           

完全編譯

在主CMakeLists.txt 檔案中注釋去掉

# add_subdirectory(examples)
# add_subdirectory(benchmark)

add_subdirectory(examples)
add_subdirectory(benchmark)


           

完全編譯可以運作例子程式。

下面以mobilenet_ssd為例:

下載下傳模型:

mobilenet_ssd 下載下傳位址

準備圖檔:

運作結果:

測試時間:

./examples/mobilenetssd ../test_imgs/111.png 
ttrack:  0.092464 
15 = 0.99992 at 204.66 26.58 238.24 x 529.39
           

測試時間92ms, cpp的運作時間, 與ssd300的gpu運作時間相當.

遇到問題:

在主工程的 CMakeLists.txt中, 添加

改變并添加:
if(NCNN_OPENMP)
    find_package(OpenMP)
    if(OpenMP_CXX_FOUND OR OPENMP_FOUND)
        set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS} ")
    endif()
endif()
為:
if(NCNN_OPENMP)
    find_package(OpenMP)
    if(OpenMP_CXX_FOUND OR OPENMP_FOUND)
        set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS} -std=c++14 -O3")
    endif()
endif()

           

編譯運作即可.

完整代碼:

// Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.
//
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software distributed
// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
// CONDITIONS OF ANY KIND, either express or implied. See the License for the
// specific language governing permissions and limitations under the License.

#include <stdio.h>
#include <vector>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>


#include <chrono>
// #include <thread>
#include "net.h"

// using namespace std; 
struct Object
{
    cv::Rect_<float> rect;
    int label;
    float prob;
};

static int detect_mobilenet(const cv::Mat &bgr, std::vector<Object> &objects)
{
    ncnn::Net mobilenet;

    // model is converted from https://github.com/chuanqi305/MobileNet-SSD
    // and can be downloaded from https://drive.google.com/open?id=0ByaKLD9QaPtucWk0Y0dha1VVY0U
    mobilenet.load_param("mobilenet_ssd_voc_ncnn.param");
    mobilenet.load_model("mobilenet_ssd_voc_ncnn.bin");

    const int target_size = 300;

    int img_w = bgr.cols;
    int img_h = bgr.rows;

    ncnn::Mat in = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR, bgr.cols, bgr.rows, target_size, target_size);

    const float mean_vals[3] = {127.5f, 127.5f, 127.5f};
    const float norm_vals[3] = {1.0 / 127.5, 1.0 / 127.5, 1.0 / 127.5};
    in.substract_mean_normalize(mean_vals, norm_vals);

    ncnn::Extractor ex = mobilenet.create_extractor();
    //     ex.set_num_threads(4);

    ex.input("data", in);

    ncnn::Mat out;
    ex.extract("detection_out", out);

    //     printf("%d %d %d\n", out.w, out.h, out.c);
    objects.clear();
    for (int i = 0; i < out.h; i++)
    {
        const float *values = out.row(i);

        Object object;
        object.label = values[0];
        object.prob = values[1];
        object.rect.x = values[2] * img_w;
        object.rect.y = values[3] * img_h;
        object.rect.width = values[4] * img_w - object.rect.x;
        object.rect.height = values[5] * img_h - object.rect.y;

        objects.push_back(object);
    }

    return 0;
}

static void draw_objects(const cv::Mat &bgr, const std::vector<Object> &objects)
{
    static const char *class_names[] = {"background",
                                        "aeroplane", "bicycle", "bird", "boat",
                                        "bottle", "bus", "car", "cat", "chair",
                                        "cow", "diningtable", "dog", "horse",
                                        "motorbike", "person", "pottedplant",
                                        "sheep", "sofa", "train", "tvmonitor"};

    cv::Mat image = bgr.clone();

    for (size_t i = 0; i < objects.size(); i++)
    {
        const Object &obj = objects[i];

        fprintf(stderr, "%d = %.5f at %.2f %.2f %.2f x %.2f\n", obj.label, obj.prob,
                obj.rect.x, obj.rect.y, obj.rect.width, obj.rect.height);

        cv::rectangle(image, obj.rect, cv::Scalar(255, 0, 0));

        char text[256];
        sprintf(text, "%s %.1f%%", class_names[obj.label], obj.prob * 100);

        int baseLine = 0;
        cv::Size label_size = cv::getTextSize(text, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);

        int x = obj.rect.x;
        int y = obj.rect.y - label_size.height - baseLine;
        if (y < 0)
            y = 0;
        if (x + label_size.width > image.cols)
            x = image.cols - label_size.width;

        cv::rectangle(image, cv::Rect(cv::Point(x, y), cv::Size(label_size.width, label_size.height + baseLine)),
                      cv::Scalar(0, 0, 255), CV_FILLED);

        cv::putText(image, text, cv::Point(x, y + label_size.height),
                    cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0));
    }

    // cv::imshow("image", image);
    // cv::waitKey(0);
    cv::imwrite("image.png", image);
}



int main(int argc, char **argv)
{
    if (argc != 2)
    {
        fprintf(stderr, "Usage: %s [imagepath]\n", argv[0]);
        return -1;
    }

    const char *imagepath = argv[1];

    cv::Mat m = cv::imread(imagepath, CV_LOAD_IMAGE_COLOR);
    if (m.empty())
    {
        fprintf(stderr, "cv::imread %s failed\n", imagepath);
        return -1;
    }

    std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now();
    std::vector<Object> objects;
    detect_mobilenet(m, objects);

    std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now();
    double ttrack = std::chrono::duration_cast<std::chrono::duration<double>>(t2 - t1).count();

    fprintf(stdout, "ttrack:  %f \n", ttrack);

    draw_objects(m, objects);

    return 0;
}