NCNN加载106点关键点检测模型（推理部署）

最近发现一款强大的开源 部署  框架，左庆博士开源的ZQCNN：https://github.com/zuoqing1988/ZQCNN

发现里面有个106个点的人脸关键点检测模型效果特别好，就将其对应mxnet模型 转换 成ncnn对应模型，测试代码如下：

#include <iostream>#include "opencv2/opencv.hpp"#include "ncnn/net.h" int main(int argc, char* argv[]) {    cv::Mat img_src = cv::imread("test.jpg");    if (img_src.empty()) {        std::cout << "the input image is empty." << std::endl;        return -1;    }    //cv::resize(img_src, img_src, cv::Size(480, 480));    ncnn::Net net;    if (net.load_param("landmark.param") == -1 ||        net.load_model("landmark.bin") == -1) {        std::cout << "load model failed." << std::endl;        return -1;    }     ncnn::Extractor ex = net.create_extractor();    ncnn::Mat ncnn_in = ncnn::Mat::from_pixels_resize(img_src.data,        ncnn::Mat::PIXEL_BGR, img_src.cols, img_src.rows, 48, 48);    const float meanVals[3] = { 127.5f, 127.5f, 127.5f };    const float normVals[3] = { 0.0078125f, 0.0078125f, 0.0078125f };    ncnn_in.substract_mean_normalize(meanVals, normVals);    ex.input("data", ncnn_in);    ncnn::Mat ncnn_out;    ex.extract("bn6_3", ncnn_out);    std::cout << "channels: " << ncnn_out.w << std::endl;    for (int i = 0; i < 106; ++i) {        std::cout << "ncnn out: " << ncnn_out[2 * i] << " " << ncnn_out[2 * i + 1] << std::endl;        float x = abs(ncnn_out[2 * i] * img_src.cols);        float y = abs(ncnn_out[2 * i + 1]  * img_src.rows);        cv::Point curr_pt = cv::Point(x, y);        cv::circle(img_src, curr_pt, 4, cv::Scalar(0, 255, 0), 2);    }     cv::imshow("result", img_src);    cv::waitKey(0);    return 0;}

效果如下：

Everything:

https://github.com/MirrorYuChen/ncnn_106landmarks

参考资料：

[1] https://github.com/zuoqing1988/ZQCNN/issues/50

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