1、运行mobilenetv2ssdlite
模型下载来自：https://github.com/nihui/ncnn-assets
git clone https://github.com/nihui/ncnn-assets.git从中找到需要的mobilenetv2_ssdlite_voc.bin和mobilenetv2_ssdlite_voc.param。
找一张图片如下图，运行：
mobilenetv2ssdlite.exe test.jpg
8 = 0.96635 at 3.13 48.38 359.55 x 350.81
2、运行yolov3
.\yolov3.exe 000000000575.jpg
12 = 0.81045 at 10.71 56.92 368.16 x 368.90
14 = 0.30102 at 312.06 56.70 156.94 x 239.00
检测还是非常准确的，分类就只能呵呵一笑了。可能是因为模型文件的问题，模型文件标签的顺序跟程序中的不一致导致结果标签标错了。所以可能是分类和检测都很准确。
算上之前的一篇 ncnn笔记（二）运行examples 这里一共运行了三个例子了，是时候说一下代码的使用了。
ncnn使用
第一步、先看包含的头文件
shufflenetv2.cpp
#include "net.h"
#include <algorithm>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <stdio.h>
#include <vector>mobilenetv2ssdlite.cpp
#include "net.h"
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <stdio.h>
#include <vector>yolov3.cpp
#include "net.h"
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <stdio.h>
#include <vector>所以使用ncnn包含头文件只需要包含一个头文件。
#include "net.h"第二步、使用的函数接口
shufflenetv2.cpp
static int detect_shufflenetv2(const cv::Mat& bgr, std::vector<float>& cls_scores)
{
ncnn::Net shufflenetv2;
shufflenetv2.opt.use_vulkan_compute = true;
// https://github.com/miaow1988/ShuffleNet_V2_pytorch_caffe
// models can be downloaded from https://github.com/miaow1988/ShuffleNet_V2_pytorch_caffe/releases
shufflenetv2.load_param("shufflenet_v2_x0.5.param");
shufflenetv2.load_model("shufflenet_v2_x0.5.bin");
ncnn::Mat in = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR, bgr.cols, bgr.rows, 224, 224);
const float norm_vals[3] = {1 / 255.f, 1 / 255.f, 1 / 255.f};
in.substract_mean_normalize(0, norm_vals);
ncnn::Extractor ex = shufflenetv2.create_extractor();
ex.input("data", in);
ncnn::Mat out;
ex.extract("fc", out);
// manually call softmax on the fc output
// convert result into probability
// skip if your model already has softmax operation
{
ncnn::Layer* softmax = ncnn::create_layer("Softmax");
ncnn::ParamDict pd;
softmax->load_param(pd);
softmax->forward_inplace(out, shufflenetv2.opt);
delete softmax;
}
out = out.reshape(out.w * out.h * out.c);
cls_scores.resize(out.w);
for (int j = 0; j < out.w; j++)
{
cls_scores[j] = out[j];
}
return 0;
}mobilenetv2ssdlite.cpp
static int detect_mobilenetv2(const cv::Mat& bgr, std::vector<Object>& objects)
{
ncnn::Net mobilenetv2;
mobilenetv2.opt.use_vulkan_compute = true;
mobilenetv2.register_custom_layer("Silence", Noop_layer_creator);
// original pretrained model from https://github.com/chuanqi305/MobileNetv2-SSDLite
// https://github.com/chuanqi305/MobileNetv2-SSDLite/blob/master/ssdlite/voc/deploy.prototxt
// the ncnn model https://github.com/nihui/ncnn-assets/tree/master/models
mobilenetv2.load_param("mobilenetv2_ssdlite_voc.param");
mobilenetv2.load_model("mobilenetv2_ssdlite_voc.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 = mobilenetv2.create_extractor();
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;
}yolov3.cpp
static int detect_yolov3(const cv::Mat& bgr, std::vector<Object>& objects)
{
ncnn::Net yolov3;
yolov3.opt.use_vulkan_compute = true;
// original pretrained model from https://github.com/eric612/MobileNet-YOLO
// param : https://drive.google.com/open?id=1V9oKHP6G6XvXZqhZbzNKL6FI_clRWdC-
// bin : https://drive.google.com/open?id=1DBcuFCr-856z3FRQznWL_S5h-Aj3RawA
// the ncnn model https://github.com/nihui/ncnn-assets/tree/master/models
yolov3.load_param("mobilenetv2_yolov3.param");
yolov3.load_model("mobilenetv2_yolov3.bin");
const int target_size = 352;
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] = {0.007843f, 0.007843f, 0.007843f};
in.substract_mean_normalize(mean_vals, norm_vals);
ncnn::Extractor ex = yolov3.create_extractor();
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;
}总结调用套路
1、构造一个对象 xxnet
ncnn::Net xxnet;2、设置valkan参数（非必须）
xxnet.opt.use_vulkan_compute = true;3、加载模型
xxnet.load_param("xxnet.param");
xxnet.load_model("xxnet.bin");4、图像预处理（一般是颜色空间转换、缩放、去均值、归一化等），下面仅供参考
const int target_size = 352;
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] = {0.007843f, 0.007843f, 0.007843f};
in.substract_mean_normalize(mean_vals, norm_vals);5、读取模型并实现推理
ncnn::Extractor ex = xxnet.create_extractor();
ex.input("data", in);
ncnn::Mat out;
ex.extract("detection_out", out);