飞腾派部署人脸识别模型教程
AI与物联网深度融合的当下，人脸识别已广泛应用于安防门禁、智慧办公、智能家居、公共服务等场景，成为智能交互核心。国产自主可控开源硬件飞腾派，凭借其高性能与灵活配置轻松实现高精准、低延迟的人脸识别应用！本次教程将分享从部署到测试的全流程。
项目准备
硬件规格：飞腾派
操作系统：飞腾派OS
（飞腾派OS系统下载：https://s.iceasy.com/nypTSLI）
具体操作：
一、设置编码器：
$ export PATH=$PATH:${YOUR_PATH}/riscv/bin
$ export CC=riscv64-unknown-linux-gnu-gcc
$ export CXX=riscv64-unknown-linux-gnu-g++
二、模型设置
MNN的使用较为简单，使用方法类似TensorFlow 1.x，简要的流程为：
// 创建会话createSession();
// 设置输入getSessionInput();
// 运行会话runSession();
// 获取输出getSessionOutput();
在该逻辑下，将模型的运行预处理，推理，后处理封装为一个模块：
// ultraFace.hpp#ifndef UltraFace_hpp#define UltraFace_hpp
#pragma once
#include "MNN/Interpreter.hpp"
#include "MNN/MNNDefine.h"#include "MNN/Tensor.hpp"#include
"MNN/ImageProcess.hpp"#include <opencv2/opencv.hpp>#include <algorithm>#include <iostream>#include <string>#include <vector>#include <memory>#include <chrono>
#define num_featuremap 4#define hard_nms 1#define blending_nms 2 /* mix nms was been proposaled in paper blaze face, aims to minimize the temporal jitter*/typedef struct FaceInfo {
float x1;
float y1;
float x2;
float y2;
float score;
} FaceInfo;
class UltraFace {public:
UltraFace(const std::string &mnn_path,
int input_width, int input_length, int num_thread_ = 4, float score_threshold_ = 0.7, float iou_threshold_ = 0.3,
int topk_ = -1);
~UltraFace();
int detect(cv::Mat &img, std::vector<FaceInfo> &face_list);
private:
void generateBBox(std::vector<FaceInfo> &bbox_collection, MNN::Tensor *scores, MNN::Tensor *boxes);
void nms(std::vector<FaceInfo> &input, std::vector<FaceInfo> &output, int type = blending_nms);
private:
std::shared_ptr<MNN::Interpreter> ultraface_interpreter;
MNN::Session *ultraface_session = nullptr;
MNN::Tensor *input_tensor = nullptr;
int num_thread;
int image_w;
int image_h;
int in_w;
int in_h;
int num_anchors;
float score_threshold;
float iou_threshold;
const float mean_vals[3] = {127, 127, 127};
const float norm_vals[3] = {1.0 / 128, 1.0 / 128, 1.0 / 128};
const float center_variance = 0.1;
const float size_variance = 0.2;
const std::vector<std::vector<float>> min_boxes = {
{10.0f, 16.0f, 24.0f},
{32.0f, 48.0f},
{64.0f, 96.0f},
{128.0f, 192.0f, 256.0f}};
const std::vector<float> strides = {8.0, 16.0, 32.0, 64.0};
std::vector<std::vector<float>> featuremap_size;
std::vector<std::vector<float>> shrinkage_size;
std::vector<int> w_h_list;
std::vector<std::vector<float>> priors = {};
};
#endif /* UltraFace_hpp */
// ultraFace.cpp
#define clip(x, y) (x < 0 ? 0 : (x > y ? y : x))
#include "UltraFace.hpp"
using namespace std;
UltraFace::UltraFace(const std::string &mnn_path,
int input_width, int input_length, int num_thread_,
float score_threshold_, float iou_threshold_, int topk_) {
num_thread = num_thread_;
score_threshold = score_threshold_;
iou_threshold = iou_threshold_;
in_w = input_width;
in_h = input_length;
w_h_list = {in_w, in_h};
for (auto size : w_h_list) {
std::vector<float> fm_item;
for (float stride : strides) {
fm_item.push_back(ceil(size / stride));
}
featuremap_size.push_back(fm_item);
}
for (auto size : w_h_list) {
shrinkage_size.push_back(strides);
}
/* generate prior anchors */
for (int index = 0; index < num_featuremap; index++) {
float scale_w = in_w / shrinkage_size[0][index];
float scale_h = in_h / shrinkage_size[1][index];
for (int j = 0; j < featuremap_size[1][index]; j++) {
for (int i = 0; i < featuremap_size[0][index]; i++) {
float x_center = (i + 0.5) / scale_w;
float y_center = (j + 0.5) / scale_h;
for (float k : min_boxes[index]) {
float w = k / in_w;
float h = k / in_h;
priors.push_back({clip(x_center, 1), clip(y_center, 1), clip(w, 1), clip(h, 1)});
}
}
}
}
/* generate prior anchors finished */
num_anchors = priors.size();
ultraface_interpreter = std::shared_ptr<MNN::Interpreter>(MNN::Interpreter::createFromFile(mnn_path.c_str()));
MNN::ScheduleConfig config;
config.numThread = num_thread;
MNN::BackendConfig backendConfig;
backendConfig.precision = (MNN::BackendConfig::PrecisionMode) 2;
config.backendConfig = &backendConfig;
ultraface_session = ultraface_interpreter->createSession(config);
input_tensor = ultraface_interpreter->getSessionInput(ultraface_session, nullptr);
}
UltraFace::~UltraFace() {
ultraface_interpreter->releaseModel();
ultraface_interpreter->releaseSession(ultraface_session);
}
int UltraFace::detect(cv::Mat &raw_image, std::vector<FaceInfo> &face_list) {
if (raw_image.empty()) {
std::cout << "image is empty ,please check!" << std::endl;
return -1;
}
image_h = raw_image.rows;
image_w = raw_image.cols;
cv::Mat image;
cv::resize(raw_image, image, cv::Size(in_w, in_h));
ultraface_interpreter->resizeTensor(input_tensor, {1, 3, in_h, in_w});
ultraface_interpreter->resizeSession(ultraface_session);
std::shared_ptr<MNN::CV::ImageProcess> pretreat(
MNN::CV::ImageProcess::create(MNN::CV::BGR, MNN::CV::RGB, mean_vals, 3,
norm_vals, 3));
pretreat->convert(image.data, in_w, in_h, image.step[0], input_tensor);
auto start = chrono::steady_clock::now();
// run network
ultraface_interpreter->runSession(ultraface_session);
// get output data
string scores = "scores";
string boxes = "boxes";
MNN::Tensor *tensor_scores = ultraface_interpreter->getSessionOutput(ultraface_session, scores.c_str());
MNN::Tensor *tensor_boxes = ultraface_interpreter->getSessionOutput(ultraface_session, boxes.c_str());
MNN::Tensor tensor_scores_host(tensor_scores, tensor_scores->getDimensionType());
tensor_scores->copyToHostTensor(&tensor_scores_host);
MNN::Tensor tensor_boxes_host(tensor_boxes, tensor_boxes->getDimensionType());
tensor_boxes->copyToHostTensor(&tensor_boxes_host);
std::vector<FaceInfo> bbox_collection;
auto end = chrono::steady_clock::now();
chrono::duration<double> elapsed = end - start;
cout << "inference time:" << elapsed.count() << " s" << endl;
generateBBox(bbox_collection, tensor_scores, tensor_boxes);
nms(bbox_collection, face_list);
return 0;
}
void UltraFace::generateBBox(std::vector<FaceInfo> &bbox_collection, MNN::Tensor *scores, MNN::Tensor *boxes) {
for (int i = 0; i < num_anchors; i++) {
if (scores->host<float>()[i * 2 + 1] > score_threshold) {
FaceInfo rects;
float x_center = boxes->host<float>()[i * 4] * center_variance * priors[i][2] + priors[i][0];
float y_center = boxes->host<float>()[i * 4 + 1] * center_variance * priors[i][3] + priors[i][1];
float w = exp(boxes->host<float>()[i * 4 + 2] * size_variance) * priors[i][2];
float h = exp(boxes->host<float>()[i * 4 + 3] * size_variance) * priors[i][3];
rects.x1 = clip(x_center - w / 2.0, 1) * image_w;
rects.y1 = clip(y_center - h / 2.0, 1) * image_h;
rects.x2 = clip(x_center + w / 2.0, 1) * image_w;
rects.y2 = clip(y_center + h / 2.0, 1) * image_h;
rects.score = clip(scores->host<float>()[i * 2 + 1], 1);
bbox_collection.push_back(rects);
}
}
}
void UltraFace::nms(std::vector<FaceInfo> &input, std::vector<FaceInfo> &output, int type) {
std::sort(input.begin(), input.end(), [](const FaceInfo &a, const FaceInfo &b) { return a.score > b.score; });
int box_num = input.size();
std::vector<int> merged(box_num, 0);
for (int i = 0; i < box_num; i++) {
if (merged[i])
continue;
std::vector<FaceInfo> buf;
buf.push_back(input[i]);
merged[i] = 1;
float h0 = input[i].y2 - input[i].y1 + 1;
float w0 = input[i].x2 - input[i].x1 + 1;
float area0 = h0 * w0;
for (int j = i + 1; j < box_num; j++) {
if (merged[j])
continue;
float inner_x0 = input[i].x1 > input[j].x1 ? input[i].x1 : input[j].x1;
float inner_y0 = input[i].y1 > input[j].y1 ? input[i].y1 : input[j].y1;
float inner_x1 = input[i].x2 < input[j].x2 ? input[i].x2 : input[j].x2;
float inner_y1 = input[i].y2 < input[j].y2 ? input[i].y2 : input[j].y2;
float inner_h = inner_y1 - inner_y0 + 1;
float inner_w = inner_x1 - inner_x0 + 1;
if (inner_h <= 0 || inner_w <= 0)
continue;
float inner_area = inner_h * inner_w;
float h1 = input[j].y2 - input[j].y1 + 1;
float w1 = input[j].x2 - input[j].x1 + 1;
float area1 = h1 * w1;
float score;
score = inner_area / (area0 + area1 - inner_area);
if (score > iou_threshold) {
merged[j] = 1;
buf.push_back(input[j]);
}
}
switch (type) {
case hard_nms: {
output.push_back(buf[0]);
break;
}
case blending_nms: {
float total = 0;
for (int i = 0; i < buf.size(); i++) {
total += exp(buf[i].score);
}
FaceInfo rects;
memset(&rects, 0, sizeof(rects));
for (int i = 0; i < buf.size(); i++) {
float rate = exp(buf[i].score) / total;
rects.x1 += buf[i].x1 * rate;
rects.y1 += buf[i].y1 * rate;
rects.x2 += buf[i].x2 * rate;
rects.y2 += buf[i].y2 * rate;
rects.score += buf[i].score * rate;
}
output.push_back(rects);
break;
}
default: {
printf("wrong type of nms.");
exit(-1);
}
}
}
}
三、测试
运行模型，利用图片进行测试
最终仅需55ms左右即可精准完成人脸识别任务！轻松满足智慧安防、智慧家居等应用场景！