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add face feature extraction pipeline

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This commit is contained in:
hongshaorou
2026-07-11 21:36:14 +08:00
parent d528379961
commit 4f82e2cdaa
28 changed files with 1270 additions and 95 deletions
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1
app/CMakeLists.txt
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@@ -13,6 +13,7 @@ add_executable(face_detection_app
target_link_libraries(face_detection_app PRIVATE
facedetection
network_camera_receiver
face_pipeline
${OpenCV_LIBS}
)

136
app/src/main.cpp
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@@ -1,98 +1,80 @@
/*
* @Author: Liu zhenyu
* @Description: 主程序（重构版）
* 流程：1. 从 network_camera_receiver 取最新帧；
* 2. 调用 libfacedetection 进行人脸检测；
* 3. 绘制并显示检测结果。
* libfacedetection 以独立动态库形式链接。
*/
#include <iostream>
#include <memory>
#include <string>
#include <vector>
#include <opencv2/opencv.hpp>
#include <iostream>
#include <string>
#include "face_pipeline/face_embedder.h"
#include "face_pipeline/face_pipeline.h"
#include "face_pipeline/result_sink.h"
#include "network_camera_receiver.h"
#include "facedetectcnn.h"
// 将 libfacedetection 的检测结果绘制到帧上
// pResults[0] = 人脸数量；其后每张脸占 FACEDETECTION_RESULT_STRIDE_SHORTS 个 short
static void drawFaces(cv::Mat& frame, const int* pResults) {
if (!pResults) return;
int faces = pResults[0];
namespace {
for (int i = 0; i < faces; i++) {
// 每个人脸结果占用 FACEDETECTION_RESULT_STRIDE_SHORTS(=16) 个 short：
// p[0]=置信度(score*100), p[1..4]=x/y/w/h,
// p[5..14]=5个关键点(x,y交替), p[15]=对齐填充
short* p = ((short*)(pResults + 1)) + FACEDETECTION_RESULT_STRIDE_SHORTS * i;
int confidence = p[0];
int x = p[1];
int y = p[2];
int w = p[3];
int h = p[4];
// 只显示置信度大于 60 的结果以过滤误检
if (confidence > 60) {
cv::rectangle(frame, cv::Rect(x, y, w, h), cv::Scalar(0, 255, 0), 2);
// 置信度标注
cv::putText(frame, std::to_string(confidence),
cv::Point(x, y - 5), cv::FONT_HERSHEY_SIMPLEX,
0.5, cv::Scalar(0, 255, 0), 1);
// 五个特征点
cv::circle(frame, cv::Point(p[5], p[6]), 2, cv::Scalar(255, 0, 0), -1);
cv::circle(frame, cv::Point(p[7], p[8]), 2, cv::Scalar(0, 0, 255), -1);
cv::circle(frame, cv::Point(p[9], p[10]), 2, cv::Scalar(0, 255, 0), -1);
cv::circle(frame, cv::Point(p[11], p[12]), 2, cv::Scalar(255, 0, 255), -1);
cv::circle(frame, cv::Point(p[13], p[14]), 2, cv::Scalar(0, 255, 255), -1);
void drawTracks(cv::Mat& frame, const std::vector<face_pipeline::TrackedFace>& tracks) {
for (const face_pipeline::TrackedFace& track : tracks) {
const face_pipeline::FaceObservation& face = track.observation;
cv::rectangle(frame, face.bbox, cv::Scalar(0, 255, 0), 2);
cv::putText(frame, "track " + std::to_string(track.track_id),
cv::Point(face.bbox.x, std::max(15, face.bbox.y - 5)),
cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 255, 0), 1);
for (const cv::Point2f& landmark : face.landmarks) {
cv::circle(frame, landmark, 2, cv::Scalar(0, 0, 255), -1);
}
}
}
int main() {
// 替换为你查到的视频流宿主机 IP
std::string host_ip = "127.0.0.1";
} // namespace
// 1) 接收端：仅负责抓帧
NetworkCameraReceiver receiver(host_ip, 5000);
if (!receiver.connect()) {
std::cerr << "连接视频流失败，退出。" << std::endl;
return -1;
}
receiver.start(); // 启动后台抓帧线程
int main(int argc, char** argv) {
const std::string model_path = argc > 1
? argv[1]
: std::string(SOURCE_ROOT) + "/models/face_recognition_sface_2021dec.onnx";
// libfacedetection 结果缓冲区（大小由库定义的宏决定）
unsigned char* pBuffer = new unsigned char[FACEDETECTION_RESULT_BUFFER_SIZE];
try {
std::unique_ptr<face_pipeline::FaceEmbedder> embedder(
new face_pipeline::SFaceEmbedder(model_path));
std::unique_ptr<face_pipeline::ResultSink> sink(
new face_pipeline::LoggingResultSink(std::cout));
face_pipeline::FacePipeline pipeline(std::move(embedder), std::move(sink));
const std::string window_name = "Face Detection";
cv::namedWindow(window_name, cv::WINDOW_AUTOSIZE);
NetworkCameraReceiver receiver("127.0.0.1", 5000);
if (!receiver.connect()) return 1;
std::cout << "人脸检测已启动，按 ESC 退出..." << std::endl;
pipeline.start();
receiver.start();
cv::Mat frame;
while (true) {
// ---- 步骤 1：接收最新帧 ----
if (receiver.getLatestFrame(frame) && !frame.empty()) {
// ---- 步骤 2：调用 libfacedetection 检测 ----
// 注意：OpenCV 采集到的帧为 BGR，正好符合 facedetect_cnn 的输入要求
int* pResults = facedetect_cnn(pBuffer,
frame.data,
frame.cols,
frame.rows,
(int)frame.step);
const std::string window_name = "Face Detection and Features";
cv::namedWindow(window_name, cv::WINDOW_AUTOSIZE);
std::cout << "Face pipeline started; press ESC to exit." << std::endl;
// ---- 步骤 3：显示检测结果 ----
drawFaces(frame, pResults);
cv::imshow(window_name, frame);
NetworkFrame network_frame;
while (true) {
if (receiver.getLatestFrame(network_frame) && !network_frame.image.empty()) {
face_pipeline::FramePacket packet;
packet.frame = network_frame.image;
packet.frame_id = network_frame.frame_id;
packet.captured_at = network_frame.captured_at;
packet.captured_at_unix_ms = network_frame.captured_at_unix_ms;
pipeline.submit(packet);
cv::Mat display = network_frame.image.clone();
drawTracks(display, pipeline.latestTracks());
cv::imshow(window_name, display);
}
if (cv::waitKey(30) == 27) break;
}
if (cv::waitKey(30) == 27) break; // ESC 退出
receiver.stop();
pipeline.stop();
cv::destroyAllWindows();
} catch (const cv::Exception& error) {
std::cerr << "OpenCV initialization failed: " << error.what() << std::endl;
return 1;
} catch (const std::exception& error) {
std::cerr << "Initialization failed: " << error.what() << std::endl;
return 1;
}
receiver.stop();
delete[] pBuffer;
cv::destroyAllWindows();
return 0;
}