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陈赣
2026-06-03 12:43:14 +08:00
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nodes/infers/vp_yunet_face_detector_node.cpp Normal file
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#include "vp_yunet_face_detector_node.h"
namespace vp_nodes {
vp_yunet_face_detector_node::vp_yunet_face_detector_node(std::string node_name,
std::string model_path,
float score_threshold,
float nms_threshold,
int top_k):
vp_primary_infer_node(node_name, model_path),
scoreThreshold(score_threshold),
nmsThreshold(nms_threshold),
topK(top_k) {
this->initialized();
}
vp_yunet_face_detector_node::~vp_yunet_face_detector_node() {
deinitialized();
}
void vp_yunet_face_detector_node::postprocess(const std::vector<cv::Mat>& raw_outputs, const std::vector<std::shared_ptr<vp_objects::vp_frame_meta>>& frame_meta_with_batch) {
using namespace cv;
// 3 heads of output
assert(raw_outputs.size() == 3);
assert(frame_meta_with_batch.size() == 1);
auto& frame_meta = frame_meta_with_batch[0];
// Extract from output_blobs
Mat loc = raw_outputs[0];
Mat conf = raw_outputs[1];
Mat iou = raw_outputs[2];
// we need generate priors if input size changed or priors is not initialized
if (loc.rows != priors.size()) {
inputW = frame_meta->frame.cols;
inputH = frame_meta->frame.rows;
generatePriors();
}
assert(loc.rows == priors.size());
assert(loc.rows == conf.rows);
assert(loc.rows == iou.rows);
// Decode from deltas and priors
const std::vector<float> variance = {0.1f, 0.2f};
float* loc_v = (float*)(loc.data);
float* conf_v = (float*)(conf.data);
float* iou_v = (float*)(iou.data);
Mat faces;
// (tl_x, tl_y, w, h, re_x, re_y, le_x, le_y, nt_x, nt_y, rcm_x, rcm_y, lcm_x, lcm_y, score)
// 'tl': top left point of the bounding box
// 're': right eye, 'le': left eye
// 'nt': nose tip
// 'rcm': right corner of mouth, 'lcm': left corner of mouth
Mat face(1, 15, CV_32FC1);
for (size_t i = 0; i < priors.size(); ++i) {
// Get score
float clsScore = conf_v[i*2+1];
float iouScore = iou_v[i];
// Clamp
if (iouScore < 0.f) {
iouScore = 0.f;
}
else if (iouScore > 1.f) {
iouScore = 1.f;
}
float score = std::sqrt(clsScore * iouScore);
face.at<float>(0, 14) = score;
// Get bounding box
float cx = (priors[i].x + loc_v[i*14+0] * variance[0] * priors[i].width) * inputW;
float cy = (priors[i].y + loc_v[i*14+1] * variance[0] * priors[i].height) * inputH;
float w = priors[i].width * exp(loc_v[i*14+2] * variance[0]) * inputW;
float h = priors[i].height * exp(loc_v[i*14+3] * variance[1]) * inputH;
float x1 = cx - w / 2;
float y1 = cy - h / 2;
face.at<float>(0, 0) = x1;
face.at<float>(0, 1) = y1;
face.at<float>(0, 2) = w;
face.at<float>(0, 3) = h;
// Get landmarks
face.at<float>(0, 4) = (priors[i].x + loc_v[i*14+ 4] * variance[0] * priors[i].width) * inputW; // right eye, x
face.at<float>(0, 5) = (priors[i].y + loc_v[i*14+ 5] * variance[0] * priors[i].height) * inputH; // right eye, y
face.at<float>(0, 6) = (priors[i].x + loc_v[i*14+ 6] * variance[0] * priors[i].width) * inputW; // left eye, x
face.at<float>(0, 7) = (priors[i].y + loc_v[i*14+ 7] * variance[0] * priors[i].height) * inputH; // left eye, y
face.at<float>(0, 8) = (priors[i].x + loc_v[i*14+ 8] * variance[0] * priors[i].width) * inputW; // nose tip, x
face.at<float>(0, 9) = (priors[i].y + loc_v[i*14+ 9] * variance[0] * priors[i].height) * inputH; // nose tip, y
face.at<float>(0, 10) = (priors[i].x + loc_v[i*14+10] * variance[0] * priors[i].width) * inputW; // right corner of mouth, x
face.at<float>(0, 11) = (priors[i].y + loc_v[i*14+11] * variance[0] * priors[i].height) * inputH; // right corner of mouth, y
face.at<float>(0, 12) = (priors[i].x + loc_v[i*14+12] * variance[0] * priors[i].width) * inputW; // left corner of mouth, x
face.at<float>(0, 13) = (priors[i].y + loc_v[i*14+13] * variance[0] * priors[i].height) * inputH; // left corner of mouth, y
faces.push_back(face);
}
if (faces.rows > 1)
{
// Retrieve boxes and scores
std::vector<Rect2i> faceBoxes;
std::vector<float> faceScores;
for (int rIdx = 0; rIdx < faces.rows; rIdx++)
{
faceBoxes.push_back(Rect2i(int(faces.at<float>(rIdx, 0)),
int(faces.at<float>(rIdx, 1)),
int(faces.at<float>(rIdx, 2)),
int(faces.at<float>(rIdx, 3))));
faceScores.push_back(faces.at<float>(rIdx, 14));
}
std::vector<int> keepIdx;
dnn::NMSBoxes(faceBoxes, faceScores, scoreThreshold, nmsThreshold, keepIdx, 1.f, topK);
// Get NMS results
Mat nms_faces;
for (int idx: keepIdx)
{
nms_faces.push_back(faces.row(idx));
}
// insert face target back to frame meta
for (int i = 0; i < nms_faces.rows; i++) {
auto x = int(nms_faces.at<float>(i, 0));
auto y = int(nms_faces.at<float>(i, 1));
auto w = int(nms_faces.at<float>(i, 2));
auto h = int(nms_faces.at<float>(i, 3));
// check value range
x = std::max(x, 0);
y = std::max(y, 0);
w = std::min(w, frame_meta->frame.cols - x);
h = std::min(h, frame_meta->frame.rows - y);
auto kp1 = std::pair<int, int>(int(nms_faces.at<float>(i, 4)), int(nms_faces.at<float>(i, 5)));
auto kp2 = std::pair<int, int>(int(nms_faces.at<float>(i, 6)), int(nms_faces.at<float>(i, 7)));
auto kp3 = std::pair<int, int>(int(nms_faces.at<float>(i, 8)), int(nms_faces.at<float>(i, 9)));
auto kp4 = std::pair<int, int>(int(nms_faces.at<float>(i, 10)), int(nms_faces.at<float>(i, 11)));
auto kp5 = std::pair<int, int>(int(nms_faces.at<float>(i, 12)), int(nms_faces.at<float>(i, 13)));
auto score = nms_faces.at<float>(i, 14);
auto face_target = std::make_shared<vp_objects::vp_frame_face_target>(x, y, w, h, score, std::vector<std::pair<int, int>>{kp1, kp2, kp3, kp4, kp5});
frame_meta->face_targets.push_back(face_target);
}
}
}
// refer to vp_infer_node::preprocess
void vp_yunet_face_detector_node::preprocess(const std::vector<cv::Mat>& mats_to_infer, cv::Mat& blob_to_infer) {
cv::dnn::blobFromImages(mats_to_infer, blob_to_infer);
}
// refer to vp_infer_node::infer
void vp_yunet_face_detector_node::infer(const cv::Mat& blob_to_infer, std::vector<cv::Mat>& raw_outputs) {
// blob_to_infer is a 4D matrix
// the first dim is number of batch, MUST be 1
assert(blob_to_infer.dims == 4);
assert(blob_to_infer.size[0] == 1);
assert(!net.empty());
net.setInput(blob_to_infer);
net.forward(raw_outputs, out_names);
}
void vp_yunet_face_detector_node::generatePriors() {
using namespace cv;
// Calculate shapes of different scales according to the shape of input image
Size feature_map_2nd = {
int(int((inputW+1)/2)/2), int(int((inputH+1)/2)/2)
};
Size feature_map_3rd = {
int(feature_map_2nd.width/2), int(feature_map_2nd.height/2)
};
Size feature_map_4th = {
int(feature_map_3rd.width/2), int(feature_map_3rd.height/2)
};
Size feature_map_5th = {
int(feature_map_4th.width/2), int(feature_map_4th.height/2)
};
Size feature_map_6th = {
int(feature_map_5th.width/2), int(feature_map_5th.height/2)
};
std::vector<Size> feature_map_sizes;
feature_map_sizes.push_back(feature_map_3rd);
feature_map_sizes.push_back(feature_map_4th);
feature_map_sizes.push_back(feature_map_5th);
feature_map_sizes.push_back(feature_map_6th);
// Fixed params for generating priors
const std::vector<std::vector<float>> min_sizes = {
{10.0f, 16.0f, 24.0f},
{32.0f, 48.0f},
{64.0f, 96.0f},
{128.0f, 192.0f, 256.0f}
};
CV_Assert(min_sizes.size() == feature_map_sizes.size()); // just to keep vectors in sync
const std::vector<int> steps = { 8, 16, 32, 64 };
// Generate priors
priors.clear();
for (size_t i = 0; i < feature_map_sizes.size(); ++i)
{
Size feature_map_size = feature_map_sizes[i];
std::vector<float> min_size = min_sizes[i];
for (int _h = 0; _h < feature_map_size.height; ++_h)
{
for (int _w = 0; _w < feature_map_size.width; ++_w)
{
for (size_t j = 0; j < min_size.size(); ++j)
{
float s_kx = min_size[j] / inputW;
float s_ky = min_size[j] / inputH;
float cx = (_w + 0.5f) * steps[i] / inputW;
float cy = (_h + 0.5f) * steps[i] / inputH;
Rect2f prior = { cx, cy, s_kx, s_ky };
priors.push_back(prior);
}
}
}
}
}
}