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Merge pull request #23020 from Wwupup:yunetv2

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upgrade FaceDetectorYN to v2
pull/23417/head^2
Alexander Smorkalov 2 years ago committed by GitHub
parent 3a83a35ab0 da3a4dcbc1
commit ce01123db2
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2 changed files with 91 additions and 132 deletions
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  1. 210
      modules/objdetect/src/face_detect.cpp
  2. 13
      modules/objdetect/test/test_face.cpp

210
modules/objdetect/src/face_detect.cpp
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@ -6,6 +6,7 @@
#include "opencv2/imgproc.hpp"
#include "opencv2/core.hpp"
#ifdef HAVE_OPENCV_DNN
#include "opencv2/dnn.hpp"
#endif
@ -27,6 +28,8 @@ public:
int top_k,
int backend_id,
int target_id)
:divisor(32),
strides({8, 16, 32})
{
net = dnn::readNet(model, config);
CV_Assert(!net.empty());
@ -37,18 +40,20 @@ public:
inputW = input_size.width;
inputH = input_size.height;
padW = (int((inputW - 1) / divisor) + 1) * divisor;
padH = (int((inputH - 1) / divisor) + 1) * divisor;
scoreThreshold = score_threshold;
nmsThreshold = nms_threshold;
topK = top_k;
generatePriors();
}
void setInputSize(const Size& input_size) override
{
inputW = input_size.width;
inputH = input_size.height;
generatePriors();
padW = ((inputW - 1) / divisor + 1) * divisor;
padH = ((inputH - 1) / divisor + 1) * divisor;
}
Size getInputSize() override
@ -97,12 +102,14 @@ public:
return 0;
}
CV_CheckEQ(input_image.size(), Size(inputW, inputH), "Size does not match. Call setInputSize(size) if input size does not match the preset size");
// Pad input_image with divisor 32
Mat pad_image = padWithDivisor(input_image);
// Build blob from input image
Mat input_blob = dnn::blobFromImage(input_image);
Mat input_blob = dnn::blobFromImage(pad_image);
// Forward
std::vector<String> output_names = { "loc", "conf", "iou" };
std::vector<String> output_names = { "cls_8", "cls_16", "cls_32", "obj_8", "obj_16", "obj_32", "bbox_8", "bbox_16", "bbox_32", "kps_8", "kps_16", "kps_32" };
std::vector<Mat> output_blobs;
net.setInput(input_blob);
net.forward(output_blobs, output_names);
@ -113,126 +120,70 @@ public:
return 1;
}
private:
void generatePriors()
{
// 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);
}
}
}
}
}
Mat postProcess(const std::vector<Mat>& output_blobs)
{
// Extract from output_blobs
Mat loc = output_blobs[0];
Mat conf = output_blobs[1];
Mat iou = output_blobs[2];
// 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;
for (size_t i = 0; i < strides.size(); ++i) {
int cols = int(padW / strides[i]);
int rows = int(padH / strides[i]);
// Extract from output_blobs
Mat cls = output_blobs[i];
Mat obj = output_blobs[i + strides.size() * 1];
Mat bbox = output_blobs[i + strides.size() * 2];
Mat kps = output_blobs[i + strides.size() * 3];
// Decode from predictions
float* cls_v = (float*)(cls.data);
float* obj_v = (float*)(obj.data);
float* bbox_v = (float*)(bbox.data);
float* kps_v = (float*)(kps.data);
// (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(int r = 0; r < rows; ++r) {
for(int c = 0; c < cols; ++c) {
size_t idx = r * cols + c;
// Get score
float cls_score = cls_v[idx];
float obj_score = obj_v[idx];
// Clamp
cls_score = MIN(cls_score, 1.f);
cls_score = MAX(cls_score, 0.f);
obj_score = MIN(obj_score, 1.f);
obj_score = MAX(obj_score, 0.f);
float score = std::sqrt(cls_score * obj_score);
face.at<float>(0, 14) = score;
// Get bounding box
float cx = ((c + bbox_v[idx * 4 + 0]) * strides[i]);
float cy = ((r + bbox_v[idx * 4 + 1]) * strides[i]);
float w = exp(bbox_v[idx * 4 + 2]) * strides[i];
float h = exp(bbox_v[idx * 4 + 3]) * strides[i];
float x1 = cx - w / 2.f;
float y1 = cy - h / 2.f;
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
for(int n = 0; n < 5; ++n) {
face.at<float>(0, 4 + 2 * n) = (kps_v[idx * 10 + 2 * n] + c) * strides[i];
face.at<float>(0, 4 + 2 * n + 1) = (kps_v[idx * 10 + 2 * n + 1]+ r) * strides[i];
}
faces.push_back(face);
}
}
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)
@ -265,16 +216,27 @@ private:
return faces;
}
}
Mat padWithDivisor(InputArray& input_image)
{
int bottom = padH - inputH;
int right = padW - inputW;
Mat pad_image;
copyMakeBorder(input_image, pad_image, 0, bottom, 0, right, BORDER_CONSTANT, 0);
return pad_image;
}
private:
dnn::Net net;
int inputW;
int inputH;
int padW;
int padH;
const int divisor;
int topK;
float scoreThreshold;
float nmsThreshold;
int topK;
std::vector<Rect2f> priors;
const std::vector<int> strides;
};
#endif

13
modules/objdetect/test/test_face.cpp
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@ -65,20 +65,16 @@ TEST(Objdetect_face_detection, regression)
{
// Pre-set params
float scoreThreshold = 0.7f;
float matchThreshold = 0.9f;
float l2disThreshold = 5.0f;
float matchThreshold = 0.7f;
float l2disThreshold = 15.0f;
int numLM = 5;
int numCoords = 4 + 2 * numLM;
// Load ground truth labels
std::map<std::string, Mat> gt = blobFromTXT(findDataFile("dnn_face/detection/cascades_labels.txt"), numCoords);
// for (auto item: gt)
// {
// std::cout << item.first << " " << item.second.size() << std::endl;
// }
// Initialize detector
std::string model = findDataFile("dnn/onnx/models/yunet-202202.onnx", false);
std::string model = findDataFile("dnn/onnx/models/yunet-202303.onnx", false);
Ptr<FaceDetectorYN> faceDetector = FaceDetectorYN::create(model, "", Size(300, 300));
faceDetector->setScoreThreshold(0.7f);
@ -137,6 +133,7 @@ TEST(Objdetect_face_detection, regression)
lmMatched[lmIdx] = true;
}
}
break;
}
EXPECT_TRUE(boxMatched) << "In image " << item.first << ", cannot match resBox " << resBox << " with any ground truth.";
if (boxMatched)
@ -178,7 +175,7 @@ TEST(Objdetect_face_recognition, regression)
}
// Initialize detector
std::string detect_model = findDataFile("dnn/onnx/models/yunet-202202.onnx", false);
std::string detect_model = findDataFile("dnn/onnx/models/yunet-202303.onnx", false);
Ptr<FaceDetectorYN> faceDetector = FaceDetectorYN::create(detect_model, "", Size(150, 150), score_thresh, nms_thresh);
std::string recog_model = findDataFile("dnn/onnx/models/face_recognizer_fast.onnx", false);

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