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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "precomp.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/core.hpp"
#ifdef HAVE_OPENCV_DNN
#include "opencv2/dnn.hpp"
#endif
#include <algorithm>
namespace cv
{
#ifdef HAVE_OPENCV_DNN
class FaceDetectorYNImpl : public FaceDetectorYN
{
public:
FaceDetectorYNImpl(const String& model,
const String& config,
const Size& input_size,
float score_threshold,
float nms_threshold,
int top_k,
int backend_id,
int target_id)
:divisor(32),
strides({8, 16, 32})
{
net = dnn::readNet(model, config);
CV_Assert(!net.empty());
net.setPreferableBackend(backend_id);
net.setPreferableTarget(target_id);
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;
}
FaceDetectorYNImpl(const String& framework,
const std::vector<uchar>& bufferModel,
const std::vector<uchar>& bufferConfig,
const Size& input_size,
float score_threshold,
float nms_threshold,
int top_k,
int backend_id,
int target_id)
:divisor(32),
strides({8, 16, 32})
{
net = dnn::readNet(framework, bufferModel, bufferConfig);
CV_Assert(!net.empty());
net.setPreferableBackend(backend_id);
net.setPreferableTarget(target_id);
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;
}
void setInputSize(const Size& input_size) override
{
inputW = input_size.width;
inputH = input_size.height;
padW = ((inputW - 1) / divisor + 1) * divisor;
padH = ((inputH - 1) / divisor + 1) * divisor;
}
Size getInputSize() override
{
Size input_size;
input_size.width = inputW;
input_size.height = inputH;
return input_size;
}
void setScoreThreshold(float score_threshold) override
{
scoreThreshold = score_threshold;
}
float getScoreThreshold() override
{
return scoreThreshold;
}
void setNMSThreshold(float nms_threshold) override
{
nmsThreshold = nms_threshold;
}
float getNMSThreshold() override
{
return nmsThreshold;
}
void setTopK(int top_k) override
{
topK = top_k;
}
int getTopK() override
{
return topK;
}
int detect(InputArray input_image, OutputArray faces) override
{
// TODO: more checkings should be done?
if (input_image.empty())
{
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");
Mat input_blob;
if(input_image.kind() == _InputArray::UMAT) {
// Pad input_image with divisor 32
UMat pad_image;
padWithDivisor(input_image, pad_image);
// Build blob from input image
input_blob = dnn::blobFromImage(pad_image);
} else {
// Pad input_image with divisor 32
Mat pad_image;
padWithDivisor(input_image, pad_image);
// Build blob from input image
input_blob = dnn::blobFromImage(pad_image);
}
// Forward
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);
// Post process
Mat results = postProcess(output_blobs);
results.convertTo(faces, CV_32FC1);
return 1;
}
private:
Mat postProcess(const std::vector<Mat>& output_blobs)
{
Mat faces;
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;
// Checking if the score meets the threshold before adding the face
if (score < scoreThreshold)
continue;
// 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);
}
}
}
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));
}
return nms_faces;
}
else
{
return faces;
}
}
void padWithDivisor(InputArray input_image, OutputArray pad_image)
{
int bottom = padH - inputH;
int right = padW - inputW;
copyMakeBorder(input_image, pad_image, 0, bottom, 0, right, BORDER_CONSTANT, 0);
}
private:
dnn::Net net;
int inputW;
int inputH;
int padW;
int padH;
const int divisor;
int topK;
float scoreThreshold;
float nmsThreshold;
const std::vector<int> strides;
};
#endif
Ptr<FaceDetectorYN> FaceDetectorYN::create(const String& model,
const String& config,
const Size& input_size,
const float score_threshold,
const float nms_threshold,
const int top_k,
const int backend_id,
const int target_id)
{
#ifdef HAVE_OPENCV_DNN
return makePtr<FaceDetectorYNImpl>(model, config, input_size, score_threshold, nms_threshold, top_k, backend_id, target_id);
#else
CV_UNUSED(model); CV_UNUSED(config); CV_UNUSED(input_size); CV_UNUSED(score_threshold); CV_UNUSED(nms_threshold); CV_UNUSED(top_k); CV_UNUSED(backend_id); CV_UNUSED(target_id);
CV_Error(cv::Error::StsNotImplemented, "cv::FaceDetectorYN requires enabled 'dnn' module.");
#endif
}
Ptr<FaceDetectorYN> FaceDetectorYN::create(const String& framework,
const std::vector<uchar>& bufferModel,
const std::vector<uchar>& bufferConfig,
const Size& input_size,
const float score_threshold,
const float nms_threshold,
const int top_k,
const int backend_id,
const int target_id)
{
#ifdef HAVE_OPENCV_DNN
return makePtr<FaceDetectorYNImpl>(framework, bufferModel, bufferConfig, input_size, score_threshold, nms_threshold, top_k, backend_id, target_id);
#else
CV_UNUSED(bufferModel); CV_UNUSED(bufferConfig); CV_UNUSED(input_size); CV_UNUSED(score_threshold); CV_UNUSED(nms_threshold); CV_UNUSED(top_k); CV_UNUSED(backend_id); CV_UNUSED(target_id);
CV_Error(cv::Error::StsNotImplemented, "cv::FaceDetectorYN requires enabled 'dnn' module.");
#endif
}
} // namespace cv
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