face_detect.cpp
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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"
#include "opencv2/dnn.hpp"
#include <algorithm>
namespace cv
{
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)
{
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;
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();
}
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");
// Build blob from input image
Mat input_blob = dnn::blobFromImage(input_image);
// Forward
std::vector<String> output_names = { "loc", "conf", "iou" };
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:
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}
};
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;
}
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));
}
return nms_faces;
}
else
{
return faces;
}
}
private:
dnn::Net net;
int inputW;
int inputH;
float scoreThreshold;
float nmsThreshold;
int topK;
std::vector<Rect2f> priors;
};
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)
{
return makePtr<FaceDetectorYNImpl>(model, config, input_size, score_threshold, nms_threshold, top_k, backend_id, target_id);
}
} // namespace cv