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authorJörg Frings-Fürst <debian@jff.email>2023-06-28 21:35:52 +0200
committerJörg Frings-Fürst <debian@jff.email>2023-06-28 21:35:52 +0200
commitb86540b743f1a87a163ffb811c8fe22a01fefa38 (patch)
treeb47cb3bb83c2377234226fb3987ab3320a987dd9 /subprojects/shotwell-facedetect/facedetect-opencv.cpp
parentac6e0b731b9f0b2efd392e3309a5c07e2a66adad (diff)
parente905d8e16eec152d19797937f13ba3cf4b8f8aca (diff)
Merge branch 'release/debian/0.32.1-1'debian/0.32.1-1
Diffstat (limited to 'subprojects/shotwell-facedetect/facedetect-opencv.cpp')
-rw-r--r--subprojects/shotwell-facedetect/facedetect-opencv.cpp281
1 files changed, 281 insertions, 0 deletions
diff --git a/subprojects/shotwell-facedetect/facedetect-opencv.cpp b/subprojects/shotwell-facedetect/facedetect-opencv.cpp
new file mode 100644
index 0000000..8b0ad10
--- /dev/null
+++ b/subprojects/shotwell-facedetect/facedetect-opencv.cpp
@@ -0,0 +1,281 @@
+// SPDX-License-Identifier: LGPL-2.1-or-later
+
+#include "shotwell-facedetect.hpp"
+
+#include <opencv2/imgcodecs.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+#include <opencv2/objdetect/objdetect.hpp>
+
+#ifdef HAS_OPENCV_DNN
+ #include <opencv2/dnn.hpp>
+#endif
+
+#include <iostream>
+#include <string>
+#include <filesystem>
+
+// Global variable for DNN to generate vector out of face
+#ifdef HAS_OPENCV_DNN
+static cv::dnn::Net faceRecogNet;
+static cv::dnn::Net faceDetectNet;
+#endif
+
+static cv::CascadeClassifier cascade;
+static cv::CascadeClassifier cascade_profile;
+static bool disableDnn{ true };
+
+constexpr std::string_view PROTOTEXT_FILE{ "deploy.prototxt" };
+constexpr std::string_view OPENFACE_RECOG_TORCH_NET{ "openface.nn4.small2.v1.t7" };
+constexpr std::string_view RESNET_DETECT_CAFFE_NET{ "res10_300x300_ssd_iter_140000_fp16.caffemodel" };
+constexpr std::string_view HAARCASCADE{ "haarcascade_frontalface_alt.xml" };
+constexpr std::string_view HAARCASCADE_PROFILE{ "haarcascade_profileface.xml" };
+
+std::vector<cv::Rect> detectFacesMat(const cv::Mat &img);
+std::vector<double> faceToVecMat(const cv::Mat& img);
+
+// Detect faces in a photo
+std::vector<FaceRect> detectFaces(const cv::String &inputName, double scale, bool infer = false) {
+ if(cascade.empty()) {
+ g_warning("No cascade file loaded. Did you call loadNet()?");
+ return {};
+ }
+
+ if (inputName.empty()) {
+ g_warning("No file to process. aborting");
+ return {};
+ }
+
+ cv::Mat const img = cv::imread(inputName, 1);
+ if (img.empty()) {
+ g_warning("Failed to load the image file: %s", inputName.c_str());
+ return {};
+ }
+
+ std::vector<cv::Rect> faces;
+ cv::Size smallImgSize;
+
+#ifdef HAS_OPENCV_DNN
+ disableDnn = faceDetectNet.empty();
+#else
+ disableDnn = true;
+#endif
+ try {
+ if (disableDnn) {
+ // Classical face detection
+ cv::Mat gray;
+ cvtColor(img, gray, cv::COLOR_BGR2GRAY);
+
+ scale = 1.0;
+ cv::Mat smallImg(cvRound(img.rows / scale), cvRound(img.cols / scale), CV_8UC1);
+ smallImgSize = smallImg.size();
+
+ cv::resize(gray, smallImg, smallImgSize, 0, 0, cv::INTER_LINEAR);
+ cv::equalizeHist(smallImg, smallImg);
+ constexpr double SCALE_FACTOR_FRONTAL{ 1.1 };
+ constexpr double SCALE_FACTOR_PROFILE{ 1.05 };
+ constexpr int MIN_NEIGHBOURS{ 2 };
+ constexpr int MIN_SIZE{ 30 };
+ cascade.detectMultiScale (smallImg,
+ faces,
+ SCALE_FACTOR_FRONTAL,
+ MIN_NEIGHBOURS,
+ cv::CASCADE_SCALE_IMAGE,
+ cv::Size (MIN_SIZE, MIN_SIZE));
+
+ // Run the cascade for profile faces, if available
+ if(not cascade_profile.empty()) {
+ g_debug("Running haarcascade detection for profile faces");
+ std::vector<cv::Rect> profiles;
+ cascade_profile.detectMultiScale (smallImg,
+ profiles,
+ SCALE_FACTOR_PROFILE,
+ MIN_NEIGHBOURS,
+ cv::CASCADE_SCALE_IMAGE,
+ cv::Size (MIN_SIZE, MIN_SIZE));
+ if(not profiles.empty()) {
+ faces.insert(faces.end(), profiles.begin(), profiles.end());
+ }
+
+ // Duplicate all rectangles so we can safely run groupRectangles with minimum 1 on it - otherwise
+ // OpenCV does weird things
+ faces.insert(faces.end(), faces.begin(), faces.end());
+
+ // Try to merge all overlapping rectangles
+ cv::groupRectangles(faces, 1);
+ }
+ } else {
+ #ifdef HAS_OPENCV_DNN
+ // DNN based face detection
+ faces = detectFacesMat(img);
+ smallImgSize = img.size(); // Not using the small image here
+ #endif
+ }
+ } catch (cv::Exception& ex) {
+ g_warning("Face detection failed: %s", ex.what());
+ return {};
+ }
+
+ std::vector<FaceRect> scaled;
+ for (std::vector<cv::Rect>::const_iterator r = faces.begin(); r != faces.end(); r++) {
+ FaceRect i;
+ i.x = (float) r->x / smallImgSize.width;
+ i.y = (float) r->y / smallImgSize.height;
+ i.width = (float) r->width / smallImgSize.width;
+ i.height = (float) r->height / smallImgSize.height;
+
+#ifdef HAS_OPENCV_DNN
+ try {
+ if (infer && !faceRecogNet.empty()) {
+ // Get colour image for vector generation
+ cv::Mat colourImg;
+ cv::resize(img, colourImg, smallImgSize, 0, 0, cv::INTER_LINEAR);
+ i.vec = faceToVecMat(colourImg(*r)); // Run vector conversion on the face
+ }
+ } catch (cv::Exception& ex) {
+ g_warning("Face recognition failed: %s", ex.what());
+ i.vec = {};
+ }
+#endif
+ scaled.push_back(i);
+ }
+
+ return scaled;
+}
+
+// Load network into global var
+bool loadNet(const cv::String &baseDir)
+{
+ // Split baseDir into multiple search paths
+ std::stringstream iss{ baseDir };
+ std::string path;
+ while(std::getline(iss, path, ':')) {
+ g_debug("Looking for face detection data files in %s", path.c_str());
+
+ std::filesystem::path const base_path{ path };
+
+ auto haarcascade = base_path / HAARCASCADE;
+ if(cascade.empty()) {
+ cascade.load(haarcascade);
+ }
+
+ if(cascade.empty()) {
+ g_info("%s not found", haarcascade.c_str());
+ }
+
+ auto haarcascade_profile = base_path / HAARCASCADE_PROFILE;
+ if(cascade_profile.empty()) {
+ cascade_profile.load(haarcascade_profile);
+ }
+
+ if(cascade_profile.empty()) {
+ g_info("%s not found", haarcascade_profile.c_str());
+ }
+
+#if HAS_OPENCV_DNN
+
+ if(faceDetectNet.empty()) {
+ try {
+ faceDetectNet =
+ cv::dnn::readNetFromCaffe(base_path / PROTOTEXT_FILE, base_path / RESNET_DETECT_CAFFE_NET);
+ } catch(cv::Exception &e) {
+ g_info("Failed to load face detect net: %s", e.what());
+ }
+ }
+
+ if(faceRecogNet.empty()) {
+ try {
+ faceRecogNet = cv::dnn::readNetFromTorch(base_path / OPENFACE_RECOG_TORCH_NET);
+ } catch(cv::Exception &e) {
+ g_info("Failed to load face recognition net: %s", e.what());
+ }
+ }
+#endif
+ }
+
+ if(cascade.empty() && cascade_profile.empty() && faceDetectNet.empty()) {
+ g_warning("No face detection method detected. Face detection fill not work.");
+ return false;
+ }
+
+#if HAS_OPENCV_DNN
+ // If there is no detection model, disable advanced face detection
+ disableDnn = faceDetectNet.empty();
+
+ if(faceRecogNet.empty()) {
+ g_warning("Face recognition net not available, disabling recognition");
+ }
+
+ return true;
+#else
+ return not cascade.empty() && not cascade_profile.empty();
+#endif
+}
+
+// Face detector
+// Adapted from OpenCV example:
+// https://github.com/opencv/opencv/blob/master/samples/dnn/js_face_recognition.html
+std::vector<cv::Rect> detectFacesMat(const cv::Mat& img) {
+ std::vector<cv::Rect> faces;
+#ifdef HAS_OPENCV_DNN
+ const cv::Mat blob = cv::dnn::blobFromImage(img, 1.0, cv::Size(128*8, 96*8),
+ cv::Scalar(104, 177, 123, 0), false, false);
+ faceDetectNet.setInput(blob);
+ cv::Mat out = faceDetectNet.forward();
+ // out is a 4D matrix [1 x 1 x n x 7]
+ // n - number of results
+ assert(out.dims == 4);
+ int outIdx[4] = { 0, 0, 0, 0 };
+ auto result_size = out.size[2];
+ for (auto i = 0; i < result_size; i++) {
+ outIdx[2] = i; outIdx[3] = 2;
+ const auto confidence = out.at<float>(outIdx);
+ outIdx[3]++;
+ auto left = out.at<float>(outIdx) * (double)img.cols;
+ outIdx[3]++;
+ auto top = out.at<float>(outIdx) * (double)img.rows;
+ outIdx[3]++;
+ auto right = out.at<float>(outIdx) * (double)img.cols;
+ outIdx[3]++;
+ auto bottom = out.at<float> (outIdx) * (double)img.rows;
+ left = std::clamp (left, 0.0, (double) img.cols - 1);
+ right = std::clamp (right, 0.0, (double) img.cols - 1);
+ bottom = std::clamp (bottom, 0.0, (double) img.rows - 1);
+ top = std::clamp (top, 0.0, (double) img.rows - 1);
+
+ constexpr double CONFIDENCE_THRESHOLD{ 0.98 };
+ if (confidence > CONFIDENCE_THRESHOLD && left < right && top < bottom) {
+ const cv::Rect rect (static_cast<int> (left),
+ static_cast<int> (top),
+ static_cast<int> (right - left),
+ static_cast<int> (bottom - top));
+ faces.push_back(rect);
+ }
+ }
+#endif // HAS_OPENCV_DNN
+ return faces;
+}
+
+// Face to vector converter
+// Adapted from OpenCV example:
+// https://github.com/opencv/opencv/blob/master/samples/dnn/js_face_recognition.html
+#ifdef HAS_OPENCV_DNN
+std::vector<double> faceToVecMat(const cv::Mat &img) {
+ std::vector<double> ret;
+ constexpr int SMALL_IMAGE_SIZE{ 96 };
+ cv::Mat smallImg(SMALL_IMAGE_SIZE, SMALL_IMAGE_SIZE, CV_8UC1);
+ const cv::Size smallImgSize = smallImg.size();
+
+ cv::resize(img, smallImg, smallImgSize, 0, 0, cv::INTER_LINEAR);
+ // Generate 128 element face vector using DNN
+ constexpr double SCALE_FACTOR{ 1.0 / 255.0 };
+ const cv::Mat blob = cv::dnn::blobFromImage (smallImg, SCALE_FACTOR, smallImgSize, cv::Scalar (), true, false);
+
+ faceRecogNet.setInput(blob);
+ cv::Mat vec = faceRecogNet.forward();
+ // Return vector
+ for (int i = 0; i < vec.rows; ++i) {
+ ret.insert(ret.end(), vec.ptr<float>(i), vec.ptr<float>(i) + vec.cols);
+ }
+ return ret;
+}
+#endif