C++ Demo - Facial Expression Recognition (#233)

DaniAffCH · web-flow · commit 821a8df3b413 · 2024-02-26T21:45:27.000+08:00
* cpp demo for facial expression recognition

* minor pr fix

* add empty line

* specified cxx version in the cmake list
diff --git a/models/facial_expression_recognition/CMakeLists.txt b/models/facial_expression_recognition/CMakeLists.txt
@@ -0,0 +1,30 @@
+cmake_minimum_required(VERSION 3.24)
+set(CMAKE_CXX_STANDARD 11)
+set(project_name "opencv_zoo_face_expression_recognition")
+
+PROJECT (${project_name})
+
+set(OPENCV_VERSION "4.9.0")
+set(OPENCV_INSTALLATION_PATH "" CACHE PATH "Where to look for OpenCV installation")
+find_package(OpenCV ${OPENCV_VERSION} REQUIRED HINTS ${OPENCV_INSTALLATION_PATH})
+# Find OpenCV, you may need to set OpenCV_DIR variable
+# to the absolute path to the directory containing OpenCVConfig.cmake file
+# via the command line or GUI
+
+file(GLOB SourceFile
+    "demo.cpp")
+# If the package has been found, several variables will
+# be set, you can find the full list with descriptions
+# in the OpenCVConfig.cmake file.
+# Print some message showing some of them
+message(STATUS "OpenCV library status:")
+message(STATUS "    config: ${OpenCV_DIR}")
+message(STATUS "    version: ${OpenCV_VERSION}")
+message(STATUS "    libraries: ${OpenCV_LIBS}")
+message(STATUS "    include path: ${OpenCV_INCLUDE_DIRS}")
+
+# Declare the executable target built from your sources
+add_executable(${project_name} ${SourceFile})
+
+# Link your application with OpenCV libraries
+target_link_libraries(${project_name} PRIVATE ${OpenCV_LIBS})
diff --git a/models/facial_expression_recognition/README.md b/models/facial_expression_recognition/README.md
@@ -19,12 +19,30 @@ Results of accuracy evaluation on [RAF-DB](http://whdeng.cn/RAF/model1.html).
 
 ***NOTE***: This demo uses [../face_detection_yunet](../face_detection_yunet) as face detector, which supports 5-landmark detection for now (2021sep).
 
+### Python
 Run the following command to try the demo:
 ```shell
 # recognize the facial expression on images
 python demo.py --input /path/to/image -v
 ```
 
+### C++
+
+Install latest OpenCV and CMake >= 3.24.0 to get started with:
+
+```shell
+# A typical and default installation path of OpenCV is /usr/local
+cmake -B build -D OPENCV_INSTALLATION_PATH=/path/to/opencv/installation .
+cmake --build build
+
+# detect on camera input
+./build/opencv_zoo_face_expression_recognition
+# detect on an image
+./build/opencv_zoo_face_expression_recognition -i=/path/to/image
+# get help messages
+./build/opencv_zoo_face_expression_recognition -h
+```
+
 ### Example outputs
 
 Note: Zoom in to to see the recognized facial expression in the top-left corner of each face boxes.
diff --git a/models/facial_expression_recognition/demo.cpp b/models/facial_expression_recognition/demo.cpp
@@ -0,0 +1,304 @@
+#include "opencv2/opencv.hpp"
+
+#include <map>
+#include <vector>
+#include <string>
+#include <iostream>
+
+using namespace std;
+using namespace cv;
+using namespace dnn;
+
+std::vector<std::pair<int, int>> backend_target_pairs = {
+    {DNN_BACKEND_OPENCV, DNN_TARGET_CPU},
+    {DNN_BACKEND_CUDA, DNN_TARGET_CUDA},
+    {DNN_BACKEND_CUDA, DNN_TARGET_CUDA_FP16},
+    {DNN_BACKEND_TIMVX, DNN_TARGET_NPU},
+    {DNN_BACKEND_CANN, DNN_TARGET_NPU}
+};
+
+class FER
+{
+private:
+    Net model;
+    string modelPath;
+    float std[5][2] = {
+        {38.2946, 51.6963},
+        {73.5318, 51.5014},
+        {56.0252, 71.7366},
+        {41.5493, 92.3655},
+        {70.7299, 92.2041}
+    };
+    vector<String> expressionEnum = {
+        "angry", "disgust", "fearful",
+        "happy", "neutral", "sad", "surprised"
+    };
+    Mat stdPoints = Mat(5, 2, CV_32F, this->std);
+    Size patchSize = Size(112,112);
+    Scalar imageMean = Scalar(0.5,0.5,0.5);
+    Scalar imageStd = Scalar(0.5,0.5,0.5);
+
+    const String inputNames = "data";
+    const String outputNames = "label";
+
+    int backend_id;
+    int target_id;
+    
+public:
+    FER(const string& modelPath, 
+        int backend_id = 0, 
+        int target_id = 0) 
+      : modelPath(modelPath), backend_id(backend_id), target_id(target_id)
+    {
+        this->model = readNet(modelPath);
+        this->model.setPreferableBackend(backend_id);
+        this->model.setPreferableTarget(target_id);
+    }
+
+    Mat preprocess(const Mat image, const Mat points)
+    {
+        // image alignment
+        Mat transformation = estimateAffine2D(points, this->stdPoints);
+        Mat aligned = Mat::zeros(this->patchSize.height, this->patchSize.width, image.type());    
+        warpAffine(image, aligned, transformation, this->patchSize);
+
+        // image normalization
+        aligned.convertTo(aligned, CV_32F, 1.0 / 255.0);
+        aligned -= imageMean;
+        aligned /= imageStd;
+        
+        return blobFromImage(aligned);;
+    }
+
+    String infer(const Mat image, const Mat facePoints)
+    {
+        Mat points = facePoints(Rect(4, 0, facePoints.cols-5, facePoints.rows)).reshape(2, 5);
+        Mat inputBlob = preprocess(image, points);
+
+        this->model.setInput(inputBlob, this->inputNames);
+        Mat outputBlob = this->model.forward(this->outputNames);
+
+        Point maxLoc;
+        minMaxLoc(outputBlob, nullptr, nullptr, nullptr, &maxLoc);
+        
+        return getDesc(maxLoc.x);
+    }
+
+    String getDesc(int ind) 
+    {
+
+        if (ind >= 0 &&  ind < this->expressionEnum.size()) 
+        {
+            return this->expressionEnum[ind];
+        } 
+        else 
+        {
+            cerr << "Error: Index out of bounds." << endl;
+            return "";
+        }
+    }
+
+};
+
+class YuNet
+{
+public:
+    YuNet(const string& model_path,
+          const Size& input_size = Size(320, 320),
+          float conf_threshold = 0.6f,
+          float nms_threshold = 0.3f,
+          int top_k = 5000,
+          int backend_id = 0,
+          int target_id = 0)
+        : model_path_(model_path), input_size_(input_size),
+          conf_threshold_(conf_threshold), nms_threshold_(nms_threshold),
+          top_k_(top_k), backend_id_(backend_id), target_id_(target_id)
+    {
+        model = FaceDetectorYN::create(model_path_, "", input_size_, conf_threshold_, nms_threshold_, top_k_, backend_id_, target_id_);
+    }
+
+    void setBackendAndTarget(int backend_id, int target_id)
+    {
+        backend_id_ = backend_id;
+        target_id_ = target_id;
+        model = FaceDetectorYN::create(model_path_, "", input_size_, conf_threshold_, nms_threshold_, top_k_, backend_id_, target_id_);
+    }
+
+    /* Overwrite the input size when creating the model. Size format: [Width, Height].
+    */
+    void setInputSize(const Size& input_size)
+    {
+        input_size_ = input_size;
+        model->setInputSize(input_size_);
+    }
+
+    Mat infer(const Mat image)
+    {
+        Mat res;
+        model->detect(image, res);
+        return res;
+    }
+
+private:
+    Ptr<FaceDetectorYN> model;
+
+    string model_path_;
+    Size input_size_;
+    float conf_threshold_;
+    float nms_threshold_;
+    int top_k_;
+    int backend_id_;
+    int target_id_;
+};
+
+cv::Mat visualize(const cv::Mat& image, const cv::Mat& faces, const vector<String> expressions, float fps = -1.f)
+{
+    static cv::Scalar box_color{0, 255, 0};
+    static std::vector<cv::Scalar> landmark_color{
+        cv::Scalar(255,   0,   0), // right eye
+        cv::Scalar(  0,   0, 255), // left eye
+        cv::Scalar(  0, 255,   0), // nose tip
+        cv::Scalar(255,   0, 255), // right mouth corner
+        cv::Scalar(  0, 255, 255)  // left mouth corner
+    };
+    static cv::Scalar text_color{0, 255, 0};
+
+    auto output_image = image.clone();
+
+    if (fps >= 0)
+    {
+        cv::putText(output_image, cv::format("FPS: %.2f", fps), cv::Point(0, 15), cv::FONT_HERSHEY_SIMPLEX, 0.5, text_color, 2);
+    }
+
+    for (int i = 0; i < faces.rows; ++i)
+    {
+        // Draw bounding boxes
+        int x1 = static_cast<int>(faces.at<float>(i, 0));
+        int y1 = static_cast<int>(faces.at<float>(i, 1));
+        int w = static_cast<int>(faces.at<float>(i, 2));
+        int h = static_cast<int>(faces.at<float>(i, 3));
+        cv::rectangle(output_image, cv::Rect(x1, y1, w, h), box_color, 2);
+
+        // Expression as text
+        String exp = expressions[i];
+        cv::putText(output_image, exp, cv::Point(x1, y1+12), cv::FONT_HERSHEY_DUPLEX, 0.5, text_color);
+
+        // Draw landmarks
+        for (int j = 0; j < landmark_color.size(); ++j)
+        {
+            int x = static_cast<int>(faces.at<float>(i, 2*j+4)), y = static_cast<int>(faces.at<float>(i, 2*j+5));
+            cv::circle(output_image, cv::Point(x, y), 2, landmark_color[j], 2);
+        }
+    }
+    return output_image;
+}
+
+string keys =
+"{ help  h          |                                                                  | Print help message. }"
+"{ model m          | facial_expression_recognition_mobilefacenet_2022july.onnx        | Usage: Path to the model, defaults to facial_expression_recognition_mobilefacenet_2022july.onnx  }"
+"{ yunet_model ym   | ../face_detection_yunet/face_detection_yunet_2023mar.onnx        | Usage: Path to the face detection yunet model, defaults to face_detection_yunet_2023mar.onnx  }"
+"{ input i          |                                                                  | Path to input image or video file. Skip this argument to capture frames from a camera.}"
+"{ backend_target t | 0                                                                | Choose one of the backend-target pair to run this demo:\n"
+                                                                                        "0: (default) OpenCV implementation + CPU,\n"
+                                                                                        "1: CUDA + GPU (CUDA),\n"
+                                                                                        "2: CUDA + GPU (CUDA FP16),\n"
+                                                                                        "3: TIM-VX + NPU,\n"
+                                                                                        "4: CANN + NPU}"
+"{ save s           | false                                                             | Specify to save results.}"
+"{ vis v            | true                                                              | Specify to open a window for result visualization.}"
+;
+
+
+int main(int argc, char** argv)
+{
+    CommandLineParser parser(argc, argv, keys);
+ 
+    parser.about("Facial Expression Recognition");
+    if (parser.has("help"))
+    {
+        parser.printMessage();
+        return 0;
+    }
+    
+    string modelPath = parser.get<string>("model");
+    string yunetModelPath = parser.get<string>("yunet_model");
+    string inputPath = parser.get<string>("input");
+    uint8_t backendTarget = parser.get<uint8_t>("backend_target");
+    bool saveFlag = parser.get<bool>("save");
+    bool visFlag = parser.get<bool>("vis");
+
+    if (modelPath.empty())
+        CV_Error(Error::StsError, "Model file " + modelPath + " not found");
+
+    if (yunetModelPath.empty())
+        CV_Error(Error::StsError, "Face Detection Model file " + yunetModelPath + " not found");
+
+    YuNet faceDetectionModel(yunetModelPath);
+    FER expressionRecognitionModel(modelPath, backend_target_pairs[backendTarget].first, backend_target_pairs[backendTarget].second);
+
+    VideoCapture cap;
+    if (!inputPath.empty())
+        cap.open(samples::findFile(inputPath));
+    else
+        cap.open(0);
+    
+    if (!cap.isOpened())
+        CV_Error(Error::StsError, "Cannot opend video or file");    
+
+    Mat frame;
+    static const std::string kWinName = "Facial Expression Demo";
+
+
+    while (waitKey(1) < 0)
+    {
+        cap >> frame;
+
+        if (frame.empty())
+        {
+            if(inputPath.empty())
+                cout << "Frame is empty" << endl;
+            break;
+        }
+
+        faceDetectionModel.setInputSize(frame.size());
+        
+        Mat faces = faceDetectionModel.infer(frame);
+        vector<String> expressions;
+
+        for (int i = 0; i < faces.rows; ++i)
+        {
+            Mat face = faces.row(i);
+            String exp = expressionRecognitionModel.infer(frame, face);
+            expressions.push_back(exp);
+
+            int x1 = static_cast<int>(faces.at<float>(i, 0));
+            int y1 = static_cast<int>(faces.at<float>(i, 1));
+            int w = static_cast<int>(faces.at<float>(i, 2));
+            int h = static_cast<int>(faces.at<float>(i, 3));
+            float conf = faces.at<float>(i, 14);
+
+            std::cout << cv::format("%d: x1=%d, y1=%d, w=%d, h=%d, conf=%.4f expression=%s\n", i, x1, y1, w, h, conf, exp.c_str());
+
+        }
+
+        Mat res_frame = visualize(frame, faces, expressions);
+
+        if(visFlag || inputPath.empty())
+        {
+            imshow(kWinName, res_frame);
+            if(!inputPath.empty())
+                waitKey(0);
+        }
+        if(saveFlag)
+        {
+            cout << "Results are saved to result.jpg" << endl;
+
+            cv::imwrite("result.jpg", res_frame);
+        }
+    }
+    
+
+    return 0;
+
+}   
+
