add Yunet C++ demo (#138)

* add cpp demo for yunet

* add a new line before end of file

* new line before eof

.gitignore

@@ -2,4 +2,8 @@
 **/__pycache__
 **/__pycache__/**
 
-.vscode
+.vscode
+
+build/
+**/build
+**/build/**

models/face_detection_yunet/CMakeLists.txt

@@ -0,0 +1,11 @@
+cmake_minimum_required(VERSION 3.24.0)
+project(opencv_zoo_face_detection_yunet)
+
+set(OPENCV_VERSION "4.7.0")
+set(OPENCV_INSTALLATION_PATH "" CACHE PATH "Where to look for OpenCV installation")
+
+# Find OpenCV
+find_package(OpenCV ${OPENCV_VERSION} REQUIRED HINTS ${OPENCV_INSTALLATION_PATH})
+
+add_executable(demo demo.cpp)
+target_link_libraries(demo ${OpenCV_LIBS})

models/face_detection_yunet/README.md

@@ -20,6 +20,8 @@ Results of accuracy evaluation with [tools/eval](../../tools/eval).
 
 ## Demo
 
+### Python
+
 Run the following command to try the demo:
 
 ```shell
@@ -32,6 +34,23 @@ python demo.py --input /path/to/image
 python demo.py --help
 ```
 
+### 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/demo
+# detect on an image
+./build/demo -i=/path/to/image
+# get help messages
+./build/demo -h
+```
+
 ### Example outputs
 
 ![webcam demo](./examples/yunet_demo.gif)

models/face_detection_yunet/demo.cpp

@@ -0,0 +1,220 @@
+#include "opencv2/opencv.hpp"
+
+#include <map>
+#include <vector>
+#include <string>
+#include <iostream>
+
+const std::map<std::string, int> str2backend{
+    {"opencv", cv::dnn::DNN_BACKEND_OPENCV}, {"cuda", cv::dnn::DNN_BACKEND_CUDA},
+    {"timvx",  cv::dnn::DNN_BACKEND_TIMVX},  {"cann", cv::dnn::DNN_BACKEND_CANN}
+};
+const std::map<std::string, int> str2target{
+    {"cpu", cv::dnn::DNN_TARGET_CPU}, {"cuda", cv::dnn::DNN_TARGET_CUDA},
+    {"npu", cv::dnn::DNN_TARGET_NPU}, {"cuda_fp16", cv::dnn::DNN_TARGET_CUDA_FP16}
+};
+
+class YuNet
+{
+public:
+    YuNet(const std::string& model_path,
+          const cv::Size& input_size = cv::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 = cv::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 = cv::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 cv::Size& input_size)
+    {
+        input_size_ = input_size;
+        model->setInputSize(input_size_);
+    }
+
+    cv::Mat infer(const cv::Mat image)
+    {
+        cv::Mat res;
+        model->detect(image, res);
+        return res;
+    }
+
+private:
+    cv::Ptr<cv::FaceDetectorYN> model;
+
+    std::string model_path_;
+    cv::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, 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);
+
+        // Confidence as text
+        float conf = faces.at<float>(i, 14);
+        cv::putText(output_image, cv::format("%.4f", conf), 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;
+}
+
+int main(int argc, char** argv)
+{
+    cv::CommandLineParser parser(argc, argv,
+        "{help  h           |                                   | Print this message}"
+        "{input i           |                                   | Set input to a certain image, omit if using camera}"
+        "{model m           | face_detection_yunet_2022mar.onnx | Set path to the model}"
+        "{backend b         | opencv                            | Set DNN backend}"
+        "{target t          | cpu                               | Set DNN target}"
+        "{save s            | false                             | Whether to save result image or not}"
+        "{vis v             | false                             | Whether to visualize result image or not}"
+        /* model params below*/
+        "{conf_threshold    | 0.9                               | Set the minimum confidence for the model to identify a face. Filter out faces of conf < conf_threshold}"
+        "{nms_threshold     | 0.3                               | Set the threshold to suppress overlapped boxes. Suppress boxes if IoU(box1, box2) >= nms_threshold, the one of higher score is kept.}"
+        "{top_k             | 5000                              | Keep top_k bounding boxes before NMS. Set a lower value may help speed up postprocessing.}"
+    );
+    if (parser.has("help"))
+    {
+        parser.printMessage();
+        return 0;
+    }
+
+    std::string input_path = parser.get<std::string>("input");
+    std::string model_path = parser.get<std::string>("model");
+    std::string backend = parser.get<std::string>("backend");
+    std::string target = parser.get<std::string>("target");
+    bool save_flag = parser.get<bool>("save");
+    bool vis_flag = parser.get<bool>("vis");
+
+    // model params
+    float conf_threshold = parser.get<float>("conf_threshold");
+    float nms_threshold = parser.get<float>("nms_threshold");
+    int top_k = parser.get<int>("top_k");
+    const int backend_id = str2backend.at(backend);
+    const int target_id = str2target.at(target);
+
+    // Instantiate YuNet
+    YuNet model(model_path, cv::Size(320, 320), conf_threshold, nms_threshold, top_k, backend_id, target_id);
+
+    // If input is an image
+    if (!input_path.empty())
+    {
+        auto image = cv::imread(input_path);
+
+        // Inference
+        model.setInputSize(image.size());
+        auto faces = model.infer(image);
+
+        // Print faces
+        std::cout << cv::format("%d faces detected:\n", faces.rows);
+        for (int i = 0; i < faces.rows; ++i)
+        {
+            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\n", i, x1, y1, w, h, conf);
+        }
+
+        // Draw reults on the input image
+        if (save_flag || vis_flag)
+        {
+            auto res_image = visualize(image, faces);
+            if (save_flag)
+            {
+                std::cout << "Results are saved to result.jpg\n";
+                cv::imwrite("result.jpg", res_image);
+            }
+            if (vis_flag)
+            {
+                cv::namedWindow(input_path, cv::WINDOW_AUTOSIZE);
+                cv::imshow(input_path, res_image);
+                cv::waitKey(0);
+            }
+        }
+    }
+    else // Call default camera
+    {
+        int device_id = 0;
+        auto cap = cv::VideoCapture(device_id);
+        int w = static_cast<int>(cap.get(cv::CAP_PROP_FRAME_WIDTH));
+        int h = static_cast<int>(cap.get(cv::CAP_PROP_FRAME_HEIGHT));
+        model.setInputSize(cv::Size(w, h));
+
+        auto tick_meter = cv::TickMeter();
+        cv::Mat frame;
+        while (cv::waitKey(1) < 0)
+        {
+            bool has_frame = cap.read(frame);
+            if (!has_frame)
+            {
+                std::cout << "No frames grabbed! Exiting ...\n";
+                break;
+            }
+
+            // Inference
+            tick_meter.start();
+            cv::Mat faces = model.infer(frame);
+            tick_meter.stop();
+
+            // Draw results on the input image
+            auto res_image = visualize(frame, faces, (float)tick_meter.getFPS());
+            // Visualize in a new window
+            cv::imshow("YuNet Demo", res_image);
+
+            tick_meter.reset();
+        }
+    }
+
+    return 0;
+}