/* Computer vision demo. Performs object tracking on pumpkins to determine velocity, launch angle, and predicted range.
 *
 */
#include <iostream>
#include <DarkHelp.hpp>
#include <unistd.h>
#include <opencv4/opencv2/core/mat.hpp>
#include <opencv4/opencv2/videoio.hpp>
#include <opencv4/opencv2/highgui.hpp>

// Default configuration values. Using YOLOv7 tiny model. Make sure to download the weights.
#define DARKNET_CFG "cfg/yolov7-tiny.cfg"
#define DARKNET_WEIGHTS "cfg/yolov7-tiny.weights"
#define DARKNET_DATA "cfg/coco.names"
#define CAMERA_ID 0
#define SHOW_GUI true

using namespace std;
using namespace cv;
using namespace DarkHelp;

int main() {
    // Configure the neural network
    NN nn(DARKNET_CFG, DARKNET_WEIGHTS, DARKNET_DATA);

    // Setup the capture stream from the webcam
    VideoCapture cap(CAMERA_ID);
    if (not cap.isOpened())
    {
        throw std::runtime_error("failed to open the webcam");
    }
    cap.set(CAP_PROP_FRAME_WIDTH, 640.0);
    cap.set(CAP_PROP_FRAME_HEIGHT, 480.0);
    cap.set(CAP_PROP_FPS, 30.0);

    while (cap.isOpened()) {
        Mat frame;

        cap >> frame;
        if (frame.empty()) break;

        const PredictionResults results = nn.predict(frame);

        // print results to console
        cout << "RESULTS" << endl;
        for (int i = 0; i < results.size(); i++) {
            const PredictionResult result = results[i];
            cout << result.name << " | (" << result.original_point.x << ", " << result.original_point.y << ")" << endl;
        }
        cout << endl;

        // Sleep between frames; if the UI is enabled, show the latest (annotated) frame
        if constexpr (SHOW_GUI) {
            frame = nn.annotate();
            imshow("pumpkin-tracker", frame);
            if (const auto key = waitKey(15); key == 27) break;
        }
        else {
            usleep(15000);
        }
    }
}