#if !(PLATFORM_LUMIN && !UNITY_EDITOR)

#if !UNITY_WSA_10_0

using System;
using System.Collections;
using System.Collections.Generic;
using System.Linq;
using UnityEngine;
using UnityEngine.UI;
using UnityEngine.SceneManagement;
using OpenCVForUnity.CoreModule;
using OpenCVForUnity.DnnModule;
using OpenCVForUnity.ImgprocModule;
using OpenCVForUnity.UnityUtils;
using OpenCVForUnity.UnityUtils.Helper;

namespace OpenCVForUnityExample
{
    /// <summary>
    /// Dnn ObjectDetection Example
    /// Referring to https://github.com/opencv/opencv/blob/master/samples/dnn/object_detection.cpp.
    /// </summary>
    [RequireComponent(typeof(WebCamTextureToMatHelper))]
    public class DnnObjectDetectionWebCamTextureExample : MonoBehaviour
    {

        [TooltipAttribute("Path to a binary file of model contains trained weights. It could be a file with extensions .caffemodel (Caffe), .pb (TensorFlow), .t7 or .net (Torch), .weights (Darknet).")]
        public string model;

        [TooltipAttribute("Path to a text file of model contains network configuration. It could be a file with extensions .prototxt (Caffe), .pbtxt (TensorFlow), .cfg (Darknet).")]
        public string config;

        [TooltipAttribute("Optional path to a text file with names of classes to label detected objects.")]
        public string classes;

        [TooltipAttribute("Optional list of classes to label detected objects.")]
        public List<string> classesList;

        [TooltipAttribute("Confidence threshold.")]
        public float confThreshold;

        [TooltipAttribute("Non-maximum suppression threshold.")]
        public float nmsThreshold;

        [TooltipAttribute("Preprocess input image by multiplying on a scale factor.")]
        public float scale;

        [TooltipAttribute("Preprocess input image by subtracting mean values. Mean values should be in BGR order and delimited by spaces.")]
        public Scalar mean;

        [TooltipAttribute("Indicate that model works with RGB input images instead BGR ones.")]
        public bool swapRB;

        [TooltipAttribute("Preprocess input image by resizing to a specific width.")]
        public int inpWidth;

        [TooltipAttribute("Preprocess input image by resizing to a specific height.")]
        public int inpHeight;


        /// <summary>
        /// The texture.
        /// </summary>
        Texture2D texture;

        /// <summary>
        /// The webcam texture to mat helper.
        /// </summary>
        WebCamTextureToMatHelper webCamTextureToMatHelper;

        /// <summary>
        /// The bgr mat.
        /// </summary>
        Mat bgrMat;

        /// <summary>
        /// The net.
        /// </summary>
        Net net;

        /// <summary>
        /// The FPS monitor.
        /// </summary>
        FpsMonitor fpsMonitor;

        List<string> classNames;
        List<string> outBlobNames;
        List<string> outBlobTypes;

        string classes_filepath;
        string config_filepath;
        string model_filepath;

#if UNITY_WEBGL && !UNITY_EDITOR
        IEnumerator getFilePath_Coroutine;
#endif

        // Use this for initialization
        void Start()
        {
            fpsMonitor = GetComponent<FpsMonitor>();

            webCamTextureToMatHelper = gameObject.GetComponent<WebCamTextureToMatHelper>();

#if UNITY_WEBGL && !UNITY_EDITOR
            getFilePath_Coroutine = GetFilePath();
            StartCoroutine(getFilePath_Coroutine);
#else
            if (!string.IsNullOrEmpty(classes)) classes_filepath = Utils.getFilePath("dnn/" + classes);
            if (!string.IsNullOrEmpty(config)) config_filepath = Utils.getFilePath("dnn/" + config);
            if (!string.IsNullOrEmpty(model)) model_filepath = Utils.getFilePath("dnn/" + model);
            Run();
#endif
        }

#if UNITY_WEBGL && !UNITY_EDITOR
        private IEnumerator GetFilePath()
        {
            if (!string.IsNullOrEmpty(classes))
            {
                var getFilePathAsync_0_Coroutine = Utils.getFilePathAsync("dnn/" + classes, (result) =>
                {
                    classes_filepath = result;
                });
                yield return getFilePathAsync_0_Coroutine;
            }

            if (!string.IsNullOrEmpty(config))
            {
                var getFilePathAsync_1_Coroutine = Utils.getFilePathAsync("dnn/" + config, (result) =>
                {
                    config_filepath = result;
                });
                yield return getFilePathAsync_1_Coroutine;
            }

            if (!string.IsNullOrEmpty(model))
            {
                var getFilePathAsync_2_Coroutine = Utils.getFilePathAsync("dnn/" + model, (result) =>
                {
                    model_filepath = result;
                });
                yield return getFilePathAsync_2_Coroutine;
            }

            getFilePath_Coroutine = null;

            Run();
        }
#endif

        // Use this for initialization
        void Run()
        {
            //if true, The error log of the Native side OpenCV will be displayed on the Unity Editor Console.
            Utils.setDebugMode(true);

            if (!string.IsNullOrEmpty(classes))
            {
                classNames = readClassNames(classes_filepath);
                if (classNames == null)
                {
                    Debug.LogError(classes_filepath + " is not loaded. Please see \"StreamingAssets/dnn/setup_dnn_module.pdf\". ");
                }
            }
            else if (classesList.Count > 0)
            {
                classNames = classesList;
            }

            if (string.IsNullOrEmpty(config_filepath) || string.IsNullOrEmpty(model_filepath))
            {
                Debug.LogError(config_filepath + " or " + model_filepath + " is not loaded. Please see \"StreamingAssets/dnn/setup_dnn_module.pdf\". ");
            }
            else
            {
                //! [Initialize network]
                net = Dnn.readNet(model_filepath, config_filepath);
                //! [Initialize network]


                outBlobNames = getOutputsNames(net);
                //for (int i = 0; i < outBlobNames.Count; i++)
                //{
                //    Debug.Log("names [" + i + "] " + outBlobNames[i]);
                //}

                outBlobTypes = getOutputsTypes(net);
                //for (int i = 0; i < outBlobTypes.Count; i++)
                //{
                //    Debug.Log("types [" + i + "] " + outBlobTypes[i]);
                //}
            }


#if UNITY_ANDROID && !UNITY_EDITOR
            // Avoids the front camera low light issue that occurs in only some Android devices (e.g. Google Pixel, Pixel2).
            webCamTextureToMatHelper.avoidAndroidFrontCameraLowLightIssue = true;
#endif
            webCamTextureToMatHelper.Initialize();
        }

        /// <summary>
        /// Raises the webcam texture to mat helper initialized event.
        /// </summary>
        public void OnWebCamTextureToMatHelperInitialized()
        {
            Debug.Log("OnWebCamTextureToMatHelperInitialized");

            Mat webCamTextureMat = webCamTextureToMatHelper.GetMat();


            texture = new Texture2D(webCamTextureMat.cols(), webCamTextureMat.rows(), TextureFormat.RGBA32, false);

            gameObject.GetComponent<Renderer>().material.mainTexture = texture;

            gameObject.transform.localScale = new Vector3(webCamTextureMat.cols(), webCamTextureMat.rows(), 1);
            Debug.Log("Screen.width " + Screen.width + " Screen.height " + Screen.height + " Screen.orientation " + Screen.orientation);

            if (fpsMonitor != null)
            {
                fpsMonitor.Add("width", webCamTextureMat.width().ToString());
                fpsMonitor.Add("height", webCamTextureMat.height().ToString());
                fpsMonitor.Add("orientation", Screen.orientation.ToString());
            }


            float width = webCamTextureMat.width();
            float height = webCamTextureMat.height();

            float widthScale = (float)Screen.width / width;
            float heightScale = (float)Screen.height / height;
            if (widthScale < heightScale)
            {
                Camera.main.orthographicSize = (width * (float)Screen.height / (float)Screen.width) / 2;
            }
            else
            {
                Camera.main.orthographicSize = height / 2;
            }


            bgrMat = new Mat(webCamTextureMat.rows(), webCamTextureMat.cols(), CvType.CV_8UC3);
        }

        /// <summary>
        /// Raises the webcam texture to mat helper disposed event.
        /// </summary>
        public void OnWebCamTextureToMatHelperDisposed()
        {
            Debug.Log("OnWebCamTextureToMatHelperDisposed");

            if (bgrMat != null)
                bgrMat.Dispose();

            if (texture != null)
            {
                Texture2D.Destroy(texture);
                texture = null;
            }
        }

        /// <summary>
        /// Raises the webcam texture to mat helper error occurred event.
        /// </summary>
        /// <param name="errorCode">Error code.</param>
        public void OnWebCamTextureToMatHelperErrorOccurred(WebCamTextureToMatHelper.ErrorCode errorCode)
        {
            Debug.Log("OnWebCamTextureToMatHelperErrorOccurred " + errorCode);
        }

        // Update is called once per frame
        void Update()
        {
            if (webCamTextureToMatHelper.IsPlaying() && webCamTextureToMatHelper.DidUpdateThisFrame())
            {

                Mat rgbaMat = webCamTextureToMatHelper.GetMat();

                if (net == null)
                {
                    Imgproc.putText(rgbaMat, "model file is not loaded.", new Point(5, rgbaMat.rows() - 30), Imgproc.FONT_HERSHEY_SIMPLEX, 0.7, new Scalar(255, 255, 255, 255), 2, Imgproc.LINE_AA, false);
                    Imgproc.putText(rgbaMat, "Please read console message.", new Point(5, rgbaMat.rows() - 10), Imgproc.FONT_HERSHEY_SIMPLEX, 0.7, new Scalar(255, 255, 255, 255), 2, Imgproc.LINE_AA, false);
                }
                else
                {

                    Imgproc.cvtColor(rgbaMat, bgrMat, Imgproc.COLOR_RGBA2BGR);

                    // Create a 4D blob from a frame.
                    Size inpSize = new Size(inpWidth > 0 ? inpWidth : bgrMat.cols(),
                                       inpHeight > 0 ? inpHeight : bgrMat.rows());
                    Mat blob = Dnn.blobFromImage(bgrMat, scale, inpSize, mean, swapRB, false);


                    // Run a model.
                    net.setInput(blob);

                    if (net.getLayer(new DictValue(0)).outputNameToIndex("im_info") != -1)
                    {  // Faster-RCNN or R-FCN
                        Imgproc.resize(bgrMat, bgrMat, inpSize);
                        Mat imInfo = new Mat(1, 3, CvType.CV_32FC1);
                        imInfo.put(0, 0, new float[] {
                            (float)inpSize.height,
                            (float)inpSize.width,
                            1.6f
                        });
                        net.setInput(imInfo, "im_info");
                    }


                    TickMeter tm = new TickMeter();
                    tm.start();

                    List<Mat> outs = new List<Mat>();
                    net.forward(outs, outBlobNames);

                    tm.stop();
                    //Debug.Log ("Inference time, ms: " + tm.getTimeMilli ());


                    postprocess(rgbaMat, outs, net);

                    for (int i = 0; i < outs.Count; i++)
                    {
                        outs[i].Dispose();
                    }
                    blob.Dispose();
                }

                Utils.fastMatToTexture2D(rgbaMat, texture);
            }
        }

        /// <summary>
        /// Raises the destroy event.
        /// </summary>
        void OnDestroy()
        {
            webCamTextureToMatHelper.Dispose();

            if (net != null)
                net.Dispose();

            Utils.setDebugMode(false);

#if UNITY_WEBGL && !UNITY_EDITOR
            if (getFilePath_Coroutine != null)
            {
                StopCoroutine(getFilePath_Coroutine);
                ((IDisposable)getFilePath_Coroutine).Dispose();
            }
#endif
        }

        /// <summary>
        /// Raises the back button click event.
        /// </summary>
        public void OnBackButtonClick()
        {
            SceneManager.LoadScene("OpenCVForUnityExample");
        }

        /// <summary>
        /// Raises the play button click event.
        /// </summary>
        public void OnPlayButtonClick()
        {
            webCamTextureToMatHelper.Play();
        }

        /// <summary>
        /// Raises the pause button click event.
        /// </summary>
        public void OnPauseButtonClick()
        {
            webCamTextureToMatHelper.Pause();
        }

        /// <summary>
        /// Raises the stop button click event.
        /// </summary>
        public void OnStopButtonClick()
        {
            webCamTextureToMatHelper.Stop();
        }

        /// <summary>
        /// Raises the change camera button click event.
        /// </summary>
        public void OnChangeCameraButtonClick()
        {
            webCamTextureToMatHelper.requestedIsFrontFacing = !webCamTextureToMatHelper.IsFrontFacing();
        }

        /// <summary>
        /// Reads the class names.
        /// </summary>
        /// <returns>The class names.</returns>
        /// <param name="filename">Filename.</param>
        private List<string> readClassNames(string filename)
        {
            List<string> classNames = new List<string>();

            System.IO.StreamReader cReader = null;
            try
            {
                cReader = new System.IO.StreamReader(filename, System.Text.Encoding.Default);

                while (cReader.Peek() >= 0)
                {
                    string name = cReader.ReadLine();
                    classNames.Add(name);
                }
            }
            catch (System.Exception ex)
            {
                Debug.LogError(ex.Message);
                return null;
            }
            finally
            {
                if (cReader != null)
                    cReader.Close();
            }

            return classNames;
        }

        /// <summary>
        /// Postprocess the specified frame, outs and net.
        /// </summary>
        /// <param name="frame">Frame.</param>
        /// <param name="outs">Outs.</param>
        /// <param name="net">Net.</param>
        private void postprocess(Mat frame, List<Mat> outs, Net net)
        {
            string outLayerType = outBlobTypes[0];


            List<int> classIdsList = new List<int>();
            List<float> confidencesList = new List<float>();
            List<OpenCVForUnity.CoreModule.Rect> boxesList = new List<OpenCVForUnity.CoreModule.Rect>();
            if (net.getLayer(new DictValue(0)).outputNameToIndex("im_info") != -1)
            {
                // Faster-RCNN or R-FCN
                // Network produces output blob with a shape 1x1xNx7 where N is a number of
                // detections and an every detection is a vector of values
                // [batchId, classId, confidence, left, top, right, bottom]

                if (outs.Count == 1)
                {

                    outs[0] = outs[0].reshape(1, (int)outs[0].total() / 7);

                    //Debug.Log ("outs[i].ToString() " + outs [0].ToString ());

                    float[] data = new float[7];

                    for (int i = 0; i < outs[0].rows(); i++)
                    {

                        outs[0].get(i, 0, data);

                        float confidence = data[2];

                        if (confidence > confThreshold)
                        {
                            int class_id = (int)(data[1]);

                            int left = (int)(data[3] * frame.cols());
                            int top = (int)(data[4] * frame.rows());
                            int right = (int)(data[5] * frame.cols());
                            int bottom = (int)(data[6] * frame.rows());
                            int width = right - left + 1;
                            int height = bottom - top + 1;

                            classIdsList.Add((int)(class_id) - 0);
                            confidencesList.Add((float)confidence);
                            boxesList.Add(new OpenCVForUnity.CoreModule.Rect(left, top, width, height));
                        }
                    }
                }
            }
            else if (outLayerType == "DetectionOutput")
            {
                // Network produces output blob with a shape 1x1xNx7 where N is a number of
                // detections and an every detection is a vector of values
                // [batchId, classId, confidence, left, top, right, bottom]

                if (outs.Count == 1)
                {

                    outs[0] = outs[0].reshape(1, (int)outs[0].total() / 7);

                    //                    Debug.Log ("outs[i].ToString() " + outs [0].ToString ());

                    float[] data = new float[7];

                    for (int i = 0; i < outs[0].rows(); i++)
                    {

                        outs[0].get(i, 0, data);

                        float confidence = data[2];

                        if (confidence > confThreshold)
                        {
                            int class_id = (int)(data[1]);

                            int left = (int)(data[3] * frame.cols());
                            int top = (int)(data[4] * frame.rows());
                            int right = (int)(data[5] * frame.cols());
                            int bottom = (int)(data[6] * frame.rows());
                            int width = right - left + 1;
                            int height = bottom - top + 1;

                            classIdsList.Add((int)(class_id) - 0);
                            confidencesList.Add((float)confidence);
                            boxesList.Add(new OpenCVForUnity.CoreModule.Rect(left, top, width, height));
                        }
                    }
                }
            }
            else if (outLayerType == "Region")
            {
                for (int i = 0; i < outs.Count; ++i)
                {
                    // Network produces output blob with a shape NxC where N is a number of
                    // detected objects and C is a number of classes + 4 where the first 4
                    // numbers are [center_x, center_y, width, height]

                    //Debug.Log ("outs[i].ToString() "+outs[i].ToString());

                    float[] positionData = new float[5];
                    float[] confidenceData = new float[outs[i].cols() - 5];

                    for (int p = 0; p < outs[i].rows(); p++)
                    {

                        outs[i].get(p, 0, positionData);

                        outs[i].get(p, 5, confidenceData);

                        int maxIdx = confidenceData.Select((val, idx) => new { V = val, I = idx }).Aggregate((max, working) => (max.V > working.V) ? max : working).I;
                        float confidence = confidenceData[maxIdx];

                        if (confidence > confThreshold)
                        {

                            int centerX = (int)(positionData[0] * frame.cols());
                            int centerY = (int)(positionData[1] * frame.rows());
                            int width = (int)(positionData[2] * frame.cols());
                            int height = (int)(positionData[3] * frame.rows());
                            int left = centerX - width / 2;
                            int top = centerY - height / 2;

                            classIdsList.Add(maxIdx);
                            confidencesList.Add((float)confidence);
                            boxesList.Add(new OpenCVForUnity.CoreModule.Rect(left, top, width, height));

                        }
                    }
                }
            }
            else
            {
                Debug.Log("Unknown output layer type: " + outLayerType);
            }


            MatOfRect boxes = new MatOfRect();
            boxes.fromList(boxesList);

            MatOfFloat confidences = new MatOfFloat();
            confidences.fromList(confidencesList);


            MatOfInt indices = new MatOfInt();
            Dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold, indices);

            //            Debug.Log ("indices.dump () "+indices.dump ());
            //            Debug.Log ("indices.ToString () "+indices.ToString());

            for (int i = 0; i < indices.total(); ++i)
            {
                int idx = (int)indices.get(i, 0)[0];
                OpenCVForUnity.CoreModule.Rect box = boxesList[idx];
                drawPred(classIdsList[idx], confidencesList[idx], box.x, box.y,
                    box.x + box.width, box.y + box.height, frame);
            }

            indices.Dispose();
            boxes.Dispose();
            confidences.Dispose();

        }

        /// <summary>
        /// Draws the pred.
        /// </summary>
        /// <param name="classId">Class identifier.</param>
        /// <param name="conf">Conf.</param>
        /// <param name="left">Left.</param>
        /// <param name="top">Top.</param>
        /// <param name="right">Right.</param>
        /// <param name="bottom">Bottom.</param>
        /// <param name="frame">Frame.</param>
        private void drawPred(int classId, float conf, int left, int top, int right, int bottom, Mat frame)
        {
            Imgproc.rectangle(frame, new Point(left, top), new Point(right, bottom), new Scalar(0, 255, 0, 255), 2);

            string label = conf.ToString();
            if (classNames != null && classNames.Count != 0)
            {
                if (classId < (int)classNames.Count)
                {
                    label = classNames[classId] + ": " + label;
                }
            }

            int[] baseLine = new int[1];
            Size labelSize = Imgproc.getTextSize(label, Imgproc.FONT_HERSHEY_SIMPLEX, 0.5, 1, baseLine);

            top = Mathf.Max(top, (int)labelSize.height);
            Imgproc.rectangle(frame, new Point(left, top - labelSize.height),
                new Point(left + labelSize.width, top + baseLine[0]), Scalar.all(255), Core.FILLED);
            Imgproc.putText(frame, label, new Point(left, top), Imgproc.FONT_HERSHEY_SIMPLEX, 0.5, new Scalar(0, 0, 0, 255));
        }

        /// <summary>
        /// Gets the outputs names.
        /// </summary>
        /// <returns>The outputs names.</returns>
        /// <param name="net">Net.</param>
        private List<string> getOutputsNames(Net net)
        {
            List<string> names = new List<string>();


            MatOfInt outLayers = net.getUnconnectedOutLayers();
            for (int i = 0; i < outLayers.total(); ++i)
            {
                names.Add(net.getLayer(new DictValue((int)outLayers.get(i, 0)[0])).get_name());
            }
            outLayers.Dispose();

            return names;
        }

        /// <summary>
        /// Gets the outputs types.
        /// </summary>
        /// <returns>The outputs types.</returns>
        /// <param name="net">Net.</param>
        private List<string> getOutputsTypes(Net net)
        {
            List<string> types = new List<string>();


            MatOfInt outLayers = net.getUnconnectedOutLayers();
            for (int i = 0; i < outLayers.total(); ++i)
            {
                types.Add(net.getLayer(new DictValue((int)outLayers.get(i, 0)[0])).get_type());
            }
            outLayers.Dispose();

            return types;
        }
    }
}
#endif

#endif