1167 lines
46 KiB
C#
1167 lines
46 KiB
C#
#if !(PLATFORM_LUMIN && !UNITY_EDITOR)
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#if !UNITY_WSA_10_0
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using System;
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using System.Collections;
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using System.Collections.Generic;
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using System.Linq;
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using System.Text;
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using UnityEngine;
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using UnityEngine.SceneManagement;
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using OpenCVForUnity.CoreModule;
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using OpenCVForUnity.DnnModule;
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using OpenCVForUnity.ImgprocModule;
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using OpenCVForUnity.ImgcodecsModule;
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using OpenCVForUnity.UnityUtils;
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using OpenCVForUnity.UnityUtils.Helper;
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using OpenCVRect = OpenCVForUnity.CoreModule.Rect;
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using OpenCVRange = OpenCVForUnity.CoreModule.Range;
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namespace OpenCVForUnityExample
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{
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/// <summary>
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/// YOLOv7 ObjectDetection Example
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/// An example of using OpenCV dnn module with YOLOv7 Object Detection (model without NMS processing).
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/// Referring to https://github.com/AlexeyAB/darknet.
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///
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/// [Tested Models]
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/// yolov7-tiny https://github.com/AlexeyAB/darknet/releases/download/yolov4/yolov7-tiny.weights, https://raw.githubusercontent.com/AlexeyAB/darknet/0faed3e60e52f742bbef43b83f6be51dd30f373e/cfg/yolov7-tiny.cfg
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/// yolov7 https://github.com/AlexeyAB/darknet/releases/download/yolov4/yolov7.weights, https://raw.githubusercontent.com/AlexeyAB/darknet/0faed3e60e52f742bbef43b83f6be51dd30f373e/cfg/yolov7.cfg
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/// </summary>
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[RequireComponent(typeof(WebCamTextureToMatHelper))]
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public class YOLOv7ObjectDetectionExample : MonoBehaviour
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{
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[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).")]
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public string model = "yolov7-tiny.weights";
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[TooltipAttribute("Path to a text file of model contains network configuration. It could be a file with extensions .prototxt (Caffe), .pbtxt (TensorFlow), .cfg (Darknet).")]
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public string config = "yolov7-tiny.cfg";
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[TooltipAttribute("Optional path to a text file with names of classes to label detected objects.")]
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public string classes = "coco.names";
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[TooltipAttribute("Confidence threshold.")]
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public float confThreshold = 0.25f;
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[TooltipAttribute("Non-maximum suppression threshold.")]
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public float nmsThreshold = 0.45f;
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//[TooltipAttribute("Maximum detections per image.")]
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//public int topK = 1000;
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[TooltipAttribute("Preprocess input image by resizing to a specific width.")]
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public int inpWidth = 416;
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[TooltipAttribute("Preprocess input image by resizing to a specific height.")]
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public int inpHeight = 416;
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[Header("TEST")]
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[TooltipAttribute("Path to test input image.")]
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public string testInputImage;
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protected string classes_filepath;
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protected string config_filepath;
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protected string model_filepath;
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/// <summary>
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/// The texture.
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/// </summary>
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Texture2D texture;
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/// <summary>
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/// The webcam texture to mat helper.
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/// </summary>
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WebCamTextureToMatHelper webCamTextureToMatHelper;
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/// <summary>
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/// The bgr mat.
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/// </summary>
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Mat bgrMat;
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/// <summary>
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/// The YOLOv7 ObjectDetector.
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/// </summary>
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YOLOv7ObjectDetector objectDetector;
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/// <summary>
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/// The FPS monitor.
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/// </summary>
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FpsMonitor fpsMonitor;
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#if UNITY_WEBGL
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IEnumerator getFilePath_Coroutine;
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#endif
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// Use this for initialization
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void Start()
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{
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fpsMonitor = GetComponent<FpsMonitor>();
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webCamTextureToMatHelper = gameObject.GetComponent<WebCamTextureToMatHelper>();
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#if UNITY_WEBGL
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getFilePath_Coroutine = GetFilePath();
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StartCoroutine(getFilePath_Coroutine);
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#else
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if (!string.IsNullOrEmpty(classes))
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{
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classes_filepath = Utils.getFilePath("OpenCVForUnity/dnn/" + classes);
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if (string.IsNullOrEmpty(classes_filepath)) Debug.Log("The file:" + classes + " did not exist in the folder “Assets/StreamingAssets/OpenCVForUnity/dnn”.");
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}
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if (!string.IsNullOrEmpty(config))
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{
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config_filepath = Utils.getFilePath("OpenCVForUnity/dnn/" + config);
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if (string.IsNullOrEmpty(config_filepath)) Debug.Log("The file:" + config + " did not exist in the folder “Assets/StreamingAssets/OpenCVForUnity/dnn”.");
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}
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if (!string.IsNullOrEmpty(model))
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{
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model_filepath = Utils.getFilePath("OpenCVForUnity/dnn/" + model);
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if (string.IsNullOrEmpty(model_filepath)) Debug.Log("The file:" + model + " did not exist in the folder “Assets/StreamingAssets/OpenCVForUnity/dnn”.");
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}
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Run();
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#endif
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}
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#if UNITY_WEBGL
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private IEnumerator GetFilePath()
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{
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if (!string.IsNullOrEmpty(classes))
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{
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var getFilePathAsync_0_Coroutine = Utils.getFilePathAsync("OpenCVForUnity/dnn/" + classes, (result) =>
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{
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classes_filepath = result;
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});
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yield return getFilePathAsync_0_Coroutine;
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if (string.IsNullOrEmpty(classes_filepath)) Debug.Log("The file:" + classes + " did not exist in the folder “Assets/StreamingAssets/OpenCVForUnity/dnn”.");
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}
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if (!string.IsNullOrEmpty(config))
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{
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var getFilePathAsync_1_Coroutine = Utils.getFilePathAsync("OpenCVForUnity/dnn/" + config, (result) =>
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{
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config_filepath = result;
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});
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yield return getFilePathAsync_1_Coroutine;
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if (string.IsNullOrEmpty(config_filepath)) Debug.Log("The file:" + config + " did not exist in the folder “Assets/StreamingAssets/OpenCVForUnity/dnn”.");
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}
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if (!string.IsNullOrEmpty(model))
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{
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var getFilePathAsync_2_Coroutine = Utils.getFilePathAsync("OpenCVForUnity/dnn/" + model, (result) =>
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{
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model_filepath = result;
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});
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yield return getFilePathAsync_2_Coroutine;
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if (string.IsNullOrEmpty(model_filepath)) Debug.Log("The file:" + model + " did not exist in the folder “Assets/StreamingAssets/OpenCVForUnity/dnn”.");
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}
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getFilePath_Coroutine = null;
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Run();
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}
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#endif
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// Use this for initialization
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void Run()
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{
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//if true, The error log of the Native side OpenCV will be displayed on the Unity Editor Console.
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Utils.setDebugMode(true);
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if (string.IsNullOrEmpty(model_filepath) || string.IsNullOrEmpty(classes_filepath))
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{
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Debug.LogError("model: " + model + " or " + "config: " + config + " or " + "classes: " + classes + " is not loaded.");
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}
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else
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{
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objectDetector = new YOLOv7ObjectDetector(model_filepath, config_filepath, classes_filepath, new Size(inpWidth, inpHeight), confThreshold, nmsThreshold/*, topK*/);
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}
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if (string.IsNullOrEmpty(testInputImage))
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{
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#if UNITY_ANDROID && !UNITY_EDITOR
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// Avoids the front camera low light issue that occurs in only some Android devices (e.g. Google Pixel, Pixel2).
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webCamTextureToMatHelper.avoidAndroidFrontCameraLowLightIssue = true;
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#endif
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webCamTextureToMatHelper.Initialize();
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}
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else
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{
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/////////////////////
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// TEST
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var getFilePathAsync_0_Coroutine = Utils.getFilePathAsync("OpenCVForUnity/dnn/" + testInputImage, (result) =>
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{
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string test_input_image_filepath = result;
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if (string.IsNullOrEmpty(test_input_image_filepath)) Debug.Log("The file:" + testInputImage + " did not exist in the folder “Assets/StreamingAssets/OpenCVForUnity/dnn”.");
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Mat img = Imgcodecs.imread(test_input_image_filepath);
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if (img.empty())
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{
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img = new Mat(424, 640, CvType.CV_8UC3, new Scalar(0, 0, 0));
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Imgproc.putText(img, testInputImage + " is not loaded.", new Point(5, img.rows() - 30), Imgproc.FONT_HERSHEY_SIMPLEX, 0.7, new Scalar(255, 255, 255, 255), 2, Imgproc.LINE_AA, false);
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Imgproc.putText(img, "Please read console message.", new Point(5, img.rows() - 10), Imgproc.FONT_HERSHEY_SIMPLEX, 0.7, new Scalar(255, 255, 255, 255), 2, Imgproc.LINE_AA, false);
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}
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else
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{
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TickMeter tm = new TickMeter();
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tm.start();
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Mat results = objectDetector.infer(img);
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tm.stop();
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Debug.Log("YOLOv7ObjectDetector Inference time (preprocess + infer + postprocess), ms: " + tm.getTimeMilli());
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objectDetector.visualize(img, results, true, false);
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}
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gameObject.transform.localScale = new Vector3(img.width(), img.height(), 1);
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float imageWidth = img.width();
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float imageHeight = img.height();
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float widthScale = (float)Screen.width / imageWidth;
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float heightScale = (float)Screen.height / imageHeight;
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if (widthScale < heightScale)
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{
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Camera.main.orthographicSize = (imageWidth * (float)Screen.height / (float)Screen.width) / 2;
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}
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else
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{
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Camera.main.orthographicSize = imageHeight / 2;
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}
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Imgproc.cvtColor(img, img, Imgproc.COLOR_BGR2RGB);
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Texture2D texture = new Texture2D(img.cols(), img.rows(), TextureFormat.RGB24, false);
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Utils.matToTexture2D(img, texture);
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gameObject.GetComponent<Renderer>().material.mainTexture = texture;
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});
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StartCoroutine(getFilePathAsync_0_Coroutine);
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/////////////////////
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}
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}
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/// <summary>
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/// Raises the webcam texture to mat helper initialized event.
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/// </summary>
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public void OnWebCamTextureToMatHelperInitialized()
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{
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Debug.Log("OnWebCamTextureToMatHelperInitialized");
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Mat webCamTextureMat = webCamTextureToMatHelper.GetMat();
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texture = new Texture2D(webCamTextureMat.cols(), webCamTextureMat.rows(), TextureFormat.RGBA32, false);
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Utils.matToTexture2D(webCamTextureMat, texture);
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gameObject.GetComponent<Renderer>().material.mainTexture = texture;
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gameObject.transform.localScale = new Vector3(webCamTextureMat.cols(), webCamTextureMat.rows(), 1);
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Debug.Log("Screen.width " + Screen.width + " Screen.height " + Screen.height + " Screen.orientation " + Screen.orientation);
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if (fpsMonitor != null)
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{
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fpsMonitor.Add("width", webCamTextureMat.width().ToString());
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fpsMonitor.Add("height", webCamTextureMat.height().ToString());
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fpsMonitor.Add("orientation", Screen.orientation.ToString());
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}
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float width = webCamTextureMat.width();
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float height = webCamTextureMat.height();
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float widthScale = (float)Screen.width / width;
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float heightScale = (float)Screen.height / height;
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if (widthScale < heightScale)
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{
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Camera.main.orthographicSize = (width * (float)Screen.height / (float)Screen.width) / 2;
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}
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else
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{
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Camera.main.orthographicSize = height / 2;
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}
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bgrMat = new Mat(webCamTextureMat.rows(), webCamTextureMat.cols(), CvType.CV_8UC3);
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}
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/// <summary>
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/// Raises the webcam texture to mat helper disposed event.
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/// </summary>
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public void OnWebCamTextureToMatHelperDisposed()
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{
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Debug.Log("OnWebCamTextureToMatHelperDisposed");
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if (bgrMat != null)
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bgrMat.Dispose();
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if (texture != null)
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{
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Texture2D.Destroy(texture);
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texture = null;
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}
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}
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/// <summary>
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/// Raises the webcam texture to mat helper error occurred event.
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/// </summary>
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/// <param name="errorCode">Error code.</param>
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public void OnWebCamTextureToMatHelperErrorOccurred(WebCamTextureToMatHelper.ErrorCode errorCode)
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{
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Debug.Log("OnWebCamTextureToMatHelperErrorOccurred " + errorCode);
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}
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// Update is called once per frame
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void Update()
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{
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if (webCamTextureToMatHelper.IsPlaying() && webCamTextureToMatHelper.DidUpdateThisFrame())
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{
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Mat rgbaMat = webCamTextureToMatHelper.GetMat();
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if (objectDetector == null)
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{
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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);
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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);
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}
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else
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{
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Imgproc.cvtColor(rgbaMat, bgrMat, Imgproc.COLOR_RGBA2BGR);
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//TickMeter tm = new TickMeter();
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//tm.start();
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Mat results = objectDetector.infer(bgrMat);
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//tm.stop();
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//Debug.Log("YOLOv7ObjectDetector Inference time (preprocess + infer + postprocess), ms: " + tm.getTimeMilli());
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Imgproc.cvtColor(bgrMat, rgbaMat, Imgproc.COLOR_BGR2RGBA);
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objectDetector.visualize(rgbaMat, results, false, true);
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}
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Utils.matToTexture2D(rgbaMat, texture);
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}
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}
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/// <summary>
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/// Raises the destroy event.
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/// </summary>
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void OnDestroy()
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{
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webCamTextureToMatHelper.Dispose();
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if (objectDetector != null)
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objectDetector.dispose();
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Utils.setDebugMode(false);
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#if UNITY_WEBGL
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if (getFilePath_Coroutine != null)
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{
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StopCoroutine(getFilePath_Coroutine);
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((IDisposable)getFilePath_Coroutine).Dispose();
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}
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#endif
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}
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/// <summary>
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/// Raises the back button click event.
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/// </summary>
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public void OnBackButtonClick()
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{
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SceneManager.LoadScene("OpenCVForUnityExample");
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}
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/// <summary>
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/// Raises the play button click event.
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/// </summary>
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public void OnPlayButtonClick()
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{
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webCamTextureToMatHelper.Play();
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}
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/// <summary>
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/// Raises the pause button click event.
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/// </summary>
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public void OnPauseButtonClick()
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{
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webCamTextureToMatHelper.Pause();
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}
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/// <summary>
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/// Raises the stop button click event.
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/// </summary>
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public void OnStopButtonClick()
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{
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webCamTextureToMatHelper.Stop();
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}
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/// <summary>
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/// Raises the change camera button click event.
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/// </summary>
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public void OnChangeCameraButtonClick()
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{
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webCamTextureToMatHelper.requestedIsFrontFacing = !webCamTextureToMatHelper.requestedIsFrontFacing;
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}
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private class YOLOv7ObjectDetector
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{
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Size input_size;
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float conf_threshold;
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float nms_threshold;
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int topK;
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int backend;
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int target;
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int num_classes = 80;
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DetectionModel detection_model;
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List<string> classNames;
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List<Scalar> palette;
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Mat maxSizeImg;
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MatOfInt classIds;
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MatOfFloat confidences;
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MatOfRect boxes;
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public YOLOv7ObjectDetector(string modelFilepath, string configFilepath, string classesFilepath, Size inputSize, float confThreshold = 0.25f, float nmsThreshold = 0.45f, int topK = 1000, int backend = Dnn.DNN_BACKEND_OPENCV, int target = Dnn.DNN_TARGET_CPU)
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{
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// initialize
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if (!string.IsNullOrEmpty(modelFilepath))
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{
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detection_model = new DetectionModel(modelFilepath, configFilepath);
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detection_model.setInputParams(1.0 / 255.0, inputSize, new Scalar(0, 0, 0), true, false);
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detection_model.setNmsAcrossClasses(false);// Perform classwise NMS.
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detection_model.setPreferableBackend(this.backend);
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detection_model.setPreferableTarget(this.target);
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}
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if (!string.IsNullOrEmpty(classesFilepath))
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{
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classNames = readClassNames(classesFilepath);
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num_classes = classNames.Count;
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}
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input_size = new Size(inputSize.width > 0 ? inputSize.width : 640, inputSize.height > 0 ? inputSize.height : 640);
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conf_threshold = Mathf.Clamp01(confThreshold);
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nms_threshold = Mathf.Clamp01(nmsThreshold);
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this.topK = topK;
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this.backend = backend;
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this.target = target;
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classIds = new MatOfInt();
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confidences = new MatOfFloat();
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boxes = new MatOfRect();
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palette = new List<Scalar>();
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palette.Add(new Scalar(255, 56, 56, 255));
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palette.Add(new Scalar(255, 157, 151, 255));
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palette.Add(new Scalar(255, 112, 31, 255));
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palette.Add(new Scalar(255, 178, 29, 255));
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palette.Add(new Scalar(207, 210, 49, 255));
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palette.Add(new Scalar(72, 249, 10, 255));
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palette.Add(new Scalar(146, 204, 23, 255));
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palette.Add(new Scalar(61, 219, 134, 255));
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palette.Add(new Scalar(26, 147, 52, 255));
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palette.Add(new Scalar(0, 212, 187, 255));
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palette.Add(new Scalar(44, 153, 168, 255));
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palette.Add(new Scalar(0, 194, 255, 255));
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palette.Add(new Scalar(52, 69, 147, 255));
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palette.Add(new Scalar(100, 115, 255, 255));
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palette.Add(new Scalar(0, 24, 236, 255));
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palette.Add(new Scalar(132, 56, 255, 255));
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palette.Add(new Scalar(82, 0, 133, 255));
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palette.Add(new Scalar(203, 56, 255, 255));
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palette.Add(new Scalar(255, 149, 200, 255));
|
|
palette.Add(new Scalar(255, 55, 199, 255));
|
|
}
|
|
|
|
protected virtual Mat preprocess(Mat image)
|
|
{
|
|
// Add padding to make it square.
|
|
int max = Mathf.Max(image.cols(), image.rows());
|
|
|
|
if (maxSizeImg == null)
|
|
maxSizeImg = new Mat(max, max, image.type(), Scalar.all(114));
|
|
if (maxSizeImg.width() != max || maxSizeImg.height() != max)
|
|
{
|
|
maxSizeImg.create(max, max, image.type());
|
|
Imgproc.rectangle(maxSizeImg, new OpenCVRect(0, 0, maxSizeImg.width(), maxSizeImg.height()), Scalar.all(114), -1);
|
|
}
|
|
|
|
Mat _maxSizeImg_roi = new Mat(maxSizeImg, new OpenCVRect((max - image.cols()) / 2, (max - image.rows()) / 2, image.cols(), image.rows()));
|
|
image.copyTo(_maxSizeImg_roi);
|
|
|
|
return maxSizeImg;// [max, max, 3]
|
|
}
|
|
|
|
public virtual Mat infer(Mat image)
|
|
{
|
|
// cheack
|
|
if (image.channels() != 3)
|
|
{
|
|
Debug.Log("The input image must be in BGR format.");
|
|
return new Mat();
|
|
}
|
|
|
|
// Preprocess
|
|
Mat input_blob = preprocess(image);
|
|
|
|
// Forward
|
|
detection_model.detect(input_blob, classIds, confidences, boxes, conf_threshold, nms_threshold);
|
|
|
|
// Postprocess
|
|
int num = classIds.rows();
|
|
Mat results = new Mat(num, 6, CvType.CV_32FC1);
|
|
|
|
float maxSize = Mathf.Max((float)image.size().width, (float)image.size().height);
|
|
float x_shift = (maxSize - (float)image.size().width) / 2f;
|
|
float y_shift = (maxSize - (float)image.size().height) / 2f;
|
|
|
|
for (int i = 0; i < num; ++i)
|
|
{
|
|
int[] classId_arr = new int[1];
|
|
classIds.get(i, 0, classId_arr);
|
|
int id = classId_arr[0];
|
|
|
|
float[] confidence_arr = new float[1];
|
|
confidences.get(i, 0, confidence_arr);
|
|
float confidence = confidence_arr[0];
|
|
|
|
int[] box_arr = new int[4];
|
|
boxes.get(i, 0, box_arr);
|
|
int x = box_arr[0] - (int)x_shift;
|
|
int y = box_arr[1] - (int)y_shift;
|
|
int w = box_arr[2];
|
|
int h = box_arr[3];
|
|
|
|
results.put(i, 0, new float[] { x, y, x + w, y + h, confidence, id });
|
|
}
|
|
|
|
return results;
|
|
}
|
|
|
|
protected virtual Mat postprocess(Mat output_blob, Size original_shape)
|
|
{
|
|
return output_blob;
|
|
}
|
|
|
|
public virtual void visualize(Mat image, Mat results, bool print_results = false, bool isRGB = false)
|
|
{
|
|
if (image.IsDisposed)
|
|
return;
|
|
|
|
if (results.empty() || results.cols() < 6)
|
|
return;
|
|
|
|
for (int i = results.rows() - 1; i >= 0; --i)
|
|
{
|
|
float[] box = new float[4];
|
|
results.get(i, 0, box);
|
|
float[] conf = new float[1];
|
|
results.get(i, 4, conf);
|
|
float[] cls = new float[1];
|
|
results.get(i, 5, cls);
|
|
|
|
float left = box[0];
|
|
float top = box[1];
|
|
float right = box[2];
|
|
float bottom = box[3];
|
|
int classId = (int)cls[0];
|
|
|
|
Scalar c = palette[classId % palette.Count];
|
|
Scalar color = isRGB ? c : new Scalar(c.val[2], c.val[1], c.val[0], c.val[3]);
|
|
|
|
Imgproc.rectangle(image, new Point(left, top), new Point(right, bottom), color, 2);
|
|
|
|
string label = String.Format("{0:0.00}", conf[0]);
|
|
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((float)top, (float)labelSize.height);
|
|
Imgproc.rectangle(image, new Point(left, top - labelSize.height),
|
|
new Point(left + labelSize.width, top + baseLine[0]), color, Core.FILLED);
|
|
Imgproc.putText(image, label, new Point(left, top), Imgproc.FONT_HERSHEY_SIMPLEX, 0.5, Scalar.all(255), 1, Imgproc.LINE_AA);
|
|
}
|
|
|
|
// Print results
|
|
if (print_results)
|
|
{
|
|
StringBuilder sb = new StringBuilder();
|
|
|
|
for (int i = 0; i < results.rows(); ++i)
|
|
{
|
|
float[] box = new float[4];
|
|
results.get(i, 0, box);
|
|
float[] conf = new float[1];
|
|
results.get(i, 4, conf);
|
|
float[] cls = new float[1];
|
|
results.get(i, 5, cls);
|
|
|
|
int classId = (int)cls[0];
|
|
string label = String.Format("{0:0.0000}", conf[0]);
|
|
if (classNames != null && classNames.Count != 0)
|
|
{
|
|
if (classId < (int)classNames.Count)
|
|
{
|
|
label = classNames[classId] + " " + label;
|
|
}
|
|
}
|
|
|
|
sb.AppendLine(String.Format("-----------object {0}-----------", i + 1));
|
|
sb.AppendLine(String.Format("conf: {0:0.0000}", conf[0]));
|
|
sb.AppendLine(String.Format("cls: {0:0}", label));
|
|
sb.AppendLine(String.Format("box: {0:0} {1:0} {2:0} {3:0}", box[0], box[1], box[2], box[3]));
|
|
}
|
|
|
|
Debug.Log(sb);
|
|
}
|
|
}
|
|
|
|
public virtual void dispose()
|
|
{
|
|
if (detection_model != null)
|
|
detection_model.Dispose();
|
|
|
|
if (maxSizeImg != null)
|
|
maxSizeImg.Dispose();
|
|
|
|
maxSizeImg = null;
|
|
|
|
if (classIds != null)
|
|
classIds.Dispose();
|
|
if (confidences != null)
|
|
confidences.Dispose();
|
|
if (boxes != null)
|
|
boxes.Dispose();
|
|
|
|
classIds = null;
|
|
confidences = null;
|
|
boxes = null;
|
|
}
|
|
|
|
protected virtual 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;
|
|
}
|
|
}
|
|
|
|
|
|
// ONNX model TEST
|
|
// yolov7-tiny.onnx
|
|
// from https://colab.research.google.com/github/WongKinYiu/yolov7/blob/main/tools/YOLOv7onnx.ipynb
|
|
//
|
|
// # export ONNX for ONNX inference
|
|
// python export.py --weights ./yolov7-tiny.pt --grid --simplify --img-size 416 416
|
|
//
|
|
// Note.
|
|
// The YOLOv7 with NMS ONNX model does not work with OpenCV 4.7.0.
|
|
// OpenCV fails to import ONNX model: error: (-215:Assertion failed) !isDynamicShape in function 'cv::dnn::dnn4_v20220524::ONNXImporter::parseShape' #22099
|
|
// https://github.com/opencv/opencv/issues/22099
|
|
private class YOLOv7ONNXObjectDetector
|
|
{
|
|
Size input_size;
|
|
float conf_threshold;
|
|
float nms_threshold;
|
|
int topK;
|
|
int backend;
|
|
int target;
|
|
|
|
int num_classes = 80;
|
|
bool keep_ratio = true;
|
|
bool class_agnostic = false;// Non-use of multi-class NMS
|
|
|
|
Net object_detection_net;
|
|
List<string> classNames;
|
|
|
|
List<Scalar> palette;
|
|
|
|
Mat maxSizeImg;
|
|
|
|
Mat pickup_blob_numx6;
|
|
Mat boxesMat;
|
|
|
|
Mat boxes_m_c4;
|
|
Mat confidences_m;
|
|
Mat class_ids_m;
|
|
MatOfRect2d boxes;
|
|
MatOfFloat confidences;
|
|
MatOfInt class_ids;
|
|
|
|
public YOLOv7ONNXObjectDetector(string modelFilepath, string configFilepath, string classesFilepath, Size inputSize, float confThreshold = 0.25f, float nmsThreshold = 0.45f, int topK = 1000, int backend = Dnn.DNN_BACKEND_OPENCV, int target = Dnn.DNN_TARGET_CPU)
|
|
{
|
|
// initialize
|
|
if (!string.IsNullOrEmpty(modelFilepath))
|
|
{
|
|
object_detection_net = Dnn.readNet(modelFilepath, configFilepath);
|
|
}
|
|
|
|
if (!string.IsNullOrEmpty(classesFilepath))
|
|
{
|
|
classNames = readClassNames(classesFilepath);
|
|
num_classes = classNames.Count;
|
|
}
|
|
|
|
input_size = new Size(inputSize.width > 0 ? inputSize.width : 640, inputSize.height > 0 ? inputSize.height : 640);
|
|
conf_threshold = Mathf.Clamp01(confThreshold);
|
|
nms_threshold = Mathf.Clamp01(nmsThreshold);
|
|
this.topK = topK;
|
|
this.backend = backend;
|
|
this.target = target;
|
|
|
|
object_detection_net.setPreferableBackend(this.backend);
|
|
object_detection_net.setPreferableTarget(this.target);
|
|
|
|
palette = new List<Scalar>();
|
|
palette.Add(new Scalar(255, 56, 56, 255));
|
|
palette.Add(new Scalar(255, 157, 151, 255));
|
|
palette.Add(new Scalar(255, 112, 31, 255));
|
|
palette.Add(new Scalar(255, 178, 29, 255));
|
|
palette.Add(new Scalar(207, 210, 49, 255));
|
|
palette.Add(new Scalar(72, 249, 10, 255));
|
|
palette.Add(new Scalar(146, 204, 23, 255));
|
|
palette.Add(new Scalar(61, 219, 134, 255));
|
|
palette.Add(new Scalar(26, 147, 52, 255));
|
|
palette.Add(new Scalar(0, 212, 187, 255));
|
|
palette.Add(new Scalar(44, 153, 168, 255));
|
|
palette.Add(new Scalar(0, 194, 255, 255));
|
|
palette.Add(new Scalar(52, 69, 147, 255));
|
|
palette.Add(new Scalar(100, 115, 255, 255));
|
|
palette.Add(new Scalar(0, 24, 236, 255));
|
|
palette.Add(new Scalar(132, 56, 255, 255));
|
|
palette.Add(new Scalar(82, 0, 133, 255));
|
|
palette.Add(new Scalar(203, 56, 255, 255));
|
|
palette.Add(new Scalar(255, 149, 200, 255));
|
|
palette.Add(new Scalar(255, 55, 199, 255));
|
|
}
|
|
|
|
protected virtual Mat preprocess(Mat image)
|
|
{
|
|
// Create a 4D blob from a frame.
|
|
Mat blob;
|
|
|
|
if (keep_ratio)
|
|
{
|
|
// Add padding to make it square.
|
|
int max = Mathf.Max(image.cols(), image.rows());
|
|
|
|
if (maxSizeImg == null)
|
|
maxSizeImg = new Mat(max, max, image.type(), Scalar.all(114));
|
|
if (maxSizeImg.width() != max || maxSizeImg.height() != max)
|
|
{
|
|
maxSizeImg.create(max, max, image.type());
|
|
Imgproc.rectangle(maxSizeImg, new OpenCVRect(0, 0, maxSizeImg.width(), maxSizeImg.height()), Scalar.all(114), -1);
|
|
}
|
|
|
|
Mat _maxSizeImg_roi = new Mat(maxSizeImg, new OpenCVRect((max - image.cols()) / 2, (max - image.rows()) / 2, image.cols(), image.rows()));
|
|
image.copyTo(_maxSizeImg_roi);
|
|
|
|
blob = Dnn.blobFromImage(maxSizeImg, 1.0 / 255.0, input_size, Scalar.all(0), true, false, CvType.CV_32F); // HWC to NCHW, BGR to RGB
|
|
}
|
|
else
|
|
{
|
|
blob = Dnn.blobFromImage(image, 1.0 / 255.0, input_size, Scalar.all(0), true, false, CvType.CV_32F); // HWC to NCHW, BGR to RGB
|
|
}
|
|
|
|
return blob;// [1, 3, h, w]
|
|
}
|
|
|
|
public virtual Mat infer(Mat image)
|
|
{
|
|
// cheack
|
|
if (image.channels() != 3)
|
|
{
|
|
Debug.Log("The input image must be in BGR format.");
|
|
return new Mat();
|
|
}
|
|
|
|
// Preprocess
|
|
Mat input_blob = preprocess(image);
|
|
|
|
// Forward
|
|
object_detection_net.setInput(input_blob);
|
|
|
|
List<Mat> output_blob = new List<Mat>();
|
|
object_detection_net.forward(output_blob, object_detection_net.getUnconnectedOutLayersNames());
|
|
|
|
// Postprocess
|
|
Mat results = postprocess(output_blob, image.size());
|
|
|
|
// scale_boxes
|
|
float x_factor;
|
|
float y_factor;
|
|
float x_shift;
|
|
float y_shift;
|
|
if (keep_ratio)
|
|
{
|
|
float maxSize = Mathf.Max((float)image.size().width, (float)image.size().height);
|
|
x_factor = maxSize / (float)input_size.width;
|
|
y_factor = maxSize / (float)input_size.height;
|
|
x_shift = (maxSize - (float)image.size().width) / 2f;
|
|
y_shift = (maxSize - (float)image.size().height) / 2f;
|
|
}
|
|
else
|
|
{
|
|
x_factor = (float)image.size().width / (float)input_size.width;
|
|
y_factor = (float)image.size().height / (float)input_size.height;
|
|
x_shift = ((float)image.size().width - (float)image.size().width) / 2f;
|
|
y_shift = ((float)image.size().height - (float)image.size().height) / 2f;
|
|
}
|
|
for (int i = 0; i < results.rows(); ++i)
|
|
{
|
|
float[] results_arr = new float[4];
|
|
results.get(i, 0, results_arr);
|
|
float x1 = Mathf.Round(results_arr[0] * x_factor - x_shift);
|
|
float y1 = Mathf.Round(results_arr[1] * y_factor - y_shift);
|
|
float x2 = Mathf.Round(results_arr[2] * x_factor - x_shift);
|
|
float y2 = Mathf.Round(results_arr[3] * y_factor - y_shift);
|
|
|
|
results.put(i, 0, new float[] { x1, y1, x2, y2 });
|
|
}
|
|
|
|
|
|
input_blob.Dispose();
|
|
for (int i = 0; i < output_blob.Count; i++)
|
|
{
|
|
output_blob[i].Dispose();
|
|
}
|
|
|
|
return results;
|
|
}
|
|
|
|
|
|
// [1, n, 5 + num_classes]
|
|
protected virtual Mat postprocess(List<Mat> output_blob, Size original_shape)
|
|
{
|
|
if (output_blob[0].size(2) < 5 + num_classes)
|
|
return new Mat();
|
|
|
|
|
|
// pre-NMS
|
|
// Pick up rows to process by conf_threshold value and calculate scores and class_ids.
|
|
if (pickup_blob_numx6 == null)
|
|
pickup_blob_numx6 = new Mat(300, 6, CvType.CV_32FC1, new Scalar(0));
|
|
|
|
Imgproc.rectangle(pickup_blob_numx6, new OpenCVRect(4, 0, 1, pickup_blob_numx6.rows()), Scalar.all(0), -1);
|
|
|
|
int index_pickup = 0;
|
|
|
|
foreach (var o_blob in output_blob)
|
|
{
|
|
Mat output_blob_numx85 = o_blob.reshape(1, (int)o_blob.total() / (5 + num_classes));
|
|
|
|
int num = output_blob_numx85.rows();
|
|
Mat box_delta = output_blob_numx85.colRange(new OpenCVRange(0, 4));
|
|
Mat confidence = output_blob_numx85.colRange(new OpenCVRange(4, 5));
|
|
Mat classes_scores_delta = output_blob_numx85.colRange(new OpenCVRange(5, 5 + num_classes));
|
|
|
|
float[] conf_arr = new float[num];
|
|
confidence.get(0, 0, conf_arr);
|
|
|
|
for (int i = 0; i < num; ++i)
|
|
{
|
|
float conf = conf_arr[i];
|
|
if (conf > conf_threshold)
|
|
{
|
|
if (index_pickup > pickup_blob_numx6.rows())
|
|
{
|
|
Mat _conf_blob_numx6 = new Mat(pickup_blob_numx6.rows() * 2, pickup_blob_numx6.cols(), pickup_blob_numx6.type(), new Scalar(0));
|
|
pickup_blob_numx6.copyTo(_conf_blob_numx6.rowRange(0, pickup_blob_numx6.rows()));
|
|
pickup_blob_numx6 = _conf_blob_numx6;
|
|
}
|
|
|
|
float[] box_arr = new float[4];
|
|
box_delta.get(i, 0, box_arr);
|
|
|
|
Mat cls_scores = classes_scores_delta.row(i);
|
|
Core.MinMaxLocResult minmax = Core.minMaxLoc(cls_scores);
|
|
|
|
pickup_blob_numx6.put(index_pickup, 0, new float[] { box_arr[0], box_arr[1], box_arr[2], box_arr[3], ((float)minmax.maxVal * conf), (float)minmax.maxLoc.x });
|
|
index_pickup++;
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
int num_pickup = pickup_blob_numx6.rows();
|
|
Mat pickup_box_delta = pickup_blob_numx6.colRange(new OpenCVRange(0, 4));
|
|
Mat pickup_confidence = pickup_blob_numx6.colRange(new OpenCVRange(4, 5));
|
|
|
|
// Convert boxes from [cx, cy, w, h] to [x, y, w, h] where Rect2d data style.
|
|
if (boxesMat == null || boxesMat.rows() != num_pickup)
|
|
boxesMat = new Mat(num_pickup, 4, CvType.CV_32FC1);
|
|
Mat cxy_delta = pickup_box_delta.colRange(new OpenCVRange(0, 2));
|
|
Mat wh_delta = pickup_box_delta.colRange(new OpenCVRange(2, 4));
|
|
Mat xy1 = boxesMat.colRange(new OpenCVRange(0, 2));
|
|
Mat xy2 = boxesMat.colRange(new OpenCVRange(2, 4));
|
|
wh_delta.copyTo(xy2);
|
|
Core.divide(wh_delta, new Scalar(2.0), wh_delta);
|
|
Core.subtract(cxy_delta, wh_delta, xy1);
|
|
|
|
|
|
if (boxes_m_c4 == null || boxes_m_c4.rows() != num_pickup)
|
|
boxes_m_c4 = new Mat(num_pickup, 1, CvType.CV_64FC4);
|
|
if (confidences_m == null || confidences_m.rows() != num_pickup)
|
|
confidences_m = new Mat(num_pickup, 1, CvType.CV_32FC1);
|
|
|
|
if (boxes == null || boxes.rows() != num_pickup)
|
|
boxes = new MatOfRect2d(boxes_m_c4);
|
|
if (confidences == null || confidences.rows() != num_pickup)
|
|
confidences = new MatOfFloat(confidences_m);
|
|
|
|
|
|
// non-maximum suppression
|
|
Mat boxes_m_c1 = boxes_m_c4.reshape(1, num_pickup);
|
|
boxesMat.convertTo(boxes_m_c1, CvType.CV_64F);
|
|
pickup_confidence.copyTo(confidences_m);
|
|
|
|
MatOfInt indices = new MatOfInt();
|
|
|
|
if (class_agnostic)
|
|
{
|
|
// NMS
|
|
Dnn.NMSBoxes(boxes, confidences, conf_threshold, nms_threshold, indices, 1f, topK);
|
|
}
|
|
else
|
|
{
|
|
Mat pickup_class_ids = pickup_blob_numx6.colRange(new OpenCVRange(5, 6));
|
|
|
|
if (class_ids_m == null || class_ids_m.rows() != num_pickup)
|
|
class_ids_m = new Mat(num_pickup, 1, CvType.CV_32SC1);
|
|
if (class_ids == null || class_ids.rows() != num_pickup)
|
|
class_ids = new MatOfInt(class_ids_m);
|
|
|
|
pickup_class_ids.convertTo(class_ids_m, CvType.CV_32S);
|
|
|
|
// multi-class NMS
|
|
Dnn.NMSBoxesBatched(boxes, confidences, class_ids, conf_threshold, nms_threshold, indices, 1f, topK);
|
|
}
|
|
|
|
Mat results = new Mat(indices.rows(), 6, CvType.CV_32FC1);
|
|
|
|
for (int i = 0; i < indices.rows(); ++i)
|
|
{
|
|
int idx = (int)indices.get(i, 0)[0];
|
|
|
|
pickup_blob_numx6.row(idx).copyTo(results.row(i));
|
|
|
|
float[] bbox_arr = new float[4];
|
|
boxesMat.get(idx, 0, bbox_arr);
|
|
float x = bbox_arr[0];
|
|
float y = bbox_arr[1];
|
|
float w = bbox_arr[2];
|
|
float h = bbox_arr[3];
|
|
results.put(i, 0, new float[] { x, y, x + w, y + h });
|
|
}
|
|
|
|
indices.Dispose();
|
|
|
|
// [
|
|
// [xyxy, conf, cls]
|
|
// ...
|
|
// [xyxy, conf, cls]
|
|
// ]
|
|
return results;
|
|
}
|
|
|
|
public virtual void visualize(Mat image, Mat results, bool print_results = false, bool isRGB = false)
|
|
{
|
|
if (image.IsDisposed)
|
|
return;
|
|
|
|
if (results.empty() || results.cols() < 6)
|
|
return;
|
|
|
|
for (int i = results.rows() - 1; i >= 0; --i)
|
|
{
|
|
float[] box = new float[4];
|
|
results.get(i, 0, box);
|
|
float[] conf = new float[1];
|
|
results.get(i, 4, conf);
|
|
float[] cls = new float[1];
|
|
results.get(i, 5, cls);
|
|
|
|
float left = box[0];
|
|
float top = box[1];
|
|
float right = box[2];
|
|
float bottom = box[3];
|
|
int classId = (int)cls[0];
|
|
|
|
Scalar c = palette[classId % palette.Count];
|
|
Scalar color = isRGB ? c : new Scalar(c.val[2], c.val[1], c.val[0], c.val[3]);
|
|
|
|
Imgproc.rectangle(image, new Point(left, top), new Point(right, bottom), color, 2);
|
|
|
|
string label = String.Format("{0:0.00}", conf[0]);
|
|
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((float)top, (float)labelSize.height);
|
|
Imgproc.rectangle(image, new Point(left, top - labelSize.height),
|
|
new Point(left + labelSize.width, top + baseLine[0]), color, Core.FILLED);
|
|
Imgproc.putText(image, label, new Point(left, top), Imgproc.FONT_HERSHEY_SIMPLEX, 0.5, Scalar.all(255), 1, Imgproc.LINE_AA);
|
|
}
|
|
|
|
// Print results
|
|
if (print_results)
|
|
{
|
|
StringBuilder sb = new StringBuilder();
|
|
|
|
for (int i = 0; i < results.rows(); ++i)
|
|
{
|
|
float[] box = new float[4];
|
|
results.get(i, 0, box);
|
|
float[] conf = new float[1];
|
|
results.get(i, 4, conf);
|
|
float[] cls = new float[1];
|
|
results.get(i, 5, cls);
|
|
|
|
int classId = (int)cls[0];
|
|
string label = String.Format("{0:0}", cls[0]);
|
|
if (classNames != null && classNames.Count != 0)
|
|
{
|
|
if (classId < (int)classNames.Count)
|
|
{
|
|
label = classNames[classId] + " " + label;
|
|
}
|
|
}
|
|
|
|
sb.AppendLine(String.Format("-----------object {0}-----------", i + 1));
|
|
sb.AppendLine(String.Format("conf: {0:0.0000}", conf[0]));
|
|
sb.AppendLine(String.Format("cls: {0:0}", label));
|
|
sb.AppendLine(String.Format("box: {0:0} {1:0} {2:0} {3:0}", box[0], box[1], box[2], box[3]));
|
|
}
|
|
|
|
Debug.Log(sb);
|
|
}
|
|
}
|
|
|
|
public virtual void dispose()
|
|
{
|
|
if (object_detection_net != null)
|
|
object_detection_net.Dispose();
|
|
|
|
if (maxSizeImg != null)
|
|
maxSizeImg.Dispose();
|
|
|
|
maxSizeImg = null;
|
|
|
|
if (pickup_blob_numx6 != null)
|
|
pickup_blob_numx6.Dispose();
|
|
if (boxesMat != null)
|
|
boxesMat.Dispose();
|
|
|
|
pickup_blob_numx6 = null;
|
|
boxesMat = null;
|
|
|
|
if (boxes_m_c4 != null)
|
|
boxes_m_c4.Dispose();
|
|
if (confidences_m != null)
|
|
confidences_m.Dispose();
|
|
if (class_ids_m != null)
|
|
class_ids_m.Dispose();
|
|
if (boxes != null)
|
|
boxes.Dispose();
|
|
if (confidences != null)
|
|
confidences.Dispose();
|
|
if (class_ids != null)
|
|
class_ids.Dispose();
|
|
|
|
boxes_m_c4 = null;
|
|
confidences_m = null;
|
|
class_ids_m = null;
|
|
boxes = null;
|
|
confidences = null;
|
|
class_ids = null;
|
|
}
|
|
|
|
protected virtual 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;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
#endif
|
|
|
|
#endif |