603 lines
24 KiB
C#
603 lines
24 KiB
C#
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#if !UNITY_WSA_10_0
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using UnityEngine;
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using UnityEngine.SceneManagement;
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using System;
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using System.Linq;
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using System.Collections;
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using System.Collections.Generic;
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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.UnityUtils;
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using OpenCVForUnity.ImgcodecsModule;
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namespace OpenCVForUnityExample
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{
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/// <summary>
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/// Dnn ObjectDetection Example
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/// Referring to https://github.com/opencv/opencv/blob/master/samples/dnn/object_detection.cpp.
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/// </summary>
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public class DnnObjectDetectionExample : MonoBehaviour
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{
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[TooltipAttribute ("Path to input image.")]
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public string input;
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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;
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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;
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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;
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[TooltipAttribute ("Optional list of classes to label detected objects.")]
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public List<string> classesList;
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[TooltipAttribute ("Confidence threshold.")]
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public float confThreshold;
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[TooltipAttribute ("Non-maximum suppression threshold.")]
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public float nmsThreshold;
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[TooltipAttribute ("Preprocess input image by multiplying on a scale factor.")]
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public float scale;
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[TooltipAttribute ("Preprocess input image by subtracting mean values. Mean values should be in BGR order and delimited by spaces.")]
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public Scalar mean;
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[TooltipAttribute ("Indicate that model works with RGB input images instead BGR ones.")]
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public bool swapRB;
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[TooltipAttribute ("Preprocess input image by resizing to a specific width.")]
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public int inpWidth;
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[TooltipAttribute ("Preprocess input image by resizing to a specific height.")]
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public int inpHeight;
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//yolov3
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// string input = "004545.jpg";
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// public string input = "person.jpg";
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// public string model = "yolov3-tiny.weights";
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// public string config = "yolov3-tiny.cfg";
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// // string model = "yolov2-tiny.weights";
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// // string config = "yolov2-tiny.cfg";
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// public string classes = "coco.names";
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//
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//
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// public float confThreshold = 0.24f;
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// public float nmsThreshold = 0.24f;
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// public float scale = 1f / 255f;
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// public Scalar mean = new Scalar (0, 0, 0);
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// public bool swapRB = false;
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// public int inpWidth = 416;
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// public int inpHeight = 416;
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//
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// List<string> classNames;
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// //MobileNetSSD
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// string input = "004545.jpg";
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// // string input = "person.jpg";
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// string model = "MobileNetSSD_deploy.caffemodel";
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// string config = "MobileNetSSD_deploy.prototxt";
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// string classes;
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// // string classes = "coco.names";
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//
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// float confThreshold = 0.2f;
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// float nmsThreshold = 0.2f;
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// float scale = 2f / 255f;
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// Scalar mean = new Scalar (127.5, 127.5, 127.5);
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// bool swapRB = false;
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// int inpWidth = 300;
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// int inpHeight = 300;
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//
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// List<string> classNames = new List<string>(new string[]{"background",
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// "aeroplane", "bicycle", "bird", "boat",
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// "bottle", "bus", "car", "cat", "chair",
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// "cow", "diningtable", "dog", "horse",
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// "motorbike", "person", "pottedplant",
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// "sheep", "sofa", "train", "tvmonitor"
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// });
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// // List<string> classNames;
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// //ResnetSSDFaceDetection
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// string input = "grace_hopper_227.png";
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// // string input = "person.jpg";
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// string model = "res10_300x300_ssd_iter_140000.caffemodel";
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// string config = "deploy.prototxt";
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// // string model = "yolov2-tiny.weights";
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// // string config = "yolov2-tiny.cfg";
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// string classes;
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//
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//
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// float confThreshold = 0.5f;
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// float nmsThreshold = 0.5f;
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// float scale = 1f;
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// Scalar mean = new Scalar (104, 177, 123);
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// bool swapRB = false;
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// int inpWidth = 300;
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// int inpHeight = 300;
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//
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// List<string> classNames;
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List<string> classNames;
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List<string> outBlobNames;
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List<string> outBlobTypes;
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string classes_filepath;
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string input_filepath;
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string config_filepath;
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string model_filepath;
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#if UNITY_WEBGL && !UNITY_EDITOR
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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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#if UNITY_WEBGL && !UNITY_EDITOR
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getFilePath_Coroutine = GetFilePath ();
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StartCoroutine (getFilePath_Coroutine);
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#else
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classes_filepath = Utils.getFilePath ("dnn/" + classes);
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input_filepath = Utils.getFilePath ("dnn/" + input);
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config_filepath = Utils.getFilePath ("dnn/" + config);
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model_filepath = Utils.getFilePath ("dnn/" + model);
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Run ();
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#endif
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}
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#if UNITY_WEBGL && !UNITY_EDITOR
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private IEnumerator GetFilePath ()
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{
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if (!string.IsNullOrEmpty (classes)) {
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var getFilePathAsync_0_Coroutine = Utils.getFilePathAsync ("dnn/" + classes, (result) => {
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classes_filepath = result;
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});
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yield return getFilePathAsync_0_Coroutine;
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}
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if (!string.IsNullOrEmpty (input)) {
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var getFilePathAsync_1_Coroutine = Utils.getFilePathAsync ("dnn/" + input, (result) => {
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input_filepath = result;
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});
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yield return getFilePathAsync_1_Coroutine;
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}
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if (!string.IsNullOrEmpty (config)) {
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var getFilePathAsync_2_Coroutine = Utils.getFilePathAsync ("dnn/" + config, (result) => {
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config_filepath = result;
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});
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yield return getFilePathAsync_2_Coroutine;
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}
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if (!string.IsNullOrEmpty (model)) {
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var getFilePathAsync_3_Coroutine = Utils.getFilePathAsync ("dnn/" + model, (result) => {
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model_filepath = result;
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});
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yield return getFilePathAsync_3_Coroutine;
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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 (classes)) {
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classNames = readClassNames (classes_filepath);
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#if !UNITY_WSA_10_0
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if (classNames == null) {
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Debug.LogError (classes_filepath + " is not loaded. Please see \"StreamingAssets/dnn/setup_dnn_module.pdf\". ");
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}
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#endif
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} else if (classesList.Count > 0) {
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classNames = classesList;
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}
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Mat img = Imgcodecs.imread (input_filepath);
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#if !UNITY_WSA_10_0
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if (img.empty ()) {
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Debug.LogError (input_filepath + " is not loaded. Please see \"StreamingAssets/dnn/setup_dnn_module.pdf\". ");
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img = new Mat (424, 640, CvType.CV_8UC3, new Scalar (0, 0, 0));
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}
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#endif
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//Adust Quad.transform.localScale.
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gameObject.transform.localScale = new Vector3 (img.width (), img.height (), 1);
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Debug.Log ("Screen.width " + Screen.width + " Screen.height " + Screen.height + " Screen.orientation " + Screen.orientation);
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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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Camera.main.orthographicSize = (imageWidth * (float)Screen.height / (float)Screen.width) / 2;
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} else {
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Camera.main.orthographicSize = imageHeight / 2;
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}
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Net net = null;
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if (string.IsNullOrEmpty (config_filepath) || string.IsNullOrEmpty (model_filepath)) {
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Debug.LogError (config_filepath + " or " + model_filepath + " is not loaded. Please see \"StreamingAssets/dnn/setup_dnn_module.pdf\". ");
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} else {
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//! [Initialize network]
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net = Dnn.readNet (model_filepath, config_filepath);
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//! [Initialize network]
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}
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if (net == null) {
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Imgproc.putText (img, "model file is not loaded.", new Point (5, img.rows () - 30), Imgproc.FONT_HERSHEY_SIMPLEX, 0.7, new Scalar (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), 2, Imgproc.LINE_AA, false);
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} else {
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outBlobNames = getOutputsNames (net);
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// for (int i = 0; i < outBlobNames.Count; i++) {
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// Debug.Log ("names [" + i + "] " + outBlobNames [i]);
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// }
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outBlobTypes = getOutputsTypes (net);
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// for (int i = 0; i < outBlobTypes.Count; i++) {
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// Debug.Log ("types [" + i + "] " + outBlobTypes [i]);
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// }
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// Create a 4D blob from a frame.
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Size inpSize = new Size (inpWidth > 0 ? inpWidth : img.cols (),
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inpHeight > 0 ? inpHeight : img.rows ());
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Mat blob = Dnn.blobFromImage (img, scale, inpSize, mean, swapRB, false);
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// Run a model.
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net.setInput (blob);
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if (net.getLayer (new DictValue (0)).outputNameToIndex ("im_info") != -1) { // Faster-RCNN or R-FCN
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Imgproc.resize (img, img, inpSize);
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Mat imInfo = new Mat (1, 3, CvType.CV_32FC1);
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imInfo.put (0, 0, new float[] {
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(float)inpSize.height,
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(float)inpSize.width,
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1.6f
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});
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net.setInput (imInfo, "im_info");
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}
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TickMeter tm = new TickMeter ();
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tm.start ();
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List<Mat> outs = new List<Mat> ();
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net.forward (outs, outBlobNames);
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tm.stop ();
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Debug.Log ("Inference time, ms: " + tm.getTimeMilli ());
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postprocess (img, outs, net);
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for (int i = 0; i < outs.Count; i++) {
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outs [i].Dispose ();
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}
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blob.Dispose ();
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net.Dispose ();
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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.RGBA32, false);
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Utils.matToTexture2D (img, texture);
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gameObject.GetComponent<Renderer> ().material.mainTexture = texture;
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Utils.setDebugMode (false);
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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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}
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/// <summary>
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/// Raises the disable event.
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/// </summary>
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void OnDisable ()
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{
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#if UNITY_WEBGL && !UNITY_EDITOR
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if (getFilePath_Coroutine != null) {
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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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/// Reads the class names.
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/// </summary>
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/// <returns>The class names.</returns>
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/// <param name="filename">Filename.</param>
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private List<string> readClassNames (string filename)
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{
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List<string> classNames = new List<string> ();
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|
|||
|
|
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
|