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Health/Assets/OpenCVForUnity/Examples/MainModules/dnn/DnnObjectDetectionExample.cs

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#if !UNITY_WSA_10_0
using UnityEngine;
using UnityEngine.SceneManagement;
using System;
using System.Linq;
using System.Collections;
using System.Collections.Generic;
using OpenCVForUnity.CoreModule;
using OpenCVForUnity.DnnModule;
using OpenCVForUnity.ImgprocModule;
using OpenCVForUnity.UnityUtils;
using OpenCVForUnity.ImgcodecsModule;
namespace OpenCVForUnityExample
{
/// <summary>
/// Dnn ObjectDetection Example
/// Referring to https://github.com/opencv/opencv/blob/master/samples/dnn/object_detection.cpp
/// </summary>
public class DnnObjectDetectionExample : MonoBehaviour
{
[TooltipAttribute("Path to input image.")]
public string input;
[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 = 0.5f;
[TooltipAttribute("Non-maximum suppression threshold.")]
public float nmsThreshold = 0.4f;
[TooltipAttribute("Preprocess input image by multiplying on a scale factor.")]
public float scale = 1.0f;
[TooltipAttribute("Preprocess input image by subtracting mean values. Mean values should be in BGR order and delimited by spaces.")]
public Scalar mean = new Scalar(0, 0, 0, 0);
[TooltipAttribute("Indicate that model works with RGB input images instead BGR ones.")]
public bool swapRB = false;
[TooltipAttribute("Preprocess input image by resizing to a specific width.")]
public int inpWidth = 320;
[TooltipAttribute("Preprocess input image by resizing to a specific height.")]
public int inpHeight = 320;
protected List<string> classNames;
protected List<string> outBlobNames;
protected List<string> outBlobTypes;
protected string classes_filepath;
protected string input_filepath;
protected string config_filepath;
protected string model_filepath;
#if UNITY_WEBGL
protected IEnumerator getFilePath_Coroutine;
#endif
// Use this for initialization
protected virtual void Start()
{
#if UNITY_WEBGL
getFilePath_Coroutine = GetFilePath();
StartCoroutine(getFilePath_Coroutine);
#else
if (!string.IsNullOrEmpty(classes))
{
classes_filepath = Utils.getFilePath("OpenCVForUnity/dnn/" + classes);
if (string.IsNullOrEmpty(classes_filepath)) Debug.Log("The file:" + classes + " did not exist in the folder “Assets/StreamingAssets/OpenCVForUnity/dnn”.");
}
if (!string.IsNullOrEmpty(input))
{
input_filepath = Utils.getFilePath("OpenCVForUnity/dnn/" + input);
if (string.IsNullOrEmpty(input_filepath)) Debug.Log("The file:" + input + " did not exist in the folder “Assets/StreamingAssets/OpenCVForUnity/dnn”.");
}
if (!string.IsNullOrEmpty(config))
{
config_filepath = Utils.getFilePath("OpenCVForUnity/dnn/" + config);
if (string.IsNullOrEmpty(config_filepath)) Debug.Log("The file:" + config + " did not exist in the folder “Assets/StreamingAssets/OpenCVForUnity/dnn”.");
}
if (!string.IsNullOrEmpty(model))
{
model_filepath = Utils.getFilePath("OpenCVForUnity/dnn/" + model);
if (string.IsNullOrEmpty(model_filepath)) Debug.Log("The file:" + model + " did not exist in the folder “Assets/StreamingAssets/OpenCVForUnity/dnn”.");
}
Run();
#endif
}
#if UNITY_WEBGL
protected virtual IEnumerator GetFilePath()
{
if (!string.IsNullOrEmpty(classes))
{
var getFilePathAsync_0_Coroutine = Utils.getFilePathAsync("OpenCVForUnity/dnn/" + classes, (result) =>
{
classes_filepath = result;
});
yield return getFilePathAsync_0_Coroutine;
if (string.IsNullOrEmpty(classes_filepath)) Debug.Log("The file:" + classes + " did not exist in the folder “Assets/StreamingAssets/OpenCVForUnity/dnn”.");
}
if (!string.IsNullOrEmpty(input))
{
var getFilePathAsync_1_Coroutine = Utils.getFilePathAsync("OpenCVForUnity/dnn/" + input, (result) =>
{
input_filepath = result;
});
yield return getFilePathAsync_1_Coroutine;
if (string.IsNullOrEmpty(input_filepath)) Debug.Log("The file:" + input + " did not exist in the folder “Assets/StreamingAssets/OpenCVForUnity/dnn”.");
}
if (!string.IsNullOrEmpty(config))
{
var getFilePathAsync_2_Coroutine = Utils.getFilePathAsync("OpenCVForUnity/dnn/" + config, (result) =>
{
config_filepath = result;
});
yield return getFilePathAsync_2_Coroutine;
if (string.IsNullOrEmpty(config_filepath)) Debug.Log("The file:" + config + " did not exist in the folder “Assets/StreamingAssets/OpenCVForUnity/dnn”.");
}
if (!string.IsNullOrEmpty(model))
{
var getFilePathAsync_3_Coroutine = Utils.getFilePathAsync("OpenCVForUnity/dnn/" + model, (result) =>
{
model_filepath = result;
});
yield return getFilePathAsync_3_Coroutine;
if (string.IsNullOrEmpty(model_filepath)) Debug.Log("The file:" + model + " did not exist in the folder “Assets/StreamingAssets/OpenCVForUnity/dnn”.");
}
getFilePath_Coroutine = null;
Run();
}
#endif
// Use this for initialization
protected virtual 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 + " is not loaded. Please see “Assets/StreamingAssets/OpenCVForUnity/dnn/setup_dnn_module.pdf”.");
}
}
else if (classesList.Count > 0)
{
classNames = classesList;
}
Mat img = Imgcodecs.imread(input_filepath);
if (img.empty())
{
Debug.LogError(input + " is not loaded. Please see “Assets/StreamingAssets/OpenCVForUnity/dnn/setup_dnn_module.pdf”.");
img = new Mat(424, 640, CvType.CV_8UC3, new Scalar(0, 0, 0));
}
//Adust Quad.transform.localScale.
gameObject.transform.localScale = new Vector3(img.width(), img.height(), 1);
Debug.Log("Screen.width " + Screen.width + " Screen.height " + Screen.height + " Screen.orientation " + Screen.orientation);
float imageWidth = img.width();
float imageHeight = img.height();
float widthScale = (float)Screen.width / imageWidth;
float heightScale = (float)Screen.height / imageHeight;
if (widthScale < heightScale)
{
Camera.main.orthographicSize = (imageWidth * (float)Screen.height / (float)Screen.width) / 2;
}
else
{
Camera.main.orthographicSize = imageHeight / 2;
}
Net net = null;
if (string.IsNullOrEmpty(model_filepath))
{
Debug.LogError(model + " is not loaded. Please see “Assets/StreamingAssets/OpenCVForUnity/dnn/setup_dnn_module.pdf”.");
}
else
{
//! [Initialize network]
net = Dnn.readNet(model_filepath, config_filepath);
//! [Initialize network]
}
if (net == null)
{
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);
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);
}
else
{
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]);
//}
// Create a 4D blob from a frame.
Size inpSize = new Size(inpWidth > 0 ? inpWidth : img.cols(),
inpHeight > 0 ? inpHeight : img.rows());
Mat blob = Dnn.blobFromImage(img, scale, inpSize, mean, swapRB, false);
// Run a model.
net.setInput(blob);
if (net.getLayer(0).outputNameToIndex("im_info") != -1)
{ // Faster-RCNN or R-FCN
Imgproc.resize(img, img, 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(img, outs, net, Dnn.DNN_BACKEND_OPENCV);
for (int i = 0; i < outs.Count; i++)
{
outs[i].Dispose();
}
blob.Dispose();
net.Dispose();
}
Imgproc.cvtColor(img, img, Imgproc.COLOR_BGR2RGB);
Texture2D texture = new Texture2D(img.cols(), img.rows(), TextureFormat.RGBA32, false);
Utils.matToTexture2D(img, texture);
gameObject.GetComponent<Renderer>().material.mainTexture = texture;
Utils.setDebugMode(false);
}
// Update is called once per frame
protected virtual void Update()
{
}
/// <summary>
/// Raises the destroy event.
/// </summary>
protected virtual void OnDestroy()
{
#if UNITY_WEBGL
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>
/// Reads the class names.
/// </summary>
/// <returns>The class names.</returns>
/// <param name="filename">Filename.</param>
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;
}
/// <summary>
/// Postprocess the specified frame, outs and net.
/// </summary>
/// <param name="frame">Frame.</param>
/// <param name="outs">Outs.</param>
/// <param name="net">Net.</param>
/// <param name="backend">Backend.</param>
protected virtual void postprocess(Mat frame, List<Mat> outs, Net net, int backend = Dnn.DNN_BACKEND_OPENCV)
{
MatOfInt outLayers = net.getUnconnectedOutLayers();
string outLayerType = outBlobTypes[0];
List<int> classIdsList = new List<int>();
List<float> confidencesList = new List<float>();
List<Rect2d> boxesList = new List<Rect2d>();
if (net.getLayer(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]);
float left = data[3] * frame.cols();
float top = data[4] * frame.rows();
float right = data[5] * frame.cols();
float bottom = data[6] * frame.rows();
float width = right - left + 1f;
float height = bottom - top + 1f;
classIdsList.Add((int)(class_id) - 1); // Skip 0th background class id.
confidencesList.Add((float)confidence);
boxesList.Add(new Rect2d(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]);
float left = data[3] * frame.cols();
float top = data[4] * frame.rows();
float right = data[5] * frame.cols();
float bottom = data[6] * frame.rows();
float width = right - left + 1f;
float height = bottom - top + 1f;
classIdsList.Add((int)(class_id) - 1); // Skip 0th background class id.
confidencesList.Add((float)confidence);
boxesList.Add(new Rect2d(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)
{
float centerX = positionData[0] * frame.cols();
float centerY = positionData[1] * frame.rows();
float width = positionData[2] * frame.cols();
float height = positionData[3] * frame.rows();
float left = centerX - width / 2;
float top = centerY - height / 2;
classIdsList.Add(maxIdx);
confidencesList.Add((float)confidence);
boxesList.Add(new Rect2d(left, top, width, height));
}
}
}
}
else
{
Debug.Log("Unknown output layer type: " + outLayerType);
}
// NMS is used inside Region layer only on DNN_BACKEND_OPENCV for another backends we need NMS in sample
// or NMS is required if number of outputs > 1
if (outLayers.total() > 1 || (outLayerType == "Region" && backend != Dnn.DNN_BACKEND_OPENCV))
{
Dictionary<int, List<int>> class2indices = new Dictionary<int, List<int>>();
for (int i = 0; i < classIdsList.Count; i++)
{
if (confidencesList[i] >= confThreshold)
{
if (!class2indices.ContainsKey(classIdsList[i]))
class2indices.Add(classIdsList[i], new List<int>());
class2indices[classIdsList[i]].Add(i);
}
}
List<Rect2d> nmsBoxesList = new List<Rect2d>();
List<float> nmsConfidencesList = new List<float>();
List<int> nmsClassIdsList = new List<int>();
foreach (int key in class2indices.Keys)
{
List<Rect2d> localBoxesList = new List<Rect2d>();
List<float> localConfidencesList = new List<float>();
List<int> classIndicesList = class2indices[key];
for (int i = 0; i < classIndicesList.Count; i++)
{
localBoxesList.Add(boxesList[classIndicesList[i]]);
localConfidencesList.Add(confidencesList[classIndicesList[i]]);
}
using (MatOfRect2d localBoxes = new MatOfRect2d(localBoxesList.ToArray()))
using (MatOfFloat localConfidences = new MatOfFloat(localConfidencesList.ToArray()))
using (MatOfInt nmsIndices = new MatOfInt())
{
Dnn.NMSBoxes(localBoxes, localConfidences, confThreshold, nmsThreshold, nmsIndices);
for (int i = 0; i < nmsIndices.total(); i++)
{
int idx = (int)nmsIndices.get(i, 0)[0];
nmsBoxesList.Add(localBoxesList[idx]);
nmsConfidencesList.Add(localConfidencesList[idx]);
nmsClassIdsList.Add(key);
}
}
}
boxesList = nmsBoxesList;
classIdsList = nmsClassIdsList;
confidencesList = nmsConfidencesList;
}
for (int idx = 0; idx < boxesList.Count; ++idx)
{
Rect2d box = boxesList[idx];
drawPred(classIdsList[idx], confidencesList[idx], box.x, box.y,
box.x + box.width, box.y + box.height, frame);
}
}
/// <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>
protected virtual void drawPred(int classId, float conf, double left, double top, double right, double 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((float)top, (float)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>
protected virtual 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((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>
protected virtual 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((int)outLayers.get(i, 0)[0]).get_type());
}
outLayers.Dispose();
return types;
}
}
}
#endif
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