void Start()
{
graph_filepath = Utils.getFilePath("dnn/graph1.pb");
webCamTextureToMatHelper = gameObject.GetComponent <WebCamTextureToMatHelper>();
webCamTextureToMatHelper.Initialize();
Mat img = webCamTextureToMatHelper.GetMat();
if (!string.IsNullOrEmpty(graph_filepath))
{
net = Dnn.readNetFromTensorflow(graph_filepath);
}
if (net == null)
{
Imgproc.putText(img, "model file is not loaded.", new Point(5, img.rows() - 30), Core.FONT_HERSHEY_SIMPLEX, 0.7, new Scalar(255, 255, 255), 2, Imgproc.LINE_AA, false);
}
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;
}
}
// Use this for initialization
void Run()
{
//if true, The error log of the Native side OpenCV will be displayed on the Unity Editor Console.
Utils.setDebugMode(true);
net = Dnn.readNetFromTensorflow(tensorflow_inception_graph_pb_filepath);
#if !UNITY_WSA_10_0
if (net.empty())
{
Debug.LogError("model file is not loaded. The model and class names list can be downloaded here: \"https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip\". Please copy to “Assets/StreamingAssets/dnn/” folder. ");
}
#endif
classes = readClassNames(imagenet_comp_graph_label_strings_txt_filepath);
#if !UNITY_WSA_10_0
if (classes == null)
{
Debug.LogError("class names list file is not loaded. The model and class names list can be downloaded here: \"https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip\". Please copy to “Assets/StreamingAssets/dnn/” folder. ");
}
#endif
#if UNITY_ANDROID && !UNITY_EDITOR
// Avoids the front camera low light issue that occurs in only some Android devices (e.g. Google Pixel, Pixel2).
webCamTextureToMatHelper.avoidAndroidFrontCameraLowLightIssue = true;
#endif
webCamTextureToMatHelper.Initialize();
}
// Start is called before the first frame update
void Start()
{
answerText = GameObject.Find("Text");
mnist_dataset_idx = 0;
mnist_dataset = read_mnist_dataset();
string model_filepath = Utils.getFilePath(MODEL_FILE_PATH);
net = Dnn.readNetFromTensorflow(model_filepath);
if (net.empty())
{
Debug.LogError("model file is not loaded.");
}
show_image();
predict();
}
// Use this for initialization
void Start()
{
net = Dnn.readNetFromTensorflow(Utils.getFilePath("dnn/tensorflow_inception_graph.pb"));
#if !UNITY_WSA_10_0
if (net.empty())
{
Debug.LogError("model file is not loaded.The model and class names list can be downloaded here: \"https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip\".Please copy to “Assets/StreamingAssets/dnn/” folder. ");
}
#endif
classes = readClassNames(Utils.getFilePath("dnn/imagenet_comp_graph_label_strings.txt"));
#if !UNITY_WSA_10_0
if (classes == null)
{
Debug.LogError("class names list file is not loaded.The model and class names list can be downloaded here: \"https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip\".Please copy to “Assets/StreamingAssets/dnn/” folder. ");
}
#endif
webCamTextureToMatHelper = gameObject.GetComponent <WebCamTextureToMatHelper> ();
webCamTextureToMatHelper.Initialize();
}
// Use this for initialization
void Run()
{
//if true, The error log of the Native side OpenCV will be displayed on the Unity Editor Console.
Utils.setDebugMode(true);
net = Dnn.readNetFromTensorflow(tensorflow_inception_graph_pb_filepath);
#if !UNITY_WSA_10_0
if (net.empty())
{
Debug.LogError("model file is not loaded.The model and class names list can be downloaded here: \"https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip\".Please copy to “Assets/StreamingAssets/dnn/” folder. ");
}
#endif
classes = readClassNames(imagenet_comp_graph_label_strings_txt_filepath);
#if !UNITY_WSA_10_0
if (classes == null)
{
Debug.LogError("class names list file is not loaded.The model and class names list can be downloaded here: \"https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip\".Please copy to “Assets/StreamingAssets/dnn/” folder. ");
}
#endif
#if UNITY_ANDROID && !UNITY_EDITOR
// Set the requestedFPS parameter to avoid the problem of the WebCamTexture image becoming low light on some Android devices. (Pixel, pixel 2)
// https://forum.unity.com/threads/android-webcamtexture-in-low-light-only-some-models.520656/
// https://forum.unity.com/threads/released-opencv-for-unity.277080/page-33#post-3445178
rearCameraRequestedFPS = webCamTextureToMatHelper.requestedFPS;
if (webCamTextureToMatHelper.requestedIsFrontFacing)
{
webCamTextureToMatHelper.requestedFPS = 15;
webCamTextureToMatHelper.Initialize();
}
else
{
webCamTextureToMatHelper.Initialize();
}
#else
webCamTextureToMatHelper.Initialize();
#endif
}
private void LoadDetector()
{
if (handDetectionMode == 0)
{
var cascadeFileName = Utils.getFilePath("palm.xml");
cascadeDetector = new CascadeClassifier();
cascadeDetector.load(cascadeFileName);
if (cascadeDetector.empty())
{
Debug.LogError("cascade file is not loaded. Please copy from “OpenCVForUnity/StreamingAssets/” to “Assets/StreamingAssets/” folder. ");
}
}
else if (handDetectionMode == 2)
{
var modelPath = Utils.getFilePath("frozen_inference_graph.pb");
var configPath = Utils.getFilePath("frozen_inference_graph.pbtxt");
tfDetector = Dnn.readNetFromTensorflow(modelPath, configPath);
if (tfDetector.empty())
{
Debug.Log("tf detector is empty");
}
}
}
// Use this for initialization
void Run()
{
//if true, The error log of the Native side OpenCV will be displayed on the Unity Editor Console.
Utils.setDebugMode(true);
net = Dnn.readNetFromTensorflow(tensorflow_inception_graph_pb_filepath);
#if !UNITY_WSA_10_0
if (net.empty())
{
Debug.LogError("model file is not loaded.The model and class names list can be downloaded here: \"https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip\".Please copy to “Assets/StreamingAssets/dnn/” folder. ");
}
#endif
classes = readClassNames(imagenet_comp_graph_label_strings_txt_filepath);
#if !UNITY_WSA_10_0
if (classes == null)
{
Debug.LogError("class names list file is not loaded.The model and class names list can be downloaded here: \"https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip\".Please copy to “Assets/StreamingAssets/dnn/” folder. ");
}
#endif
webCamTextureToMatHelper.Initialize();
}
void Run(string jpg_path)
{
Utils.setDebugMode(true);
Mat img = Imgcodecs.imread(jpg_path);
gameObject.transform.localScale = new Vector3(img.width(), img.height(), 1);
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(graph_filepath))
{
net = Dnn.readNetFromTensorflow(graph_filepath);
}
if (net == null)
{
Imgproc.putText(img, "Model file is not loaded.", new Point(5, img.rows() - 30), Core.FONT_HERSHEY_SIMPLEX, 0.7, new Scalar(255, 255, 255), 2, Imgproc.LINE_AA, false);
}
else
{
float frameWidth = img.cols();
float frameHeight = img.rows();
Mat input = Dnn.blobFromImage(img, 1.0, new Size(inWidth, inHeight), new Scalar(0, 0, 0), false, false);
net.setInput(input, "image");
Mat output = net.forward("Openpose/concat_stage7");
output = output.reshape(1, 57);
List <Point> points = new List <Point>();
for (int i = 0; i < BODY_PARTS.Count; i++)
{
Mat heatMap = output.row(i).reshape(1, 46);
Core.MinMaxLocResult result = Core.minMaxLoc(heatMap);
heatMap.Dispose();
double x = (frameWidth * result.maxLoc.x) / 46;
double y = (frameHeight * result.maxLoc.y) / 46;
if (result.maxVal > 0.3)
{
points.Add(new Point(x, y));
}
else
{
points.Add(null);
}
}
for (int i = 0; i < POSE_PAIRS.GetLength(0); i++)
{
string partFrom = POSE_PAIRS[i, 0];
string partTo = POSE_PAIRS[i, 1];
int idFrom = BODY_PARTS[partFrom];
int idTo = BODY_PARTS[partTo];
if (points[idFrom] != null && points[idTo] != null)
{
Imgproc.line(img, points[idFrom], points[idTo], new Scalar(0, 255, 0), 3);
Imgproc.ellipse(img, points[idFrom], new Size(3, 3), 0, 0, 360, new Scalar(0, 0, 255), Core.FILLED);
Imgproc.ellipse(img, points[idTo], new Size(3, 3), 0, 0, 360, new Scalar(0, 0, 255), Core.FILLED);
}
}
}
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);
}
// Use this for initialization
void Run()
{
//if true, The error log of the Native side OpenCV will be displayed on the Unity Editor Console.
Utils.setDebugMode(true);
classNames = readClassNames(classes_filepath);
if (classNames == null)
{
Debug.LogError(classes_filepath + " is not loaded. Please see \"StreamingAssets/dnn/setup_dnn_module.pdf\". ");
}
classColors = new List <Scalar> ();
for (int i = 0; i < classNames.Count; i++)
{
classColors.Add(new Scalar(UnityEngine.Random.Range(0, 255), UnityEngine.Random.Range(0, 255), UnityEngine.Random.Range(0, 255)));
}
Mat img = Imgcodecs.imread(image_filepath);
if (img.empty())
{
Debug.LogError(image_filepath + " is not loaded. Please see \"StreamingAssets/dnn/setup_dnn_module.pdf\". ");
img = new Mat(height, width, 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) || string.IsNullOrEmpty(config_filepath))
{
Debug.LogError(model_filepath + " or " + config_filepath + " is not loaded. Please see \"StreamingAssets/dnn/setup_dnn_module.pdf\". ");
}
else
{
net = Dnn.readNetFromTensorflow(model_filepath, config_filepath);
}
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
{
float frameWidth = img.cols();
float frameHeight = img.rows();
Mat blob = Dnn.blobFromImage(img, 1.0, new Size(width, height), new Scalar(0, 0, 0), true, false);
net.setInput(blob);
List <Mat> outputBlobs = new List <Mat> ();
List <string> outputName = new List <string> ();
outputName.Add("detection_out_final");
outputName.Add("detection_masks");
net.forward(outputBlobs, outputName);
Mat boxes = outputBlobs [0];
Mat masks = outputBlobs [1];
//int numClasses = masks.size (1);
//int numDetections = boxes.size (2);
Debug.Log("boxes.size(0) " + boxes.size(0));
Debug.Log("boxes.size(1) " + boxes.size(1));
Debug.Log("boxes.size(2) " + boxes.size(2));
Debug.Log("boxes.size(3) " + boxes.size(3));
Debug.Log("masks.size(0) " + masks.size(0));
Debug.Log("masks.size(1) " + masks.size(1));
Debug.Log("masks.size(2) " + masks.size(2));
Debug.Log("masks.size(3) " + masks.size(3));
//reshape from 4D to two 2D.
float[] data = new float[boxes.size(3)];
boxes = boxes.reshape(1, (int)boxes.total() / boxes.size(3));
// Debug.Log ("boxes.ToString() " + boxes.ToString ());
//reshape from 4D to two 2D.
float[] mask_data = new float[masks.size(2) * masks.size(3)];
masks = masks.reshape(1, (int)masks.total() / (masks.size(2) * masks.size(3)));
// Debug.Log ("masks.ToString(): " + masks.ToString ());
for (int i = 0; i < boxes.rows(); i++)
{
boxes.get(i, 0, data);
float score = data [2];
if (score > thr)
{
int class_id = (int)(data [1]);
float left = (float)(data [3] * frameWidth);
float top = (float)(data [4] * frameHeight);
float right = (float)(data [5] * frameWidth);
float bottom = (float)(data [6] * frameHeight);
left = (int)Mathf.Max(0, Mathf.Min(left, frameWidth - 1));
top = (int)Mathf.Max(0, Mathf.Min(top, frameHeight - 1));
right = (int)Mathf.Max(0, Mathf.Min(right, frameWidth - 1));
bottom = (int)Mathf.Max(0, Mathf.Min(bottom, frameHeight - 1));
Debug.Log("class_id: " + class_id + " class_name " + classNames [class_id] + " left: " + left + " top: " + top + " right: " + right + " bottom: " + bottom);
//draw masks
masks.get((i * 90) + class_id, 0, mask_data);
Mat objectMask = new Mat(15, 15, CvType.CV_32F);
Utils.copyToMat <float> (mask_data, objectMask);
Imgproc.resize(objectMask, objectMask, new Size(right - left + 1, bottom - top + 1));
Core.compare(objectMask, new Scalar(mask_thr), objectMask, Core.CMP_GT);
// Debug.Log ("objectMask.ToString(): " + objectMask.ToString ());
// Debug.Log ("objectMask.dump(): " + objectMask.dump ());
Mat roi = new Mat(img, new OpenCVForUnity.CoreModule.Rect(new Point(left, top), new Point(right + 1, bottom + 1)));
Mat coloredRoi = new Mat(roi.size(), CvType.CV_8UC3);
Imgproc.rectangle(coloredRoi, new Point(0, 0), new Point(coloredRoi.width(), coloredRoi.height()), classColors [class_id], -1);
Core.addWeighted(coloredRoi, 0.7, roi, 0.3, 0, coloredRoi);
// Debug.Log ("coloredRoi.ToString(): " + coloredRoi.ToString ());
// Debug.Log ("roi.ToString(): " + roi.ToString ());
coloredRoi.copyTo(roi, objectMask);
coloredRoi.Dispose();
objectMask.Dispose();
//draw boxes
Imgproc.rectangle(img, new Point(left, top), new Point(right, bottom), new Scalar(0, 255, 0), 2);
string label = score.ToString();
if (classNames != null && classNames.Count != 0)
{
if (class_id < (int)classNames.Count)
{
label = classNames [class_id] + ": " + 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(img, new Point(left, top - labelSize.height),
new Point(left + labelSize.width, top + baseLine [0]), Scalar.all(255), Core.FILLED);
Imgproc.putText(img, label, new Point(left, top), Imgproc.FONT_HERSHEY_SIMPLEX, 0.5, new Scalar(0, 0, 0, 255));
}
}
boxes.Dispose();
masks.Dispose();
blob.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;
net.Dispose();
Utils.setDebugMode(false);
}
private void LoadModel()
{
string model_filepath = Utils.getFilePath(MODEL_FILE_PATH);
this.net = Dnn.readNetFromTensorflow(model_filepath);
}
// Use this for initialization
void Run()
{
//if true, The error log of the Native side OpenCV will be displayed on the Unity Editor Console.
Utils.setDebugMode(true);
classNames = readClassNames(classes_filepath);
if (classNames == null)
{
Debug.LogError(classes_filepath + " is not loaded. Please see \"StreamingAssets/dnn/setup_dnn_module.pdf\". ");
}
classColors = new List <Scalar>();
for (int i = 0; i < classNames.Count; i++)
{
classColors.Add(new Scalar(UnityEngine.Random.Range(0, 255), UnityEngine.Random.Range(0, 255), UnityEngine.Random.Range(0, 255)));
}
Mat img = Imgcodecs.imread(image_filepath);
if (img.empty())
{
Debug.LogError(image_filepath + " is not loaded. Please see \"StreamingAssets/dnn/setup_dnn_module.pdf\". ");
img = new Mat(height, width, 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) || string.IsNullOrEmpty(config_filepath))
{
Debug.LogError(model_filepath + " or " + config_filepath + " is not loaded. Please see \"StreamingAssets/dnn/setup_dnn_module.pdf\". ");
}
else
{
net = Dnn.readNetFromTensorflow(model_filepath, config_filepath);
}
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
{
float frameW = img.cols();
float frameH = img.rows();
// Create a 4D blob from a frame.
Mat blob = Dnn.blobFromImage(img, 1.0, new Size(width, height), new Scalar(0, 0, 0), true, false);
//Run a model
net.setInput(blob);
List <Mat> outputBlobs = new List <Mat>();
List <string> outputName = new List <string>();
outputName.Add("detection_out_final");
outputName.Add("detection_masks");
TickMeter tm = new TickMeter();
tm.start();
net.forward(outputBlobs, outputName);
tm.stop();
Debug.Log("Inference time, ms: " + tm.getTimeMilli());
Mat boxes = outputBlobs[0];
Mat masks = outputBlobs[1];
//int numClasses = masks.size(1);
int numDetections = boxes.size(2);
int mask_sizeH = masks.size(2);
int mask_sizeW = masks.size(3);
float[] box_data = new float[boxes.size(3)];
float[] mask_data = new float[masks.size(2) * masks.size(3)];
for (int i = 0; i < numDetections; i++)
{
boxes.get(new int[] { 0, 0, i, 0 }, box_data);
float score = box_data[2];
if (score > thr)
{
int classId = (int)box_data[1];
float left = (int)frameW * box_data[3];
float top = (int)frameH * box_data[4];
float right = (int)frameW * box_data[5];
float bottom = (int)frameH * box_data[6];
left = (int)Mathf.Max(0, Mathf.Min(left, frameW - 1));
top = (int)Mathf.Max(0, Mathf.Min(top, frameH - 1));
right = (int)Mathf.Max(0, Mathf.Min(right, frameW - 1));
bottom = (int)Mathf.Max(0, Mathf.Min(bottom, frameH - 1));
masks.get(new int[] { i, classId, 0, 0 }, mask_data);
Mat classMask = new Mat(mask_sizeH, mask_sizeW, CvType.CV_32F);
classMask.put(0, 0, mask_data);
Imgproc.resize(classMask, classMask, new Size(right - left + 1, bottom - top + 1));
Core.compare(classMask, new Scalar(0.5), classMask, Core.CMP_GT);
Mat roi = new Mat(img, new OpenCVForUnity.CoreModule.Rect(new Point(left, top), new Point(right + 1, bottom + 1)));
Mat coloredRoi = new Mat(roi.size(), CvType.CV_8UC3);
Imgproc.rectangle(coloredRoi, new Point(0, 0), new Point(coloredRoi.width(), coloredRoi.height()), classColors[classId], -1);
Core.addWeighted(coloredRoi, 0.7, roi, 0.3, 0, coloredRoi);
coloredRoi.copyTo(roi, classMask);
coloredRoi.Dispose();
classMask.Dispose();
drawPred(classId, score, left, top, right, bottom, img);
Debug.Log("classId:" + classId + " cnof:" + score + " l:" + left + " t:" + top + " r:" + right + " b:" + bottom);
}
}
boxes.Dispose();
masks.Dispose();
blob.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;
net.Dispose();
Utils.setDebugMode(false);
}
public void Run()
{
//if true, The error log of the Native side OpenCV will be displayed on the Unity Editor Console.
Utils.setDebugMode(true);
classNames = readClassNames(classes_filepath);
if (classNames == null)
{
Debug.LogError(classes_filepath +
" is not loaded. Please see \"StreamingAssets/dnn/setup_dnn_module.pdf\". ");
}
//Mat img = new Mat(imageTex.height, imageTex.width, CvType.);
//Utils.texture2DToMat(imageTex, img);
Mat img = Imgcodecs.imread(input_filepath);
if (img.empty())
{
Debug.LogError(input_filepath + " is not loaded. Please see \"StreamingAssets/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) || string.IsNullOrEmpty(config_filepath))
{
Debug.LogError(model_filepath + " or " + config_filepath +
" is not loaded. Please see \"StreamingAssets/dnn/setup_dnn_module.pdf\". ");
}
else
{
net = Dnn.readNetFromTensorflow(model_filepath, config_filepath);
}
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);
outBlobTypes = getOutputsTypes(net);
Mat blob = Dnn.blobFromImage(img, 0.007843, new Size(300, 300), new Scalar(127.5, 127.5, 127.5));
net.setInput(blob);
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);
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);
}
}