#if !UNITY_WSA_10_0 using OpenCVForUnity.CoreModule; using OpenCVForUnity.DnnModule; using OpenCVForUnity.ImgprocModule; using System; using System.Collections.Generic; using System.Text; using UnityEngine; using OpenCVRange = OpenCVForUnity.CoreModule.Range; using OpenCVRect = OpenCVForUnity.CoreModule.Rect; namespace OpenCVForUnityExample.DnnModel { /// /// Referring to https://github.com/opencv/opencv_zoo/tree/main/models/person_detection_mediapipe /// public class MediaPipePersonDetector { float nms_threshold; float score_threshold; int topK; int backend; int target; Size input_size = new Size(224, 224); Net person_detection_net; Mat anchors; Mat input_sizeMat; Mat maxSizeImg; Mat boxesMat; Mat boxes_m_c4; Mat confidences_m; MatOfRect2d boxes; MatOfFloat confidences; public MediaPipePersonDetector(string modelFilepath, float nmsThreshold = 0.3f, float scoreThreshold = 0.5f, int topK = 1, int backend = Dnn.DNN_BACKEND_OPENCV, int target = Dnn.DNN_TARGET_CPU) { // initialize if (!string.IsNullOrEmpty(modelFilepath)) { person_detection_net = Dnn.readNet(modelFilepath); } nms_threshold = Mathf.Clamp01(nmsThreshold); score_threshold = Mathf.Clamp01(scoreThreshold); this.topK = topK; this.backend = backend; this.target = target; person_detection_net.setPreferableBackend(this.backend); person_detection_net.setPreferableTarget(this.target); anchors = load_anchors(); } 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(0)); 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(0), -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); // Create a 4D blob from a frame. Mat blob = Dnn.blobFromImage(maxSizeImg, 1.0 / 255.0, input_size, Scalar.all(0), true, false, CvType.CV_32F); // HWC to NCHW, BGR to RGB int c = image.channels(); int h = (int)input_size.height; int w = (int)input_size.width; Mat blob_cxhxw = blob.reshape(1, new int[] { c, h, w });// [c, h, w] for (int i = 0; i < c; ++i) { Mat blob_1xhw = blob_cxhxw.row(i).reshape(1, 1);// [1, h, w] => [1, h * w] // # [0, 1] -> [-1, 1] Core.subtract(blob_1xhw, new Scalar(0.5), blob_1xhw); Core.multiply(blob_1xhw, new Scalar(2.0), blob_1xhw); } return blob;// [1, 3, 224, 224] } 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 person_detection_net.setInput(input_blob); List output_blob = new List(); person_detection_net.forward(output_blob, person_detection_net.getUnconnectedOutLayersNames()); // Postprocess Mat results = postprocess(output_blob); // scale_boxes float maxSize = Mathf.Max((float)image.size().width, (float)image.size().height); float x_factor = maxSize; float y_factor = maxSize; float x_shift = (maxSize - (float)image.size().width) / 2f; float y_shift = (maxSize - (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.Floor(results_arr[0] * x_factor - x_shift); float y1 = Mathf.Floor(results_arr[1] * y_factor - y_shift); float x2 = Mathf.Floor(results_arr[2] * x_factor - x_shift); float y2 = Mathf.Floor(results_arr[3] * y_factor - y_shift); results.put(i, 0, new float[] { x1, y1, x2, y2 }); float[] landmarks_arr = new float[8]; results.get(i, 4, landmarks_arr); for (int j = 0; j < 8; ++j) { if (j % 2 == 0) { landmarks_arr[j] = Mathf.Floor(landmarks_arr[j] * x_factor - x_shift); } else { landmarks_arr[j] = Mathf.Floor(landmarks_arr[j] * y_factor - y_shift); } } results.put(i, 4, landmarks_arr); } input_blob.Dispose(); for (int i = 0; i < output_blob.Count; i++) { output_blob[i].Dispose(); } return results; } protected virtual Mat postprocess(List output_blob) { int num = output_blob[0].size(1); Mat output_blob_1_numx1 = output_blob[1].reshape(1, num); Mat score = output_blob_1_numx1.colRange(new OpenCVRange(0, 1)); Mat output_blob_0_numx12 = output_blob[0].reshape(1, num); Mat box_delta = output_blob_0_numx12.colRange(new OpenCVRange(0, 4)); Mat landmark_delta = output_blob_0_numx12.colRange(new OpenCVRange(4, 12)); // get scores // # score = np.clip(score, -100, 100) numpy_clip(score, -100, 100); // # score = 1 / (1 + np.exp(-score)) Core.multiply(score, Scalar.all(1.0), score, -1.0); Core.exp(score, score); Core.add(score, Scalar.all(1.0), score); Core.divide(1.0, score, score); // get boxes Mat cxy_delta = box_delta.colRange(new OpenCVRange(0, 2)); Mat _cxy_delta_numx1_c2 = cxy_delta.reshape(2, cxy_delta.rows()); Core.divide(_cxy_delta_numx1_c2, new Scalar(input_size.width, input_size.height), _cxy_delta_numx1_c2); Mat wh_delta = box_delta.colRange(new OpenCVRange(2, 4)); Mat _wh_delta_numx1_c2 = wh_delta.reshape(2, wh_delta.rows()); Core.divide(_wh_delta_numx1_c2, new Scalar(input_size.width, input_size.height), _wh_delta_numx1_c2); if (boxesMat == null) boxesMat = new Mat(num, 4, CvType.CV_32FC1); Mat xy1 = boxesMat.colRange(new OpenCVRange(0, 2)); Mat xy2 = boxesMat.colRange(new OpenCVRange(2, 4)); Core.divide(wh_delta, new Scalar(2.0), wh_delta); Core.subtract(cxy_delta, wh_delta, xy1); Core.add(xy1, anchors, xy1); Core.add(cxy_delta, wh_delta, xy2); Core.add(xy2, anchors, xy2); // NMS if (boxes_m_c4 == null) boxes_m_c4 = new Mat(num, 1, CvType.CV_64FC4); if (confidences_m == null) confidences_m = new Mat(num, 1, CvType.CV_32FC1); if (boxes == null) boxes = new MatOfRect2d(boxes_m_c4); if (confidences == null) confidences = new MatOfFloat(confidences_m); Mat boxes_m_c1 = boxes_m_c4.reshape(1, num); boxesMat.convertTo(boxes_m_c1, CvType.CV_64F); score.copyTo(confidences_m); MatOfInt indices = new MatOfInt(); Dnn.NMSBoxes(boxes, confidences, score_threshold, nms_threshold, indices, 1f, topK); // get landmarks Mat results = new Mat(indices.rows(), 13, CvType.CV_32FC1); for (int i = 0; i < indices.total(); ++i) { int idx = (int)indices.get(i, 0)[0]; float[] bbox_arr = new float[4]; boxesMat.get(idx, 0, bbox_arr); results.put(i, 0, bbox_arr); float[] confidence_arr = new float[1]; confidences.get(idx, 0, confidence_arr); results.put(i, 12, confidence_arr); float[] landmarks_arr = new float[8]; landmark_delta.get(idx, 0, landmarks_arr); float[] anchors_arr = new float[2]; anchors.get(idx, 0, anchors_arr); for (int j = 0; j < 8; ++j) { if (j % 2 == 0) { landmarks_arr[j] /= (float)input_size.width; landmarks_arr[j] += anchors_arr[0]; } else { landmarks_arr[j] /= (float)input_size.height; landmarks_arr[j] += anchors_arr[1]; } } results.put(i, 4, landmarks_arr); } indices.Dispose(); // # TODO: still don't know the meaning of face bbox // # each landmark: hip center point; full body point; shoulder center point; upper body point; // # // # [ // # [face_bbox, landmarks, score] // # ... // # [face_bbox, landmarks, score] // # ] 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() < 13) return; StringBuilder sb = null; if (print_results) sb = new StringBuilder(); for (int i = 0; i < results.rows(); ++i) { float[] score = new float[1]; results.get(i, 12, score); float[] face_box = new float[4]; results.get(i, 0, face_box); float[] person_landmarks = new float[8]; results.get(i, 4, person_landmarks); float hip_point_x = person_landmarks[0]; float hip_point_y = person_landmarks[1]; float full_body_x = person_landmarks[2]; float full_body_y = person_landmarks[3]; float shoulder_point_x = person_landmarks[4]; float shoulder_point_y = person_landmarks[5]; float upper_body_x = person_landmarks[6]; float upper_body_y = person_landmarks[7]; // put score Imgproc.putText(image, String.Format("{0:0.0000}", score[0]), new Point(face_box[0], face_box[1] + 12), Imgproc.FONT_HERSHEY_DUPLEX, 0.5, new Scalar(0, 255, 0, 255)); // draw box Imgproc.rectangle(image, new Point(face_box[0], face_box[1]), new Point(face_box[2], face_box[3]), new Scalar(0, 255, 0, 255), 2); // draw circle for full body // # radius = np.linalg.norm(hip_point - full_body).astype(np.int32) Mat full_body_vector = new Mat(1, 1, CvType.CV_32FC2, new Scalar(hip_point_x - full_body_x, hip_point_y - full_body_y)); int radius = (int)Core.norm(full_body_vector); Imgproc.circle(image, new Point(hip_point_x, hip_point_y), radius, (isRGB) ? new Scalar(0, 0, 255, 255) : new Scalar(255, 0, 0, 255), 2); // draw circle for upper body // # radius = np.linalg.norm(shoulder_point - upper_body).astype(np.int32) Mat upper_body_vector = new Mat(1, 1, CvType.CV_32FC2, new Scalar(shoulder_point_x - upper_body_x, shoulder_point_y - upper_body_y)); radius = (int)Core.norm(upper_body_vector); Imgproc.circle(image, new Point(shoulder_point_x, shoulder_point_y), radius, (isRGB) ? new Scalar(255, 255, 0, 255) : new Scalar(0, 255, 255, 255), 2); // draw points for (int j = 0; j < person_landmarks.Length; j += 2) { Imgproc.circle(image, new Point(person_landmarks[j], person_landmarks[j + 1]), 2, (isRGB) ? new Scalar(255, 0, 0, 255) : new Scalar(0, 0, 255, 255), 2); } // Print results if (print_results) { sb.AppendLine(String.Format("-----------person {0}-----------", i + 1)); sb.AppendLine(String.Format("score: {0:0.00}", score[0])); sb.AppendLine(String.Format("face box: {0:0} {1:0} {2:0} {3:0}", face_box[0], face_box[1], face_box[2], face_box[3])); sb.Append("person landmarks: "); foreach (var p in person_landmarks) { sb.Append(String.Format("{0:0} ", p)); } sb.AppendLine(); } } if (print_results) Debug.Log(sb); } public virtual void dispose() { if (person_detection_net != null) person_detection_net.Dispose(); if (input_sizeMat != null) input_sizeMat.Dispose(); input_sizeMat = null; if (maxSizeImg != null) maxSizeImg.Dispose(); maxSizeImg = null; if (boxesMat != null) boxesMat.Dispose(); boxesMat = null; if (boxes_m_c4 != null) boxes_m_c4.Dispose(); if (confidences_m != null) confidences_m.Dispose(); if (boxes != null) boxes.Dispose(); if (confidences != null) confidences.Dispose(); boxes_m_c4 = null; confidences_m = null; boxes = null; confidences = null; } // OpenCV port of numpy.clip(a, a_min, a_max) in Python. // Restrict the range to [a_min, a_max]. protected virtual void numpy_clip(Mat a, double a_min, double a_max) { if (a == null) throw new ArgumentNullException("a"); if (a != null) a.ThrowIfDisposed(); // Bring values within [0, a_max - a_min]. Core.subtract(a, new Scalar(a_min), a); // Set negative values to 0. Imgproc.threshold(a, a, 0, 0, Imgproc.THRESH_TOZERO); // Set values above range to a_max - a_min. Imgproc.threshold(a, a, a_max - a_min, 0, Imgproc.THRESH_TRUNC); // Adjust the result to the desired range. Core.add(a, new Scalar(a_min), a); } protected virtual Mat load_anchors() { Mat anchors = new Mat(2254, 2, CvType.CV_32FC1); float[] anchors_arr = new float[] { 0.017857142857142856f, 0.017857142857142856f, 0.017857142857142856f, 0.017857142857142856f, 0.05357142857142857f, 0.017857142857142856f, 0.05357142857142857f, 0.017857142857142856f, 0.08928571428571429f, 0.017857142857142856f, 0.08928571428571429f, 0.017857142857142856f, 0.125f, 0.017857142857142856f, 0.125f, 0.017857142857142856f, 0.16071428571428573f, 0.017857142857142856f, 0.16071428571428573f, 0.017857142857142856f, 0.19642857142857142f, 0.017857142857142856f, 0.19642857142857142f, 0.017857142857142856f, 0.23214285714285715f, 0.017857142857142856f, 0.23214285714285715f, 0.017857142857142856f, 0.26785714285714285f, 0.017857142857142856f, 0.26785714285714285f, 0.017857142857142856f, 0.30357142857142855f, 0.017857142857142856f, 0.30357142857142855f, 0.017857142857142856f, 0.3392857142857143f, 0.017857142857142856f, 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0.19642857142857142f, 0.05357142857142857f, 0.19642857142857142f, 0.05357142857142857f, 0.23214285714285715f, 0.05357142857142857f, 0.23214285714285715f, 0.05357142857142857f, 0.26785714285714285f, 0.05357142857142857f, 0.26785714285714285f, 0.05357142857142857f, 0.30357142857142855f, 0.05357142857142857f, 0.30357142857142855f, 0.05357142857142857f, 0.3392857142857143f, 0.05357142857142857f, 0.3392857142857143f, 0.05357142857142857f, 0.375f, 0.05357142857142857f, 0.375f, 0.05357142857142857f, 0.4107142857142857f, 0.05357142857142857f, 0.4107142857142857f, 0.05357142857142857f, 0.44642857142857145f, 0.05357142857142857f, 0.44642857142857145f, 0.05357142857142857f, 0.48214285714285715f, 0.05357142857142857f, 0.48214285714285715f, 0.05357142857142857f, 0.5178571428571429f, 0.05357142857142857f, 0.5178571428571429f, 0.05357142857142857f, 0.5535714285714286f, 0.05357142857142857f, 0.5535714285714286f, 0.05357142857142857f, 0.5892857142857143f, 0.05357142857142857f, 0.5892857142857143f, 0.05357142857142857f, 0.625f, 0.05357142857142857f, 0.625f, 0.05357142857142857f, 0.6607142857142857f, 0.05357142857142857f, 0.6607142857142857f, 0.05357142857142857f, 0.6964285714285714f, 0.05357142857142857f, 0.6964285714285714f, 0.05357142857142857f, 0.7321428571428571f, 0.05357142857142857f, 0.7321428571428571f, 0.05357142857142857f, 0.7678571428571429f, 0.05357142857142857f, 0.7678571428571429f, 0.05357142857142857f, 0.8035714285714286f, 0.05357142857142857f, 0.8035714285714286f, 0.05357142857142857f, 0.8392857142857143f, 0.05357142857142857f, 0.8392857142857143f, 0.05357142857142857f, 0.875f, 0.05357142857142857f, 0.875f, 0.05357142857142857f, 0.9107142857142857f, 0.05357142857142857f, 0.9107142857142857f, 0.05357142857142857f, 0.9464285714285714f, 0.05357142857142857f, 0.9464285714285714f, 0.05357142857142857f, 0.9821428571428571f, 0.05357142857142857f, 0.9821428571428571f, 0.05357142857142857f, 0.017857142857142856f, 0.08928571428571429f, 0.017857142857142856f, 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