add pedestrian detection demo

dnn_tutorial/pedestrian_demo.cpp

@@ -0,0 +1,41 @@
+#include <opencv2/opencv.hpp>
+#include <opencv2/dnn.hpp>
+#include <iostream>
+
+using namespace cv;
+using namespace cv::dnn;
+using namespace std;
+
+int main(int argc, char** argv) {
+	string inference_pb = "D:/pedestrian_data/test/frozen_inference_graph.pb";
+	string graph_txt = "D:/pedestrian_data/test/graph.pbtxt";
+	Net net = readNetFromTensorflow(inference_pb, graph_txt);
+	Mat image = imread("D:/python/Pedestrian-Detection/test_images/3600.jpg");
+	int h = image.rows;
+	int w = image.cols;
+	imshow("input", image);
+
+	Mat im_tensor = blobFromImage(image, 1.0, Size(300, 300), Scalar(), true, false);
+	net.setInput(im_tensor);
+	Mat cvOut = net.forward();
+	Mat detectOut(cvOut.size[2], cvOut.size[3], CV_32F, cvOut.ptr<float>());
+	for (int row = 0; row < detectOut.rows; row++) {
+		float confidence = detectOut.at<float>(row, 2);
+		if (confidence > 0.4) {
+			int left = detectOut.at<float>(row, 3) * w;
+			int top = detectOut.at<float>(row, 4) * h;
+			int right = detectOut.at<float>(row, 5) * w;
+			int bottom = detectOut.at<float>(row, 6) * h;
+
+			Rect rect;
+			rect.x = left;
+			rect.y = top;
+			rect.width = right - left;
+			rect.height = bottom - top;
+			rectangle(image, rect, Scalar(255, 0, 255), 2, 8, 0);
+		}
+	}
+	imshow("detection out", image);
+	waitKey(0);
+	return 0;
+}