add new tutorial

hzyu-hub · Apr 8, 2018 · 31ac7d3 · 31ac7d3
1 parent eb182a5
commit 31ac7d3
Showing 1 changed file with 178 additions and 0 deletions.
diff --git a/dnn_tutorial/yolov3_demo.cpp b/dnn_tutorial/yolov3_demo.cpp
@@ -0,0 +1,178 @@
+#include <opencv2/opencv.hpp>
+#include <opencv2/dnn.hpp>
+
+#include <fstream>
+#include <iostream>
+#include <algorithm>
+#include <cstdlib>
+using namespace std;
+using namespace cv;
+using namespace cv::dnn;
+
+float confidenceThreshold = 0.25;
+void video_detection();
+void image_detection();
+int main(int argc, char** argv)
+{
+	image_detection();
+}
+
+void video_detection() {
+	String modelConfiguration = "D:/vcprojects/images/dnn/yolov2-tiny-voc/yolov2-tiny-voc.cfg";
+	String modelBinary = "D:/vcprojects/images/dnn/yolov2-tiny-voc/yolov2-tiny-voc.weights";
+	dnn::Net net = readNetFromDarknet(modelConfiguration, modelBinary);
+	if (net.empty())
+	{
+		printf("Could not load net...\n");
+		return;
+	}
+
+	vector<string> classNamesVec;
+	ifstream classNamesFile("D:/vcprojects/images/dnn/yolov2-tiny-voc/voc.names");
+	if (classNamesFile.is_open())
+	{
+		string className = "";
+		while (std::getline(classNamesFile, className))
+			classNamesVec.push_back(className);
+	}
+
+	// VideoCapture capture(0); 
+	VideoCapture capture;
+	capture.open("D:/vcprojects/images/fbb.avi");
+	if (!capture.isOpened()) {
+		printf("could not open the camera...\n");
+		return;
+	}
+
+	Mat frame;
+	while (capture.read(frame))
+	{
+		if (frame.empty())
+			if (frame.channels() == 4)
+				cvtColor(frame, frame, COLOR_BGRA2BGR);
+		Mat inputBlob = blobFromImage(frame, 1 / 255.F, Size(416, 416), Scalar(), true, false);
+		net.setInput(inputBlob, "data");
+		Mat detectionMat = net.forward("detection_out");
+		vector<double> layersTimings;
+		double freq = getTickFrequency() / 1000;
+		double time = net.getPerfProfile(layersTimings) / freq;
+		ostringstream ss;
+		ss << "FPS: " << 1000 / time << " ; time: " << time << " ms";
+		putText(frame, ss.str(), Point(20, 20), 0, 0.5, Scalar(0, 0, 255));
+
+		for (int i = 0; i < detectionMat.rows; i++)
+		{
+			const int probability_index = 5;
+			const int probability_size = detectionMat.cols - probability_index;
+			float *prob_array_ptr = &detectionMat.at<float>(i, probability_index);
+			size_t objectClass = max_element(prob_array_ptr, prob_array_ptr + probability_size) - prob_array_ptr;
+			float confidence = detectionMat.at<float>(i, (int)objectClass + probability_index);
+			if (confidence > confidenceThreshold)
+			{
+				float x = detectionMat.at<float>(i, 0);
+				float y = detectionMat.at<float>(i, 1);
+				float width = detectionMat.at<float>(i, 2);
+				float height = detectionMat.at<float>(i, 3);
+				int xLeftBottom = static_cast<int>((x - width / 2) * frame.cols);
+				int yLeftBottom = static_cast<int>((y - height / 2) * frame.rows);
+				int xRightTop = static_cast<int>((x + width / 2) * frame.cols);
+				int yRightTop = static_cast<int>((y + height / 2) * frame.rows);
+				Rect object(xLeftBottom, yLeftBottom,
+					xRightTop - xLeftBottom,
+					yRightTop - yLeftBottom);
+				rectangle(frame, object, Scalar(0, 255, 0));
+				if (objectClass < classNamesVec.size())
+				{
+					ss.str("");
+					ss << confidence;
+					String conf(ss.str());
+					String label = String(classNamesVec[objectClass]) + ": " + conf;
+					int baseLine = 0;
+					Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
+					rectangle(frame, Rect(Point(xLeftBottom, yLeftBottom),
+						Size(labelSize.width, labelSize.height + baseLine)),
+						Scalar(255, 255, 255), CV_FILLED);
+					putText(frame, label, Point(xLeftBottom, yLeftBottom + labelSize.height),
+						FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 0));
+				}
+			}
+		}
+		imshow("YOLOv3: Detections", frame);
+		if (waitKey(1) >= 0) break;
+	}
+}
+
+void image_detection() {
+	String modelConfiguration = "D:/vcprojects/images/dnn/yolov2-tiny-voc/yolov2-tiny-voc.cfg";
+	String modelBinary = "D:/vcprojects/images/dnn/yolov2-tiny-voc/yolov2-tiny-voc.weights";
+	dnn::Net net = readNetFromDarknet(modelConfiguration, modelBinary);
+	if (net.empty())
+	{
+		printf("Could not load net...\n");
+		return;
+	}
+	vector<string> classNamesVec;
+	ifstream classNamesFile("D:/vcprojects/images/dnn/yolov2-tiny-voc/voc.names");
+	if (classNamesFile.is_open())
+	{
+		string className = "";
+		while (std::getline(classNamesFile, className))
+			classNamesVec.push_back(className);
+	}
+
+	// ¼ÓÔØÍ¼Ïñ
+	Mat frame = imread("D:/vcprojects/images/fastrcnn.jpg");
+	Mat inputBlob = blobFromImage(frame, 1 / 255.F, Size(416, 416), Scalar(), true, false);
+	net.setInput(inputBlob, "data");
+
+	// ¼ì²â
+	Mat detectionMat = net.forward("detection_out");
+	vector<double> layersTimings;
+	double freq = getTickFrequency() / 1000;
+	double time = net.getPerfProfile(layersTimings) / freq;
+	ostringstream ss;
+	ss << "detection time: " << time << " ms";
+	putText(frame, ss.str(), Point(20, 20), 0, 0.5, Scalar(0, 0, 255));
+
+	// Êä³ö½á¹û
+	for (int i = 0; i < detectionMat.rows; i++)
+	{
+		const int probability_index = 5;
+		const int probability_size = detectionMat.cols - probability_index;
+		float *prob_array_ptr = &detectionMat.at<float>(i, probability_index);
+		size_t objectClass = max_element(prob_array_ptr, prob_array_ptr + probability_size) - prob_array_ptr;
+		float confidence = detectionMat.at<float>(i, (int)objectClass + probability_index);
+		if (confidence > confidenceThreshold)
+		{
+			float x = detectionMat.at<float>(i, 0);
+			float y = detectionMat.at<float>(i, 1);
+			float width = detectionMat.at<float>(i, 2);
+			float height = detectionMat.at<float>(i, 3);
+			int xLeftBottom = static_cast<int>((x - width / 2) * frame.cols);
+			int yLeftBottom = static_cast<int>((y - height / 2) * frame.rows);
+			int xRightTop = static_cast<int>((x + width / 2) * frame.cols);
+			int yRightTop = static_cast<int>((y + height / 2) * frame.rows);
+			Rect object(xLeftBottom, yLeftBottom,
+				xRightTop - xLeftBottom,
+				yRightTop - yLeftBottom);
+			rectangle(frame, object, Scalar(0, 0, 255), 2, 8);
+			if (objectClass < classNamesVec.size())
+			{
+				ss.str("");
+				ss << confidence;
+				String conf(ss.str());
+				String label = String(classNamesVec[objectClass]) + ": " + conf;
+				int baseLine = 0;
+				Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
+				rectangle(frame, Rect(Point(xLeftBottom, yLeftBottom),
+					Size(labelSize.width, labelSize.height + baseLine)),
+					Scalar(255, 255, 255), CV_FILLED);
+				putText(frame, label, Point(xLeftBottom, yLeftBottom + labelSize.height),
+					FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 0));
+			}
+		}
+	}
+	imshow("YOLO-Detections", frame);
+	waitKey(0);
+	return;
+}