DNN tutorial

data/models/yolov2-tiny-voc/voc.names

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+aeroplane
+bicycle
+bird
+boat
+bottle
+bus
+car
+cat
+chair
+cow
+diningtable
+dog
+horse
+motorbike
+person
+pottedplant
+sheep
+sofa
+train
+tvmonitor

data/models/yolov2-tiny-voc/yolov2-tiny-voc.cfg

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+[net]
+# Testing
+batch=1
+subdivisions=1
+# Training
+# batch=64
+# subdivisions=2
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.001
+max_batches = 40200
+policy=steps
+steps=-1,100,20000,30000
+scales=.1,10,.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=16
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=1
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+###########
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=125
+activation=linear
+
+[region]
+anchors = 1.08,1.19,  3.42,4.41,  6.63,11.38,  9.42,5.11,  16.62,10.52
+bias_match=1
+classes=20
+coords=4
+num=5
+softmax=1
+jitter=.2
+rescore=1
+
+object_scale=5
+noobject_scale=1
+class_scale=1
+coord_scale=1
+
+absolute=1
+thresh = .6
+random=1

dnn_tutorial/resnet_demo.cpp

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+#include <opencv2/dnn.hpp>
+#include <opencv2/imgproc.hpp>
+#include <opencv2/highgui.hpp>
+
+using namespace cv;
+using namespace cv::dnn;
+
+#include <iostream>
+#include <cstdlib>
+using namespace std;
+
+const size_t inWidth = 300;
+const size_t inHeight = 300;
+const double inScaleFactor = 1.0;
+const Scalar meanVal(104.0, 177.0, 123.0);
+const float confidenceThreshold = 0.5;
+int main(int argc, char** argv)
+{
+	String modelDesc = "D:/vcprojects/images/dnn/face/deploy.prototxt";
+	String modelBinary = "D:/vcprojects/images/dnn/face/res10_300x300_ssd_iter_140000.caffemodel";
+
+	// 初始化网络
+	dnn::Net net = readNetFromCaffe(modelDesc, modelBinary);
+	if (net.empty())
+	{
+		printf("could not load net...\n");
+		return -1;
+	}
+
+	// 打开摄像头
+	VideoCapture capture(0);
+	if (!capture.isOpened()) {
+		printf("could not load camera...\n");
+		return -1;
+	}
+
+	Mat frame;
+	int index = 0;
+	while (capture.read(frame)) {
+		if (frame.empty())
+		{
+			waitKey();
+			break;
+		}
+		// 水平镜像调整
+		flip(frame, frame, 1);
+		imshow("input", frame);
+		if (frame.channels() == 4);
+		cvtColor(frame, frame, COLOR_BGRA2BGR);
+
+		// 输入数据调整
+		Mat inputBlob = blobFromImage(frame, inScaleFactor,
+			Size(inWidth, inHeight), meanVal, false, false);
+		net.setInput(inputBlob, "data");
+
+		// 人脸检测
+		Mat detection = net.forward("detection_out");
+		vector<double> layersTimings;
+		double freq = getTickFrequency() / 1000;
+		double time = net.getPerfProfile(layersTimings) / freq;
+		Mat detectionMat(detection.size[2], detection.size[3], CV_32F, detection.ptr<float>());
+
+		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++)
+		{
+			// 置信度 0～1之间
+			float confidence = detectionMat.at<float>(i, 2);
+			if (confidence > confidenceThreshold)
+			{
+				int xLeftBottom = static_cast<int>(detectionMat.at<float>(i, 3) * frame.cols);
+				int yLeftBottom = static_cast<int>(detectionMat.at<float>(i, 4) * frame.rows);
+				int xRightTop = static_cast<int>(detectionMat.at<float>(i, 5) * frame.cols);
+				int yRightTop = static_cast<int>(detectionMat.at<float>(i, 6) * frame.rows);
+
+				Rect object((int)xLeftBottom, (int)yLeftBottom,
+					(int)(xRightTop - xLeftBottom),
+					(int)(yRightTop - yLeftBottom));
+
+				rectangle(frame, object, Scalar(0, 255, 0));
+
+				ss.str("");
+				ss << confidence;
+				String conf(ss.str());
+				String label = "Face: " + conf;
+				int baseLine = 0;
+				Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
+				rectangle(frame, Rect(Point(xLeftBottom, yLeftBottom - labelSize.height),
+					Size(labelSize.width, labelSize.height + baseLine)),
+					Scalar(255, 255, 255), CV_FILLED);
+				putText(frame, label, Point(xLeftBottom, yLeftBottom),
+					FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 0));
+			}
+		}
+		index++;
+		imwrite(format("D:/gloomyfish/picture/face_0%d.png", index), frame);
+		imshow("dnn_face_detection", frame);
+		if (waitKey(1) >= 0) break;
+	}
+
+	waitKey(0);
+	return 0;
+}