WINDOWS下 YOLOV3/YOLOV4 CPU 檢測驗證

 

實踐環境：

VS2015 + OPENCV4.5 （release build）

cpu ：i7 

對比了 darket 和 opencv dnn 

darknet：

 ./darknet_no_gpu.exe detector test data/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights -i 0 -thresh 0.25 data/dog.jpg --img-size 416 -ext_output

opencv-dnn：

#include <iostream>
#include <cstdlib>
#include <fstream>
#include<opencv2\dnn\dnn.hpp>
#include<opencv2\core.hpp>
#include<opencv2\highgui\highgui.hpp>
#include<opencv2\imgproc\imgproc.hpp>



using namespace cv;
using namespace cv::dnn;
using namespace std;


// Initialize the parameters
float confThreshold = 0.5; // Confidence threshold
float nmsThreshold = 0.4;  // Non-maximum suppression threshold
int inpWidth = 416;        // Width of network's input image
int inpHeight = 416;       // Height of network's input imag
std::vector<std::string> classes;


						   // Draw the predicted bounding box
void drawPred(int classId, float conf, int left, int top, int right, int bottom, cv::Mat& frame)
{
	//Draw a rectangle displaying the bounding box
	cv::rectangle(frame, cv::Point(left, top), cv::Point(right, bottom), cv::Scalar(0, 0, 255));

	//Get the label for the class name and its confidence
	std::string label = cv::format("%.2f", conf);
	if (!classes.empty())
	{
		CV_Assert(classId < (int)classes.size());
		label = classes[classId] + ":" + label;
	}
	else
	{
		std::cout << "classes is empty..." << std::endl;
	}

	//Display the label at the top of the bounding box
	int baseLine;
	cv::Size labelSize = cv::getTextSize(label, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
	top = std::max(top, labelSize.height);
	cv::putText(frame, label, cv::Point(left, top), cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(255, 255, 255));
}
						   // Remove the bounding boxes with low confidence using non-maxima suppression
void postprocess(cv::Mat& frame, std::vector<cv::Mat>& outs)
{
	std::vector<int> classIds;
	std::vector<float> confidences;
	std::vector<cv::Rect> boxes;

	for (size_t i = 0; i < outs.size(); ++i)
	{
		// Scan through all the bounding boxes output from the network and keep only the
		// ones with high confidence scores. Assign the box's class label as the class
		// with the highest score for the box.
		float* data = (float*)outs[i].data;
		for (int j = 0; j < outs[i].rows; ++j, data += outs[i].cols)
		{
			cv::Mat scores = outs[i].row(j).colRange(5, outs[i].cols);
			cv::Point classIdPoint;
			double confidence;
			// Get the value and location of the maximum score
			cv::minMaxLoc(scores, 0, &confidence, 0, &classIdPoint);

			if (confidence > confThreshold)
			{
				int centerX = (int)(data[0] * frame.cols);
				int centerY = (int)(data[1] * frame.rows);
				int width = (int)(data[2] * frame.cols);
				int height = (int)(data[3] * frame.rows);
				int left = centerX - width / 2;
				int top = centerY - height / 2;

				classIds.push_back(classIdPoint.x);
				confidences.push_back((float)confidence);
				boxes.push_back(cv::Rect(left, top, width, height));
			}
		}
	}


	// Perform non maximum suppression to eliminate redundant overlapping boxes with
	// lower confidences
	std::vector<int> indices;
	cv::dnn::NMSBoxes(boxes, confidences, confThreshold, nmsThreshold, indices);
	for (size_t i = 0; i < indices.size(); ++i)
	{
		int idx = indices[i];
		cv::Rect box = boxes[idx];
		drawPred(classIds[idx], confidences[idx], box.x, box.y,
			box.x + box.width, box.y + box.height, frame);
	}
}


 // Get the names of the output layers
std::vector<cv::String> getOutputsNames(const cv::dnn::Net& net)
{
	static std::vector<cv::String> names;
	if (names.empty())
	{
		//Get the indices of the output layers, i.e. the layers with unconnected outputs
		std::vector<int> outLayers = net.getUnconnectedOutLayers();

		//get the names of all the layers in the network
		std::vector<cv::String> layersNames = net.getLayerNames();

		// Get the names of the output layers in names
		names.resize(outLayers.size());
		for (size_t i = 0; i < outLayers.size(); ++i)
			names[i] = layersNames[outLayers[i] - 1];
	}
	return names;
}


int main()
{
	// Give the configuration and weight files for the model
	string modelConfiguration = "D:\\yolov3\\cfg\\yolov3.cfg";
	string modelWeights = "D:\\yolov3\\yolov3.weights";
	// Load names of classes
	string classesFile = "D:\\yolov3\\data\\coco.names";
	ifstream ifs(classesFile.c_str());
	string line;
	clock_t start, finish;



	while (getline(ifs, line))
	{
		classes.push_back(line);
	}
	//Load the network
	Net net = readNetFromDarknet(modelConfiguration, modelWeights);
	cv::Mat blob;
	Mat frame0;
	//Load test image
	for (int i = 0; i < 100; ++i) {
		Mat frame = imread("D:\\yolov3\\data\\dog.jpg");
		//start time
		start = clock();
		//Create a 4D blob from a frame. 建立神經網路輸入影像                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               
		blobFromImage(frame, blob, 1 / 255.0, cv::Size(inpWidth, inpHeight), Scalar(0, 0, 0), true, false);

		//Sets the input to the network 設定輸出
		net.setInput(blob);
		//Runs the forward pass to get output of the output layers 獲取輸出層結果
		vector<Mat> outs;
		net.forward(outs, getOutputsNames(net));
		//Remove the bounding boxes with low confidence
		postprocess(frame, outs);
		finish = clock();
		cout << "Run time is " << double(finish - start) / CLOCKS_PER_SEC << endl;
		frame0 = frame;
	}
	//Put efficiency information. The function getPerfProfile returns the overall time for inference(t) and the timings for each of the layers(in layersTimes)
	//輸出前向傳播的時間
	vector<double> layersTimes;
	double freq = getTickFrequency() / 1000;
	double t = net.getPerfProfile(layersTimes) / freq;
	string label = format("Inference time for a frame : %.2f ms", t);
	putText(frame0, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 255));
	imshow("result", frame0);
	//儲存影像
	imwrite("D:\\yolov3\\result.jpg", frame0);

	cv::waitKey(0);

}

檢測結果 上看，幾位速度快的自行車都不要了。 唉。 yolov3-tiny opencv dnn 還多認了一輛跑車。

 

再看看  yolov3 opencv dnn