ImageRecognition/AmpereMeterALGO.cpp

#define _CRT_SECURE_NO_WARNINGS
#include <iostream>
#include <fstream>
#include <string>
#include <math.h>
#include <cmath>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/highgui/highgui.hpp>
#include "opencv2/imgproc/types_c.h"
#include <onnxruntime_cxx_api.h>
#include "AmpereMeterALGO.h"
#include <vector>

void AmpereMeterALGO::Init(bool isCuda)
{
	string model_path = "models/VoltageAmpereMeter.onnx";
	std::wstring widestr = std::wstring(model_path.begin(), model_path.end());
	sessionOptions.SetGraphOptimizationLevel(ORT_ENABLE_BASIC);
	ort_session = new Session(env, widestr.c_str(), sessionOptions);
	size_t numInputNodes = ort_session->GetInputCount();
	size_t numOutputNodes = ort_session->GetOutputCount();
	AllocatorWithDefaultOptions allocator;
	for (int i = 0; i < numInputNodes; i++)
	{
		input_names.push_back(ort_session->GetInputName(i, allocator));
		Ort::TypeInfo input_type_info = ort_session->GetInputTypeInfo(i);
		auto input_tensor_info = input_type_info.GetTensorTypeAndShapeInfo();
		auto input_dims = input_tensor_info.GetShape();
		input_node_dims.push_back(input_dims);
	}
	for (int i = 0; i < numOutputNodes; i++)
	{
		output_names.push_back(ort_session->GetOutputName(i, allocator));
		Ort::TypeInfo output_type_info = ort_session->GetOutputTypeInfo(i);
		auto output_tensor_info = output_type_info.GetTensorTypeAndShapeInfo();
		auto output_dims = output_tensor_info.GetShape();
		output_node_dims.push_back(output_dims);
	}
	this->inpHeight = input_node_dims[0][2];
	this->inpWidth = input_node_dims[0][3];
	this->outHeight = output_node_dims[0][2];
	this->outWidth = output_node_dims[0][3];
}

float AmpereMeterALGO::detect(Mat& srcimg)
{
	/*namedWindow("矩形", WINDOW_NORMAL);
	imshow("矩形", srcimg);
	waitKey(0);*/
	vector<float> input_image_ = { 1, 3, 512, 512 };
	Mat dstimg;
	Size resize_size(input_image_[2], input_image_[3]);
	resize(srcimg, dstimg, resize_size, 0, 0, cv::INTER_LINEAR);
	int channels = dstimg.channels();
	input_image_.resize((this->inpWidth * this->inpHeight * dstimg.channels()));

	for (int c = 0; c < channels; c++)
	{
		for (int i = 0; i < this->inpHeight; i++)
		{
			for (int j = 0; j < this->inpWidth; j++)
			{
				float pix = dstimg.ptr<uchar>(i)[j * 3 + 2 - c];
				input_image_[(c * this->inpHeight * this->inpWidth + i * this->inpWidth + j)] = (pix / 255.0 - mean[c]) / stds[c];
			}
		}
	}

	array<int64_t, 4> input_shape_{ 1, 3, this->inpHeight, this->inpWidth };
	auto allocator_info = MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU);
	Value input_tensor_ = Value::CreateTensor<float>(allocator_info, input_image_.data(), input_image_.size(), input_shape_.data(), input_shape_.size());

	// 开始推理
	vector<Value> ort_outputs = ort_session->Run(RunOptions{ nullptr }, &input_names[0], &input_tensor_, 1, output_names.data(), output_names.size());
	float* pred = ort_outputs[0].GetTensorMutableData<float>();
	Mat result(outHeight, outWidth, CV_32FC1, pred);
	result = 3 - result;
	result *= 255;
	result.convertTo(result, CV_8UC1);
	Mat binary;
	threshold(result, binary, 150, 255, THRESH_BINARY);//二值化阈值处理
	Mat result_line_image = creat_line_image(binary, 258, 128);
	/*namedWindow("矩形", WINDOW_NORMAL);
	imshow("矩形", result_line_image);
	waitKey(0);*/
	return get_meter_reader(result_line_image);
}

Mat AmpereMeterALGO::creat_line_image(const Mat& circle, int Radius, int RingStride)
{
	Mat rectangle;
	float theta;
	int rho;

	rectangle = Mat::zeros(Size(Radius * pi * 2, RingStride), CV_8UC1);
	int nl = rectangle.rows;   // number of lines					
	int nc = rectangle.cols * rectangle.channels();   // total number of elements per line
	for (int j = 0; j < nl; j++)
	{
		// get the address of row j
		try
		{
			uchar* data = rectangle.ptr<uchar>(j);
			for (int i = 0; i < nc; i++)
			{
				theta = pi * 2.0 / LINE_WIDTH * float(i + 1);
				rho = (Radius - j - 1);
				int position_y = (float)circle_center[0] + rho * (float)std::cos(theta) + 0.5;
				int position_x = (float)circle_center[1] - rho * (float)std::sin(theta) + 0.5;
				data[i] = circle.at<uchar>(position_y, position_x);
			}
		}
		catch (exception &e) { /* Please, at least do some logging or other error handling here*/ }
	}
	return rectangle;
}

//像素值提取及累加
float AmpereMeterALGO::get_meter_reader(const Mat& image)
{
	Mat histogram = Mat::zeros(Size(256, 1), CV_8UC1);    	 												 
	int rows = LINE_HEIGH;  	 //输入图像的行数
	int cols = LINE_WIDTH; 		 //输入图像的列数
	int scale_num = 0;
	int pointer_num = 0;
	int sum_horsum = 0;   //水平方向列相加和
	
	vector<int>num1;      
	vector<int>num2;     
	for (int c = 0; c < 1696; c++)
	{
		int versum = 0;
		for (int r = 0; r < rows; r++)
		{
			int index = int(image.at<uchar>(r, c));	
			versum += index;                        
		}
		if (versum != 0)
		{
			//int max_sum_horsum = 0;
			sum_horsum += versum;
			//cout << "和：" << sum_horsum << endl;
			num1.push_back(sum_horsum);
		}
		if (versum == 0)
		{
			int maxValue = 0;
			for (auto v : num1) 
			{
				if (maxValue < v) maxValue = v;
			}
			if (maxValue != 0)
			{
				//cout << "最大值：" << maxValue << endl;
				num2.push_back(maxValue);
			}
			sum_horsum = 0;            
			vector<int>().swap(num1);  
		}
	}

	//计算表盘读数
	int maxPosition = max_element(num2.begin(), num2.end()) - num2.begin();
    scale_num = num2.size();
	pointer_num = maxPosition ;
	float result_ratio = (1.0 * pointer_num  / scale_num );
	float result_value = (result_ratio * METER_RANGE);
	if (result_value > 0)
	{
		result_value += 2;
	}
	cout << "scale_num:" << scale_num << "  pointer_num:" << pointer_num << "  result_ratio:" << result_ratio << "  result_value:" << result_value << endl;
	return result_value;
}