Yolov5身份证检测——C++ OpenCV DNN推理

1.由于OpenCV DNN中的slice层不支持step为2，所以在转换模型时需要修改代码，修改的地方在models/common.py中Focus类

• 修改前：

class Focus(nn.Module):
    # Focus wh information into c-space
    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
        super(Focus, self).__init__()
        self.conv = Conv(c1 * 4, c2, k, s, p, g, act)

    def forward(self, x):  # x(b,c,w,h) -> y(b,4c,w/2,h/2)
        return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))

• 修改后

class Focus(nn.Module):
    # Focus wh information into c-space
    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
        super(Focus, self).__init__()
        self.conv = Conv(c1 * 4, c2, k, s, p, g, act)

    def forward(self, x):  # x(b,c,w,h) -> y(b,4c,w/2,h/2)
        #return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
        return self.conv(x)

2.转换模型

python models/export.py --weights runs/exp/weights/best.pt
# --weights: 训练得到的模型

运行后，onnx模型保存为了runs/exp/weights/best.onnx，这个模型就可以用OpenCV DNN进行推理。

3.DNN C++推理

#include <iostream>
#include <string>
#include <vector>
#include <fstream>
#include <sstream>
#include <opencv2/opencv.hpp>
#include <opencv2/dnn.hpp>


void imshow(std::string name, const cv::Mat& cv_src) 
{
	cv::namedWindow(name, 0);
	int max_rows = 800;
	int max_cols = 800;
	if (cv_src.rows >= cv_src.cols && cv_src.rows > max_rows)
	{
		cv::resizeWindow(name, cv::Size(cv_src.cols * max_rows / cv_src.rows, max_rows));
	}
	else if (cv_src.cols >= cv_src.rows && cv_src.cols > max_cols)
	{
		cv::resizeWindow(name, cv::Size(max_cols, cv_src.rows * max_cols / cv_src.cols));
	}
	cv::imshow(name, cv_src);
}

inline float sigmoid(float x)
{
	return 1.f / (1.f + exp(-x));
}

void sliceAndConcat(cv::Mat& img, cv::Mat* input)
{
	const float* srcData = img.ptr<float>();
	float* dstData = input->ptr<float>();
	using Vec12f = cv::Vec<float, 12>;
	for (int i = 0; i < input->size[2]; i++)
	{
		for (int j = 0; j < input->size[3]; j++)
		{
			for (int k = 0; k < 3; ++k)
			{
				dstData[k * input->size[2] * input->size[3] + i * input->size[3] + j] =
					srcData[k * img.size[2] * img.size[3] + 2 * i * img.size[3] + 2 * j];
			}
			for (int k = 0; k < 3; ++k)
			{
				dstData[(3 + k) * input->size[2] * input->size[3] + i * input->size[3] + j] =
					srcData[k * img.size[2] * img.size[3] + (2 * i + 1) * img.size[3] + 2 * j];
			}
			for (int k = 0; k < 3; ++k) 
			{
				dstData[(6 + k) * input->size[2] * input->size[3] + i * input->size[3] + j] =
					srcData[k * img.size[2] * img.size[3] + 2 * i * img.size[3] + 2 * j + 1];
			}
			for (int k = 0; k < 3; ++k)
			{
				dstData[(9 + k) * input->size[2] * input->size[3] + i * input->size[3] + j] =
					srcData[k * img.size[2] * img.size[3] + (2 * i + 1) * img.size[3] + 2 * j + 1];
			}
		}
	}
}

std::vector<cv::String> getOutputNames(const cv::dnn::Net& net)
{
	static std::vector<cv::String> names;
	if (names.empty())
	{
		std::vector<int> outLayers = net.getUnconnectedOutLayers();
		std::vector<cv::String> layersNames = net.getLayerNames();
		names.resize(outLayers.size());
		for (size_t i = 0; i < outLayers.size(); i++)
		{
			names[i] = layersNames[outLayers[i] - 1];
		}
	}
	return names;
}

void drawPred(int classId, float conf, int left, int top, int right, int bottom, cv::Mat& frame,
	const std::vector<std::string> &classes)
{
	cv::rectangle(frame, cv::Point(left, top), cv::Point(right, bottom), cv::Scalar(0, 255, 0), 3);
	std::string label = cv::format("%.2f", conf);
	if (!classes.empty()) {
		CV_Assert(classId < (int)classes.size());
		label = classes[classId] + ": " + label;
	}
	int baseLine;
	cv::Size labelSize = cv::getTextSize(label, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
	top = std::max(top, labelSize.height);
	cv::rectangle(frame, cv::Point(left, top - round(1.5 * labelSize.height)), cv::Point(left + round(1.5 * labelSize.width), top + baseLine), cv::Scalar(0, 255, 0), cv::FILLED);
	cv::putText(frame, label, cv::Point(left, top), cv::FONT_HERSHEY_SIMPLEX, 0.75, cv::Scalar(), 2);
}

void postprocess(cv::Mat& cv_src, std::vector<cv::Mat>& outs, const std::vector<std::string>& classes, int net_size) 
{
	float confThreshold = 0.4f;
	float nmsThreshold = 0.5f;
	std::vector<int> classIds;
	std::vector<float> confidences;
	std::vector<cv::Rect> boxes;
	int strides[] = { 8, 16, 32 };
	std::vector<std::vector<int> > anchors = 
	{
		{ 10,13, 16,30, 33,23 },
		{ 30,61, 62,45, 59,119 },
		{ 116,90, 156,198, 373,326 }
	};
	for (size_t k = 0; k < outs.size(); k++)
	{
		float* data = outs[k].ptr<float>();
		int stride = strides[k];
		int num_classes = outs[k].size[4] - 5;
		for (int i = 0; i < outs[k].size[2]; i++)
		{
			for (int j = 0; j < outs[k].size[3]; j++)
			{
				for (int a = 0; a < outs[k].size[1]; ++a)
				{
					float* record = data + a * outs[k].size[2] * outs[k].size[3] * outs[k].size[4] +
						i * outs[k].size[3] * outs[k].size[4] + j * outs[k].size[4];
					float* cls_ptr = record + 5;
					for (int cls = 0; cls < num_classes; cls++) 
					{
						float score = sigmoid(cls_ptr[cls]) * sigmoid(record[4]);
						if (score > confThreshold)
						{
							float cx = (sigmoid(record[0]) * 2.f - 0.5f + (float)j) * (float)stride;
							float cy = (sigmoid(record[1]) * 2.f - 0.5f + (float)i) * (float)stride;
							float w = pow(sigmoid(record[2]) * 2.f, 2) * anchors[k][2 * a];
							float h = pow(sigmoid(record[3]) * 2.f, 2) * anchors[k][2 * a + 1];
							float x1 = std::max(0, std::min(cv_src.cols, int((cx - w / 2.f) * (float)cv_src.cols / (float)net_size)));
							float y1 = std::max(0, std::min(cv_src.rows, int((cy - h / 2.f) * (float)cv_src.rows / (float)net_size)));
							float x2 = std::max(0, std::min(cv_src.cols, int((cx + w / 2.f) * (float)cv_src.cols / (float)net_size)));
							float y2 = std::max(0, std::min(cv_src.rows, int((cy + h / 2.f) * (float)cv_src.rows / (float)net_size)));
							classIds.push_back(cls);
							confidences.push_back(score);
							boxes.push_back(cv::Rect(cv::Point(x1, y1), cv::Point(x2, y2)));
						}
					}
				}
			}
		}
	}
	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, cv_src, classes);
	}
}

int main(int argc, char* argv[])
{
	std::string path = "images";
	std::vector<std::string> filenames;
	cv::glob(path, filenames, false);

	for (auto name : filenames)
	{
		cv::Mat cv_src = cv::imread(name);
		if (cv_src.empty())
		{
			continue;
		}

		std::vector<std::string> class_names{ "ida","idb" };
		
		int net_size = 640;
		cv::Mat blob = cv::dnn::blobFromImage(cv_src, 1.0 / 255, cv::Size(net_size, net_size),
			cv::Scalar(0, 0, 0), true, false);
		cv::dnn::Net net = cv::dnn::readNet("model/ODID_DNN.onnx");
		const int sz[] = { 1, 12, net_size / 2, net_size / 2 };
		cv::Mat input = cv::Mat(4, sz, blob.type());

		sliceAndConcat(blob, &input);
		net.setInput(input);
		auto t0 = cv::getTickCount();
		std::vector<cv::Mat> outs;
		net.forward(outs, getOutputNames(net));
		postprocess(cv_src, outs, class_names, net_size);
		auto t1 = cv::getTickCount();
		std::cout << "elapsed time: " << (t1 - t0) * 1000.0 / cv::getTickFrequency() << "ms" << std::endl;

		imshow("img", cv_src);
		cv::waitKey();
	}

	return 0;
}

检测一张图像大概要0.1秒左右。

4.模型和源码以上传优快云。地址：https://download.youkuaiyun.com/download/matt45m/46982918