Opencv C++ 调用 tensorflow 模型、caffe模型

最新推荐文章于 2025-04-28 14:14:16 发布

woha1yo

最新推荐文章于 2025-04-28 14:14:16 发布

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分类专栏： Tensorflow C++开发目标检测文章标签： tensorflow opencv 深度学习

本文链接：https://blog.youkuaiyun.com/woha1yo/article/details/117907025

版权

C++ Opencv 部署模型

调用 TensorFlow 模型

重要参考链接：

使用VS2015新建 空项目。
配置项目的包含目录，库目录和附加依赖项

新建源文件 main.cpp，内容如下：

#include<opencv2\opencv.hpp>
#include<opencv2\dnn.hpp>
#include <iostream>
#include<map>
#include<string>
#include<time.h>

using namespace std;
using namespace cv;


const char* classNames[]= {
     "background", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
"fire hydrant", "background", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "background", "backpack",
"umbrella", "background", "background", "handbag", "tie", "suitcase", "frisbee","skis", "snowboard", "sports ball", "kite", "baseball bat","baseball glove", "skateboard", "surfboard", "tennis racket",
"bottle", "background", "wine glass", "cup", "fork", "knife", "spoon","bowl", "banana",  "apple", "sandwich", "orange","broccoli", "carrot", "hot dog",  "pizza", "donut",
"cake", "chair", "couch", "potted plant", "bed", "background", "dining table", "background", "background", "toilet", "background","tv", "laptop", "mouse", "remote", "keyboard",
"cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "background","book", "clock", "vase", "scissors","teddy bear", "hair drier", "toothbrush"};

int main() 
{
     
	String weights = "models/frozen_inference_graph.pb";
	String prototxt = "models/ssd_mobilenet_v1_coco.pbtxt";
	const size_t width = 300;
	const size_t height = 300;

	VideoCapture capture;
	capture.open(0);
	namedWindow("input", CV_WINDOW_AUTOSIZE);
	int w = capture.get(CAP_PROP_FRAME_WIDTH);
	int h = capture.get(CAP_PROP_FRAME_HEIGHT);

	printf("frame width : %d, frame height : %d", w, h);

	// set up net
	dnn::Net net = cv::dnn::readNetFromTensorflow(weights, prototxt);

	Mat frame;
	/*
	while (1)	// 模式1：测试单张图像
	{
		frame = imread("models/car.jpg");
		imshow("input", frame);
	*/

	while (capture.read(frame))		// 模式2：调用摄像头
	{
     

		//预测
		cv::Mat inputblob = cv::dnn::blobFromImage(frame, 1. / 255, Size(width, height));
		net.setInput(inputblob);
		Mat output = net.forward();

		//检测
		Mat detectionMat(output.size[2], output.size[3], CV_32F, output.ptr<float>());
		float confidence_threshold = 0.5;
		for (int i = 0; i < detectionMat.rows; i++) {
     
			float confidence = detectionMat.at<float>(i, 2);
			if (confidence > confidence_threshold) {
     
				size_t objIndex = (size_t)(detectionMat.at<float>(i, 1));
				float tl_x = detectionMat.at<float>(i, 3) * frame.cols;
				float tl_y = detectionMat.at<float>(i, 4) * frame.rows;
				float br_x = detectionMat.at<float>(i, 5) * frame.cols;
				float br_y = detectionMat.at<float>(i, 6) * frame.rows;

				Rect object_box((int)tl_x, (int)tl_y, (int)(br_x - tl_x), (int)(br_y - tl_y));
				rectangle(frame, object_box, Scalar(0, 255, 0), 2, 8, 0);
				putText(frame, format("%s", classNames[objIndex