opencv-122-DNN 实现图像分类

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知识点

使用ImageNet数据集支持1000分类的GoogleNet网络模型， 分别演示了Python与C++语言中的使用该模型实现图像分类标签预测。其中label标签是在一个单独的文本文件中读取，模型从上面的链接中下载即可。读取模型的API：

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Net cv::dnn::readNetFromCaffe(
    const String & 	prototxt,
    const String & 	caffeModel = String() 
)
prototxt表示模型的配置文件
caffeModel表示模型的权重二进制文件

使用模型实现预测的时候，需要读取图像作为输入，网络模型支持的输入数据是四维的输入，所以要把读取到的Mat对象转换为四维张量，OpenCV的提供的API为如下：

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Mat cv::dnn::blobFromImage(
    InputArray 	image,
    double 	scalefactor = 1.0,
    const Size & 	size = Size(),
    const Scalar & 	mean = Scalar(),
    bool 	swapRB = false,
    bool 	crop = false,
    int 	ddepth = CV_32F 
)
image输入图像
scalefactor 默认1.0
size表示网络接受的数据大小
mean表示训练时数据集的均值
swapRB 是否互换Red与Blur通道
crop剪切
ddepth 数据类型

代码（c++,python）

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#include <opencv2/opencv.hpp>
#include <opencv2/dnn.hpp>
#include <iostream>
#include <fstream>
/******************************************************
*
********************************************************/
using namespace cv;
using namespace cv::dnn;
using namespace std;

String labels_txt_file = "D:/projects/opencv_tutorial/data/models/inception5h/imagenet_comp_graph_label_strings.txt";
String tf_pb_file = "D:/projects/opencv_tutorial/data/models/inception5h/tensorflow_inception_graph.pb";
vector<String> readClassNames();
int main(int argc, char** argv) {
	Mat src = imread("D:/images/space_shuttle.jpg");
	if (src.empty()) {
		printf("could not load image...\n");
		return -1;
	}
	namedWindow("input", WINDOW_AUTOSIZE);
	imshow("input", src);
	vector<String> labels = readClassNames();

	Mat rgb;
	cvtColor(src, rgb, COLOR_BGR2RGB);
	int w = 224;
	int h = 224;

	// 加载网络
	Net net = readNetFromTensorflow(tf_pb_file);
	if (net.empty()) {
		printf("read caffe model data failure...\n");
		return -1;
	}
	Mat inputBlob = blobFromImage(src, 1.0f, Size(224, 224), Scalar(), true, false);
	inputBlob -= 117.0; // 均值

						// 执行图像分类
	Mat prob;
	net.setInput(inputBlob, "input");
	prob = net.forward("softmax2");

	// 得到最可能分类输出
	Mat probMat = prob.reshape(1, 1);
	Point classNumber;
	double classProb;
	minMaxLoc(probMat, NULL, &classProb, NULL, &classNumber);
	int classidx = classNumber.x;
	printf("\n current image classification : %s, possible : %.2f", labels.at(classidx).c_str(), classProb);

	// 显示文本
	putText(src, labels.at(classidx), Point(20, 20), FONT_HERSHEY_SIMPLEX, 1.0, Scalar(0, 0, 255), 2, 8);
	imshow("Image Classification", src);
	imwrite("D:/result.png", src);
	waitKey(0);
	return 0;
}

std::vector<String> readClassNames()
{
	std::vector<String> classNames;

	std::ifstream fp(labels_txt_file);
	if (!fp.is_open())
	{
		printf("could not open file...\n");
		exit(-1);
	}
	std::string name;
	while (!fp.eof())
	{
		std::getline(fp, name);
		if (name.length())
			classNames.push_back(name);
	}
	fp.close();
	return classNames;
}
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"""
DNN 实现图像分类
"""

import cv2 as cv
import numpy as np

bin_model = "bvlc_googlenet.caffemodel"
protxt = "bvlc_googlenet.prototxt"

# load names of classes
classes = None
with open("classification_classes_ILSVRC2012.txt", 'rt') as f:
    classes = f.read().rstrip('\n').split('\n')

# load CNN model
net = cv.dnn.readNetFromCaffe(protxt, bin_model)

# read input data
image = cv.imread("images/airplane.jpg")
blob = cv.dnn.blobFromImage(image, 1.0, (224, 224), (104, 117, 123),
                            False, False)
result = np.copy(image)
cv.imshow("input", image)

# run a model
net.setInput(blob)
out = net.forward()

# get a class with a highest score
out = out.flatten()
classId = np.argmax(out)
confidence = out[classId]

# put efficiency information
t, _ = net.getPerfProfile()
label = 'Inference time: %.2f ms' % (t * 1000.0 / cv.getTickFrequency())
cv.putText(result, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0))

# print predicted class
label = '%s : %.4f' % (classes[classId], confidence)
cv.putText(result, label, (50, 50), cv.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)
cv.imshow("googlenet-demo", result)

cv.waitKey(0)
cv.destroyAllWindows()

结果

代码地址

github