opencv-116-决策树算法 介绍与使用

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知识点

OpenCV中机器学习模块的决策树算法分为两个类别，一个是随机森林(Random Trees)、另外一个强化分类(Boosting Classification)。这两个算法都属于决策树算法。

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virtual float cv::ml::StatModel::predict(
    InputArray 	samples,
    OutputArray results = noArray(),
    int flags = 0 
)const
sample输入样本
result预测结果

代码（c++,python）

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#include <opencv2/opencv.hpp>
#include <iostream>

using namespace cv;
using namespace cv::ml;
using namespace std;

int main(int argc, char** argv) {
	Mat data = imread("D:/projects/opencv_tutorial/data/images/digits.png");
	Mat gray;
	cvtColor(data, gray, COLOR_BGR2GRAY);

	// 分割为5000个cells
	Mat images = Mat::zeros(5000, 400, CV_8UC1);
	Mat labels = Mat::zeros(5000, 1, CV_8UC1);

	int index = 0;
	Rect roi;
	roi.x = 0;
	roi.height = 1;
	roi.width = 400;
	for (int row = 0; row < 50; row++) {
		int label = row / 5;
		int offsety = row * 20;
		for (int col = 0; col < 100; col++) {
			int offsetx = col * 20;
			Mat digit = Mat::zeros(Size(20, 20), CV_8UC1);
			for (int sr = 0; sr < 20; sr++) {
				for (int sc = 0; sc < 20; sc++) {
					digit.at<uchar>(sr, sc) = gray.at<uchar>(sr + offsety, sc + offsetx);
				}
			}
			Mat one_row = digit.reshape(1, 1);
			printf("index : %d \n", index);
			roi.y = index;
			one_row.copyTo(images(roi));
			labels.at<uchar>(index, 0) = label;
			index++;
		}
	}
	printf("load sample hand-writing data...\n");
	imwrite("D:/result.png", images);

	// 转换为浮点数
	images.convertTo(images, CV_32FC1);
	labels.convertTo(labels, CV_32SC1);

	printf("load sample hand-writing data...\n");


	// 开始训练
	printf("Start to Random Trees train...\n");
	Ptr<RTrees> model = RTrees::create();
	/*model->setMaxDepth(10);
	model->setMinSampleCount(10);
	model->setRegressionAccuracy(0);
	model->setUseSurrogates(false);
	model->setMaxCategories(15);
	model->setPriors(Mat());
	model->setCalculateVarImportance(true);
	model->setActiveVarCount(4);
	*/
	TermCriteria tc = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 100, 0.01);
	model->setTermCriteria(tc);
	Ptr<ml::TrainData> tdata = ml::TrainData::create(images, ml::ROW_SAMPLE, labels);
	model->train(tdata);
	model->save("D:/vcworkspaces/rtrees_knowledge.yml");
	printf("Finished Random trees...\n");

	waitKey(0);
	return true;
}
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"""
决策树算法 介绍与使用
"""

import cv2 as cv
import numpy as np

# 读取数据
img = cv.imread('images/digits.png')
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
cells = [np.hsplit(row, 100) for row in np.vsplit(gray, 50)]
x = np.array(cells)

# 创建训练与测试数据
train = x[:, :50].reshape(-1, 400).astype(np.float32)
test = x[:, 50:100].reshape(-1, 400).astype(np.float32)
k = np.arange(10)
train_labels = np.repeat(k, 250)[:, np.newaxis]
test_labels = train_labels.copy()

# 训练随机树
dt = cv.ml.RTrees_create()

dt.train(train, cv.ml.ROW_SAMPLE, train_labels)
retval, results = dt.predict(test)

# 计算准确率
matches = results == test_labels
correct = np.count_nonzero(matches)
accuracy = correct / results.size
print(accuracy)

cv.waitKey(0)
cv.destroyAllWindows()

代码地址

github