基于阈值的分割算法opencv代码
时间: 2023-09-10 14:09:08 浏览: 51
以下是基于阈值的分割算法的OpenCV代码示例:
```python
import cv2
# 读取图像
img = cv2.imread('image.jpg')
# 将图像转为灰度图像
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 使用自适应阈值算法进行分割
thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)
# 显示分割结果
cv2.imshow('thresh', thresh)
cv2.waitKey(0)
cv2.destroyAllWindows()
```
首先读取图像,将其转为灰度图像。然后使用`cv2.adaptiveThreshold()`函数进行分割,该函数采用自适应阈值算法,可以自动调整阈值。最后将分割结果显示出来。
相关问题
实现灰度图像和彩色图像的迭代阈值分割算法opencv c++代码实现
以下是基于OpenCV的灰度图像和彩色图像迭代阈值分割算法的C++代码实现:
灰度图像迭代阈值分割:
```cpp
#include <opencv2/opencv.hpp>
#include <iostream>
using namespace cv;
using namespace std;
double thresholdValue(Mat& img) {
double T = 0;
double T_last = -1;
int rows = img.rows;
int cols = img.cols;
int total = rows * cols;
while(abs(T - T_last) > 1) {
double sum1 = 0, sum2 = 0, count1 = 0, count2 = 0;
for(int i = 0; i < rows; ++i) {
for(int j = 0; j < cols; ++j) {
if(img.at<uchar>(i, j) > T) {
sum1 += img.at<uchar>(i, j);
count1++;
} else {
sum2 += img.at<uchar>(i, j);
count2++;
}
}
}
T_last = T;
T = (sum1 / count1 + sum2 / count2) / 2;
}
return T;
}
int main() {
Mat img = imread("lena.jpg", IMREAD_GRAYSCALE);
if(img.empty()) {
cout << "Could not read the image" << endl;
return -1;
}
double T = thresholdValue(img);
Mat dst;
threshold(img, dst, T, 255, THRESH_BINARY);
namedWindow("Original Image", WINDOW_NORMAL);
namedWindow("Thresholded Image", WINDOW_NORMAL);
imshow("Original Image", img);
imshow("Thresholded Image", dst);
waitKey(0);
return 0;
}
```
彩色图像迭代阈值分割:
```cpp
#include <opencv2/opencv.hpp>
#include <iostream>
using namespace cv;
using namespace std;
double thresholdValue(Mat& img) {
double T = 0;
double T_last = -1;
int rows = img.rows;
int cols = img.cols;
int total = rows * cols;
while(abs(T - T_last) > 1) {
double sum1 = 0, sum2 = 0, count1 = 0, count2 = 0;
for(int i = 0; i < rows; ++i) {
for(int j = 0; j < cols; ++j) {
Vec3b pixel = img.at<Vec3b>(i, j);
int gray = (pixel[0] + pixel[1] + pixel[2]) / 3;
if(gray > T) {
sum1 += gray;
count1++;
} else {
sum2 += gray;
count2++;
}
}
}
T_last = T;
T = (sum1 / count1 + sum2 / count2) / 2;
}
return T;
}
int main() {
Mat img = imread("lena.jpg");
if(img.empty()) {
cout << "Could not read the image" << endl;
return -1;
}
double T = thresholdValue(img);
Mat dst;
cvtColor(img, dst, COLOR_BGR2GRAY);
threshold(dst, dst, T, 255, THRESH_BINARY);
namedWindow("Original Image", WINDOW_NORMAL);
namedWindow("Thresholded Image", WINDOW_NORMAL);
imshow("Original Image", img);
imshow("Thresholded Image", dst);
waitKey(0);
return 0;
}
```
基于区域分割算法代码
好的,以下是一个简单的基于区域分割算法的Python代码示例:
```python
import cv2
import numpy as np
# 读取图像
img = cv2.imread('test.jpg')
# 转换为灰度图像
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 阈值分割
ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
# 形态学操作,去除噪点和填充小洞
kernel = np.ones((3,3), np.uint8)
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=2)
# 膨胀操作,使得区域更加连通
sure_bg = cv2.dilate(opening, kernel, iterations=3)
# 距离变换
dist_transform = cv2.distanceTransform(opening, cv2.DIST_L2, 5)
ret, sure_fg = cv2.threshold(dist_transform, 0.7*dist_transform.max(), 255, 0)
# 找到未知区域
sure_fg = np.uint8(sure_fg)
unknown = cv2.subtract(sure_bg, sure_fg)
# 标记连通区域
ret, markers = cv2.connectedComponents(sure_fg)
# 将未知区域标记为0
markers[unknown==255] = 0
# Watershed算法分割图像
markers = cv2.watershed(img, markers)
img[markers == -1] = [255, 0, 0]
# 显示分割结果
cv2.imshow('Segmentation', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
```
这个代码示例使用了OpenCV库实现了基于区域分割算法的图像分割。具体步骤如下:
1. 读取图像并将其转换为灰度图像。
2. 使用Otsu阈值分割算法将图像二值化,并使用形态学操作去除噪点和填充小洞。
3. 对处理后的图像进行膨胀操作,使得区域更加连通。
4. 使用距离变换算法找到前景区域。
5. 找到未知区域。
6. 标记连通区域。
7. 使用Watershed算法分割图像。
8. 显示分割结果。