otsu阈值分割代码python
时间: 2023-11-09 13:00:08 浏览: 57
下面是 python 中使用 Otsu 阈值分割的代码:
```python
import cv2
# 读入图像
img = cv2.imread('image.jpg', 0)
# 使用 Otsu 阈值法自动计算合适的阈值
ret, th = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
# 显示结果
cv2.imshow('Otsu threshold', th)
cv2.waitKey(0)
cv2.destroyAllWindows()
```
其中,`cv2.threshold()` 函数中的参数含义如下:
- `img`: 输入图像,灰度图。
- `0`: 阈值初始值,通常为 0。
- `255`: 阈值最大值,通常为 255。
- `cv2.THRESH_BINARY + cv2.THRESH_OTSU`: 阈值类型,使用 Otsu 阈值分割。
相关问题
otsu阈值分割算法python
OTSU阈值分割算法是一种对图像进行二值化的高效算法,由日本学者OTSU于1979年提出。在OpenCV中,可以使用Python来实现OTSU阈值分割算法。以下是一个示例的Python代码:
```python
import cv2
import numpy as np
from matplotlib import pyplot as plt
image = cv2.imread("E:/python/cv/OTSU/test.jpg")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
ret1, th1 = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU)
plt.subplot(131), plt.imshow(image, "gray")
plt.title("source image"), plt.xticks([]), plt.yticks([])
plt.subplot(132), plt.hist(image.ravel(), 256)
plt.title("Histogram"), plt.xticks([]), plt.yticks([])
plt.subplot(133), plt.imshow(th1, "gray")
plt.title("OTSU,
#### 引用[.reference_title]
- *1* *3* [阈值化分割(二)OTSU法-附Python实现](https://blog.csdn.net/u010128736/article/details/52801310)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 50%"]
- *2* [Opencv之Otsu阈值处理(Python)](https://blog.csdn.net/qq_44116998/article/details/124645657)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 50%"]
[ .reference_list ]
Otsu阈值分割代码
Otsu阈值分割是一种常用的图像分割方法,用于将图像分成背景和前景两部分。下面是一个简单的Otsu阈值分割的代码示例:
```python
import cv2
# 读取图像
image = cv2.imread('image.jpg', 0)
# 计算图像的直方图
histogram = cv2.calcHist([image], [0], None, [256], [0, 256])
# 归一化直方图
histogram /= histogram.sum()
# 初始化类间方差和最佳阈值
inter_class_variance = 0
best_threshold = 0
# 遍历所有可能的阈值
for threshold in range(256):
# 计算背景和前景的像素概率和均值
background_probability = histogram[:threshold].sum()
foreground_probability = histogram[threshold:].sum()
background_mean = (histogram[:threshold] * range(threshold)).sum() / background_probability
foreground_mean = (histogram[threshold:] * range(threshold, 256)).sum() / foreground_probability
# 计算类间方差
current_inter_class_variance = background_probability * foreground_probability * (background_mean - foreground_mean) ** 2
# 更新最佳阈值和类间方差
if current_inter_class_variance > inter_class_variance:
inter_class_variance = current_inter_class_variance
best_threshold = threshold
# 应用最佳阈值进行二值化
binary_image = (image > best_threshold).astype('uint8') * 255
# 显示结果
cv2.imshow('Original Image', image)
cv2.imshow('Binary Image', binary_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
```
这段代码使用OpenCV库实现了Otsu阈值分割。首先,读取图像并计算图像的直方图。然后,遍历所有可能的阈值,计算背景和前景的像素概率和均值,并根据类间方差更新最佳阈值。最后,应用最佳阈值进行二值化,并显示结果。