ubuntu22.04@laptop OpenCV Get Started: 009_image_thresholding

ubuntu22.04@laptop OpenCV Get Started: 009_image_thresholding

  • [1. 源由](#1. 源由)
  • [2. image_thresholding应用Demo](#2. image_thresholding应用Demo)
    • [2.1 C++应用Demo](#2.1 C++应用Demo)
    • [2.2 Python应用Demo](#2.2 Python应用Demo)
  • [3. 重点分析](#3. 重点分析)
    • [3.1 Binary Thresholding ( THRESH_BINARY )](#3.1 Binary Thresholding ( THRESH_BINARY ))
    • [3.2 Inverse-Binary Thresholding ( THRESH_BINARY_INV )](#3.2 Inverse-Binary Thresholding ( THRESH_BINARY_INV ))
    • [3.3 Truncate Thresholding ( THRESH_TRUNC )](#3.3 Truncate Thresholding ( THRESH_TRUNC ))
    • [3.4 Threshold to Zero ( THRESH_TOZERO )](#3.4 Threshold to Zero ( THRESH_TOZERO ))
    • [3.5 Inverted Threshold to Zero ( THRESH_TOZERO_INV )](#3.5 Inverted Threshold to Zero ( THRESH_TOZERO_INV ))
  • [4. 总结](#4. 总结)
  • [5. 参考资料](#5. 参考资料)
  • [6. 补充](#6. 补充)

1. 源由

阈值过滤也是OpenCV图像最基本的操作之一。

其主要方法就是:

  1. 通过一个阈值(阈值)来判断数据的有效性
  2. 通过加强对比度来让肉眼更易识别图像

比如:一张灰度图上,当灰度相近似的时候,肉眼其实很难判断出来。但是通过阈值判断和加强,就可以非常容易的让肉眼轻易识别图形。

2. image_thresholding应用Demo

009_image_thresholding是OpenCV通过阈值对图像过滤的示例程序。

2.1 C++应用Demo

C++应用Demo工程结构:

复制代码
009_image_thresholding/CPP$ tree .
.
├── CMakeLists.txt
├── image_threshold.cpp
└── threshold.png

0 directories, 3 files

确认OpenCV安装路径:

复制代码
$ find /home/daniel/ -name "OpenCVConfig.cmake"
/home/daniel/OpenCV/installation/opencv-4.9.0/lib/cmake/opencv4/
/home/daniel/OpenCV/opencv/build/OpenCVConfig.cmake
/home/daniel/OpenCV/opencv/build/unix-install/OpenCVConfig.cmake


$ export OpenCV_DIR=/home/daniel/OpenCV/installation/opencv-4.9.0/lib/cmake/opencv4/

C++应用Demo工程编译执行:

复制代码
$ mkdir build
$ cd build
$ cmake ..
$ cmake --build . --config Release
$ cd ..
$ ./build/image_threshold

2.2 Python应用Demo

Python应用Demo工程结构:

复制代码
009_image_thresholding/Python$ tree .
.
├── image_threshold.py
├── requirements.txt
└── threshold.png

0 directories, 3 files

Python应用Demo工程执行:

复制代码
$ workoncv-4.9.0
$ python image_threshold.py

3. 重点分析

3.1 Binary Thresholding ( THRESH_BINARY )

过滤规则:阈值两端极化操作

复制代码
# Binary Threshold
if src(x,y) > thresh
  dst(x,y) = maxValue
else
  dst(x,y) = 0

C++:

复制代码
// Thresholding with threshold value set 127 
threshold(src,dst,127,255, THRESH_BINARY); 

Python:

复制代码
# Thresholding with threshold value set 127 
th, dst = cv2.threshold(src,127,255, cv2.THRESH_BINARY) 

3.2 Inverse-Binary Thresholding ( THRESH_BINARY_INV )

过滤规则:阈值两端反向极化操作

复制代码
# Inverse Binary Threshold
if src(x,y) > thresh
  dst(x,y) = 0
else
  dst(x,y) = maxValue

C++:

复制代码
// Thresholding using THRESH_BINARY_INV 
threshold(src,dst,127,255, THRESH_BINARY_INV); 

Python:

复制代码
# Thresholding using THRESH_BINARY_INV 
th, dst = cv2.threshold(src,127,255, cv2.THRESH_BINARY_INV) 

3.3 Truncate Thresholding ( THRESH_TRUNC )

过滤规则:超过阈值截断操作

复制代码
# Truncate Threshold
if src(x,y) > thresh
  dst(x,y) = thresh
else
  dst(x,y) = src(x,y)

C++:

复制代码
// Thresholding using THRESH_TRUNC 
threshold(src,dst,127,255, THRESH_TRUNC); 

Python:

复制代码
# Thresholding using THRESH_TRUNC 
th, dst = cv2.threshold(src,127,255, cv2.THRESH_TRUNC) 

3.4 Threshold to Zero ( THRESH_TOZERO )

过滤规则:低于阈值归零

复制代码
# Threshold to Zero
if src(x,y) > thresh
  dst(x,y) = src(x,y)
else
  dst(x,y) = 0

C++:

复制代码
// Thresholding using THRESH_TOZERO 
threshold(src,dst,127,255, THRESH_TOZERO); 

Python:

复制代码
# Thresholding using THRESH_TOZERO 
th, dst = cv2.threshold(src,127,255, cv2.THRESH_TOZERO) 

3.5 Inverted Threshold to Zero ( THRESH_TOZERO_INV )

过滤规则:超过阈值归零

复制代码
# Inverted Threshold to Zero
if src(x,y) > thresh
  dst(x,y) = 0
else
  dst(x,y) = src(x,y)

C++:

复制代码
// Thresholding using THRESH_TOZERO_INV 
threshold(src,dst,127,255, THRESH_TOZERO_INV); 

Python:

复制代码
# Thresholding using THRESH_TOZERO_INV 
th, dst = cv2.threshold(src,127,255, cv2.THRESH_TOZERO_INV) 

4. 总结

前面《ubuntu22.04@laptop OpenCV Get Started: 008_image_filtering_using_convolution》对图像进行卷积的计算机操作,从而对数据进行有效性过滤。

本文通过对图像进行阈值的计算机操作,从而对数据进行有效性过滤,在特定的场景下,依然能够实现很好的图像数据分析作用。

  • src Source array (single-channel).
  • dst Destination array with the same size and type as src .
  • thresh Threshold value.
  • maxval Maximum value to use with THRESH_BINARY and THRESH_BINARY_INV threshold types.
  • type Threshold type. For details, see threshold . The THRESH_MASK, THRESH_OTSU and THRESH_TRIANGLE threshold types are not supported.

5. 参考资料

【1】ubuntu22.04@laptop OpenCV Get Started

【2】ubuntu22.04@laptop OpenCV安装

【3】ubuntu22.04@laptop OpenCV定制化安装

6. 补充

学习是一种过程,对于前面章节学习讨论过的,就不在文中重复了。

有兴趣了解更多的朋友,请从《ubuntu22.04@laptop OpenCV Get Started》开始,一个章节一个章节的了解,循序渐进。

相关推荐
小阿鑫11 分钟前
不要太信任Cursor,这位网友被删库了。。。
人工智能·aigc·cursor·部署mcp
说私域1 小时前
基于定制开发开源 AI 智能名片 S2B2C 商城小程序的热点与人工下发策略研究
人工智能·小程序
GoGeekBaird2 小时前
GoHumanLoopHub开源上线,开启Agent人际协作新方式
人工智能·后端·github
Jinkxs2 小时前
测试工程师的AI转型指南:从工具使用到测试策略重构
人工智能·重构
别惹CC2 小时前
Spring AI 进阶之路01:三步将 AI 整合进 Spring Boot
人工智能·spring boot·spring
stbomei4 小时前
当 AI 开始 “理解” 情感:情感计算技术正在改写人机交互规则
人工智能·人机交互
Moshow郑锴9 小时前
人工智能中的(特征选择)数据过滤方法和包裹方法
人工智能
TY-20259 小时前
【CV 目标检测】Fast RCNN模型①——与R-CNN区别
人工智能·目标检测·目标跟踪·cnn
CareyWYR10 小时前
苹果芯片Mac使用Docker部署MinerU api服务
人工智能
失散1311 小时前
自然语言处理——02 文本预处理(下)
人工智能·自然语言处理