【Python】OpenCV库学习笔记（六）

红太狼 2023-01-08 10:26 98阅读 0赞

### OpenCV库学习笔记（六） ###

*  1. 图像二值化
 *   *  1.1 二值图像（Binary Image）
     *  1.2 图像二值化方法
     *  1.3 OpenCV相关API
     *   *  1.3.1 OTSU
         *  1.3.2 Triangle
         *  1.3.3 自动与手动
         *  1.3.4 局部阈值
 *  2. 图像金字塔
 *   *  图像金字塔原理
     *  高斯金字塔和拉普拉斯金字塔

# 1. 图像二值化 #

## 1.1 二值图像（Binary Image） ##

（1）二值图像的概念

*  一张图像只有`黑白`两个颜色
 *  下面图像是就是一个`二值图像`，其中的`1`代表`黑色`，其中的`0`代表`白`色

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NTQzNzAyMg_size_16_color_FFFFFF_t_70]

（2）二值图像的存储

*  使用`四叉树`存储平面图像

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NTQzNzAyMg_size_16_color_FFFFFF_t_70 1]

## 1.2 图像二值化方法 ##

> 图像二值化的方法：
> 
> 1.  全局阈值
> 2.  局部阈值

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NTQzNzAyMg_size_16_color_FFFFFF_t_70 2]

## 1.3 OpenCV相关API ##

### 1.3.1 OTSU ###

import cv2 as cv
    import numpy
    
    def threshold_demo(image):
        gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
        ret, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)
        print("threshold value %s" % ret)
        cv.imshow("Binary IMage", binary)
    
    image = cv.resize(cv.imread("01.jpg"), (300,400))
    cv.imshow("image", image)
    threshold_demo(image)
    cv.waitKey(0)
    cv.destroyAllWindows()

threshold value 171.0

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NTQzNzAyMg_size_16_color_FFFFFF_t_70 3]

### 1.3.2 Triangle ###

import cv2 as cv
    import numpy
    
    
    def threshold_demo(image):
        gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
        ret, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY | cv.THRESH_TRIANGLE)
        print("threshold value %s" % ret)
        cv.imshow("Binary IMage", binary)
    
    
    image = cv.resize(cv.imread("01.jpg"), (300,400))
    cv.imshow("image", image)
    threshold_demo(image)
    cv.waitKey(0)
    cv.destroyAllWindows()

threshold value 189.0

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NTQzNzAyMg_size_16_color_FFFFFF_t_70 4]

### 1.3.3 自动与手动 ###

import cv2 as cv
    import numpy
    
    
    def threshold_demo(image):
        gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
        ret, binary = cv.threshold(gray, 150, 255, cv.THRESH_BINARY)
        print("threshold value %s" % ret)
        cv.imshow("Binary IMage", binary)
    
    
    image = cv.resize(cv.imread("01.jpg"), (300,400))
    cv.imshow("image", image)
    threshold_demo(image)
    cv.waitKey(0)
    cv.destroyAllWindows()

threshold value 150.0

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NTQzNzAyMg_size_16_color_FFFFFF_t_70 5]

### 1.3.4 局部阈值 ###

*  使用`ADAPTIVE_THRESH_MEAN_C`

import cv2 as cv
    import numpy
    
    def local_threshold_demo(image):
        gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
        binary = cv.adaptiveThreshold(gray, 255, cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY, 25, 10)
        cv.imshow("Binary IMage", binary)
    
    image = cv.resize(cv.imread("01.jpg"), (300,400))
    cv.imshow("image", image)
    local_threshold_demo(image)
    cv.waitKey(0)
    cv.destroyAllWindows()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NTQzNzAyMg_size_16_color_FFFFFF_t_70 6]

*  使用`ADAPTIVE_THRESH_GAUSSIAN_C`

import cv2 as cv
    import numpy
    
    def local_threshold_demo(image):
        gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
        binary = cv.adaptiveThreshold(gray, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY, 25, 10)
        cv.imshow("Binary IMage", binary)
    
    image = cv.resize(cv.imread("01.jpg"), (300,400))
    cv.imshow("image", image)
    local_threshold_demo(image)
    cv.waitKey(0)
    cv.destroyAllWindows()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NTQzNzAyMg_size_16_color_FFFFFF_t_70 7]

# 2. 图像金字塔 #

## 图像金字塔原理 ##

*  图像金字塔是图像多尺度表达的一种，是一种以多分辨率来解释图像的有效但概念简单的结构。
 *  一幅图像的金字塔是一系列以金字塔形状排列的分辨率逐步降低，且来源于同一张原始图的图像集合。其通过梯次向下采样获得，直到达到某个终止条件才停止采样。
 *  我们将一层一层的图像比喻成金字塔，层级越高，则图像越小，分辨率越低。

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NTQzNzAyMg_size_16_color_FFFFFF_t_70 8]

## 高斯金字塔和拉普拉斯金字塔 ##

*  Reduce：先对图像进行高斯模糊，再取出所有行和列中的偶数行和偶数列进行降采样

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NTQzNzAyMg_size_16_color_FFFFFF_t_70 9]

*  Expand：先对图像进行扩大，再对扩大的图像进行卷积

import cv2 as cv
    
    
    def pyramid_demo(image):
       level = 3
       temp = image.copy()
       pyramid_images = []
       for i in range(level):
           dst = cv.pyrDown(temp)
           pyramid_images.append(dst)
           cv.imshow("pyramid_down_" + str(i), dst)
           temp = dst.copy()
       return pyramid_images
    
    
    def ladpalian_demo(image):
       pyramid_images = pyramid_demo(image)
       level = len(pyramid_images)
       for i in range(level - 1, -1, -1):
           if (i - 1) < 0:
               expand = cv.pyrUp(pyramid_images[i], dstsize=image.shape[:2])
               lpls = cv.subtract(image, expand)
               cv.imshow("laplalian_down_" + str(i), lpls)
           else:
               expand = cv.pyrUp(pyramid_images[i], dstsize=pyramid_images[i - 1].shape[:2])
               lpls = cv.subtract(pyramid_images[i - 1], expand)
               cv.imshow("laplalian_down_" + str(i), lpls)
    
    image = cv.imread("01.jpg")
    pyramid_demo(image)
    cv.waitKey(0)
    cv.destroyAllWindows()

*  pyramid\_demo

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NTQzNzAyMg_size_16_color_FFFFFF_t_70 10]

*  ladpalian\_demo  
    ![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NTQzNzAyMg_size_16_color_FFFFFF_t_70 11]

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