【Python】OpenCV库学习笔记（七）

蔚落 2023-01-09 15:58 193阅读 0赞

### OpenCV库学习笔记（七） ###

*  1. 图像梯度
 *   *  1.1 Soble算子
     *  1.2 拉普拉斯算子
 *  2. Canny边缘提取
 *   *  2.1 Canny算法介绍
     *  2.2 代码演示
 *  3. 直线检测
 *   *  3.1 霍夫直线变换介绍
     *  3.2 代码演示

# 1. 图像梯度 #

## 1.1 Soble算子 ##

*  求x,y方向图像梯度

import cv2 as cv
    
    def sobel_demo(image):
        grad_x = cv.Sobel(image, cv.CV_32F, 1, 0)	# 求x方向的梯度，对x求一阶导数
        grad_y = cv.Sobel(image, cv.CV_32F, 0, 1)	# 求x方向的梯度，对y求一阶导数
        gradx = cv.convertScaleAbs(grad_x)
        grady = cv.convertScaleAbs(grad_y)
        cv.imshow("grandient-x", gradx)
        cv.imshow("grandient-y", grady)
    
    image = cv.resize(cv.imread("01.jpg"), (300,400))
    cv.imshow("image", image)
    sobel_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]

*  求整体梯度

import cv2 as cv
    
    def sobel_demo(image):
        grad_x = cv.Sobel(image, cv.CV_32F, 1, 0)
        grad_y = cv.Sobel(image, cv.CV_32F, 0, 1)
        gradx = cv.convertScaleAbs(grad_x)
        grady = cv.convertScaleAbs(grad_y)
        gradxy = cv.addWeighted(gradx, 0.5, grady, 0.5, 0)
        cv.imshow("gradxy",gradxy)
    
    image = cv.resize(cv.imread("01.jpg"), (300,400))
    cv.imshow("image", image)
    sobel_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 1]

## 1.2 拉普拉斯算子 ##

*  拉普拉斯算子

import cv2 as cv
    
    def lapalian_demo(image):
        dst = cv.Laplacian(image, cv.CV_32F)
        lpls = cv.convertScaleAbs(dst)
        cv.imshow("lapalian_demo", lpls)
    
    image = cv.resize(cv.imread("01.jpg"), (300,400))
    cv.imshow("image", image)
    lapalian_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 2]

*  自定义拉普拉斯算子

import cv2 as cv
    import numpy
    
    def lapalian_demo(image):
        kernel = numpy.array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
        lpls = cv.filter2D(image, cv.CV_32F, kernel=kernel)
        cv.imshow("lapalian_demo", lpls)
    
    image = cv.resize(cv.imread("01.jpg"), (300,400))
    cv.imshow("image", image)
    lapalian_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 3]

# 2. Canny边缘提取 #

## 2.1 Canny算法介绍 ##

*  `Canny`是边缘检测算法，是1986年提出的

> 步骤：对彩色图像进行高斯模糊，去除噪声，再进行灰度转换，对图像求取梯度，根据图像角度实现非最大信号压制，进行高低阈值过滤
> 
> 1.  高斯模糊：GaussianBlur
> 2.  灰度转换：cvtColor
> 3.  计算梯度：Sobel/Scharr
> 4.  非最大信号抑制
> 5.  高低阈值输出二值图像

## 2.2 代码演示 ##

import cv2 as cv
    import numpy
    
    def edge_demo(image):
        blurred = cv.GaussianBlur(image, (3, 3), 0)
        gray = cv.cvtColor(blurred, cv.COLOR_BGR2GRAY)
        # x gradient
        gradx = cv.Sobel(gray, cv.CV_16SC1, 1, 0)
        grady = cv.Sobel(gray, cv.CV_16SC1, 0, 1)
        # edge 高阈值为150 低阈值为50
        edge_output = cv.Canny(gradx, grady, 50, 150)
        cv.imshow("Canny Edge", edge_output)
    
        dst = cv.bitwise_and(image, image, mask=edge_output)
        cv.imshow("Color Edge", dst)
    
    image = cv.resize(cv.imread("01.jpg"), (300,400))
    cv.imshow("image", image)
    edge_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 4]

# 3. 直线检测 #

## 3.1 霍夫直线变换介绍 ##

*  Hough Line Transform用来做直线检测
 *  前提条件：边缘检测已经完成
 *  需要理解：平面空间到极坐标空间转换

## 3.2 代码演示 ##

import cv2 as cv
    import numpy
    
    def line_detection(image):
        gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
        edges = cv.Canny(gray, 50, 150, apertureSize=3)
        lines = cv.HoughLines(edges, 1, numpy.pi/180, 200)
        for line in lines:
            rho, theta = line[0]
            a = numpy.cos(theta)
            b = numpy.sin(theta)
            x0 = a * rho
            y0 = b * rho
            x1 = int(x0 + 1000 * (-b))
            y1 = int(y0 + 1000 * (a))
            x2 = int(x0 - 1000 * (-b))
            y2 = int(y0 - 1000 * (a))
            cv.line(image, (x1, y1), (x2, y2), (0, 0, 255), 2)
        cv.imshow("image lines", image)
    
    
    image = cv.resize(cv.imread("star.jpg"), (300,400))
    cv.imshow("image", image)
    line_detection(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 5]

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