OpenCV for Python之圖像的開閉操作、頂帽黑帽、形態學梯度

Opencv4 官方文檔 : https://docs.opencv.org/4.2.0/
Opencv4 for Python中文文檔點擊下載:OpenCV4 for Python 中文文檔

1 開閉操作

1. 開運算:先腐蝕再膨脹.就叫做開運算,它被用來去除噪聲。
   閉運算:先膨脹再腐蝕。它經常被用來填充前景物體中的小洞，或者前景物體上的小黑點。
2. api:

cv.morphologyEx(src, op, kernel, dst=None, anchor=None, iterations=None, borderType=None, borderValue=None)

開操作：
demo1：

def open_demo(image):
    print(image.shape)
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    ret, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)
    cv.imshow("binary", binary)
    kernel = cv.getStructuringElement(cv.MORPH_RECT, (5, 5))
    dst = cv.morphologyEx(binary, cv.MORPH_OPEN, kernel=kernel)
    cv.imshow("open_demo", dst)

result1:

demo2:

def open_line(image):
    print(image.shape)
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    ret, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY_INV | cv.THRESH_OTSU)
    cv.imshow("binary", binary)
    # kernel = cv.getStructuringElement(cv.MORPH_RECT, (15, 1))#去掉豎線
    # kernel = cv.getStructuringElement(cv.MORPH_RECT, (15, 1))#去掉水平線
    kernel = cv.getStructuringElement(cv.MORPH_RECT, (3, 3))#去掉背景干擾線
    dst = cv.morphologyEx(binary, cv.MORPH_OPEN, kernel=kernel)
    cv.imshow("open_line", dst)

result2:

閉操作：
demo:

def open_line(image):
    print(image.shape)
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    ret, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY_INV | cv.THRESH_OTSU)
    cv.imshow("binary", binary)
    # kernel = cv.getStructuringElement(cv.MORPH_RECT, (15, 1))#去掉豎線
    # kernel = cv.getStructuringElement(cv.MORPH_RECT, (15, 1))#去掉水平線
    kernel = cv.getStructuringElement(cv.MORPH_RECT, (3, 3))#去掉背景干擾線
    dst = cv.morphologyEx(binary, cv.MORPH_OPEN, kernel=kernel)
    cv.imshow("open_line", dst)

result:

2 頂帽 黑帽

黑帽:

demo:

def black_hat_demo(image):
    gray=cv.cvtColor(image,cv.COLOR_BGR2GRAY)
    #構建形態學元素 提高內核矩陣(10,10)可以提取更多元素
    kernel=cv.getStructuringElement(cv.MORPH_RECT,(10,10))
    #黑帽處理
    dst=cv.morphologyEx(gray,cv.MORPH_BLACKHAT,kernel)

    #如果圖像較黑可以用圖像增強看看效果
    cimage=np.array(gray.shape,np.uint8)
    cimage=50
    dst=cv.add(dst,cimage)

    cv.imshow('black_hat',dst)

result:

頂帽:
demo:

def top_hat_demo(image):
    gray=cv.cvtColor(image,cv.COLOR_BGR2GRAY)
    #構建形態學元素 提高內核矩陣(10,10)可以提取更多元素
    kernel=cv.getStructuringElement(cv.MORPH_RECT,(10,10))
    #黑帽處理
    dst=cv.morphologyEx(gray,cv.MORPH_TOPHAT,kernel)
    #如果圖像較黑可以用圖像增強看看效果
    cimage=np.array(gray.shape,np.uint8)
    cimage=50
    dst=cv.add(dst,cimage)
    cv.imshow('top_hat',dst)

result:

3 形態學梯度

就是膨脹圖像減去腐蝕圖像的結果，,得到圖像的輪廓。分爲基本梯度，內、外梯度
demo1:基本梯度

def gradient_demo(image):  #基本梯度
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    ret, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)
    kernel = cv.getStructuringElement(cv.MORPH_RECT, (3, 3))
    dst = cv.morphologyEx(binary, cv.MORPH_GRADIENT, kernel)
    cv.imshow("gradient", dst)

result:

demo2:內外梯度

def gradient2_demo(image):
    kernel = cv.getStructuringElement(cv.MORPH_RECT, (3, 3))
    dm = cv.dilate(image, kernel)
    em = cv.erode(image, kernel)
    dst1 = cv.subtract(image, em) # internal gradient
    dst2 = cv.subtract(dm, image) # external gradient
    cv.imshow("internal", dst1)
    cv.imshow("external", dst2)

result:

轉載請註明轉自:https://leejason.blog.csdn.net/article/details/106443742