Other morphological operations of image processing Python + OpenCV

Top hat (Top Hat):

A difference image between the original image and the opening operation, the original projection image brighter than the surrounding area

Black Hat (Black Hat):

Closing operation image difference image and the original image, the original projection image darker than the surrounding area

Morphological Gradient (Gradient):

Gradient base: based gradient image by subtracting the expanded image to obtain a difference image after etching, referred to as a method of calculating a gradient image morphological gradient opencv is supported, and this method has been known as basic gradient gradient.

Internal Gradient: is an image with the original image after the difference image obtained by subtracting the corrosion, known as internal gradient image.

Outer gradient: difference minus the original image obtained after image expansion, referred to as external gradient image.

Top hat and the results achieved python

def top_hat_demo(image):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
    dst = cv2.morphologyEx(gray, cv2.MORPH_TOPHAT, kernel)
    # 提升亮度
    cimage = np.array(gray.shape, np.uint8)
    cimage = 100
    dst = cv2.add(dst, cimage)
    cv2.imshow("top_hat_demo", dst)

 

Black Hat python implementation and results

def black_hat_demo(image):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
    dst = cv2.morphologyEx(gray, cv2.MORPH_BLACKHAT, kernel)
    # 提升亮度
    cimage = np.array(gray.shape, np.uint8)
    cimage = 100
    dst = cv2.add(dst, cimage)
    cv2.imshow("black_hat_demo", dst)

 

 Overcap and black binary image capper

def threshold_top_hat_demo(image):  # 二值图像顶帽操作
    gray = cv2.cvtColor(image, cv2.COLOR_BGRA2GRAY)
    ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
    dst = cv2.morphologyEx(thresh, cv2.MORPH_TOPHAT, kernel)
    cv2.imshow("dst", dst)


def threshold_black_hat_demo(image):  # 二值图像黑帽操作
    gray = cv2.cvtColor(image, cv2.COLOR_BGRA2GRAY)
    ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
    dst = cv2.morphologyEx(thresh, cv2.MORPH_BLACKHAT, kernel)
    cv2.imshow("dst", dst)

形态学梯度操作

def gradient1_demo(image):
    cv2.imshow("image", image)
    gray = cv2.cvtColor(image, cv2.COLOR_BGRA2GRAY)
    ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
    dst = cv2.morphologyEx(thresh, cv2.MORPH_GRADIENT, kernel)  # 基本梯度
    cv2.imshow("dst", dst)


def gradients2_demo(image):
    cv2.imshow("image", image)
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
    dm = cv2.dilate(image, kernel)
    em = cv2.erode(image, kernel)
    dst1 = cv2.subtract(image, em)  # 内部梯度
    dst2 = cv2.subtract(dm, image)  # 外部梯度
    cv2.imshow("internal", dst1)
    cv2.imshow("external", dst2)

内部梯度，外部梯度结果