传统图像算法

#腐蚀:选取kernel区域内的最小值

# 矩形
kernel1 = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
print(kernel1)
# 椭圆
kernel2 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))
print(kernel2)
# 十字形
kernel3 = cv2.getStructuringElement(cv2.MORPH_CROSS, (5,5))
print(kernel3)

image = imread('image.jpg')
show(image)
erosion = cv2.erode(image, kernel1)
show(erosion)

#膨胀：选取kernel区域内的最大值

#膨胀与腐蚀相反，取的是局部最大值。cv2.dilate()
dilation = cv2.dilate(image, kernel)
show(dilation)
for i in range(3):
    dilation = cv2.dilate(image, kernel1, iterations=i+1)
    #输出的是迭代i+1次的结果
    show(dilation)
 
#Opening 开运算
#先符识后膨胀叫做开运算，消除小白点儿
image2 = imread('image2.jpg')
show(image2)
# 去除白点
opening = cv2.morphologyEx(image2, cv2.MORPH_OPEN, kernel1)
show(opening)

#Closing 闭运算
#先膨胀后腐蚀叫做闭运算，消除小黑点
# 去除黑点
closing = cv2.morphologyEx(image2, cv2.MORPH_CLOSE, kernel1)
show(closing)

#先开后闭的运算，消除黑白的小点
opening = cv2.morphologyEx(image2, cv2.MORPH_OPEN, kernel1)
closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel1)
show(closing)

#Gradient 形态学梯度
#膨胀图减去俯视图，得到罗阔图
gradient = cv2.morphologyEx(image, cv2.MORPH_GRADIENT, kernel1)
show(gradient)

#TOP Hat/White Hat顶帽，白帽
#原图减去开运算后的图：小白点
tophat = cv2.morphologyEx(image2, cv2.MORPH_TOPHAT, kernel1)
show(tophat)

#Black Hat黑帽
#闭运算的图减去原图：小黑点
blackhat = cv2.morphologyEx(image2, cv2.MORPH_BLACKHAT, kernel1)
show(blackhat)