ｏpencv图像处理（八）canny 边缘检测、轮廓检测

"""首先，由于Canny只能处理灰度图，所以将读取的图像转成灰度图。
用高斯平滑处理原图像降噪。
调用Canny函数，指定最大和最小阈值，其中apertureSize默认为3
edge = cv2.Canny(image, threshold1, threshold2[, edges[, apertureSize[, L2gradient ]]])
"""
import cv2
import numpy as np

img = cv2.imread("cat.jpg", 0)
img = cv2.GaussianBlur(img, (3, 3), 0)
canny = cv2.Canny(img, 50, 150)
cv2.imshow('Canny', canny)
cv2.waitKey(0)
cv2.destroyAllWindows()

#这个程序只是静态的，在github上有一个可以在运行时调整阈值大小的程序
def CannyThreshold(lowThreshold):
    detected_edges = cv2.GaussianBlur(gray, (3, 3), 0)
    detected_edges = cv2.Canny(detected_edges, lowThreshold, lowThreshold * ratio, apertureSize=kernel_size)
    dst = cv2.bitwise_and(img, img, mask=detected_edges)  # just add some colours to edges from original image.
    cv2.imshow('canny demo', dst)


lowThreshold = 0
max_lowThreshold = 100
ratio = 3
kernel_size = 3

img = cv2.imread('D:/lion.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

cv2.namedWindow('canny demo')

cv2.createTrackbar('Min threshold', 'canny demo', lowThreshold, max_lowThreshold, CannyThreshold)

CannyThreshold(0)  # initialization
if cv2.waitKey(0) == 27:
    cv2.destroyAllWindows()
    
# 轮廓检测
"""轮廓检测也是图像处理中经常用到的。OpenCV-Python接口中使用cv2.findContours()函数来查找检测物体的轮廓。
cv2.findContours(image, mode, method[, contours[, hierarchy[, offset ]]])
cv2.drawContours(image, contours, contourIdx, color[, thickness[, lineType[, hierarchy[, maxLevel[, offset ]]]]])
"""

img = cv2.imread("water.jpg")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)

contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(img, contours, -1, (0, 0, 255), 3)

cv2.imshow("img", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
"""需要注意的是cv2.findContours()函数接受的参数为二值图，即黑白的（不是灰度图），所以读取的图像要先转成灰度的，再转成二值图"""

参考：https://blog.csdn.net/sunny2038/article/details/12889059