OpenCV教程-OpenCV Canny边缘检测
边缘检测是一种识别图像中物体边界的技术。我们将学习使用Canny边缘检测技术进行边缘检测。Canny边缘检测函数的语法如下:
edges = cv2.Canny('/path/to/img', minVal, maxVal, apertureSize, L2gradient)
参数-
- /path/to/img: 图像文件路径(必填)
- minVal: 最小强度梯度(必填)
- maxVal: 最大强度梯度(必填)
- aperture: 这是可选参数。
- L2gradient: 默认值为false,如果值为true,则Canny()使用更消耗计算资源的方程来检测边缘,以提供更高的准确性。
示例: 1
import cv2
img = cv2.imread(r'C:\Users\DEVANSH SHARMA\cat_16x9.jpg')
edges = cv2.Canny(img, 100, 200)
cv2.imshow("Edge Detected Image", edges)
cv2.imshow("Original Image", img)
cv2.waitKey(0) # waits until a key is pressed
cv2.destroyAllWindows() # destroys the window showing image
输出:
示例: 实时边缘检测
# import libraries of python OpenCV
import cv2
# import Numpy by alias name np
import numpy as np
# capture frames from a camera
cap = cv2.VideoCapture(0)
# loop runs if capturing has been initialized
while (1):
# reads frames from a camera
ret, frame = cap.read()
# converting BGR to HSV
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# define range of red color in HSV
lower_red = np.array([30, 150, 50])
upper_red = np.array([255, 255, 180])
# create a red HSV colour boundary and
# threshold HSV image
mask = cv2.inRange(hsv, lower_red, upper_red)
# Bitwise-AND mask and original image
res = cv2.bitwise_and(frame, frame, mask=mask)
# Display an original image
cv2.imshow('Original', frame)
# discovers edges in the input image image and
# marks them in the output map edges
edges = cv2.Canny(frame, 100, 200)
# Display edges in a frame
cv2.imshow('Edges', edges)
# Wait for Esc key to stop
k = cv2.waitKey(5) & 0xFF
if k == 27:
break
# Close the window
cap.release()
# De-allocate any associated memory usage
cv2.destroyAllWindows()
输出: