用OpenCV处理图像

傅里叶变换

• 高通滤波器

import cv2
import numpy as np
from scipy import ndimage

kernel_3x3 = np.array([[-1,-1,-1],
                       [-1,8,-1],
                       [-1,-1,-1]])

kernel_5x5 = np.array([[-1,-1,-1,-1,-1],
                       [-1, 1, 2, 1,-1],
                       [-1, 2, 4, 2,-1],
                       [-1, 1, 2, 1,-1],
                       [-1,-1,-1,-1,-1]])

img = cv2.imread("",0)

k3 = ndimage.convolve(img,kernel_3x3)
k5 = ndimage.convolve(img,kernel_5x5)

blurred = cv2.GaussianBlur(img,(11,11),0)
g_hpf = img - blurred

cv2.imshow("3x3",k3)
cv2.imshow("5x5",k5)
cv2.imshow("g_hpf",g_hpf)
cv2.waitKey()
cv2.destroyAllWindows()

• 低通滤波器
  高通滤波器是根据像素与邻近像素的亮度差值来提升该像素的亮度。低通滤波器则是在像素与周围像素的亮度差值小于一个特定值时，平滑该像素的亮度。它主要用于去噪和模糊化。比如说，高斯模糊是最常用的模糊滤波器（平滑滤波器）之一，他是一个削弱高频信号强度的低通滤波器。

Canny边缘检测

OpenCV提供了Canny函数。这个算法以发明者Jhon F.Canny命名。

import cv2
import numpy as np

img = cv2.imread("MyPic.jpg", 0)
cv2.imwrite("canny.jpg", cv2.Canny(img, 200, 300))
cv2.imshow("canny", cv2.imread("canny.jpg"))
cv2.waitKey()
cv2.destroyAllWindows()

轮廓检测

import cv2
import numpy as np

img = np.zeros((200, 200), dtype=np.uint8)
img[50:150, 50:150] = 255

ret, thresh = cv2.threshold(img, 127, 255, 0)
image, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
color = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
img = cv2.drawContours(color, contours, -1, (0,255,0), 2)
cv2.imshow("contours", color)
cv2.waitKey()
cv2.destroyAllWindows()

边界框、最小矩形区域和最小闭圆的轮廓

import cv2
import numpy as np

img = cv2.pyrDown(cv2.imread("MyPic.jpg", cv2.IMREAD_UNCHANGED))

ret, thresh = cv2.threshold(cv2.cvtColor(img.copy(), cv2.COLOR_BGR2GRAY) , 127, 255, cv2.THRESH_BINARY)
image, contours, hier = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

for c in contours:
  # find bounding box coordinates
  x,y,w,h = cv2.boundingRect(c)
  cv2.rectangle(img, (x,y), (x+w, y+h), (0, 255, 0), 2)

  # find minimum area
  rect = cv2.minAreaRect(c)
  # calculate coordinates of the minimum area rectangle
  box = cv2.boxPoints(rect)
  # normalize coordinates to integers
  box = np.int0(box)
  # draw contours
  cv2.drawContours(img, [box], 0, (0,0, 255), 3)
  
  # calculate center and radius of minimum enclosing circle
  (x,y),radius = cv2.minEnclosingCircle(c)
  # cast to integers
  center = (int(x),int(y))
  radius = int(radius)
  # draw the circle
  img = cv2.circle(img,center,radius,(0,255,0),2)

cv2.drawContours(img, contours, -1, (255, 0, 0), 1)
cv2.imshow("contours", img)

cv2.waitKey()
cv2.destroyAllWindows()

直线检测

import cv2
import numpy as np

img = cv2.imread('MyLinesPic.jpg')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray,50,120)
minLineLength = 20
maxLineGap = 5
lines = cv2.HoughLinesP(edges,1,np.pi/180,20,minLineLength,maxLineGap)
for x1,y1,x2,y2 in lines[0]:
  cv2.line(img,(x1,y1),(x2,y2),(0,255,0),2)

cv2.imshow("edges", edges)
cv2.imshow("lines", img)
cv2.waitKey()
cv2.destroyAllWindows()

圆检测

import cv2
import numpy as np

planets = cv2.imread('planet_glow.jpg')
gray_img = cv2.cvtColor(planets, cv2.COLOR_BGR2GRAY)
img = cv2.medianBlur(gray_img, 5)
cimg = cv2.cvtColor(img,cv2.COLOR_GRAY2BGR)

circles = cv2.HoughCircles(img,cv2.HOUGH_GRADIENT,1,120,
                            param1=100,param2=30,minRadius=0,maxRadius=0)

circles = np.uint16(np.around(circles))

for i in circles[0,:]:
    # draw the outer circle
    cv2.circle(planets,(i[0],i[1]),i[2],(0,255,0),2)
    # draw the center of the circle
    cv2.circle(planets,(i[0],i[1]),2,(0,0,255),3)

cv2.imwrite("planets_circles.jpg", planets)
cv2.imshow("HoughCirlces", planets)
cv2.waitKey()
cv2.destroyAllWindows()