jiqixuexiyushenduxuexi/图像识别CV/pythoncv_learn/main.py

import cv2
import numpy as np

image=cv2.imread('10076.jpg')
image = cv2.resize(image, (600, 600))

'''
imgray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 127, 255, 0)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
'''

#(3, 3)表示高斯矩阵的长与宽都是3，标准差取1
InputArray = image.copy()
cv2.GaussianBlur(InputArray, (3, 3), 1, InputArray)
edgeImage = cv2.Canny(InputArray, 20, 80)
cv2.imshow('GaussianBlur', edgeImage)

ret, thresh = cv2.threshold(edgeImage, 127, 255, 0)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

'''
blur = cv2.bilateralFilter(edgeImage,9,75,75)
cv2.imshow('blur', blur)
'''

kernel = np.ones((5,5),np.uint8)
edgeImage = cv2.morphologyEx(edgeImage, cv2.MORPH_CLOSE, kernel)
cv2.imshow('morphologyEx', edgeImage)

'''
kernel = np.ones((5,5),np.uint8)
edgeImage = cv2.dilate(edgeImage,kernel,iterations = 1) 
cv2.imshow('dilate', edgeImage)
'''

laplacian = cv2.Laplacian(edgeImage,cv2.CV_8U)
sobelx = cv2.Sobel(edgeImage,cv2.CV_8U,1,0,ksize=5)
sobely = cv2.Sobel(edgeImage,cv2.CV_8U,0,1,ksize=5)
# laplacian = cv2.Laplacian(edgeImage,cv2.CV_64F)
# sobelx = cv2.Sobel(edgeImage,cv2.CV_64F,1,0,ksize=5)
# sobely = cv2.Sobel(edgeImage,cv2.CV_64F,0,1,ksize=5)
cv2.imshow('laplacian', laplacian)
cv2.imshow('sobelx', sobelx)
cv2.imshow('sobely', sobely)
edgeImage = sobely

def cnt_area(cnt):
  area = cv2.contourArea(cnt)
  return area
contours.sort(key = cnt_area, reverse=True)
print(len(contours))
for i in range(0, 10):
    cnt = contours[i]
    img = image.copy()
    cv2.drawContours(img, [cnt], 0, (0,255,0), 3)
    cv2.imshow('drawContours_' + str(i), img)
cv2.drawContours(image, contours, -1, (0,255,0), 1)
cv2.imshow('drawContours', image)

houghLinesImage = image.copy()
lines = cv2.HoughLines(edgeImage,1,np.pi/180,200)
for line in lines:
    rho,theta = line[0]
    a = np.cos(theta)
    b = np.sin(theta)
    x0 = a*rho
    y0 = b*rho
    x1 = int(x0 + 1000*(-b))
    y1 = int(y0 + 1000*(a))
    x2 = int(x0 - 1000*(-b))
    y2 = int(y0 - 1000*(a))
    if (x2-x1) == 0 :
        xielv = (y2-y1)/0.00000001
    else:
        xielv = (y2-y1)/(x2-x1)
    if (xielv > -0.08 and xielv < 0.08)  or xielv > 8.0 or xielv < -8.0 :
        # print(xielv)
        cv2.line(houghLinesImage,(x1,y1),(x2,y2),(0,0,255),2)
cv2.imshow('houghLinesImage', houghLinesImage)

houghLinesPImage = image.copy()
thresh_min = min(edgeImage.shape)
lines = cv2.HoughLinesP(edgeImage, 1.2, np.pi / 180, 160, minLineLength=int(edgeImage.shape[0] * 0.7),maxLineGap=int(thresh_min * 0.5))
print(len(lines))
for line in lines:
    x1,y1,x2,y2 = line[0]
    xielv = (y2-y1)/(x2-x1)
    if (xielv > -0.08 and xielv < 0.08)  or xielv > 8.0 or xielv < -8.0 :
        # print(xielv)
        cv2.line(houghLinesPImage,(x1,y1),(x2,y2),(255,0,0),2)
cv2.imshow('HoughLinesP', houghLinesPImage)

'''
circles = cv2.HoughCircles(edgeImage,cv2.HOUGH_GRADIENT,1,20,param1=50,param2=30,minRadius=0,maxRadius=0)
circles = np.uint16(np.around(circles))
for i in circles[0,:]:
    # 绘制外圆
    cv2.circle(image,(i[0],i[1]),i[2],(0,255,0),2)
    # 绘制圆心
    cv2.circle(image,(i[0],i[1]),2,(0,0,255),3)
'''

pts1 = np.float32([[56,65],[368,52],[28,387],[389,390]])
pts2 = np.float32([[0,0],[300,0],[0,300],[300,300]])
M = cv2.getPerspectiveTransform(pts1, pts2)
image = cv2.warpPerspective(image, M, (600, 600))

cv2.imshow('show', image)
cv2.waitKey(0)
cv2.destroyAllWindows()