python 精明等边缘检测的方法

Posted 2021-05-09

import numpy as np
import cv2

def resize_img(filename):
    image = cv2.imread(filename)
    resized = cv2.resize(image,None,fx=0.125, fy=0.125, interpolation=cv2.INTER_AREA)
    cv2.imwrite(filename,resized)

# resize_img("./page_1.jpg")
def canny(filename):
    img = cv2.imread(filename,0)
    edges = cv2.Canny(img,50,150,apertureSize = 3)
    cv2.imshow('edges', edges)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    return edges
   
def laplacian(filename):
    img = cv2.imread(filename, 0)
    gray_lap = cv2.Laplacian(img,cv2.CV_16S,ksize = 3)
    dst = cv2.convertScaleAbs(gray_lap)
    cv2.imshow('laplacian',dst)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    return dst

def sobel(filename):
 
 
    img = cv2.imread(filename, 0)
    
    x = cv2.Sobel(img,cv2.CV_16S,1,0)
    y = cv2.Sobel(img,cv2.CV_16S,0,1)
    
    absX = cv2.convertScaleAbs(x)# 转回uint8
    absY = cv2.convertScaleAbs(y)
    
    dst = cv2.addWeighted(absX,0.5,absY,0.5,0)
    
    cv2.imshow("absX", absX)
    cv2.imshow("absY", absY)
    
    cv2.imshow("Result", dst)
    
    cv2.waitKey(0)
    cv2.destroyAllWindows() 
    return absX,absY,dst


def holine_img(img):
 
    height,wight=img.shape[:2]
    lines_hor = cv2.HoughLines(img,1,np.pi/180,wight*8//10)
    for lineone in lines_hor:
        for rho,theta in lineone:
            a = np.cos(theta)
            b = np.sin(theta)
            if b==1:
                x0 = a*rho
                y0 = b*rho
                x1 = 0
                y1 = int(y0 + 1000*(a))
                x2 = wight-1
                y2 = int(y0 - 1000*(a))
            
                cv2.line(img,(x1,y1),(x2,y2),(255,25,0),2)
                
    lines_ver = cv2.HoughLines(img,1,np.pi/180,height*8//10)
    for lineone in lines_ver:
        for rho,theta in lineone:
            a = np.cos(theta)
            b = np.sin(theta)
            if b==0:
                x0 = a*rho
                y0 = b*rho
                x1 = x0
                y1 =0
                x2 = x0
                y2 =height-1
            
                cv2.line(img,(x1,y1),(x2,y2),(0,25,255),2) 

    cv2.imwrite("houghline_demo.jpg",img)
    cv2.imshow('img', img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

def corrective_transform(image, pos):
    height = image.shape[0]
    width = image.shape[1]
    # 获取四个点，顺时针获取
    left_top = pos[0]
    right_top = pos[1]
    right_down = pos[2]
    left_down = pos[3]
    # print('pos:', pos)
    dx = width
    dy = height
    pts1 = np.float32([left_top, right_top, right_down, left_down])
    pts2 = np.float32([[0, 0], [dx, 0], [dx, dy], [0, dy]])
    # 计算透视变换的矩阵
    M = cv2.getPerspectiveTransform(pts1, pts2)
    # 透视变换
    dst = cv2.warpPerspective(image, M, (int(dx), int(dy)))
    return dst
    # 结果展示
    # savepath = './corrective/' + image_name + '_c.jpg'
    # cv2.imwrite(savepath, dst)
    
img=laplacian("./page_1.jpg")
holine_img(img)
# sobel("./page_1.jpg")