openCV+python实现倾斜校正

示例代码

import cv2
import imutils
import numpy as np

img = cv2.imread(r'C:\Users\ml\Desktop\0.png')

result3 = img.copy()

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
dilate = cv2.dilate(blurred, cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3)))
edged = cv2.Canny(dilate, 30, 120, 3)            # 边缘检测

cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)  # 轮廓检测
cnts = cnts[0] if imutils.is_cv2() else cnts[1]  # 判断是opencv2还是opencv3
docCnt = None

if len(cnts) > 0:
    cnts = sorted(cnts, key=cv2.contourArea, reverse=True) # 根据轮廓面积从大到小排序
    for c in cnts:
        peri = cv2.arcLength(c, True)                                       # 计算轮廓周长
        approx = cv2.approxPolyDP(c, 0.02*peri, True)           # 轮廓多边形拟合
        # 轮廓为4个点表示找到纸张
        if len(approx) == 4:
            docCnt = approx
            break
print(docCnt)
box = [docCnt[0][0],docCnt[1][0],docCnt[2][0],docCnt[3][0]]
rectangle_L = int(np.sqrt(pow((box[0][0]-box[1][0]), 2) + pow((box[0][1]-box[1][1]), 2)))
rectangle_W = int(np.sqrt(pow((box[0][0]-box[3][0]), 2) + pow((box[0][1]-box[3][1]), 2)))
print(rectangle_L)
print(rectangle_W)

src = np.float32(docCnt)
dst = np.float32([[0, 0], [0, rectangle_L], [rectangle_W, rectangle_L], [rectangle_W, 0]])
m = cv2.getPerspectiveTransform(src, dst)
result = cv2.warpPerspective(result3, m, (rectangle_W, rectangle_L))

cv2.imshow('result', result)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2
import imutils
from imutils.perspective import four_point_transform

img = cv2.imread(r'C:\Users\ml\Desktop\0.png')

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)       # 转化为灰度图，加快处理速度
binary = cv2.medianBlur(gray,7)       # 中值滤波
cv2.imshow('binary',binary)
# blurred = cv2.GaussianBlur(gray, (5, 5), 0)
# cv2.imshow('blurred',blurred)
dilate = cv2.dilate(binary, cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3)))
cv2.imshow('dilate',dilate)
edged = cv2.Canny(dilate, 30, 120, 3)            # 边缘检测
cv2.imshow('edged',edged)

cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)  # 轮廓检测
cnts = cnts[0] if imutils.is_cv2() else cnts[1]  # 判断是opencv2还是opencv3

docCnt = None

if len(cnts) > 0:
    cnts = sorted(cnts, key=cv2.contourArea, reverse=True) # 根据轮廓面积从大到小排序
    for c in cnts:
        peri = cv2.arcLength(c, True)                                       # 计算轮廓周长
        approx = cv2.approxPolyDP(c, 0.02*peri, True)           # 轮廓多边形拟合
        # 轮廓为4个点表示找到纸张
        if len(approx) == 4:
            docCnt = approx
            break
print(docCnt)
result = four_point_transform(img, docCnt.reshape(4,2)) # 对原始图像进行四点透视变换

cv2.imshow('result', result)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2
import operator

ix,iy = 0,0
box =[]

img = cv2.imread(r'C:\Users\ml\Desktop\tng.jpeg')

# mouse callback function
def draw_circle(event,x,y,flags,param):
    global ix, iy
    if event == cv2.EVENT_LBUTTONDBLCLK:  # 设置鼠标事件
        ix, iy = x, y
        cv2.circle(img,(x,y), 6, (0,0,255), -1)
        cv2.imshow('image', img)
        
cv2.namedWindow('image')
cv2.imshow('image', img)
cv2.setMouseCallback('image',draw_circle)
print(ix,iy)
box.append((ix,iy))
print(box)
i=1

'''
while(1):
    i=i+1
    cv2.setMouseCallback('image',draw_circle)
    if operator.eq(box[i], box[1])==True:
        break
'''
cv2.waitKey(0)
cv2.destroyAllWindows()

函数列表

函数	作用
cv2.GaussianBlur()	高斯滤波：去除噪音，使图像变得平滑
cv2.cvtColor()	颜色空间转换
cv2.Canny()	边缘检测
cv2.approxPolyDP(curve, epsilon, closed)	多边形逼近
cv2.getPerspectiveTransform(src, dst)	透视变换

参考资料

资料1
资料2
资料3
资料4：利用透视变换进行图像纠正
资料5：傅里叶变换和透视变换
资料6：透视变换项目
资料7
文档
图像平滑
颜色空间转换
边缘检测
轮廓处理
鱼眼矫正