opencv实战之透视变换

import cv2

import numpy as np

import pytesseract

def cv_show(imgname,img):

    cv2.imshow(imgname,img)

    cv2.waitKey(0)

    cv2.destroyAllWindows()

#按左上，右上，右下，左下排序坐标

def order_points(pts):

    rec = np.zeros((4,2),dtype='float32')

    #找出左上，右下

    s = np.sum(pts,axis=1)

    rec[0] = pts[np.argmin(s)]

    rec[2] = pts[np.argmax(s)]

    #找出右上，左下 （x,y） y-x相差最小最大

    diff = np.diff(pts)

    rec[1] = pts[np.argmin(diff)]

    rec[3] = pts[np.argmax(diff)]

    return rec

#透视变换后的对应坐标

def four_point_wrap(img,pts):

    rec = order_points(pts)

    tl,tr,br,bl = rec

#找到最大的宽和高

    widthA = np.sqrt(((tr[0]-tl[0])**2)+((tr[1]-tl[1])**2))

    widthB = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))

    width = max(int(widthB),int(widthA))

    lengthA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))

    lengthB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))

    length = max(int(lengthA),int(lengthB))

    print('width:{},length:{}'.format(width,length))

#width,length都减去100，完整的图像才会显示出来，小了的话会显示不完整，这一部分笔者也有点困惑

    dst = np.array([

        (0,0),

        (width-100,0),

        (width-100,length-100),

        (0,length-100)

    ],dtype='float32')

    M = cv2.getPerspectiveTransform(rec,dst)

    wrap = cv2.warpPerspective(img,M,(width,length))

    return wrap

def resize(img,height=None,width=None,inter=cv2.INTER_AREA):   #cv2.INIER_AREA 插值方法

    dim = None

    (h,w) = img.shape[:2]

    if width is None and height is None:

        return img

    if width is None:

        r = height / float(h)

        dim = (int(w*r),height)

    else:

        r = width / float(h)

        dim = (width,int(h*r))

    resized = cv2.resize(img,dim,interpolation=inter)

    return resized

img =  cv2.imread('ocrbook.jpg')

print(img.shape)

ratio = img.shape[0]/500.0

orig = img.copy()

#等比例缩小

img = resize(orig,height=400)

#转化成灰度图

gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

# cv_show('gray',gray)

kernel = np.array([[0, -1, 0],[-1, 5, -1],[0, -1, 0]], np.float32)

kernel1 = np.ones([3,3])

#锐度处理 对于此图像，词操作会使噪音点变多，边缘检测效果变差，故舍弃

# dst = cv2.filter2D(gray, -1, kernel=kernel)

# cv_show('dst',dst)

#高斯模糊

gaussian = cv2.GaussianBlur(gray,(1,1),0)

# cv_show('gaussian',gaussian)

#二值化处理

# thresh = cv2.threshold(gray,20,250,cv2.THRESH_BINARY|cv2.THRESH_OTSU)[1]

# cv_show('thresh',thresh)

kernel = np.ones([3,3])

#边缘检测

canny = cv2.Canny(gaussian,120,200)

cv_show('canny',canny)

#膨胀操作 使细小的未连接的边缘连接起来，使得图像有封闭区域

dilate = cv2.morphologyEx(canny,cv2.MORPH_DILATE,kernel1,iterations=2)

cv_show('dilate',dilate)

#腐蚀

erode  = cv2.morphologyEx(dilate,cv2.MORPH_ERODE,kernel1)

cv_show('erode',erode)

#轮廓检测

cnts = cv2.findContours(erode,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)[0]

#按轮廓面积升序排列

cnts = sorted(cnts,key=cv2.contourArea,reverse=True )[:4]

for cnt in cnts:

    perix = cv2.arcLength(cnt,True)

    print(perix)

    approx = cv2.approxPolyDP(cnt,0.03*perix,True)

    print('len(approx): ',len(approx))

#找到矩形

    if len(approx)==4:

        screen = approx

        break

cv2.drawContours(img,[screen],-1,(0,25,155),2)

cv_show('cnts',img)

screen = screen.reshape(4,2)*ratio

print(screen)

#透视变换

wraped = four_point_wrap(orig,screen)

cv_show('wrapwd',wraped)

#ocr检测  准确度需要训练

text = pytesseract.image_to_string(wraped)

print(text)

#图片来源百度图片，只为学习交流专用，如有侵权，联系笔者，立刻删除

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