openCV：适用于各种尺寸图像的“变形透视图”

问题描述

我正在学习计算机视觉，并试图扭曲用于OCR的单张纸图像的透视图。示例图片为

enter image description

我成功地对图像进行了二值化并检测了轮廓。但是我很难根据轮廓来包裹透视。

def display_cv_image(image,format='.png'):
    """
    display image from 2d array
    """

    decoded_bytes = cv2.imencode(format,image)[1].tobytes()
    display(Image(data=decoded_bytes))
    
def get_contour(img,original,thresh):
    contours,hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
    areas = []
    for cnt in contours:
        area = cv2.contourArea(cnt)
        if area > 10000:
            epsilon = 0.1*cv2.arcLength(cnt,True)
            approx = cv2.approxpolyDP(cnt,epsilon,True)
            areas.append(approx)

    cv2.drawContours(original,areas,-1,(0,255,0),3)
    display_cv_image(original)

    return areas[0]

def perspective(original,target):
    dst = []

    pts1 = np.float32(target)
    pts2 = np.float32([[1000,2000],[1000,0],[0,2000]])

    M = cv2.getPerspectiveTransform(pts1,pts2)
    dst = cv2.warpPerspective(original,M,(1000,2000))

    display_cv_image(dst)

# Driver codes
original = cv2.imread('image.jpg')
thresh,grey = binarize(original)
target = get_contour(grey,thresh)
perspective(original,target)

问题是pts2函数中的perspective。我正在尝试为变量设置多个值，但它们都不起作用。我想回算地图矩阵，并可能使函数适应各种尺寸的图像。

解决方法

可以从Adrian的教程中获得关于四点透视变换的良好描述：https://www.pyimagesearch.com/2014/08/25/4-point-opencv-getperspective-transform-example/

four_point_transform模块中有一个功能imutils。

就上述图片而言，以下是可用于OCR输入的扭曲和二进制化的代码段。

import cv2
import numpy as np
from imutils.perspective import four_point_transform
import imutils

original = cv2.imread('image.jpg')

blurred = cv2.GaussianBlur(original,(3,3),0)
blurred_float = blurred.astype(np.float32) / 255.0
edgeDetector = cv2.ximgproc.createStructuredEdgeDetection('model.yml')
edged = edgeDetector.detectEdges(blurred_float)
edged = (255 * edged).astype("uint8")
edged = cv2.threshold(edged,255,cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]

cnts = cv2.findContours(edged,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
cnts = sorted(cnts,key = cv2.contourArea,reverse = True)[:5]

for c in cnts:
    peri = cv2.arcLength(c,True)
    approx = cv2.approxPolyDP(c,0.02 * peri,True)
    if len(approx) == 4:
        screenCnt = approx
        break

if len(screenCnt) == 4:
    warped = four_point_transform(original,screenCnt.reshape(4,2))

warped = cv2.cvtColor(warped,cv2.COLOR_BGR2GRAY)
T = cv2.ximgproc.niBlackThreshold(warped,maxValue=255,type=cv2.THRESH_BINARY_INV,blockSize=81,k=0.1,binarizationMethod=cv2.ximgproc.BINARIZATION_WOLF)
warped = (warped > T).astype("uint8") * 255

cv2.imshow("Original",imutils.resize(original,height = 650))
cv2.imshow("Edged",imutils.resize(edged,height = 650))
cv2.imshow("Warped",imutils.resize(warped,height = 650))
cv2.waitKey(0)

以下是原始的，有边的和最终的扭曲二值化输出：

请注意，StructuredEdgeDetection用于更好的边缘检测。您可以从以下链接下载model.yml文件：https://cdn.rawgit.com/opencv/opencv_extra/3.3.0/testdata/cv/ximgproc/model.yml.gz

还请注意，使用Wolf＆Julion二值化技术可获得更好的输出。

您需要通过pip为opencv-contrib-python和StructuredEdgeDetection安装niBlackThreshold软件包。

cv2 opencv python