我正在尝试变换非水平的图像,因为它们可能会倾斜。
事实证明,当测试2张图像时,这张照片是水平的,而这张照片不是水平的。使用水平照片可以给我很好的效果,但是当尝试更改倾斜的第二张照片时,它并没有达到预期的效果。
[fist image]的工作原理如下,带有theta 1.6406095
。目前,它看起来很糟糕,因为我正在尝试使两张照片在水平方向上看起来正确。
second image说theta只是1.9198622
我认为这是此行的错误:
lines= cv2.HoughLines(edges, 1, np.pi/90.0, 60, np.array([]))
我对此link with colab进行了一些模拟。
欢迎任何帮助。
到目前为止,这就是我所得到的。
import cv2
import numpy as np
img=cv2.imread('test.jpg',1)
imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
imgBlur=cv2.GaussianBlur(imgGray,(5,5),0)
imgCanny=cv2.Canny(imgBlur,90,200)
contours,hierarchy =cv2.findContours(imgCanny,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
rectCon=[]
for cont in contours:
area=cv2.contourArea(cont)
if area >100:
#print(area) #prints all the area of the contours
peri=cv2.arcLength(cont,True)
approx=cv2.approxPolyDP(cont,0.01*peri,True)
#print(len(approx)) #prints the how many corner points does the contours have
if len(approx)==4:
rectCon.append(cont)
#print(len(rectCon))
rectCon=sorted(rectCon,key=cv2.contourArea,reverse=True) # Sort out the contours based on largest area to smallest
bigPeri=cv2.arcLength(rectCon[0],True)
cornerPoints=cv2.approxPolyDP(rectCon[0],0.01*peri,True)
# Reorder bigCornerPoints so I can prepare it for warp transform (bird eyes view)
cornerPoints=cornerPoints.reshape((4,2))
mynewpoints=np.zeros((4,1,2),np.int32)
add=cornerPoints.sum(1)
mynewpoints[0]=cornerPoints[np.argmin(add)]
mynewpoints[3]=cornerPoints[np.argmax(add)]
diff=np.diff(cornerPoints,axis=1)
mynewpoints[1]=cornerPoints[np.argmin(diff)]
mynewpoints[2]=cornerPoints[np.argmax(diff)]
# Draw my corner points
#cv2.drawContours(img,mynewpoints,-1,(0,0,255),10)
##cv2.imshow('Corner Points in Red',img)
##print(mynewpoints)
# Bird Eye view of your region of interest
pt1=np.float32(mynewpoints) #What are your corner points
pt2=np.float32([[0,0],[300,0],[0,200],[300,200]])
matrix=cv2.getPerspectiveTransform(pt1,pt2)
imgWarpPers=cv2.warpPerspective(img,matrix,(300,200))
cv2.imshow('Result',imgWarpPers)
现在,您只需要解决倾斜问题(opencv有歪斜),然后使用一些阈值来检测字母,然后识别每个字母。