问题描述
我想阅读对象上的文字.但是OCR程序无法识别它.当我给出一小部分时,它可以识别.我必须将圆形文本转换为线性文本.我怎样才能做到这一点?谢谢.
I want to read text on the object. But OCR program can't recognize it. When I give the small part, it can recognize. I have to transform circle text to linear text. How can I do this? Thanks.
推荐答案
,您可以将图像从笛卡尔坐标系转换为极坐标系,以准备用于OCR程序的圆路径文本图像.此功能logPolar()
可以提供帮助.
you can transform the image from Cartesian coordinate system to Polar coordinate system to prepare circle path text image for OCR program. This function logPolar()
can help.
以下是准备圆形路径文本图像的一些步骤:
Here are some steps to prepare circle path text image:
- 使用
HoughCircles()
查找圈子的中心. - 获取均值并进行一些偏移,因此获取中心.
- (最佳)从中心裁剪图像的正方形.
- 执行
logPolar()
,然后在必要时旋转它.
- Find the circles' centers using
HoughCircles()
. - Get the mean and do some offset, so get the center.
- (Optinal) Crop a square of the image from the center.
- Do
logPolar()
, then rotate it if necessary.
检测到圆并获取中心均值并进行偏移后.
After detect circles and get the mean of centers and do offset.
裁剪后的图像:
在logPolar()
和rotate()
此处显示了我的Python3-OpenCV3.3
代码,也许有帮助.
My Python3-OpenCV3.3
code is presented here, maybe it helps.
#!/usr/bin/python3
# 2017.10.10 12:44:37 CST
# 2017.10.10 14:08:57 CST
import cv2
import numpy as np
##(1) Read and resize the original image(too big)
img = cv2.imread("circle.png")
img = cv2.resize(img, (W//4, H//4))
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
## (2) Detect circles
circles = cv2.HoughCircles(gray, method=cv2.HOUGH_GRADIENT, dp=1, minDist=3, circles=None, param1=200, param2=100, minRadius = 200, maxRadius=0 )
## make canvas
canvas = img.copy()
## (3) Get the mean of centers and do offset
circles = np.int0(np.array(circles))
x,y,r = 0,0,0
for ptx,pty, radius in circles[0]:
cv2.circle(canvas, (ptx,pty), radius, (0,255,0), 1, 16)
x += ptx
y += pty
r += radius
cnt = len(circles[0])
x = x//cnt
y = y//cnt
r = r//cnt
x+=5
y-=7
## (4) Draw the labels in red
for r in range(100, r, 20):
cv2.circle(canvas, (x,y), r, (0, 0, 255), 3, cv2.LINE_AA)
cv2.circle(canvas, (x,y), 3, (0,0,255), -1)
## (5) Crop the image
dr = r + 20
croped = img[y-dr:y+dr+1, x-dr:x+dr+1].copy()
## (6) logPolar and rotate
polar = cv2.logPolar(croped, (dr,dr),80, cv2.WARP_FILL_OUTLIERS )
rotated = cv2.rotate(polar, cv2.ROTATE_90_COUNTERCLOCKWISE)
## (7) Display the result
cv2.imshow("Canvas", canvas)
cv2.imshow("croped", croped)
cv2.imshow("polar", polar)
cv2.imshow("rotated", rotated)
cv2.waitKey();cv2.destroyAllWindows()
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