从图像中分割字符 -
方法 -
- 对图像进行阈值处理(将其转换为黑白)
- 执行扩张
- 检查轮廓是否足够大
- 查找矩形轮廓
- 获取 ROI 并保存角色
Python 代码 -
# import the necessary packages
import numpy as np
import cv2
import imutils
# load the image, convert it to grayscale, and blur it to remove noise
image = cv2.imread("sample1.jpg")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (7, 7), 0)
# threshold the image
ret,thresh1 = cv2.threshold(gray ,127,255,cv2.THRESH_BINARY_INV)
# dilate the white portions
dilate = cv2.dilate(thresh1, None, iterations=2)
# find contours in the image
cnts = cv2.findContours(dilate.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
orig = image.copy()
i = 0
for cnt in cnts:
# Check the area of contour, if it is very small ignore it
if(cv2.contourArea(cnt) < 100):
continue
# Filtered countours are detected
x,y,w,h = cv2.boundingRect(cnt)
# Taking ROI of the cotour
roi = image[y:y+h, x:x+w]
# Mark them on the image if you want
cv2.rectangle(orig,(x,y),(x+w,y+h),(0,255,0),2)
# Save your contours or characters
cv2.imwrite("roi" + str(i) + ".png", roi)
i = i + 1
cv2.imshow("Image", orig)
cv2.waitKey(0)
首先,我对图像进行阈值处理,将其转换为黑白。我得到图像白色部分的字符和黑色背景。然后我放大图像以使字符(白色部分)变粗,这将很容易找到合适的轮廓。然后find使用findContours方法来寻找轮廓。然后我们需要检查轮廓是否足够大,如果轮廓不够大则被忽略(因为该轮廓是噪声)。然后使用boundingRect方法来找到轮廓的矩形。最后,保存并绘制检测到的轮廓。
输入图像 -
临界点 -
扩张 -
轮廓 -
已保存的字符 -