投影法多用于图像的阈值分割。闲话不多说，现用Python实现。

上代码。

import cv2
import numpy
img = cv2.imread('D:/0.jpg', cv2.COLOR_BGR2GRAY)
height, width = img.shape[:2]
#resized = cv2.resize(img, (3*width,3*height), interpolation=cv2.INTER_CUBIC)
#二值化
(_, thresh) = cv2.threshold(img, 150, 255, cv2.THRESH_BINARY) 
#cv2.imshow('thresh', thresh)
#扩大黑色面积，使效果更明显
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10, 10))#形态学处理，定义矩形结构
closed = cv2.erode(thresh, None, iterations = 5)
cv2.imshow('erode',closed)
height, width = closed.shape[:2]
v = [0]*width
z = [0]*height
a = 0
#垂直投影
#统计并存储每一列的黑点数
for x in range(0, width):    
 for y in range(0, height):
  if closed[y,x][0] == 0:
   a = a + 1
  else :
   continue
 v[x] = a
 a = 0
l = len(v)
#print l
#print width
#创建空白图片，绘制垂直投影图
emptyImage = numpy.zeros((height, width, 3), numpy.uint8) 
for x in range(0,width):
 for y in range(0, v[x]):
  b = (255,255,255)
  emptyImage[y,x] = b
cv2.imshow('chuizhi', emptyImage)
#水平投影
#统计每一行的黑点数
a = 0
emptyImage1 = numpy.zeros((height, width, 3), numpy.uint8) 
for y in range(0, height):
 for x in range(0, width):
  if closed[y,x][0] == 0:
   a = a + 1
  else :
   continue
 z[y] = a
 a = 0
l = len(z)
#print l
#print height
#绘制水平投影图
for y in range(0,height):
 for x in range(0, z[y]):
  b = (255,255,255)
  emptyImage1[y,x] = b
cv2.imshow('shuipin', emptyImage1)
cv2.waitKey(0)

原图

垂直投影图

水平投影图

由这两图可以确定我们所需的分割点，从而可以进行下一步的文本分割。这将在下一篇博客中实现。

以上这篇Python实现投影法分割图像示例就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持IIS7站长之家。