python 은 재 귀적 인 방식 으로 의미 이미지 분할 기능 을 실현 합 니 다.

# -*-coding:utf-8-*-
import numpy as np
import cv2

#----------------------------------------------------------------------
def obj_clip(img, foreground, border):
  result = []
  height ,width = np.shape(img)
  visited = set()
  for h in range(height):
    for w in range(width):
      if img[h,w] == foreground and not (h,w) in visited:
        obj = visit(img, height, width, h, w, visited, foreground, border)
        result.append(obj)
  return result
#----------------------------------------------------------------------
def visit(img, height, width, h, w, visited, foreground, border):
  visited.add((h,w))
  result = [(h,w)]
  if w > 0 and not (h, w-1) in visited:
    if img[h, w-1] == foreground: 
      result += visit(img, height, width, h, w-1, visited , foreground, border)
    elif border is not None and img[h, w-1] == border:
      result.append((h, w-1))
  if w < width-1 and not (h, w+1) in visited:
    if img[h, w+1] == foreground:
      result += visit(img, height, width, h, w+1, visited, foreground, border)
    elif border is not None and img[h, w+1] == border:
      result.append((h, w+1))
  if h > 0 and not (h-1, w) in visited:
    if img[h-1, w] == foreground:
      result += visit(img, height, width, h-1, w, visited, foreground, border)
    elif border is not None and img[h-1, w] == border:
      result.append((h-1, w))
  if h < height-1 and not (h+1, w) in visited:
    if img[h+1, w] == foreground :
      result += visit(img, height, width, h+1, w, visited, foreground, border) 
    elif border is not None and img[h+1, w] == border:
      result.append((h+1, w))
  return result
#----------------------------------------------------------------------
if __name__ == "__main__":
  import cv2
  import sys
  sys.setrecursionlimit(100000)
  img = np.zeros([400,400])
  cv2.rectangle(img, (10,10), (150,150), 1.0, 5)
  cv2.circle(img, (270,270), 70, 1.0, 5)
  cv2.line(img, (100,10), (100,150), 0.5, 5)
  #cv2.putText(img, "Martin",(200,200), 1.0, 5)
  cv2.imshow("img", img*255)
  cv2.waitKey(0)
  for obj in obj_clip(img, 1.0, 0.5):
    clip = np.zeros([400, 400])
    for h, w in obj:
      clip[h, w] = 0.2
    cv2.imshow("aa", clip*255)
    cv2.waitKey(0)