使用Opencv python从Image中裁剪凹面多边形

时间:2018-01-17 12:29:11

标签: python opencv image-processing crop

如何从图像裁剪凹面多边形。我的输入图像看起来像 this

关闭多边形的坐标是 [10150],[150100],[300150],[350100],[310,20],[35,10]。我希望使用opencv裁剪由凹多边形限定的区域。我搜索了其他类似的问题,但我找不到正确的答案。那就是我问的原因?你能帮帮我吗?

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2 个答案:

答案 0 :(得分:10)

  

步骤

     
      
  1. 使用多边形点找到区域
  2.   
  3. 使用多边形点创建蒙版
  4.   
  5. 掩饰op to crop
  6.   
  7. 如果需要,添加白色bg
  8.   

代码:

# 2018.01.17 20:39:17 CST
# 2018.01.17 20:50:35 CST
import numpy as np
import cv2

img = cv2.imread("test.png")
pts = np.array([[10,150],[150,100],[300,150],[350,100],[310,20],[35,10]])

## (1) Crop the bounding rect
rect = cv2.boundingRect(pts)
x,y,w,h = rect
croped = img[y:y+h, x:x+w].copy()

## (2) make mask
pts = pts - pts.min(axis=0)

mask = np.zeros(croped.shape[:2], np.uint8)
cv2.drawContours(mask, [pts], -1, (255, 255, 255), -1, cv2.LINE_AA)

## (3) do bit-op
dst = cv2.bitwise_and(croped, croped, mask=mask)

## (4) add the white background
bg = np.ones_like(croped, np.uint8)*255
cv2.bitwise_not(bg,bg, mask=mask)
dst2 = bg+ dst


cv2.imwrite("croped.png", croped)
cv2.imwrite("mask.png", mask)
cv2.imwrite("dst.png", dst)
cv2.imwrite("dst2.png", dst2)

来源图片:

enter image description here

结果:

enter image description here

答案 1 :(得分:2)

您可以分三步完成:

1)从图像中创建一个蒙版

mask = np.zeros((height, width))
points = np.array([[[10,150],[150,100],[300,150],[350,100],[310,20],[35,10]]])
cv2.fillPoly(mask, points, (255))

2)将遮罩应用于原始图像

res = cv2.bitwise_and(img,img,mask = mask)

3)您可以选择删除将图像裁剪为较小的图像

rect = cv2.boundingRect(points) # returns (x,y,w,h) of the rect
cropped = res[rect[1]: rect[1] + rect[3], rect[0]: rect[0] + rect[2]]

有了这个,你应该在最后裁剪图像

更新

为了完整起见,这里是完整的代码:

import numpy as np
import cv2

img = cv2.imread("test.png")
height = img.shape[0]
width = img.shape[1]

mask = np.zeros((height, width), dtype=np.uint8)
points = np.array([[[10,150],[150,100],[300,150],[350,100],[310,20],[35,10]]])
cv2.fillPoly(mask, points, (255))

res = cv2.bitwise_and(img,img,mask = mask)

rect = cv2.boundingRect(points) # returns (x,y,w,h) of the rect
cropped = res[rect[1]: rect[1] + rect[3], rect[0]: rect[0] + rect[2]]

cv2.imshow("cropped" , cropped )
cv2.imshow("same size" , res)
cv2.waitKey(0)