用Opencv python裁剪图像中的凹多边形

Question

如何从图像中裁剪凹多边形。我的输入图像看起来像 。

闭合多边形的坐标是 [10150]，[150100]，[300150]，[350100]，[310,20]，[35,10]。我想用opencv裁剪以凹多边形为边界的区域。我搜索了其他类似的问题，但没有找到正确的答案。所以我才问这个？你能帮助我吗。

任何帮助都将不胜感激。！！！

Answer 1

Steps

1. find region using the poly points
2. create mask using the poly points
3. do mask op to crop
4. add white bg if needed

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)

---

源图像：

结果：

Answer 2

您可以通过三个步骤完成：

1）从图像中创建一个遮罩

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）对原始图像应用蒙版

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

3）您可以选择将图像裁剪为更小的图像

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，你应该在最后剪掉图像

更新

为了完整起见，下面是完整的代码：

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)