将转换矩阵应用于opencv中的点列表（Python）

Question

我无法理解the documentation for cv2.transform。请问有人可惜（并用Python解释）？

我有一个绘制多边形，在填充它，并且将图像旋转

import numpy as np 
import cv2 

dx,dy = 400,400 
centre = dx//2,dy//2 
img = np.zeros((dy,dx),np.uint8) 

# construct a long thin triangle with the apex at the centre of the image 
polygon = np.array([(0,0),(100,10),(100,-10)],np.int32) 
polygon += np.int32(centre) 

# draw the filled-in polygon and then rotate the image 
cv2.fillConvexPoly(img,polygon,(255)) 
M = cv2.getRotationMatrix2D(centre,20,1) # M.shape = (2, 3) 
rotatedimage = cv2.warpAffine(img,M,img.shape) 

我想先旋转多边形，然后代码绘制

# this is clumsy, I have to extend each vector, 
# apply the matrix to each extended vector, 
# and turn the list of transformed vectors into an numpy array 
extendedpolygon = np.hstack([polygon,np.ones((3,1))]) 
rotatedpolygon = np.int32([M.dot(x) for x in extendedpolygon]) 
cv2.fillConvexPoly(img,rotatedpolygon,(127)) 

我想这样做在一个函数调用，但我无法得到它的权利

# both of these calls produce the same error 
cv2.transform(polygon,M) 
cv2.transform(polygon.T,M) 
# OpenCV Error: Assertion failed (scn == m.cols || scn + 1 == m.cols) in transform, file /home/david/opencv/modules/core/src/matmul.cpp, line 1947 

我怀疑它的那句“src - 输入数组必须具有与m.cols或m.cols-1一样多的通道（1到4）“，我正在处理它。我认为polygon.T作为三个坐标与X和Y部分作为单独的渠道将资格，但可悲的是不。

我知道同样的问题已经被问到C++，我想在Python中得到答案。

Answer 1

我感谢Micka指出我的一个实例。这里是代码，提供了我的问题的答案。

import numpy as np 
import cv2 

dx,dy = 400,400 
centre = dx//2,dy//2 
img = np.zeros((dy,dx),np.uint8) 

# construct a long thin triangle with the apex at the centre of the image 
polygon = np.array([(0,0),(100,10),(100,-10)],np.int32) 
polygon += np.int32(centre) 

# draw the filled-in polygon and then rotate the image 
cv2.fillConvexPoly(img,polygon,(255)) 
M = cv2.getRotationMatrix2D(centre,20,1) # M.shape = (2, 3) 
rotatedimage = cv2.warpAffine(img,M,img.shape) 

# as an alternative, rotate the polygon first and then draw it 

# these are alternative ways of coding the working example 
# polygon.shape is 3,2 

# magic that makes sense if one understands numpy arrays 
poly1 = np.reshape(polygon,(3,1,2)) 
# slightly more accurate code that doesn't assumy the polygon is a triangle 
poly2 = np.reshape(polygon,(polygon.shape[0],1,2)) 
# turn each point into an array of points 
poly3 = np.array([[p] for p in polygon]) 
# use an array of array of points 
poly4 = np.array([polygon]) 
# more magic 
poly5 = np.reshape(polygon,(1,3,2)) 

for poly in (poly1,poly2,poly3,poly4,poly5): 
    newimg = np.zeros((dy,dx),np.uint8) 
    rotatedpolygon = cv2.transform(poly,M) 
    cv2.fillConvexPoly(newimg,rotatedpolygon,(127)) 

说实话，我不明白为什么cv2.fillConvexPoly（）接受前三个答案，但它似乎是非常宽容的，我还是不明白，这些答案如何映射到文档。