將轉換矩陣應用於opencv中的點列表（Python）

Question

我無法理解the documentation for cv2.transform。請問有人可惜（並用Python解釋）？

我有一個繪製多邊形，在填充它，並且將圖像旋轉

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) 

我想先旋轉多邊形，然後代碼繪製

# 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)) 

我想這樣做在一個函數調用，但我無法得到它的權利

# 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 

我懷疑它的那句「src - 輸入數組必須具有與m.cols或m.cols-1一樣多的通道（1到4）「，我正在處理它。我認爲polygon.T作爲三個座標與X和Y部分作爲單獨的渠道將資格，但可悲的是不。

我知道同樣的問題已經被問到C++，我想在Python中得到答案。

Answer 1

我感謝Micka指出我的一個實例。這裏是代碼，提供了我的問題的答案。

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)) 

說實話，我不明白爲什麼cv2.fillConvexPoly（）接受前三個答案，但它似乎是非常寬容的，我還是不明白，這些答案如何映射到文檔。