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Given a numpy array like the one below, can you convert it to a multi-channel cv mat, collapsing the data into a single column? 

arr = [[x1, y1],
       [x2, y2],  
       [x3, y3]]

arr = np.array(arr, dtype='f4')

mat = cv.fromarray(arr)

Done this way, the resulting mat has 3 rows and 2 single-channel columns (the mat is of type 32FC1). I need the resulting mat to have 3 rows and a single 2-channel column (specifically, to be of type 32FC2).

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  • Does this thread helps ? stackoverflow.com/questions/12535715/… Commented Jul 9, 2015 at 10:57
  • Unfortunately no :( Attempting to use cv.Convert gives the following error: cv2.error: src.size == dst.size && src.channels() == dst.channels(). Looks like you can't convert between mats with different numbers of channels. Commented Jul 9, 2015 at 14:14

2 Answers 2

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You can use np.array() on an existing array to create an array that will convert to an OpenCV Mat with additional channels:

a = np.array([1,2,3], dtype='float32')
a = np.array([a])

Will convert to Mat of type CV_32FC2 (two channels) under cv.fromarray

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The answer to your question is YES.

Given the example:

arr = [[x1, y1],
       [x2, y2],  
       [x3, y3]]
arr = np.array(arr, dtype='f4')
mat = arr[:, np.newaxis, :]

The resulting mat will be 3 rows, 1 column and 2 channels interpreted by OpenCV. The official numpy doc: Shape Manipulation explains more details.

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