I have a function that draws a polygon around some primary features in images by making use of OpenCV's contour detection. I simplify these contours using the approxPolyDP function in the snippet below to return a closed trapezoid around the regions, and it works fine:

top_poly = cv.approxPolyDP(top_cnt, 0.05 * top_perimeter, closed=True)

top_poly = np.squeeze(top_poly) # get rid of the singleton dimension

However, the approxPolyDP returns a strange type of ndarray which has a shape of N x 1 x 2, when the expected output according to the documentation linked below is an array of N x 2D points (N x 2). I had to debug for a while until I found that singleton dimension in there can be carved out using np.squeeze in the second line of the snippet. Thanks to this answer: What does cv2.approxPolydp() return?

My question is, what is the purpose of this singleton dimension? I worry I might be dropping some useful information and I don't enjoy having to use np.squeeze() in a way I don't completely understand. Thanks for any input that can shed some light on this.

https://docs.opencv.org/4.5.4/d3/dc0/group__imgproc__shape.html#ga0012a5fdaea70b8a9970165d98722b4c

CodePudding user response：

The result shape happens because of OpenCV's requirements, i.e. it has to map numpy arrays to cv::Mat and back.

A cv::Mat is a 2D thing with channels, which can be color (RGB or whatever), 2D points, 3D points, 4D, ..., or any other purpose.

The shape is generally (height, width, channels).

OpenCV returns the points as a column vector (Nx1) of 2-channel data, hence (N, 1, 2).

OpenCV is somewhat tolerant of different shapes like (N, 2, 1) which is (N, 2) (N rows, 2 columns, single-channel).