Question:  I have a bunch of images such as the one above. They each contain a data matrix, but do not guarantee that it is oriented to an axis. Nevertheless, I can read these matrices with libdmtx pretty reliably regardless of their rotation. However, I also need to rotate the image so that the label is oriented right-side-up. My thought process is that I need to get the angle of rotation of the data matrix so that I can rotate the image with PIL to orient it correctly. pylibdmtx.decode returns the data that the matrix contains, as well as a rectangle which I originally thought was the bounding box of the data matrix. To test this, I ran the following code with the image above: from PIL import Image from pylibdmtx.pylibdmtx import decode def segment_qr_code(image: Image.Image): data = decode(image)[0] print(data.rect) if __name__ == "__main__": segment_qr_code(Image.open('<path to image>'))  Unfortunately, this code returned Rect(left=208, top=112, width=94, height=-9). Because the height is negative, I don't think it is the bounding box to the data matrix, and if it is, I don't know how to use it to get the angle of rotation. My question is, what is the best way to obtain the angle of rotation of the data matrix? I originally thought that I could crop the image with the bounding box to get a segmented image of just the data matrix. Then I could use image thresholding or contouring to get an angle of rotation. However, I'm not sure how to get the correct bounding box, and even if I did I don't know how to use thresholding. I would also prefer to not use thresholding because it isn't always accurate. The data matrix always has a solid border on the bottom and left sides, so I think it may be possible to use it as a fiducial to align the image, however I was unable to find any libraries that were able to return the angle of rotation of the data matrix. I am open to any suggestions. Thanks in advance.

Answer: Thank you to @flakes for the suggestion. Combining code from the PR and issue, I created the following solution: from pylibdmtx.pylibdmtx import _region, _decoder, _image, _pixel_data, _decoded_matrix_region from pylibdmtx.wrapper import c_ubyte_p, DmtxPackOrder, DmtxVector2, dmtxMatrix3VMultiplyBy, DmtxUndefined from ctypes import cast, string_at from collections import namedtuple import numpy _pack_order = { 8: DmtxPackOrder.DmtxPack8bppK, 16: DmtxPackOrder.DmtxPack16bppRGB, 24: DmtxPackOrder.DmtxPack24bppRGB, 32: DmtxPackOrder.DmtxPack32bppRGBX, } Decoded = namedtuple('Decoded', 'data rect') def decode_with_region(image): results = [] pixels, width, height, bpp = _pixel_data(image) with _image(cast(pixels, c_ubyte_p), width, height, _pack_order[bpp]) as img: with _decoder(img, 1) as decoder: while True: with _region(decoder, None) as region: if not region: break else: res = _decode_region(decoder, region) if res: open_cv_image = numpy.array(image) # Convert RGB to BGR open_cv_image = open_cv_image[:, :, ::-1].copy() height, width, _ = open_cv_image.shape topLeft = (res.rect['01']['x'], height - res.rect['01']['y']) topRight = (res.rect['11']['x'], height - res.rect['11']['y']) bottomRight = (res.rect['10']['x'], height - res.rect['10']['y']) bottomLeft = (res.rect['00']['x'], height - res.rect['00']['y']) results.append(Decoded(res.data, (topLeft, topRight, bottomRight, bottomLeft))) return results def _decode_region(decoder, region): with _decoded_matrix_region(decoder, region, DmtxUndefined) as msg: if msg: vector00 = DmtxVector2() vector11 = DmtxVector2(1.0, 1.0) vector10 = DmtxVector2(1.0, 0.0) vector01 = DmtxVector2(0.0, 1.0) dmtxMatrix3VMultiplyBy(vector00, region.contents.fit2raw) dmtxMatrix3VMultiplyBy(vector11, region.contents.fit2raw) dmtxMatrix3VMultiplyBy(vector01, region.contents.fit2raw) dmtxMatrix3VMultiplyBy(vector10, region.contents.fit2raw) return Decoded( string_at(msg.contents.output), { '00': { 'x': int((vector00.X) + 0.5), 'y': int((vector00.Y) + 0.5) }, '01': { 'x': int((vector01.X) + 0.5), 'y': int((vector01.Y) + 0.5) }, '10': { 'x': int((vector10.X) + 0.5), 'y': int((vector10.Y) + 0.5) }, '11': { 'x': int((vector11.X) + 0.5), 'y': int((vector11.Y) + 0.5) } } ) else: return None  To decode an image, use decode_with_region() instead of pylibdmtx's decode(). It outputs a dictionary of coordinates, which I can plot on an image and get the following output:  I can then use these coordinates to obtain an angle of rotation: def get_data_from_matrix(image): decoded = decode_with_region(image)[0] topLeft, topRight = decoded.rect[2], decoded.rect[3] rotation = -math.atan2(topLeft[1] - topRight[1], topLeft[0] - topRight[0]) * (180 / math.pi) image = image.rotate(rotation, expand=True)