OpenCV Python YAML persistance

I wasn’t able to find online a complete example on how to persist OpenCV matrices in Python (so really NumPy arrays) to YAML like what cv::FileStorage will give you.
So here’s a short snippet:

import numpy as np
import yaml
# A yaml constructor is for loading from a yaml node.
# This is taken from: http://stackoverflow.com/a/15942429
def opencv_matrix_constructor(loader, node):
    mapping = loader.construct_mapping(node, deep=True)
    mat = np.array(mapping["data"])
    mat.resize(mapping["rows"], mapping["cols"])
    return mat
yaml.add_constructor(u"tag:yaml.org,2002:opencv-matrix", opencv_matrix_constructor)
# A yaml representer is for dumping structs into a yaml node.
# So for an opencv_matrix type (to be compatible with c++'s FileStorage) we save the rows, cols, type and flattened-data
def opencv_matrix_representer(dumper, mat):
    mapping = {'rows': mat.shape[0], 'cols': mat.shape[1], 'dt': 'd', 'data': mat.reshape(-1).tolist()}
    return dumper.represent_mapping(u"tag:yaml.org,2002:opencv-matrix", mapping)
yaml.add_representer(np.ndarray, opencv_matrix_representer)
#example
with open('output.yaml', 'w') as f:
    f.write("%YAML:1.0")
    yaml.dump({"a matrix": np.zeros((10,10)), "another_one": np.zeros((2,4))}, f)
#   a matrix: !!opencv-matrix
#     cols: 10
#     data: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
#       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
#       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
#       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
#       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
#       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
#       0.0, 0.0, 0.0, 0.0, 0.0]
#     dt: d
#     rows: 10
#   another_one: !!opencv-matrix
#     cols: 4
#     data: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
#     dt: d
#     rows: 2
with open('output.yaml', 'r') as f:
    print yaml.load(f)
#  {'a matrix': array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
#         [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
#         [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
#         [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
#         [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
#         [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
#         [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
#         [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
#         [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
#         [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]]), 'another_one': array([[ 0.,  0.,  0.,  0.],
#         [ 0.,  0.,  0.,  0.]])}

There you go