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Computing a bounding box for a numpy array

I am struggling with a simple question. I have an array of numpy forms:

[[[ 1152.07507324   430.84799194]
  [ 4107.82910156   413.95199585]
  [ 4127.64941406  2872.32006836]
  [ 1191.71643066  2906.11206055]]]

And I want to calculate the bounding rectangle, that is, I want to have the leftmost, highest, rightmost and lowest point.

It must be the right decision.

[[[ 1152.07507324   413.95199585]
  [ 4127.64941406   413.95199585]
  [ 4127.64941406  2906.11206055]
  [ 1152.07507324  2906.11206055]]]

I developed a nasty function that does the trick, but I am very dissatisfied with it, since its not very pythonic / numpyic

def bounding_box(iterable):
    minimum_x = min(iterable[0], key=lambda x:x[0])[0]
    maximum_x = max(iterable[0], key=lambda x:x[0])[0]
    minimum_y = min(iterable[0], key=lambda x:x[1])[1]
    maximum_y = max(iterable[0], key=lambda x:x[1])[1]

    return numpy.array([[(minimum_x, minimum_y), (maximum_x, minimum_y), (maximum_x, maximum_y), (minimum_x, maximum_y)]], dtype=numpy.float32)

Do you have an idea to optimize the function above, possibly using numpy built-in functions?

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2 answers

Use the built-in functions numpy.minand numpy.max:

def bounding_box(iterable):
    min_x, min_y = numpy.min(iterable[0], axis=0)
    max_x, max_y = numpy.max(iterable[0], axis=0)
    return numpy.array([(min_x, min_y), (max_x, min_y), (max_x, max_y), (min_x, max_y)])
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returns the same as the previous one, but shorter and cleaner

returns 2 * 2 ndarray

def bbox(points):
    """
    [xmin xmax]
    [ymin ymax]
    """
    a = zeros((2,2))
    a[:,0] = np.min(points, axis=0)
    a[:,1] = np.max(points, axis=0)
    return a
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