Beispiel #1
0
def shape_divide(arr, scale, reduction='mean'):
    '''Scale down an array (shape N x M x ...) by the specified scale
       in each dimension (n x m x ...)
       Each dimension in arr must be divisible by its scale
       (throws an error otherwise)
       This is reduces each sub-array (n x m x ...) to a single element
       according to the reduction parameter, which is one of:
        * mean (default): mean of each sub-array
        * median: median of each sub-array
        * first: the [0,0,0, ...] element of the sub-array
        * all: all the possible (N x M x ...) sub-arrays;
               returns an array of shape (n, m, ..., N, M, ...)
       This is a downsampling operation, similar to
       scipy.misc.imresize and scipy.ndimage.interpolate'''
    arr = np.asanyarray(arr)
    reduction_options = ['mean', 'median', 'first', 'all']
    assert reduction in reduction_options, \
        'reduction must be one of: ' + ' '.join(reduction_options)
    scale = coerce_to_target_length(scale, arr.ndim)
    assert all([sh % sc == 0 for sh, sc in zip(arr.shape,scale)]), \
        'all dimensions must be divisible by their respective scale!'
    new_shape = flatten([sh//sc, sc] for sh, sc in zip(arr.shape, scale))
    # group pixes into smaller sub-arrays that can then be modified by standard operations
    subarrays = _transpose_interleaved(arr.reshape(new_shape))
    flat_subarrays = subarrays.reshape([np.product(scale)] + new_shape[::2])
    return (np.mean(flat_subarrays, axis=0) if reduction == 'mean' else
            np.median(flat_subarrays, axis=0) if reduction == 'median' else
            flat_subarrays[0] if reduction == 'first' else
            subarrays if reduction == 'all' else
            None)
Beispiel #2
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 def zeroFill(t, a, sc):
     t *= 0
     middle_slice = flatten([slice(None), i//2] for i in sc)
     t[middle_slice] = a
     return t
Beispiel #3
0
def GetTriangleBorderPoints(p0,p1,p2):
    '''Collect all the border points for a raster triangle and remove any duplicates'''
    allPts = [BresenhamFunction(pa,pb)
              for pa,pb in ((p0,p1),(p1,p2),(p2,p0))]
    return list(set(totuple(flatten(allPts))))