Image Indexing, Array Views, and Bounding Boxes

Pixel Indexing Conventions

LSST’s image classes (Image, Mask, MaskedImage, and Exposure) use a pixel indexing convention that is different from both the convention used by numpy.ndarray objects and the convention used in FITS images.

Like FITS but unlike NumPy, points and pixel indices in LSST software are always ordered (x, y) (with x the column index and y the row index); this includes both geometry objects (Point2D, Point2I, Extent2D, Extent2I) and the images classes themselves.

Like NumPy and unlike FITS, LSST typically labels the center of the lower left pixel of an image (0, 0). Note that because we label the centers of pixels with integer coordinates, the exact coordinate bounding box of an image (which correspond to the edges of pixels) have half-integer values.

LSST image classes also have the ability to use a custom origin, which we frequently refer to as xy0. The image coordinate system that uses xy0 as the coordinates of the lower left pixel is called the PARENT coordinate system, because when a subimage is created, it allows the subimage to use the same coordinate system as that of the “parent” image it is derived from. Most image operations can also utilize the LOCAL system, which uses (0,0) as the coordinates of the lower left pixel regardless of the value of xy0.


NumPy array indices are ordered (y, x) and always start at (0, 0), while LSST image indices are ordered (x, y) and sometimes have a nonzero origin.

In Python, all image operations use PARENT coordinates by default. In C++, most image operations use PARENT, including all those with direct equivalents in Python; only iterator and operator()-based direct pixel access do not (purely for historical/backwards-compatibility reasons). The coordinate system to use is typically indicated via the afw.image.ImageOrigin enum type, which has two values, PARENT and LOCAL.

To illustrate how this works, let’s start with a 10×12 image with xy0=(0,0):

>>> import numpy as np
>>> from lsst.afw.image import Image
>>> from lsst.geom import Box2I, Point2I, Extent2I
>>> img = Image(Extent2I(x=10, y=12), dtype=np.float32)
>>> print(img.getBBox(LOCAL))
(minimum=(0, 0), maximum=(9, 11))
>>> print(img.getBBox(PARENT))
(minimum=(0, 0), maximum=(9, 11))

Because xy0=(0,0), the bounding box is the same in both coordinate systems. We’ll now extract a subimage:

>>> box1 = Box2I(minimum=Point2I(x=2, y=3), maximum=Point2I(x=7, y=9))
>>> sub1 = img[box1]

This image has a nonzero xy0:

>>> print(sub1.getXY0())
(2, 3)

This makes sense; the lower left pixel in the subimage corresponds to pixel (2, 3) in the original image.

As expected, the bounding box of the subimage is the same as the one we used to construct it:

>>> print(sub1.getBBox())
(minimum=(2, 3), maximum=(7, 9))

This is the PARENT bounding box; the LOCAL bounding box has the same dimensions but a different offset:

>>> print(sub1.getBBox(LOCAL))
Box2I(minimum=Point2I(0, 0), dimensions=Extent2I(6, 7))


The PARENT bounding box’s minimum point is xy0, while the LOCAL bounding box’s minimum point is (0, 0); this is always true.

The operation that creates a subimage can also accept an ImageOrigin argument:

>>> sub1a = img[box1, LOCAL]

This argument indicates which of the original image’s coordinate systems the given box is in. But in this case, the original image has xy0 = (0, 0), and hence those two coordinate systems are the same, so sub1a is exactly the same subimage as sub1.

That’s not the case if we make a subimage of our subimage:

>>> box2 = Box2I(minimum=Point2I(x=3, y=4), maximum=Point2I(x=5, y=5))
>>> sub2a = sub1[box2, PARENT]   # same as no ImageOrigin argument
>>> sub2b = sub1[box2, LOCAL]
>>> sub2a.getBBox(PARENT)
(minimum=(3, 4), maximum=(5, 5))
>>> sub2b.getBBox(PARENT)
(minimum=(5, 7), maximum=(7, 8))
>>> sub2a.getBBox(LOCAL)
(minimum=(0, 0), maximum=(2, 1))
>>> sub2b.getBBox(LOCAL)
(minimum=(0, 0), maximum=(2, 1))

As in the previous case, when we make a subimage using a box in PARENT coordinates, the PARENT bounding box of the result is that same box. When we make a subimage using a box in LOCAL coordinates, that input box is different from both the resulting subimage’s LOCAL bounding box and its PARENT bounding box.


We strongly recommend using the PARENT convention whenever possible (which usually just means not explicitly selecting LOCAL, of course, since PARENT is the default).

FITS Reading and Writing

The flexibility of our xy0 functionality makes it possible to make LSST images use the FITS convention by setting xy0 = (1, 1), but LSST code does not do this, even when reading and writing FITS images. Instead, we read a FITS image with an origin of (1, 1) into LSST image objects with an origin of (0, 0) (and do the reverse when writing, of course).

We also adjust any FITS WCS in the image headers to account for this change in conventions, and also write an extra (trivial, shift-only) WCS that offsets the pixel grid by xy0, providing FITS access to our PARENT coordinate system.

Floating-Point and Integer Bounding Boxes

One consequence of using integer labels for pixel centers is that integer boxes (Box2I) behave fundamentally differently from floating-point bounding boxes (Box2D).

The width and height of an image’s integer bounding box are of course the same as those of the image itself:

>>> img = Image(Extent2I(x=10, y=12), dtype=np.float32)
>>> boxI = img.getBBox()
>>> print(boxI.getDimensions())
(10, 12)

But this is not the same as the difference between the minimum and maximum points of that box:

>>> print(boxI.getMax() - boxI.getMin())
(9, 11)

That’s because those values correspond to the centers of the minimum and maximum pixels, and hence this naive subtraction does not include that half-pixel-width boundary.

This same discrepancy can also be seen when converting a Box2I to a Box2D:

>>> from lsst.geom import Box2D, Point2D, Extent2D
>>> boxD = Box2D(boxI)
>>> print(boxD)
(minimum=(-0.5, -0.5), maximum=(9.5, 11.5))
>>> print(boxD.getDimensions())
(10, 12)

When converting a Box2I to a Box2D:

  • the dimensions are preserved;

  • the minimum and maximum points are not (instead, they are expanded by half a pixel in all directions).

This means that the difference between the minimum and maximum points of a Box2D is equivalent to its size:

>>> print(boxD.getMax() - boxD.getMin())
(10, 12)

The conversion from a Box2D to a Box2I is not as straightforward, because there are many Box2D regions that cannot be represented exactly by Box2I objects. Instead, the Box2I.EdgeHandlingEnum is used to specify whether the Box2I is the smallest integer box that contains the Box2D (Box2I.EXPAND), or the Box2I is the largest integer box that is contained by the Box2D (Box2I.SHRINK).

In fact, because of the half-pixel boundary discrepancy noted above, Box2D objects with integer-valued minimum and maximum points are among those that cannot be converted exactly to Box2Is, even though it looks like they are (when Box2I.EXPAND is used):

>>> smallBox = Box2D(Point2D(0.0, 0.0), Point2D(10.0, 12.0))
>>> expandedBox = Box2I(smallBox, Box2I.EXPAND)
>>> print(smallBox)
(minimum=(0, 0), maximum=(10, 12))
>>> print(expandedbox)
(minimum=(0, 0), maximum=(10, 12))

While smallBox and expandedBox appear to have the same minimum and maximum points, they actually represent different regions: smallBox does not enclose that half-pixel boundary around the edges, and this is reflected by their dimensions:

>>> print(smallBox.getDimensions())
(10, 12)
>>> print(expandedBox.getDimensions())
(11, 13)

Converting with Box2I.SHRINK of course creates a box that is smaller than the Box2D:

>>> shrunkBox = Box2I(smallBox, Box2I.SHRINK)
>>> print(shrunkBox)
(minimum=(1, 1), maximum=(9, 11))
>>> print(shrunkBox.getDimensions())
(9, 11)

Image Slicing

We’ve already shown how Box2I objects can be used to access subimage views. This is usually the most concise syntax, and we recommend using it when a box object is available.

It’s also possible, however, to create subimages using Python’s built-in slice syntax; the sub1 and sub2 views below are thus equivalent:

>>> box = Box2I(minimum=Point2I(x=2, y=3), maximum=Point2I(x=7, y=9))
>>> sub1 = img[box]
>>> sub2 = img[2:8, 3:9]

Note that again that Box2I maximum points are inclusive, while slice upper endpoints are exclusive. The indices still follow the LSST conventions: the slices are ordered (x, y) and assumed to be PARENT coordinates unless LOCAL is explicitly passed as the last argument.

It is also possible to use scalar indices or Point2I objects when indexing Images and Masks:

>>> scalar = img[3, 4]
>>> scalar = img[Point2I(x=3, y=4)]

Indexing with a slice for one dimension and a scalar for the other is not supported, because LSST image objects are intrinsically 2-d. 1-d array views can be obtained by first accessing the 2-d array view and slicing that (see Array Views to Images).


Python slicing typically allows negative indices to be used to indicate positions relative to the end of the sequence. This is supported when slicing LSST image objects when the LOCAL coordinate system is used. When xy0 is negative, negative indices in PARENT coordinates could be either positions relative to the end or true negative pixel indices, and to avoid confusion image classes will raise IndexError instead of assuming either. To obtain a subimage containing a region that includes negative-index pixels, use a Box2I.

Array Views to Images

The Image and Mask classes also provide NumPy views to their internal data via an array property. These are writeable views that can be used to modify the contents of the Image or Mask.

Because these are just numpy.ndarray objects, these views conform to NumPy’s conventions, not LSST’s: indices are ordered (y, x), and are ignorant of xy0. That means the following two array views are equivalent:

>>> view1 = img[x1:x2, y1:y2, LOCAL].array
>>> view2 = img.array[y1:y2, x1:x2]