By Cyrus Harmon email@example.com, February 2011. See COPYRIGHT file for license details.
opticl is designed to be a high-performance, but relatively lightweight, library for representing, processing, loading, and saving 2-dimensional pixel-based images. opticl aims to improve upon my first attempt at an image processing library -- ch-image, and also borrows some ideas from Matthieu Villeneuve's excellent imago image processing library. Representing and processing images provides an excellent illustration of the trade-offs between generality, and complexity, on the one hand, and simplicity and efficiency, on the other hand. All other things being equal, one generally wants a simple system that is both efficient and general enough to be suitable for use in a variety of different contexts -- opticl aims to strike this balance and to be both broadly applicable in different contexts and to provide a core set of functionality that is of high-enough performance to be useful in time-(and resource-)sensitive operations.
The easiest way to install opticl is to use Zachary Beane's fabulous quicklisp library:
For a quick example, let's load an image (of a truck) from a JPEG file, invert the red channel and save the image back out to another jpeg file:
(defpackage #:impatient (:use #:cl #:opticl)) (in-package #:impatient) (let ((img (read-jpeg-file "test/images/truck.jpeg"))) (typecase img (8-bit-rgb-image (locally (declare (type 8-bit-rgb-image img)) (with-image-bounds (height width) img (time (loop for i below height do (loop for j below width do (multiple-value-bind (r g b) (pixel img i j) (declare (type (unsigned-byte 8) r g b)) (setf (pixel img i j) (values (- 255 r) g b)))))))))) (write-jpeg-file "test/output/inv-r-truck.jpeg" img))
If we time the
(loop for i below...) using the time macro, with SBCL we see
Evaluation took: 0.006 seconds of real time 0.005708 seconds of total run time (0.005538 user, 0.000170 system) 100.00% CPU 11,694,688 processor cycles 0 bytes consed
Which shows that we're able to perform simple arithmetic operations on each pixel of the image in 6 milliseconds, and that we don't need to cons to do so.
In ch-image, images were represented by a set of CLOS classes which, in turn, either extended or wrapped classes from the CLEM matrix-processing library. The idea was that CLEM could do the heavy lifting and ch-image could take advantage of CLEM's relatively efficient routines for storing arrayed sets of 2-dimensional numbers. This worked reasonably well, and allowed for ch-image to have a great variety of, at least conceptual, image types, such as various sizes of RGB and grayscale images, multichannel images, floating point images, binary images, etc..., but this approach had to fundamental costs. First, it required that client programs wishing to use ch-image use CLEM as well -- and CLEM brings along a host of other things that may not be desired by the image-library-using programmer. Second, and more problematic, it relied on CLEM's facilities for accessing image data, or digging deeply into CLEM data structures to get access to the underlying data, which seems to be missing the point.
So... I've taken a different approach with opticl, which is to largely eschew CLOS classes and to provide the image data directly as native CL arrays. Clearly, some measure of abstraction can be useful to insulate programmers from subsequent changes in the implementation, but this abstraction should be kept to a minimum and should not get in the way of programmers seeking to use the data. Therefore, the fundamental data structure of opticl is the CL array, but the API to create and access the data in these arrays is a set of functions that are used to make images and to get and set the data in the images. These functions are implemented as non-generic functions, which can be inlined (with a sufficiently smart compiler) for efficient access to image data. To date, opticl has only been tested on SBCL, and, conversely, has been designed to exploit the performance-enhancing characteristics of the SBCL compiler, such as efficient access to specialized arrays (given proper type declarations). opticl contains CL types (not classes) and the core functions for creating and accessing and setting pixel values use these type declarations to enable SBCL to generate relatively efficient code for accessing image data.
Common Lisp's multidimensional arrays provide some attractive
qualities for representing images. At the core, it is desirable to
have a representation that lends itself to efficient operations --
many languages offer high performance one-dimensional array access,
and some offer efficient access to multidimensional arrays. However,
merely the bytes that comprise the underlying array may not be
sufficient for one to intelligently use the array. But the bytes that
make up the image are only part of the story, the other critical
pieces are the data that describes the bytes in those arrays, the
dimensions of the image, the number of image channels, etc... In
ch-image I used CLOS classes for this data and for holding a reference
to the underlying pixels themselves. Fortunately, CL's array type
itself enables us to store this metadata directly in a
multidimensional array. We define a mapping between various image
types and various specialized CL array types, such that, for instance,
an 8-bit RGB array is represented by the type
(UNSIGNED-BYTE 8) (* * 3)). Any 3-dimensional simple-array with a
third dimension of size 3 and an element-type of
will satisfy the conditions of being an
This enables both opticl code and user code to infer the dimensions
and the kind of pixels represented in a(n appropriate) CL array that
happens to be on opticl
image. This, in turn, allows for both opticl
code and user code to use type declarations to enable the compiler to
generate high-performance code for processing images. It is chiefly
this facility that distinguishes opticl from other CL image processing
libraries such as ch-image and imago.
Another facility afforded by CL, is the notion of multiple values. If one wants to represent a pixel of an 8-bit RGB image, and to perform an operation on the individual color values of this pixel, one is presented with a number of alternatives. Without using multiple-values, one can treat the pixel as a single 24-bit unsigned integer, knowing which bits correspond to the red, green and blue channels; one can get the values as a list of three 8-bit integers; or one can rely on reader/writer functions. Each of these alternatives has some drawbacks.
The 24-bit unsigned integer approach is relatively clean, but requires that user code unpack the image into it's respective components. Easy enough to do, but we just lost two things. First, the image would now be represented as an array of unsigned 24-bit integers -- or in the case of an RGBA image, unsigned 32-bit integers. How would one distinguish this from a 32-bit grayscale image? One would need additional information. Second, one would be relying on either user code or library-provided facilities for unpacking the color information. It is my belief that the compiler is going to do at least as good of a job as user code in pulling those values out of an additional array dimension than user or library code would. On the other hand, using a list or reader/writer functions would likely involve heap-allocation of data structures to store this information.
CL offers a facility that has the potential to alleviate these issues,
multiple-values. This allows us to return multiple (perhaps
stack-allocated) values from a function and for us to to efficiently
update the values in multiple places using
setf. Furthermore, it
allows for a unified treatment of grayscale and RGB pixels as a
grayscale pixel is just a single value, while an RGB pixel is
represented by multiple values, as opposed to treating grayscale
values as an integer and RGB values as a list of integers. All of this
would just be theoretical navel-gazing if the implementations didn't
take advantage of the features of multiple values to provide efficient
compiled implementations of code that uses these
features. Fortunately, SBCL's implementation of multiple-values allows
us to define (possibly inline) reader and writer functions that can
access the pixel and color-value data efficiently and without
allocating additional memory on the heap (consing).
The trade-off in this approach is that doing so requires that we know the kind of image with which are dealing, at least if we want to do so efficiently. Fortunately, CL's type system gets us most of the way there. I say most of the way there, as there is one limitation in standard, which we will see in a moment. In the example above you'll notice a line which reads:
(declare (type 8-bit-rgb-image img))
This declaration tells the compiler that the variable image is of the
type 8-bit-rgb-image and the compiler is able to optimize the code
effectively. The problem is that this is great for things inside the
compiler, the compiler sees the declaration and can act accordingly,
but only the compiler can do so. In CL, these declarations are opaque
to the user/library programmer. This limitation wasn't lost on the
early CL implementors, but facilities for inspecting declarations
didn't make it into the CL spec, but rather, eventually, found there
way into the less-widely implemented Common Lisp the Lanuage, 2nd
Edition (CLtL2) book by Guy Steele. SBCL has a contrib library called
sb-cltl2 that provides the key facility we need,
cltl2:variable-information. We can use that function in code called
from our define-setf-expander, as shown below in get-image-dimensions,
to see if there is a declaration in effect:
(defun get-image-dimensions (image-var env) (multiple-value-bind (binding-type localp declarations) (cltl2:variable-information image-var env) (declare (ignore binding-type localp)) (let ((type-decl (find 'type declarations :key #'car))) (and type-decl (listp type-decl) (= (length type-decl) 4) (fourth type-decl)))))
This allows us to glean information from the information provided to the compiler that enables opticl to efficiently operate on its images, when given appropriate declarations, and still work, albeit less efficiently, in the absence of the appropriate type declarations.
(Note: I still need to look into the availability of the CLtL2 functionality on other CL implementations.)
It is the representation of image data as native CL arrays and the efficient performance of these reader and writer functions that offer the hope that opticl can serve as a general purpose image processing library suitable for use by a wide variety of CL programs.
While opticl is designed to have minimal dependencies, I have decided that it is better to use existing libraries, where possible, especially for file I/O of various formats. In ch-image, I tried to make the file I/O sections optional dependencies, but this proved merely to sow confusion into the minds of the user. With the advent of quicklisp, dependencies on libraries that are in quicklisp are much less painful (for the quicklisp user anyway) than they used to be.
- retrospectiff (new version -- as of??)
- com.gigamonkeys.binary-data (also known as monkeylib-binary-data)
opticl and all of its dependencies should be automatically installed by:
Some examples of using opticl code can be found here:
Thanks to Ivan Chernetsky for contributing code thresholding grayscale images