|Title:||Standard image protocol and class|
|Author:||Lino Mastrodomenico <l.mastrodomenico at gmail.com>|
The current situation of image storage and manipulation in the Python world is extremely fragmented: almost every library that uses image objects has implemented its own image class, incompatible with everyone else's and often not very pythonic. A basic RGB image class exists in the standard library (Tkinter.PhotoImage), but is pretty much unusable, and unused, for anything except Tkinter programming.
This fragmentation not only takes up valuable space in the developers minds, but also makes the exchange of images between different libraries (needed in relatively common use cases) slower and more complex than it needs to be.
This PEP proposes to improve the situation by defining a simple and pythonic image protocol/interface that can be hopefully accepted and implemented by existing image classes inside and outside the standard library without breaking backward compatibility with their existing user bases. In practice this is a definition of how a minimal image-like object should look and act (in a similar way to the read() and write() methods in file-like objects).
The inclusion in the standard library of a class that provides basic image manipulation functionality and implements the new protocol is also proposed, together with a mixin class that helps adding support for the protocol to existing image classes.
Further exploration of the concepts covered in this PEP has been deferred for lack of a current champion interested in promoting the goals of the PEP and collecting and incorporating feedback, and with sufficient available time to do so effectively.
A good way to have high quality modules ready for inclusion in the Python standard library is to simply wait for natural selection among competing external libraries to provide a clear winner with useful functionality and a big user base. Then the de facto standard can be officially sanctioned by including it in the standard library.
Unfortunately this approach hasn't worked well for the creation of a dominant image class in the Python world: almost every third-party library that requires an image object creates its own class incompatible with the ones from other libraries. This is a real problem because it's entirely reasonable for a program to create and manipulate an image using, e.g., PIL (the Python Imaging Library) and then display it using wxPython or pygame. But these libraries have different and incompatible image classes, and the usual solution is to manually "export" an image from the source to a (width, height, bytes_string) tuple and "import" it creating a new instance in the target format. This approach works, but is both uglier and slower than it needs to be.
Another "solution" that has been sometimes used is the creation of specific adapters and/or converters from a class to another (e.g. PIL offers the ImageTk module for converting PIL images to a class compatible with the Tkinter one). But this approach doesn't scale well with the number of libraries involved and it's still annoying for the user: if I have a perfectly good image object why should I convert before passing it to the next method, why can't it simply accept my image as-is?
The problem isn't by any stretch limited to the three mentioned libraries and has probably multiple causes, including two that IMO are very important to understand before solving it:
- in today's computing world an image is a basic type not strictly tied to a specific domain. This is why there will never be a clear winner between the image classes from the three libraries mentioned above (PIL, wxPython and pygame): they cover different domains and don't really compete with each other;
- the Python standard library has never provided a good image class that can be adopted or imitated by third part modules. Tkinter.PhotoImage provides basic RGB functionality, but it's by far the slowest and ugliest of the bunch and it can be instantiated only after the Tkinter root window has been created.
This PEP tries to improve this situation in four ways:
- It defines a simple and pythonic image protocol/interface (both on the Python and the C side) that can be hopefully accepted and implemented by existing image classes inside and outside the standard library without breaking backward compatibility with their existing user bases.
- It proposes the inclusion in the standard library of three new
- ImageMixin provides almost everything necessary to implement the new protocol; its main purpose is to make as simple as possible to support this interface for existing libraries, in some cases as simple as adding it to the list of base classes and doing minor additions to the constructor.
- Image is a subclass of ImageMixin and will add a constructor that can resize and/or convert an image between different pixel formats. This is intended to provide a fast and efficient default implementation of the new protocol.
- ImageSize is a minor helper class. See below for details.
- Tkinter.PhotoImage will implement the new protocol (mostly through the ImageMixin class) and all the Tkinter methods that can receive an image will be modified the accept any object that implements the interface. As an aside the author of this PEP will collaborate with the developers of the most common external libraries to achieve the same goal (supporting the protocol in their classes and accepting any class that implements it).
- New PyImage_* functions will be added to the CPython C API: they implement the C side of the protocol and accept as first parameter any object that supports it, even if it isn't an instance of the Image/ImageMixin classes.
The main effects for the end user will be a simplification of the interchange of images between different libraries (if everything goes well, any Python library will accept images from any other library) and the out-of-the-box availability of the new Image class. The new class is intended to cover simple but common use cases like cropping and/or resizing a photograph to the desired size and passing it an appropriate widget for displaying it on a window, or darkening a texture and passing it to a 3D library.
The Image class is not intended to replace or compete with PIL, Pythonmagick or NumPy, even if it provides a (very small) subset of the functionality of these three libraries. In particular PIL offers very rich image manipulation features with dozens of classes, filters, transformations and file formats. The inclusion of PIL (or something similar) in the standard library may, or may not, be a worthy goal but it's completely outside the scope of this PEP.
The imageop module is used as the default location for the new classes and objects because it has for a long time hosted functions that provided a somewhat similar functionality, but a new module may be created if preferred (e.g. a new "image" or "media" module; the latter may eventually include other multimedia classes).
MODES is a new module level constant: it is a set of the pixel formats supported by the Image class. Any image object that implements the new protocol is guaranteed to be formatted in one of these modes, but libraries that accept images are allowed to support only a subset of them.
These modes are in turn also available as module level constants (e.g. imageop.RGB).
The following table is a summary of the modes currently supported and their properties:
|Name||Component names||Bits per component||Subsampling||Valid intervals|
|L||l (lowercase L)||8||no||full range|
|LA||l, a||8||no||full range|
|LA32||l, a||16||no||full range|
|RGB||r, g, b||8||no||full range|
|RGB48||r, g, b||16||no||full range|
|RGBA||r, g, b, a||8||no||full range|
|RGBA64||r, g, b, a||16||no||full range|
|YV12||y, cr, cb||8||1, 2, 2||16-235, 16-240, 16-240|
|JPEG_YV12||y, cr, cb||8||1, 2, 2||full range|
|CMYK||c, m, y, k||8||no||full range|
|CMYK64||c, m, y, k||16||no||full range|
When the name of a mode ends with a number, it represents the average number of bits per pixel. All the other modes simply use a byte per component per pixel.
No palette modes or modes with less than 8 bits per component are supported. Welcome to the 21st century.
Here's a quick description of the modes and the rationale for their inclusion; there are four groups of modes:
grayscale (L* modes): they are heavily used in scientific computing (those people may also need a very high dynamic range and precision, hence L32, the only mode with 32 bits per component) and sometimes it can be useful to consider a single component of a color image as a grayscale image (this is used by the individual planes of the planar images, see YV12 below); the name of the component ('l', lowercase letter L) stands for luminance, the second optional component ('a') is the alpha value and represents the opacity of the pixels: alpha = 0 means full transparency, alpha = 255/65535 represents a fully opaque pixel;
RGB* modes: the garden variety color images. The optional alpha component has the same meaning as in grayscale modes;
YCbCr, a.k.a. YUV (*YV12 modes). These modes are planar (i.e. the values of all the pixel for each component are stored in a consecutive memory area, instead of the usual arrangement where all the components of a pixel reside in consecutive bytes) and use a 1, 2, 2 (a.k.a. 4:2:0) subsampling (i.e. each pixel has its own Y value, but the Cb and Cr components are shared between groups of 2x2 adjacent pixels) because this is the format that's by far the most common for YCbCr images. Please note that the V (Cr) plane is stored before the U (Cb) plane.
YV12 is commonly used for MPEG2 (including DVDs), MPEG4 (both ASP/DivX and AVC/H.264) and Theora video frames. Valid values for Y are in range(16, 236) (excluding 236), and valid values for Cb and Cr are in range(16, 241). JPEG_YV12 is similar to YV12, but the three components can have the full range of 256 values. It's the native format used by almost all JPEG/JFIF files and by MJPEG video frames. The "strangeness" of these two wrt all the other supported modes derives from the fact that they are widely used that way by a lot of existing libraries and applications; this is also the reason why they are included (and the fact that they can't losslessly converted to RGB because YCbCr is a bigger color space); the funny 4:2:0 planar arrangement of the pixel values is relatively easy to support because in most cases the three planes can be considered three separate grayscale images;
CMYK* modes (cyan, magenta, yellow and black) are subtractive color modes, used for printing color images on dead trees. Professional designers love to pretend that they can't live without them, so here they are.
See the examples below.
In Python 2.x, all the new classes defined here are new-style classes.
The mode objects offer a number of attributes and methods that can be used for implementing generic algorithms that work on different types of images:
The number of components per pixel (e.g. 4 for an RGBA image).
A tuple of strings; see the column "Component names" in the above table.
8, 16 or 32; see "Bits per component" in the above table.
components * bits_per_component // 8, only available for non planar modes (see below).
Boolean; True if the image components reside each in a separate plane. Currently this happens if and only if the mode uses subsampling.
A tuple that for each component in the mode contains a tuple of two integers that represent the amount of downsampling in the horizontal and vertical direction, respectively. In practice it's ((1, 1), (2, 2), (2, 2)) for YV12 and JPEG_YV12 and ((1, 1),) * components for everything else.
max(x for x, y in subsampling); the width of an image that uses this mode must be divisible for this value.
max(y for x, y in subsampling); the height of an image that uses this mode must be divisible for this value.
A tuple that for each component in the mode contains a tuple of two integers: the minimum and maximum valid value for the component. Its value is ((16, 235), (16, 240), (16, 240)) for YV12 and ((0, 2 ** bits_per_component - 1),) * components for everything else.
get_length(iterable[integer]) -> int
The parameter must be an iterable that contains two integers: the width and height of an image; it returns the number of bytes needed to store an image of these dimensions with this mode.
Implementation detail: the modes are instances of a subclass of str and have a value equal to their name (e.g. imageop.RGB == 'RGB') except for L32 that has value 'I'. This is only intended for backward compatibility with existing PIL users; new code that uses the image protocol proposed here should not rely on this detail.
Any object that supports the image protocol must provide the following methods and attributes:
The format and the arrangement of the pixels in this image; it's one of the constants in the MODES set.
An instance of the ImageSize class; it's a named tuple of two integers: the width and the height of the image in pixels; both of them must be >= 1 and can also be accessed as the width and height attributes of size.
A sequence of integers between 0 and 255; they are the actual bytes used for storing the image data (i.e. modifying their values affects the image pixels and vice versa); the data has a row-major/C-contiguous order without padding and without any special memory alignment, even when there are more than 8 bits per component. The only supported methods are __len__, __getitem__/__setitem__ (with both integers and slice indexes) and __iter__; on the C side it implements the buffer protocol.
This is a pretty low level interface to the image and the user is responsible for using the correct (native) byte order for modes with more than 8 bit per component and the correct value ranges for YV12 images. A buffer may or may not keep a reference to its image, but it's still safe (if useless) to use the buffer even after the corresponding image has been destroyed by the garbage collector (this will require changes to the image class of wxPython and possibly other libraries). Implementation detail: this can be an array('B'), a bytes() object or a specialized fixed-length type.
A dict object that can contain arbitrary metadata associated with the image (e.g. DPI, gamma, ICC profile, exposure time...); the interpretation of this data is beyond the scope of this PEP and probably depends on the library used to create and/or to save the image; if a method of the image returns a new image, it can copy or adapt metadata from its own info attribute (the ImageMixin implementation always creates a new image with an empty info dictionary).
Shortcuts for the corresponding mode.* attributes.
map(function[, function...]) -> None
For every pixel in the image, maps each component through the corresponding function. If only one function is passed, it is used repeatedly for each component. This method modifies the image in place and is usually very fast (most of the time the functions are called only a small number of times, possibly only once for simple functions without branches), but it imposes a number of restrictions on the function(s) passed:
- it must accept a single integer argument and return a number (map will round the result to the nearest integer and clip it to range(0, 2 ** bits_per_component), if necessary);
- it must not try to intercept any BaseException, Exception or any unknown subclass of Exception raised by any operation on the argument (implementations may try to optimize the speed by passing funny objects, so even a simple "if n == 10:" may raise an exception: simply ignore it, map will take care of it); catching any other exception is fine;
- it should be side-effect free and its result should not depend on values (other than the argument) that may change during a single invocation of map.
Return a copy of the image rotated 90, 180 or 270 degrees counterclockwise around its center.
clip() -> None
Saturates invalid component values in YV12 images to the minimum or the maximum allowed (see mode.intervals), for other image modes this method does nothing, very fast; libraries that save/export YV12 images are encouraged to always call this method, since intermediate operations (e.g. the map method) may assign to pixels values outside the valid intervals.
split() -> tuple[image]
Returns a tuple of L, L16 or L32 images corresponding to the individual components in the image.
Planar images also supports attributes with the same names defined in component_names: they contain grayscale (mode L) images that offer a view on the pixel values for the corresponding component; any change to the subimages is immediately reflected on the parent image and vice versa (their buffers refer to the same memory location).
Non-planar images offer the following additional methods:
pixels() -> iterator[pixel]
Returns an iterator that iterates over all the pixels in the image, starting from the top line and scanning each line from left to right. See below for a description of the pixel objects.
__iter__() -> iterator[line]
Returns an iterator that iterates over all the lines in the image, from top to bottom. See below for a description of the line objects.
__len__() -> int
Returns the number of lines in the image (size.height).
__getitem__(integer) -> line
Returns the line at the specified (y) position.
__getitem__(tuple[integer]) -> pixel
The parameter must be a tuple of two integers; they are interpreted respectively as x and y coordinates in the image (0, 0 is the top left corner) and a pixel object is returned.
__getitem__(slice | tuple[integer | slice]) -> image
The parameter must be a slice or a tuple that contains two slices or an integer and a slice; the selected area of the image is copied and a new image is returned; image[x:y:z] is equivalent to image[:, x:y:z].
__setitem__(tuple[integer], integer | iterable[integer]) -> None
Modifies the pixel at specified position; image[x, y] = integer is a shortcut for image[x, y] = (integer,) for images with a single component.
__setitem__(slice | tuple[integer | slice], image) -> None
Selects an area in the same way as the corresponding form of the __getitem__ method and assigns to it a copy of the pixels from the image in the second argument, that must have exactly the same mode as this image and the same size as the specified area; the alpha component, if present, is simply copied and doesn't affect the other components of the image (i.e. no alpha compositing is performed).
The mode, size and buffer (including the address in memory of the buffer) never change after an image is created.
It is expected that, if PEP 3118 is accepted, all the image objects will support the new buffer protocol, however this is beyond the scope of this PEP.
The ImageMixin class implements all the methods and attributes described above except mode, size, buffer and info. Image is a subclass of ImageMixin that adds support for these four attributes and offers the following constructor (please note that the constructor is not part of the image protocol):
__init__(mode, size, color, source)
mode must be one of the constants in the MODES set, size is a sequence of two integers (width and height of the new image); color is a sequence of integers, one for each component of the image, used to initialize all the pixels to the same value; source can be a sequence of integers of the appropriate size and format that is copied as-is in the buffer of the new image or an existing image; in Python 2.x source can also be an instance of str and is interpreted as a sequence of bytes. color and source are mutually exclusive and if they are both omitted the image is initialized to transparent black (all the bytes in the buffer have value 16 in the YV12 mode, 255 in the CMYK* modes and 0 for everything else). If source is present and is an image, mode and/or size can be omitted; if they are specified and are different from the source mode and/or size, the source image is converted.
The exact algorithms used for resizing and doing color space conversions may differ between Python versions and implementations, but they always give high quality results (e.g.: a cubic spline interpolation can be used for upsampling and an antialias filter can be used for downsampling images); any combination of mode conversion is supported, but the algorithm used for conversions to and from the CMYK* modes is pretty naïve: if you have the exact color profiles of your devices you may want to use a good color management tool such as LittleCMS. The new image has an empty info dict.
The line objects (returned, e.g., when iterating over an image) support the following attributes and methods:
The mode of the image from where this line comes.
__iter__() -> iterator[pixel]
Returns an iterator that iterates over all the pixels in the line, from left to right. See below for a description of the pixel objects.
__len__() -> int
Returns the number of pixels in the line (the image width).
__getitem__(integer) -> pixel
Returns the pixel at the specified (x) position.
__getitem__(slice) -> image
The selected part of the line is copied and a new image is returned; the new image will always have height 1.
__setitem__(integer, integer | iterable[integer]) -> None
Modifies the pixel at the specified position; line[x] = integer is a shortcut for line[x] = (integer,) for images with a single component.
__setitem__(slice, image) -> None
Selects a part of the line and assigns to it a copy of the pixels from the image in the second argument, that must have height 1, a width equal to the specified slice and the same mode as this line; the alpha component, if present, is simply copied and doesn't affect the other components of the image (i.e. no alpha compositing is performed).
The pixel objects (returned, e.g., when iterating over a line) support the following attributes and methods:
The mode of the image from where this pixel comes.
A tuple of integers, one for each component. Any iterable of the correct length can be assigned to value (it will be automagically converted to a tuple), but you can't assign to it an integer, even if the mode has only a single component: use, e.g., pixel.l = 123 instead.
r, g, b, a, l, c, m, y, k
The integer values of each component; only those applicable for the current mode (in mode.component_names) will be available.
These four methods emulate a fixed length list of integers, one for each pixel component.
ImageSize is a named tuple, a class identical to tuple except that:
- its constructor only accepts two integers, width and height; they are converted in the constructor using their __index__() methods, so all the ImageSize objects are guaranteed to contain only int (or possibly long, in Python 2.x) instances;
- it has a width and a height property that are equivalent to the first and the second number in the tuple, respectively;
- the string returned by its __repr__ method is 'imageop.ImageSize(width=%d, height=%d)' % (width, height).
ImageSize is not usually instantiated by end-users, but can be used when creating a new class that implements the image protocol, since the size attribute must be an ImageSize instance.
The available image modes are visible at the C level as PyImage_* constants of type PyObject * (e.g.: PyImage_RGB is imageop.RGB).
The following functions offer a C-friendly interface to mode and image objects (all the functions return NULL or -1 on failure):
int PyImageMode_Check(PyObject *obj)
Returns true if the object obj is a valid image mode.
These functions are equivalent to their corresponding Python attributes or methods.
int PyImage_Check(PyObject *obj)
Returns true if the object obj is an Image object or an instance of a subtype of the Image type; see also PyObject_CheckImage below.
int PyImage_CheckExact(PyObject *obj)
Returns true if the object obj is an Image object, but not an instance of a subtype of the Image type.
Returns a new Image instance, initialized to transparent black (see Image.__init__ above for the details).
Returns a new Image instance, initialized with the contents of the image object rescaled and converted to the specified mode, if necessary.
Returns a new Image instance, initialized with the contents of the buffer object.
int PyObject_CheckImage(PyObject *obj)
Returns true if the object obj implements a sufficient subset of the image protocol to be accepted by the functions defined below, even if its class is not a subclass of ImageMixin and/or Image. Currently it simply checks for the existence and correctness of the attributes mode, size and buffer.
These functions are equivalent to their corresponding Python attributes or methods; the image memory can be accessed only with the GIL and a reference to the image or its buffer held, and extra care should be taken for modes with more than 8 bits per component: the data is stored in native byte order and it can be not aligned on 2 or 4 byte boundaries.
A few examples of common operations with the new Image class and protocol:
# create a new black RGB image of 6x9 pixels rgb_image = imageop.Image(imageop.RGB, (6, 9)) # same as above, but initialize the image to bright red rgb_image = imageop.Image(imageop.RGB, (6, 9), color=(255, 0, 0)) # convert the image to YCbCr yuv_image = imageop.Image(imageop.JPEG_YV12, source=rgb_image) # read the value of a pixel and split it into three ints r, g, b = rgb_image[x, y] # modify the magenta component of a pixel in a CMYK image cmyk_image[x, y].m = 13 # modify the Y (luma) component of a pixel in a *YV12 image and # its corresponding subsampled Cr (red chroma) yuv_image.y[x, y] = 42 yuv_image.cr[x // 2, y // 2] = 54 # iterate over an image for line in rgb_image: for pixel in line: # swap red and blue, and set green to 0 pixel.value = pixel.b, 0, pixel.r # find the maximum value of the red component in the image max_red = max(pixel.r for pixel in rgb_image.pixels()) # count the number of colors in the image num_of_colors = len(set(tuple(pixel) for pixel in image.pixels())) # copy a block of 4x2 pixels near the upper right corner of an # image and paste it into the lower left corner of the same image image[:4, -2:] = image[-6:-2, 1:3] # create a copy of the image, except that the new image can have a # different (usually empty) info dict new_image = image[:] # create a mirrored copy of the image, with the left and right # sides flipped flipped_image = image[::-1, :] # downsample an image to half its original size using a fast, low # quality operation and a slower, high quality one: low_quality_image = image[::2, ::2] new_size = image.size.width // 2, image.size.height // 2 high_quality_image = imageop.Image(size=new_size, source=image) # direct buffer access rgb_image[0, 0] = r, g, b assert tuple(rgb_image.buffer[:3]) == (r, g, b)
There are three areas touched by this PEP where backwards compatibility should be considered:
- Python 2.6: new classes and objects are added to the imageop module without touching the existing module contents; new methods and attributes will be added to Tkinter.PhotoImage and its __getitem__ and __setitem__ methods will be modified to accept integers, tuples and slices (currently they only accept strings). All the changes provide a superset of the existing functionality, so no major compatibility issues are expected.
- Python 3.0: the legacy contents of the imageop module will be deleted, according to PEP 3108; everything defined in this proposal will work like in Python 2.x with the exception of the usual 2.x/3.0 differences (e.g. support for long integers and for interpreting str instances as sequences of bytes will be dropped).
- external libraries: the names and the semantics of the standard image methods and attributes are carefully chosen to allow some external libraries that manipulate images (including at least PIL, wxPython and pygame) to implement the new protocol in their image classes without breaking compatibility with existing code. The only blatant conflicts between the image protocol and NumPy arrays are the value of the size attribute and the coordinates order in the image[x, y] expression.
If this PEP is accepted, the author will provide a reference implementation of the new classes in pure Python (that can run in CPython, PyPy, Jython and IronPython) and a second one optimized for speed in Python and C, suitable for inclusion in the CPython standard library. The author will also submit the required Tkinter patches. For all the code will be available a version for Python 2.x and a version for Python 3.0 (it is expected that the two version will be very similar and the Python 3.0 one will probably be generated almost completely automatically).
The implementation of this PEP, if accepted, is sponsored by Google through the Google Summer of Code program.
This document has been placed in the public domain.