#### /Src/Dependencies/Boost/libs/gil/doc/doxygen/tutorial.dox

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  1////////////////////////////////////////////////////////////////////////////////////////
2/// \file
3/// \brief Doxygen documentation
4/// \author Lubomir Bourdev and Hailin Jin \n
6///
7///
8////////////////////////////////////////////////////////////////////////////////////////
9
10/**
11\page GILTutorial Generic Image Library Tutorial
12
15
16\version 2.1
17\date    September 15, 2007
18
19The Generic Image Library (GIL) is a C++ library that abstracts image representations from algorithms and allows writing code that can work on
20a variety of images with performance similar to hand-writing for a specific image type.
21<p>This document will give you a jump-start in using GIL. It does not discuss the underlying design
22of the library and does not cover all aspects of it. You can find a detailed library design document on the main GIL web page
24
25- \ref InstallSec
26- \ref ExampleSec
27   - \ref InterfaceSec
28   - \ref FirstImplementationSec
30   - \ref GenericVersionSec
31   - \ref ImageViewTransformationSec
33   - \ref STLEquivalentsSec
34   - \ref ColorConversionSec
35   - \ref ImagesSec
36   - \ref VirtualViewSec
37   - \ref DynamicImageSec
38   - \ref ConclusionSec
39- \ref AppendixSec
40   - \ref AppendixConventionSec
41
42\section InstallSec     Installation
43
45GIL is approved for integration into Boost and in the future will be installed simply by installing Boost from http://www.boost.org.
46GIL consists of header files only and does not require any libraries to link against. It does not require Boost to be built.
47Including \p boost/gil/gil_all.hpp will be sufficient for most projects.
48
49\section ExampleSec     Example - Computing the Image Gradient
50
51This tutorial will walk through an example of using GIL to compute the image gradients.
52We will start with some very simple and non-generic code and make it more generic as we go along.
54The gradient at pixel x can be approximated with the half-difference of its two neighboring pixels:
55D[x] = (I[x-1] - I[x+1]) / 2
56
57For simplicity, we will also ignore the boundary cases - the pixels along the edges of the image for which one of the neighbors is not defined.
58The focus of this document is how to use GIL, not how to create a good gradient generation algorithm.
59
60\subsection InterfaceSec Interface and Glue Code
61
62Let us first start with 8-bit unsigned grayscale image as the input and 8-bit signed grayscale image as the output.
63Here is how the interface to our algorithm looks like:
64
65\code
66#include <boost/gil/gil_all.hpp>
67using namespace boost::gil;
68
69void x_gradient(const gray8c_view_t& src, const gray8s_view_t& dst) {
70    assert(src.dimensions() == dst.dimensions());
71    ...    // compute the gradient
72}
73\endcode
74
75\p gray8c_view_t is the type of the source image view - an 8-bit grayscale view, whose pixels are read-only (denoted by the \p "c"). The output
76is a grayscale view with a 8-bit signed (denoted by the \p "s") integer channel type. See Appendix 1 for the complete convension GIL uses to name concrete types.
77
78GIL makes a distinction between an image and an image view. A GIL <em>image view</em>, is a shallow, lightweight view of a rectangular grid of pixels. It provides access to the pixels
79but does not own the pixels. Copy-constructing a view does not deep-copy the pixels. Image views do not propagate their constness to the pixels and should
80always be taken by a const reference. Whether a view is mutable or read-only (immutable) is a property of the view type.
81
82A GIL \e image, on the other hand, is a view with associated ownership. It is a container of pixels; its constructor/destructor allocates/deallocates the pixels, its copy-constructor
83performs deep-copy of the pixels and its operator== performs deep-compare of the pixels. Images also propagate their constness to their pixels - a constant reference to an image will not
84allow for modifying its pixels.
85
86Most GIL algorithms operate on image views; images are rarely needed. GIL's design is very similar to that of the STL. The STL equivalent of GIL's image is a container, like \p std::vector, whereas
87GIL's image view corresponds to STL's range, which is often represented with a pair of iterators. STL algorithms operate on ranges, just like GIL algorithms operate on image views.
88
89GIL's image views can be constructed from raw data - the dimensions, the number of bytes per row and the pixels, which for chunky views are represented with one pointer. Here is how to provide
90the glue between your code and GIL:
91
92\code
93void ComputeXGradientGray8(const unsigned char* src_pixels, ptrdiff_t src_row_bytes, int w, int h,
94                                   signed char* dst_pixels, ptrdiff_t dst_row_bytes) {
95    gray8c_view_t src = interleaved_view(w, h, (const gray8_pixel_t*)src_pixels,src_row_bytes);
96    gray8s_view_t dst = interleaved_view(w, h, (     gray8s_pixel_t*)dst_pixels,dst_row_bytes);
98}
99\endcode
100
101This glue code is very fast and views are lightweight - in the above example the views have a size of 16 bytes. They consist of a pointer to the top left pixel and three integers - the width, height,
102and number of bytes per row.
103
104\subsection FirstImplementationSec First Implementation
105
106Focusing on simplicity at the expense of speed, we can compute the horizontal gradient like this:
107
108\code
109void x_gradient(const gray8c_view_t& src, const gray8s_view_t& dst) {
110    for (int y=0; y<src.height(); ++y)
111        for (int x=1; x<src.width()-1; ++x)
112            dst(x,y) = (src(x-1,y) - src(x+1,y)) / 2;
113}
114\endcode
115
116We use image view's \p operator(x,y) to get a reference to the pixel at a given location and we set it to the half-difference of its left and right neighbors.
117operator() returns a reference to a grayscale pixel. A grayscale pixel is convertible to its channel type (<tt>unsigned char</tt> for \p src) and it can be copy-constructed from a channel.
118(This is only true for grayscale pixels).
119While the above code is easy to read, it is not very fast, because the binary \p operator() computes the location of the pixel in a 2D grid, which involves addition and multiplication. Here is
120a faster version of the above:
121
122\code
123void x_gradient(const gray8c_view_t& src, const gray8s_view_t& dst) {
124    for (int y=0; y<src.height(); ++y) {
125        gray8c_view_t::x_iterator src_it = src.row_begin(y);
126        gray8s_view_t::x_iterator dst_it = dst.row_begin(y);
127
128        for (int x=1; x<src.width()-1; ++x)
129            dst_it[x] = (src_it[x-1] - src_it[x+1]) / 2;
130    }
131}
132\endcode
133
134We use pixel iterators initialized at the beginning of each row. GIL's iterators are Random Access Traversal iterators. If you are not familiar with random access iterators, think of them as if they
135were pointers. In fact, in the above example the two iterator types are raw C pointers and their \p operator[] is a fast pointer indexing operator.
136
137The code to compute gradient in the vertical direction is very similar:
138
139\code
140void y_gradient(const gray8c_view_t& src, const gray8s_view_t& dst) {
141    for (int x=0; x<src.width(); ++x) {
142        gray8c_view_t::y_iterator src_it = src.col_begin(x);
143        gray8s_view_t::y_iterator dst_it = dst.col_begin(x);
144
145        for (int y=1; y<src.height()-1; ++y)
146            dst_it[y] = (src_it[y-1] - src_it[y+1])/2;
147    }
148}
149\endcode
150
151Instead of looping over the rows, we loop over each column and create a \p y_iterator, an iterator moving vertically. In this case a simple pointer cannot be used because the distance
152between two adjacent pixels equals the number of bytes in each row of the image. GIL uses here a special step iterator class whose size is 8 bytes - it contains a raw C pointer and a step.
153Its \p operator[] multiplies the index by its step.
154
155The above version of \p y_gradient, however, is much slower (easily an order of magnitude slower) than \p x_gradient because of the memory access pattern; traversing an image vertically
156results in lots of cache misses. A much more efficient and cache-friendly version will iterate over the columns in the inner loop:
157
158\code
159void y_gradient(const gray8c_view_t& src, const gray8s_view_t& dst) {
160    for (int y=1; y<src.height()-1; ++y) {
161        gray8c_view_t::x_iterator src1_it = src.row_begin(y-1);
162        gray8c_view_t::x_iterator src2_it = src.row_begin(y+1);
163        gray8s_view_t::x_iterator dst_it = dst.row_begin(y);
164
165        for (int x=0; x<src.width(); ++x) {
166            *dst_it = ((*src1_it) - (*src2_it))/2;
167            ++dst_it;
168            ++src1_it;
169            ++src2_it;
170        }
171    }
172}
173\endcode
174
175This sample code also shows an alternative way of using pixel iterators - instead of \p operator[] one could use increments and dereferences.
176
177
178
179
180
182
183Unfortunately this cache-friendly version requires the extra hassle of maintaining two separate iterators in the source view. For every pixel,
184we want to access its neighbors above and below it. Such relative access can be done with GIL locators:
185
186\code
187void y_gradient(const gray8c_view_t& src, const gray8s_view_t& dst) {
188    gray8c_view_t::xy_locator src_loc = src.xy_at(0,1);
189    for (int y=1; y<src.height()-1; ++y) {
190        gray8s_view_t::x_iterator dst_it  = dst.row_begin(y);
191
192        for (int x=0; x<src.width(); ++x) {
193            (*dst_it) = (src_loc(0,-1) - src_loc(0,1)) / 2;
194            ++dst_it;
195            ++src_loc.x();                  // each dimension can be advanced separately
196        }
197        src_loc+=point2<std::ptrdiff_t>(-src.width(),1);    // carriage return
198    }
199}
200\endcode
201
202The first line creates a locator pointing to the first pixel of the second row of the source view. A GIL pixel locator is very similar to an iterator,
203except that it can move both horizontally and vertically. \p src_loc.x() and \p src_loc.y() return references to a horizontal and a vertical iterator
204respectively, which can be used to move the locator along the desired dimension, as shown above. Additionally, the locator can be advanced in both dimensions
205simultaneously using its \p operator+= and \p operator-=. Similar to image views, locators provide binary \p operator() which returns a reference to a pixel
206with a relative offset to the current locator position. For example, \p src_loc(0,1) returns a reference to the neighbor below the current pixel.
207Locators are very lightweight objects - in the above example the locator has a size of 8 bytes - it consists of a raw pointer to the current pixel and an int
208indicating the number of bytes from one row to the next (which is the step when moving vertically). The call to \p ++src_loc.x() corresponds to a single C pointer increment.
209However, the example above performs more computations than necessary. The code src_loc(0,1) has to compute the offset of the pixel in two dimensions, which is slow.
210Notice though that the offset of the two neighbors is the same, regardless of the pixel location. To improve the performance, GIL can cache and reuse this offset:
211
212\code
213void y_gradient(const gray8c_view_t& src, const gray8s_view_t& dst) {
214    gray8c_view_t::xy_locator src_loc = src.xy_at(0,1);
215    gray8c_view_t::xy_locator::cached_location_t above = src_loc.cache_location(0,-1);
216    gray8c_view_t::xy_locator::cached_location_t below = src_loc.cache_location(0, 1);
217
218    for (int y=1; y<src.height()-1; ++y) {
219        gray8s_view_t::x_iterator dst_it = dst.row_begin(y);
220
221        for (int x=0; x<src.width(); ++x) {
222            (*dst_it) = (src_loc[above] - src_loc[below])/2;
223            ++dst_it;
224            ++src_loc.x();
225        }
226        src_loc+=point2<std::ptrdiff_t>(-src.width(),1);
227    }
228}
229\endcode
230
231In this example \p "src_loc[above]" corresponds to a fast pointer indexing operation and the code is efficient.
232
233\subsection GenericVersionSec Creating a Generic Version of GIL Algorithms
234
235Let us make our \p x_gradient more generic. It should work with any image views, as long as they have the same number of channels.
236The gradient operation is to be computed for each channel independently. Here is how the new interface looks like:
237
238\code
239template <typename SrcView, typename DstView>
240void x_gradient(const SrcView& src, const DstView& dst) {
241    gil_function_requires<ImageViewConcept<SrcView> >();
242    gil_function_requires<MutableImageViewConcept<DstView> >();
243    gil_function_requires<ColorSpacesCompatibleConcept<
244                                typename color_space_type<SrcView>::type,
245                                typename color_space_type<DstView>::type> >();
246
247    ... // compute the gradient
248}
249\endcode
250
251The new algorithm now takes the types of the input and output image views as template parameters.
252That allows using both built-in GIL image views, as well as any user-defined image view classes.
253The first three lines are optional; they use \p boost::concept_check to ensure that the two arguments
254are valid GIL image views, that the second one is mutable and that their color spaces are compatible (i.e. have the same set of channels).
255
256GIL does not require using its own built-in constructs. You are free to use your own channels, color spaces, iterators, locators, views and images.
257However, to work with the rest of GIL they have to satisfy a set of requirements; in other words, they have to \e model the corresponding GIL \e concept.
258GIL's concepts are defined in the user guide.
259
260One of the biggest drawbacks of using
261templates and generic programming in C++ is that compile errors can be very difficult to comprehend.
262This is a side-effect of the lack of early type checking - a generic argument may not satisfy the requirements of a function,
263but the incompatibility may be triggered deep into a nested call, in code unfamiliar and hardly related to the problem.
264GIL uses \p boost::concept_check to mitigate this problem. The above three lines of code check whether the
265template parameters are valid models of their corresponding concepts.
266If a model is incorrect, the compile error will be inside \p gil_function_requires, which is much closer to the problem
267and easier to track. Furthermore, such checks get compiled out and have zero performance overhead. The disadvantage of using
268concept checks is the sometimes severe impact they have on compile time. This is why GIL performs concept checks only in
269debug mode, and only if \p BOOST_GIL_USE_CONCEPT_CHECK is defined (off by default).
270
271The body of the generic function is very similar to that of the concrete one. The biggest difference is that we need to loop over the
272channels of the pixel and compute the gradient for each channel:
273
274\code
275template <typename SrcView, typename DstView>
276void x_gradient(const SrcView& src, const DstView& dst) {
277    for (int y=0; y<src.height(); ++y) {
278        typename SrcView::x_iterator src_it = src.row_begin(y);
279        typename DstView::x_iterator dst_it = dst.row_begin(y);
280
281        for (int x=1; x<src.width()-1; ++x)
282            for (int c=0; c<num_channels<SrcView>::value; ++c)
283                dst_it[x][c] = (src_it[x-1][c]- src_it[x+1][c])/2;
284    }
285}
286\endcode
287
288Having an explicit loop for each channel could be a performance problem. GIL allows us to abstract out such per-channel operations:
289
290\code
291template <typename Out>
292struct halfdiff_cast_channels {
293    template <typename T> Out operator()(const T& in1, const T& in2) const {
294        return Out((in1-in2)/2);
295    }
296};
297
298template <typename SrcView, typename DstView>
299void x_gradient(const SrcView& src, const DstView& dst) {
300    typedef typename channel_type<DstView>::type dst_channel_t;
301
302    for (int y=0; y<src.height(); ++y) {
303        typename SrcView::x_iterator src_it = src.row_begin(y);
304        typename DstView::x_iterator dst_it = dst.row_begin(y);
305
306        for (int x=1; x<src.width()-1; ++x)
307            static_transform(src_it[x-1], src_it[x+1], dst_it[x],
308                               halfdiff_cast_channels<dst_channel_t>());
309    }
310}
311\endcode
312
313\p static_transform is an example of a channel-level GIL algorithm. Other such algorithms are \p static_generate, \p static_fill and \p static_for_each. They are the channel-level equivalents
314of STL's \p generate, \p transform, \p fill and \p for_each respectively. GIL channel algorithms use static recursion to unroll the loops; they never loop over the channels explicitly.
315Note that sometimes modern compilers (at least Visual Studio 8) already unroll channel-level loops, such as the one above. However, another advantage of using
316GIL's channel-level algorithms is that they pair the channels semantically, not based on their order in memory. For example, the above example will properly match an RGB source
317with a BGR destination.
318
319Here is how we can use our generic version with images of different types:
320
321\code
322// Calling with 16-bit grayscale data
323void XGradientGray16_Gray32(const unsigned short* src_pixels, ptrdiff_t src_row_bytes, int w, int h,
324                                  signed int* dst_pixels, ptrdiff_t dst_row_bytes) {
325    gray16c_view_t src=interleaved_view(w,h,(const gray16_pixel_t*)src_pixels,src_row_bytes);
326    gray32s_view_t dst=interleaved_view(w,h,(     gray32s_pixel_t*)dst_pixels,dst_row_bytes);
328}
329
330// Calling with 8-bit RGB data into 16-bit BGR
331void XGradientRGB8_BGR16(const unsigned char* src_pixels, ptrdiff_t src_row_bytes, int w, int h,
332                                 signed short* dst_pixels, ptrdiff_t dst_row_bytes) {
333    rgb8c_view_t  src = interleaved_view(w,h,(const rgb8_pixel_t*)src_pixels,src_row_bytes);
334    rgb16s_view_t dst = interleaved_view(w,h,(    rgb16s_pixel_t*)dst_pixels,dst_row_bytes);
336}
337
338// Either or both the source and the destination could be planar - the gradient code does not change
340           const unsigned short* src_r, const unsigned short* src_g, const unsigned short* src_b,
341           ptrdiff_t src_row_bytes, int w, int h,
342           signed int* dst_pixels, ptrdiff_t dst_row_bytes) {
343    rgb16c_planar_view_t src=planar_rgb_view (w,h, src_r,src_g,src_b,         src_row_bytes);
344    rgb32s_view_t        dst=interleaved_view(w,h,(rgb32s_pixel_t*)dst_pixels,dst_row_bytes);
346}
347\endcode
348
349As these examples illustrate, both the source and the destination can be interleaved or planar, of any channel depth (assuming the destination channel is
350assignable to the source), and of any compatible color spaces.
351
352GIL 2.1 can also natively represent images whose channels are not byte-aligned, such as 6-bit RGB222 image or a 1-bit Gray1 image.
353GIL algorithms apply to these images natively. See the design guide or sample files for more on using such images.
354
355
356
357
358
359
360
361
362
363
364
365
366\subsection ImageViewTransformationSec Image View Transformations
367
368One way to compute the y-gradient is to rotate the image by 90 degrees, compute the x-gradient and rotate the result back. Here is how to do this in GIL:
369
370\code
371template <typename SrcView, typename DstView>
372void y_gradient(const SrcView& src, const DstView& dst) {
374}
375\endcode
376
377\p rotated90ccw_view takes an image view and returns an image view representing 90-degrees counter-clockwise rotation of its input. It is an example of a GIL view transformation function. GIL provides
378a variety of transformation functions that can perform any axis-aligned rotation, transpose the view, flip it vertically or horizontally, extract a rectangular subimage,
379perform color conversion, subsample view, etc. The view transformation functions are fast and shallow - they don't copy the pixels, they just change the "coordinate system" of
380accessing the pixels. \p rotated90cw_view, for example, returns a view whose horizontal iterators are the vertical iterators of the original view. The above code to compute \p y_gradient
381is slow because of the memory access pattern; using \p rotated90cw_view does not make it any slower.
382
383Another example: suppose we want to compute the gradient of the N-th channel of a color image. Here is how to do that:
384
385\code
386template <typename SrcView, typename DstView>
387void nth_channel_x_gradient(const SrcView& src, int n, const DstView& dst) {
389}
390\endcode
391
392\p nth_channel_view is a view transformation function that takes any view and returns a single-channel (grayscale) view of its N-th channel.
393For interleaved RGB view, for example, the returned view is a step view - a view whose horizontal iterator skips over two channels when incremented.
394If applied on a planar RGB view, the returned type is a simple grayscale view whose horizontal iterator is a C pointer.
395Image view transformation functions can be piped together. For example, to compute the y gradient of the second channel of the even pixels in the view, use:
396
397\code
399\endcode
400
401GIL can sometimes simplify piped views. For example, two nested subsampled views (views that skip over pixels in X and in Y) can be represented as a single subsampled view whose step
402is the product of the steps of the two views.
403
405
406Let's go back to \p x_gradient one more time.
407Many image view algorithms apply the same operation for each pixel and GIL provides an abstraction to handle them. However, our algorithm has an unusual access pattern, as it skips the
408first and the last column. It would be nice and instructional to see how we can rewrite it in canonical form. The way to do that in GIL is to write a version that works for every pixel, but
409apply it only on the subimage that excludes the first and last column:
410
411\code
412void x_gradient_unguarded(const gray8c_view_t& src, const gray8s_view_t& dst) {
413    for (int y=0; y<src.height(); ++y) {
414        gray8c_view_t::x_iterator src_it = src.row_begin(y);
415        gray8s_view_t::x_iterator dst_it = dst.row_begin(y);
416
417        for (int x=0; x<src.width(); ++x)
418            dst_it[x] = (src_it[x-1] - src_it[x+1]) / 2;
419    }
420}
421
422void x_gradient(const gray8c_view_t& src, const gray8s_view_t& dst) {
423    assert(src.width()>=2);
424    x_gradient_unguarded(subimage_view(src, 1, 0, src.width()-2, src.height()),
425                         subimage_view(dst, 1, 0, src.width()-2, src.height()));
426}
427\endcode
428
429\p subimage_view is another example of a GIL view transformation function. It takes a source view and a rectangular region (in this case, defined as x_min,y_min,width,height) and
430returns a view operating on that region of the source view. The above implementation has no measurable performance degradation from the version that operates on the original views.
431
432Now that \p x_gradient_unguarded operates on every pixel, we can rewrite it more compactly:
433
434\code
435void x_gradient_unguarded(const gray8c_view_t& src, const gray8s_view_t& dst) {
436    gray8c_view_t::iterator src_it = src.begin();
437    for (gray8s_view_t::iterator dst_it = dst.begin(); dst_it!=dst.end(); ++dst_it, ++src_it)
438        *dst_it = (src_it.x()[-1] - src_it.x()[1]) / 2;
439}
440\endcode
441
442GIL image views provide \p begin() and \p end() methods that return one dimensional pixel iterators which iterate over each pixel in the view,
443left to right and top to bottom. They do a proper "carriage return" - they skip any unused bytes at the end of a row. As such, they are slightly suboptimal, because they need to keep
444track of their current position with respect to the end of the row. Their increment operator performs one extra check (are we at the end of the row?), a check that is avoided if two
445nested loops are used instead. These iterators have a method \p x() which returns the more lightweight horizontal iterator that we used previously. Horizontal iterators have no
446notion of the end of rows. In this case, the horizontal iterators are raw C pointers. In our example, we must use the horizontal iterators to access the two neighbors properly, since they
447could reside outside the image view.
448
449\subsection STLEquivalentsSec STL Equivalent Algorithms
450
451GIL provides STL equivalents of many algorithms. For example, \p std::transform is an STL algorithm that sets each element in a destination range the result of a generic function taking the
452corresponding element of the source range. In our example, we want to assign to each destination pixel the value of the half-difference of the horizontal neighbors of the corresponding source pixel.
453If we abstract that operation in a function object, we can use GIL's \p transform_pixel_positions to do that:
454
455\code
456struct half_x_difference {
457    int operator()(const gray8c_loc_t& src_loc) const {
458        return (src_loc.x()[-1] - src_loc.x()[1]) / 2;
459    }
460};
461
462void x_gradient_unguarded(const gray8c_view_t& src, const gray8s_view_t& dst) {
463    transform_pixel_positions(src, dst, half_x_difference());
464}
465\endcode
466
467GIL provides the algorithms \p for_each_pixel and \p transform_pixels which are image view equivalents of STL's \p std::for_each and \p std::transform. It also provides
468\p for_each_pixel_position and \p transform_pixel_positions, which instead of references to pixels, pass to the generic function pixel locators. This allows for more powerful functions
469that can use the pixel neighbors through the passed locators.
470GIL algorithms iterate through the pixels using the more efficient two nested loops (as opposed to the single loop using 1-D iterators)
471
472\subsection ColorConversionSec Color Conversion
473
474Instead of computing the gradient of each color plane of an image, we often want to compute the gradient of the luminosity. In other words, we want to convert the
475color image to grayscale and compute the gradient of the result. Here how to compute the luminosity gradient of a 32-bit float RGB image:
476
477\code
478void x_gradient_rgb_luminosity(const rgb32fc_view_t& src, const gray8s_view_t& dst) {
480}
481\endcode
482
483\p color_converted_view is a GIL view transformation function that takes any image view and returns a view in a target color space and channel depth (specified
484as template parameters). In our example, it constructs an 8-bit integer grayscale view over 32-bit float RGB pixels. Like all other view transformation functions, \p color_converted_view is very
485fast and shallow. It doesn't copy the data or perform any color conversion. Instead it returns a view that performs color conversion every time its pixels are accessed.
486
487In the generic version of this algorithm we might like to convert the color space to grayscale, but keep the channel depth the same. We do that by constructing the
488type of a GIL grayscale pixel with the same channel as the source, and color convert to that pixel type:
489
490\code
491template <typename SrcView, typename DstView>
492void x_luminosity_gradient(const SrcView& src, const DstView& dst) {
493    typedef pixel<typename channel_type<SrcView>::type, gray_layout_t> gray_pixel_t;
495}
496\endcode
497
498When the destination color space and channel type happens to be the same as the source one, color conversion is unnecessary. GIL detects this case and avoids calling the color conversion
499code at all - i.e. \p color_converted_view returns back the source view unchanged.
500
501
502\subsection ImagesSec Image
503
504The above example has a performance problem - \p x_gradient dereferences most source pixels twice, which will cause the above code to perform color conversion twice.
505Sometimes it may be more efficient to copy the color converted image into a temporary buffer and use it to compute the gradient - that way color conversion is invoked once per pixel.
506Using our non-generic version we can do it like this:
507
508\code
509void x_luminosity_gradient(const rgb32fc_view_t& src, const gray8s_view_t& dst) {
510    gray8_image_t ccv_image(src.dimensions());
511    copy_pixels(color_converted_view<gray8_pixel_t>(src), view(ccv_image));
512
514}
515\endcode
516
517First we construct an 8-bit grayscale image with the same dimensions as our source. Then we copy a color-converted view of the source into the temporary image.
518Finally we use a read-only view of the temporary image in our \p x_gradient algorithm. As the example shows, GIL provides global functions \p view and \p const_view
519that take an image and return a mutable or an immutable view of its pixels.
520
521Creating a generic version of the above is a bit trickier:
522
523\code
524template <typename SrcView, typename DstView>
525void x_luminosity_gradient(const SrcView& src, const DstView& dst) {
526    typedef typename channel_type<DstView>::type d_channel_t;
527    typedef typename channel_convert_to_unsigned<d_channel_t>::type channel_t;
528    typedef pixel<channel_t, gray_layout_t>  gray_pixel_t;
529    typedef image<gray_pixel_t, false>       gray_image_t;
530
531    gray_image_t ccv_image(src.dimensions());
532    copy_pixels(color_converted_view<gray_pixel_t>(src), view(ccv_image));
534}
535\endcode
536
537First we use the \p channel_type metafunction to get the channel type of the destination view. A metafunction is a function operating on types. In GIL metafunctions
538are structs which take their parameters as template parameters and return their result in a nested typedef called \p type. In this case, \p channel_type is
539a unary metafunction which in this example is called with the type of an image view and returns the type of the channel associated with that image view.
540
541GIL constructs that have an associated pixel type, such as pixels, pixel iterators, locators, views and images, all model \p PixelBasedConcept, which means
542that they provide a set of metafunctions to query the pixel properties, such as \p channel_type, \p color_space_type, \p channel_mapping_type, and \p num_channels.
543
544After we get the channel type of the destination view, we use another metafunction to remove its sign (if it is a signed integral type) and then use it
545to generate the type of a grayscale pixel. From the pixel type we create the image type. GIL's image class is templated over the pixel type and a boolean
546indicating whether the image should be planar or interleaved.
547Single-channel (grayscale) images in GIL must always be interleaved. There are multiple ways of constructing types in GIL. Instead of instantiating the classes
548directly we could have used type factory metafunctions. The following code is equivalent:
549
550\code
551template <typename SrcView, typename DstView>
552void x_luminosity_gradient(const SrcView& src, const DstView& dst) {
553    typedef typename channel_type<DstView>::type d_channel_t;
554    typedef typename channel_convert_to_unsigned<d_channel_t>::type channel_t;
555    typedef typename image_type<channel_t, gray_layout_t>::type gray_image_t;
556    typedef typename gray_image_t::value_type gray_pixel_t;
557
558    gray_image_t ccv_image(src.dimensions());
559    copy_and_convert_pixels(src, view(ccv_image));
561}
562\endcode
563
564GIL provides a set of metafunctions that generate GIL types - \p image_type is one such meta-function that constructs the type of an image from
565a given channel type, color layout, and planar/interleaved option (the default is interleaved). There are also similar meta-functions to
566construct the types of pixel references, iterators, locators and image views. GIL also has metafunctions \p derived_pixel_reference_type, \p derived_iterator_type,
567\p derived_view_type and \p derived_image_type that construct the type of a GIL construct from a given source one by changing one or more properties of
568the type and keeping the rest.
569
570From the image type we can use the nested typedef \p value_type to obtain the type of a pixel. GIL images, image views and locators have nested typedefs
571\p value_type and \p reference to obtain the type of the pixel and a reference to the pixel. If you have a pixel iterator, you can get these types from its
572\p iterator_traits. Note also the algorithm \p copy_and_convert_pixels, which is an abbreviated version of \p copy_pixels with a color converted source view.
573
574\subsection VirtualViewSec Virtual Image Views
575
576So far we have been dealing with images that have pixels stored in memory. GIL allows you to create an image view of an arbitrary image, including
577a synthetic function. To demonstrate this, let us create a view of the Mandelbrot set.
578First, we need to create a function object that computes the value of the Mandelbrot set at a given location (x,y) in the image:
579\code
581struct mandelbrot_fn {
582    typedef point2<ptrdiff_t>   point_t;
583
584    typedef mandelbrot_fn       const_t;
585    typedef gray8_pixel_t       value_type;
586    typedef value_type          reference;
587    typedef value_type          const_reference;
588    typedef point_t             argument_type;
589    typedef reference           result_type;
590    BOOST_STATIC_CONSTANT(bool, is_mutable=false);
591
592    mandelbrot_fn() {}
593    mandelbrot_fn(const point_t& sz) : _img_size(sz) {}
594
595    result_type operator()(const point_t& p) const {
596        // normalize the coords to (-2..1, -1.5..1.5)
597        double t=get_num_iter(point2<double>(p.x/(double)_img_size.x*3-2, p.y/(double)_img_size.y*3-1.5f));
598        return value_type((bits8)(pow(t,0.2)*255));   // raise to power suitable for viewing
599    }
600private:
601    point_t _img_size;
602
603    double get_num_iter(const point2<double>& p) const {
604        point2<double> Z(0,0);
605        for (int i=0; i<100; ++i) {     // 100 iterations
606            Z = point2<double>(Z.x*Z.x - Z.y*Z.y + p.x, 2*Z.x*Z.y + p.y);
607            if (Z.x*Z.x + Z.y*Z.y > 4)
608                return i/(double)100;
609        }
610        return 0;
611    }
612};
613\endcode
614
615We can now use GIL's \p virtual_2d_locator with this function object to construct a Mandelbrot view of size 200x200 pixels:
616\code
617typedef mandelbrot_fn::point_t point_t;
618typedef virtual_2d_locator<mandelbrot_fn,false> locator_t;
619typedef image_view<locator_t> my_virt_view_t;
620
621point_t dims(200,200);
622
623// Construct a Mandelbrot view with a locator, taking top-left corner (0,0) and step (1,1)
624my_virt_view_t mandel(dims, locator_t(point_t(0,0), point_t(1,1), mandelbrot_fn(dims)));
625\endcode
626
627We can treat the synthetic view just like a real one. For example, let's invoke our \p x_gradient algorithm to compute
628the gradient of the 90-degree rotated view of the Mandelbrot set and save the original and the result:
629
630\code
631gray8s_image_t img(dims);
633
634// Save the Mandelbrot set and its 90-degree rotated gradient (jpeg cannot save signed char; must convert to unsigned char)
635jpeg_write_view("mandel.jpg",mandel);
637\endcode
638
639Here is what the two files look like:
640
641\image html mandel.jpg
642
643\subsection DynamicImageSec Run-Time Specified Images and Image Views
644
645So far we have created a generic function that computes the image gradient of a templated image view.
646Sometimes, however, the properties of an image view, such as its color space and channel depth, may not be available at compile time.
647GIL's \p dynamic_image extension allows for working with GIL constructs that are specified at run time, also called \e variants. GIL provides
648models of a run-time instantiated image, \p any_image, and a run-time instantiated image view, \p any_image_view. The mechanisms are in place to create
649other variants, such as \p any_pixel, \p any_pixel_iterator, etc.
650Most of GIL's algorithms and all of the view transformation functions also work with run-time instantiated image views and binary algorithms, such
651as \p copy_pixels can have either or both arguments be variants.
652
653Lets make our \p x_luminosity_gradient algorithm take a variant image view. For simplicity, let's assume that only the source view can be a variant.
654(As an example of using multiple variants, see GIL's image view algorithm overloads taking multiple variants.)
655
656First, we need to make a function object that contains the templated destination view and has an application operator taking a templated source view:
657
658\code
659#include <boost/gil/extension/dynamic_image/dynamic_image_all.hpp>
660
661template <typename DstView>
663    typedef void result_type;        // required typedef
664
665    const DstView& _dst;
666    x_gradient_obj(const DstView& dst) : _dst(dst) {}
667
668    template <typename SrcView>
669    void operator()(const SrcView& src) const { x_luminosity_gradient(src, _dst); }
670};
671\endcode
672
673The second step is to provide an overload of \p x_luminosity_gradient that takes image view variant and calls GIL's \p apply_operation
674passing it the function object:
675
676\code
677template <typename SrcViews, typename DstView>
678void x_luminosity_gradient(const any_image_view<SrcViews>& src, const DstView& dst) {
680}
681\endcode
682
683\p any_image_view<SrcViews> is the image view variant. It is templated over \p SrcViews, an enumeration of all possible view types the variant can take.
684\p src contains inside an index of the currently instantiated type, as well as a block of memory containing the instance.
685\p apply_operation goes through a switch statement over the index, each case of which casts the memory to the correct view type and invokes the
686function object with it. Invoking an algorithm on a variant has the overhead of one switch statement. Algorithms that perform an operation for
687each pixel in an image view have practically no performance degradation when used with a variant.
688
689Here is how we can construct a variant and invoke the algorithm:
690
691\code
692#include <boost/mpl/vector.hpp>
693#include <boost/gil/extension/io/jpeg_dynamic_io.hpp>
694
695typedef mpl::vector<gray8_image_t, gray16_image_t, rgb8_image_t, rgb16_image_t> my_img_types;
696any_image<my_img_types> runtime_image;
698
702\endcode
703
704In this example, we create an image variant that could be 8-bit or 16-bit RGB or grayscale image. We then use GIL's I/O extension to load the image from file
705in its native color space and channel depth. If none of the allowed image types matches the image on disk, an exception will be thrown.
706We then construct a 8 bit signed (i.e. \p char) image to store the gradient and invoke \p x_gradient on it. Finally we save the result into another file.
707We save the view converted to 8-bit unsigned, because JPEG I/O does not support signed char.
708
709Note how free functions and methods such as \p jpeg_read_image, \p dimensions, \p view and \p const_view work on both templated and variant types.
710For templated images \p view(img) returns a templated view, whereas for image variants it returns a view variant.
711For example, the return type of \p view(runtime_image) is \p any_image_view<Views> where \p Views enumerates four views corresponding to the four image types.
712\p const_view(runtime_image) returns a \p any_image_view of the four read-only view types, etc.
713
714A warning about using variants: instantiating an algorithm with a variant effectively instantiates it with every possible type the variant can take.
715For binary algorithms, the algorithm is instantiated with every possible combination of the two input types! This can take a toll on both the compile time
716and the executable size.
717
718\section ConclusionSec   Conclusion
719
720This tutorial provides a glimpse at the challenges associated with writing generic and efficient image processing algorithms in GIL.
721We have taken a simple algorithm and shown how to make it work with image representations that vary in bit depth, color space, ordering of the
722channels, and planar/interleaved structure. We have demonstrated that the algorithm can work with fully abstracted virtual images, and even images
723whose type is specified at run time. The associated video presentation also demonstrates that even for complex scenarios the generated assembly
724is comparable to that of a C version of the algorithm, hand-written for the specific image types.
725
726Yet, even for such a simple algorithm, we are far from making a fully generic and optimized code. In particular, the presented algorithms work on homogeneous
727images, i.e. images whose pixels have channels that are all of the same type. There are examples of images, such as a packed 565 RGB format, which contain
728channels of different types. While GIL provides concepts and algorithms operating on heterogeneous pixels, we leave the task of extending x_gradient as an
730Second, after computing the value of the gradient we are simply casting it to the destination channel type. This may not always be the desired operation. For
731example, if the source channel is a float with range [0..1] and the destination is unsigned char, casting the half-difference to unsigned char will result in
732either 0 or 1. Instead, what we might want to do is scale the result into the range of the destination channel. GIL's channel-level algorithms might be useful
733in such cases. For example, \p channel_convert converts between channels by linearly scaling the source channel value into the range of the destination channel.
734
735There is a lot to be done in improving the performance as well. Channel-level operations, such as the half-difference, could be abstracted out into atomic
736channel-level algorithms and performance overloads could be provided for concrete channel types. Processor-specific operations could be used, for example,
737to perform the operation over an entire row of pixels simultaneously, or the data could be prefetched. All of these optimizations can be realized as performance
738specializations of the generic algorithm. Finally, compilers, while getting better over time, are still failing to fully optimize generic code in some cases, such
739as failing to inline some functions or put some variables into registers. If performance is an issue, it might be worth trying your code with different compilers.
740
741\section AppendixSec     Appendix
742
743\subsection AppendixConventionSec Naming convention for GIL concrete types
744
745Concrete (non-generic) GIL types follow this naming convention:
746
747<p>
748\e ColorSpace + \e BitDepth + [\p f | \p s]+ [\p c] + [\p _planar] + [\p _step] + \e ClassType + \p _t
749<p>
750
751Where \e ColorSpace also indicates the ordering of components. Examples are \p rgb, \p bgr, \p cmyk, \p rgba.
752\e BitDepth indicates the bit depth of the color channel. Examples are \p 8,\p 16,\p 32. By default the type of channel is unsigned integral; using \p s indicates
753signed integral and \p f - a floating point type, which is always signed. \p c indicates object operating over immutable pixels. \p _planar indicates planar organization
754(as opposed to interleaved). \p _step indicates special image views,
755locators and iterators which traverse the data in non-trivial way (for example, backwards or every other pixel).
756\e ClassType is \p _image (image), \p _view (image view), \p _loc (pixel 2D locator) \p _ptr (pixel iterator), \p _ref (pixel reference),
757\p _pixel (pixel value).
758
759\code
760bgr8_image_t             a;    // 8-bit interleaved BGR image
761cmyk16_pixel_t;          b;    // 16-bit CMYK pixel value;
762cmyk16c_planar_ref_t     c(b); // const reference to a 16-bit planar CMYK pixel x.
763rgb32f_planar_step_ptr_t d;    // step pointer to a 32-bit planar RGB pixel.
764\endcode
765