OpenCV (Open Source Computer Vision Library: http://opencv.willowgarage.com/wiki/) is an open-source BSD-licensed library that includes several hundreds of computer vision algorithms. The document describes the so-called OpenCV 2.x API, which is essentially a C++ API, as opposite to the C-based OpenCV 1.x API. The latter is described in opencv1x.pdf.
***core** - a compact module defining basic data structures, including the dense multi-dimensional array ``Mat`` and basic functions used by all other modules.
***imgproc** - an image processing module that includes linear and non-linear image filtering, geometrical image transformations (resize, affine and perspective wraping, generic table-based remapping), color space conversion, histograms, and so on.
***video** - a video analysis module that includes motion estimation, background subtraction, and object tracking algorithms.
***calib3d** - basic multiple-view geometry algorithms, single and stereo camera calibration, object pose estimation, stereo correspondence algorithms, and elements of 3D reconstruction.
***features2d** - salient feature detectors, descriptors, and descriptor matchers.
The further chapters of the document describe functionality of each module. But first, make sure to get familiar with the common API concepts used thoroughly in the library.
All the OpenCV classes and functions are placed into the *``cv``* namespace. Therefore, to access this functionality from your code, use the ``cv::`` specifier or ``using namespace cv;`` directive:
First of all, ``std::vector``, ``Mat``, and other data structures used by the functions and methods have destructors that deallocate the underlying memory buffers when needed. This means that the destructors do not always deallocate the buffers as in case of ``Mat``. They take into account possible data sharing. A destructor decrements the reference counter associated with the matrix data buffer. The buffer is deallocated if and only if the reference counter reaches zero, that is, when no other structures refer to the same buffer. Similarly, when a ``Mat`` instance is copied, no actual data is really copied. Instead, the counter associated with its reference is incremented to memorize that there is another owner of the same data. There is also the ``Mat::clone`` method that creates a full copy of the matrix data. See the example below: ::
You see that the use of ``Mat`` and other basic structures is simple. But what about high-level classes or even user data types created without taking automatic memory management into account? For them, OpenCV offers the ``Ptr<>`` template class that is similar to ``std::shared_ptr`` from C++ TR1. So, instead of using plain pointers::
OpenCV does not only deallocate the memory automatically, it can also automatically allocate the memory for output function parameters most of the time. So, if a function has one or more input arrays (``cv::Mat`` instances) and some output arrays, the output arrays are automatically allocated or reallocated. The size and type of the output arrays are determined from the size and type of input arrays. If needed, the functions take extra parameters that help to figure out the output array properties.
The array ``frame`` is automatically allocated by the ``>>`` operator, since the video frame resolution and bit-depth is known to the video capturing module. The array ``edges`` is automatically allocated by the ``cvtColor`` function. It has the same size and the bit-depth as the input array. The number of channels is 1 because the color conversion code ``CV_BGR2GRAY`` is passed, which means color to grayscale conversion. Note that ``frame`` and ``edges`` are allocated only once during the first execution of the loop body, since all the next video frames have the same resolution. If you somehow change the video resolution, the arrays are automatically reallocated.
The key component of this technology is the ``Mat::create`` method. It takes the desired array size and type. If the array already has the specified size and type, the method does nothing. Otherwise, it releases the previously allocated data, if any (this part involves decrementing the reference counter and comparing it with zero), and then allocates a new buffer of the required size. Most functions call this the ``Mat::create`` method for each output array and so the automatic output data allocation is implemented.
Some notable exceptions from this scheme are ``cv::mixChannels``, ``cv::RNG::fill``, and a few other functions and methods. They are not able to allocate the output array, so you have to do this in advance.
As a computer vision library, OpenCV deals a lot with image pixels that are often encoded in a compact, 8- or 16-bit per channel, form and thus have a limited value range. Furthermore, certain operations on images, like color space conversions, brightness/contrast adjustments, sharpening, complex interpolation (bi-cubic, Lanczos) can produce values out of the available range. If you just store the lowest 8 (16) bit of the result, this will result in some visual artifacts and may affect the further image analysis. To solve this problem, the so-called *saturation* arithmetics is used. For example, to store ``r``, a result of an operation, to an 8-bit image, we find the nearest value within 0..255 range:
The similar rules are applied to 8-bit signed and 16-bit signed and unsigned types. This semantics is used everywhere in the library. In C++ code, it is done using ``saturate_cast<>`` functions that resemble standard C++ cast operations. Here is the implementation of the formula provided above::
where ``cv::uchar`` is an OpenCV 8-bit unsigned integer type. In the optimized SIMD code, instructions like SSE2' ``paddusb``, ``packuswb``, and so on are used. They help achieve exactly the same behavior as in C++ code.
Templates is a great feature of C++ that enables implementation of very powerful, efficient and yet safe data structures and algorithms. However, the extensive use of templates may dramatically increase compilation time and code size. Besides, it is difficult to separate an interface and implementation when templates are used exclusively. This could be fine for basic algorithms but not good for computer vision libraries where a single algorithm may span a thousand lines of code. Because of this and also to simplify development of bindings for other languages, like Python*, Java*, Matlab* that do not have templates at all or have limited template capabilities, the current OpenCV implementation is based on polymorphism and runtime dispatching over templates. In those places where runtime dispatching would be too slow (like pixel access operators), impossible (generic ``Ptr<>`` implementation), or just very inconvenient (``saturate_cast<>()``) the current implementation introduces small template classes, methods, and functions. Anywhere else in this implementation templates are not used.
* a tuple of several elements, where all elements have the same type (one of the above). An array, whose elements are such tuples, are called multi-channel arrays, as opposite to the single-channel arrays, whose elements are scalar values. The maximum possible number of channels is defined by the ``CV_CN_MAX`` constant (which is not smaller than 32).
Multi-channel (``n``-channel) types can be specified using the ``CV_8UC1`` ... ``CV_64FC4`` constants (for number of channels from 1 to 4), or using the ``CV_8UC(n)`` ... ``CV_64FC(n)`` or ``CV_MAKETYPE(CV_8U, n)`` ... ``CV_MAKETYPE(CV_64F, n)`` macros when the number of channels is more than 4 or unknown at compilation time.
..note::``CV_32FC1 == CV_32F``, ``CV_32FC2 == CV_32FC(2) == CV_MAKETYPE(CV_32F, 2)``, and ``CV_MAKETYPE(depth, n) == ((x&7)<<3) + (n-1)``, that is, the constant type is formed from the ``depth``, taking the lowest 3 bits, and the number of channels minus 1, taking the next ``log2(CV_CN_MAX)`` bits.
Arrays, whose elements are more complex, cannot be constructed or processed using OpenCV. Furthermore, each function or method can handle only a subset of all possible array types. Usually, the more complex is the algorithm, the smaller the supported subset of formats is. Here are some typical examples of such limitations:
The subset of supported types for each functions has been defined from practical needs. All this information about supported types can be put together into a special table. In different implementations of the standard, the tables may look differently. For example, on embedded platforms the double-precision floating-point type (``CV_64F``) may be unavailable.
OpenCV uses exceptions to signal critical errors. When the input data has a correct format and within the specified value range, but the algorithm cannot succeed for some reason (for exampl, the optimization algorithm did not converge), it returns a special error code (typically, just a boolean variable).
The exceptions can be instances of the ``cv::Exception`` class or its derivatives. In its turn, ``cv::Exception`` is a derivative of ``std::exception``, so it can be gracefully handled in the code using other standard C++ library components.
The exception is typically thrown using either the ``CV_Error(errcode, description)`` macro, or its printf-like ``CV_Error_(errcode, printf-spec, (printf-args))`` variant, or using the ``CV_Assert(condition)`` macro that checks the condition and throws an exception when it is not satisfied. For performance-critical code, there is ``CV_DbgAssert(condition)`` that is only retained in the Debug configuration. Thanks to the automatic memory management, all the intermediate buffers are automatically deallocated in the case of sudden error. You only need to add a try statement to catch the exceptions, if needed: ::
The current OpenCV implementation is fully re-enterable. That is, the same function, the same *constant* method of a class instance, or the same *non-constant* method of different class instances can be called from different threads. Also, the same ``cv::Mat`` can be used in different threads because the reference-counting operations use the architecture-specific atomic instructions.
