Download Applied C++: Practical Techniques for Building Better by Philip Romanik, Amy Muntz PDF

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By Philip Romanik, Amy Muntz

This publication is set utilizing C++ to resolve the issues inherent in development advertisement software program. these of you who've labored on engineering groups development complicated software program will recognize precisely what we suggest by way of calling it advertisement software program.

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Here is the final version of this implementation: T* alignPointer (void* raw) { T* p = reinterpret_cast( (reinterpret_cast(raw) + align_ - 1) & ~(align_ - 1)); return p; } 37 38 Here is how we arrived at this implementation: in order to perform the alignment arithmetic, we need to change the data type to some type of numeric value. The following statement accomplishes this by casting our raw pointer to a uintptr_t (a type large enough to hold a pointer): reinterpret_cast(raw) By subsequently casting the raw pointer to an uintptr_t, we're able to do the actual alignment arithmetic, as follows: (reinterpret_cast(raw) + align_ - 1) & ~(align_ - 1); Basically, we want to round up the address, if necessary, so that the address is of the desired alignment.

The reason is that when the apAllocatorBase_<> constructor runs, it obtains its definition for allocate() and deallocate() from the base class, because the derived versions are not available until the object is fully constructed. Watch out for bugs introduced in the constructor of base and other parent classes. Make sure an object is fully constructed before calling any virtual function. We found it cleaner to define a base class, apAllocatorBase_<>, and later derive the object apAllocator_<> from it.

4 Prototype 1: Simple Image Objects Prototype 1 is designed to explore the similarities between images of different pixel types. Remember that our test application defined a pixel as an unsigned char to specify monochrome images with 256 levels of gray (this is frequently called an 8-bit image, or an 8-bit grayscale image). 8-bit images are still very popular in applications for security, medical imaging, and machine vision, but they represent just one of many image formats. Other popular monochrome formats define pixels with depths of 16 bits and 32 bits per pixel.

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