Learning OpenCV

A Not-So-Simple Transformation

That was pretty good, and we are learning to do more interesting things. In Example 2-4 we chose to allocate a new IplImage structure, and into this new structure we wrote the output of a single transformation. As mentioned, we could have applied the transformation in such a way that the output overwrites the original, but this is not always a good idea. In particular, some operators do not produce images with the same size, depth, and number of channels as the input image. Typically, we want to perform a sequence of operations on some initial image and so produce a chain of transformed images.

In such cases, it is often useful to introduce simple wrapper functions that both allocate the output image and perform the transformation we are interested in. Consider, for example, the reduction of an image by a factor of 2 [Rosenfeld80]. In OpenCV this is accomplished by the function cvPyrDown(), which performs a Gaussian smooth and then removes every other line from an image. This is useful in a wide variety of important vision algorithms. We can implement the simple function described in Example 2-5.

Example 2-5. Using cvPyrDown() to create a new image that is half the width and height of the input image

IplImage* doPyrDown( IplImage* in, int filter = IPL_GAUSSIAN_5x5 ) { // Best to make sure input image is divisible by two. // assert( in->width%2 == 0 && in->height%2 == 0 ); IplImage* out = cvCreateImage( cvSize( in->width/2, in->height/2 ), in->depth, in->nChannels ...