Random forest implementations in opencv 3.3

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the tree count is hidden in the TermCriteria:

/** The termination criteria that specifies when the training algorithm stops.
Either when the specified number of trees is trained and added to the ensemble or when
sufficient accuracy (measured as OOB error) is achieved. Typically the more trees you have the
better the accuracy. However, the improvement in accuracy generally diminishes and asymptotes
pass a certain number of trees. Also to keep in mind, the number of tree increases the
prediction time linearly. Default value is TermCriteria(TermCriteria::MAX_ITERS +
TermCriteria::EPS, 50, 0.1)*/
/** @see setTermCriteria */
CV_WRAP virtual TermCriteria getTermCriteria() const = 0;
/** @copybrief getTermCriteria @see getTermCriteria */
CV_WRAP virtual void setTermCriteria(const TermCriteria &val) = 0;

and getVotes() and create() can be found here