To calculate my confusion matrix with recall and precision, my test set need to be equal(balanced)?
I don't think there is any reason to modify the matrix so keep it as it is. Even if you scale it what purpose does it serve? At the end of the day your model does not change even if you modify your confusion matrix.

In my opinion you can use other metrics e.g. f1-score (or f beta score), AUC score, etc to judge your model. Confusion matrix only provides visualization where your model "confused" and I would say it is less useful for binary classification (as you only have False positive or False negative). Metrics above serve as better judge for evaluating your model. 

This is a related question which you can probably check.