Crowd counting is a crucial task in computer vision, which poses a significant challenge yet holds vast potential for practical applications in public safety and transportation. Traditional crowd counting approaches typically rely on a single framework to predict density maps or head point distributions. However, the straightforward architectures often fall short in cases of over-counting or omission, particularly in diverse crowded scenes. To address these limitations, we introduce the Density to Point Transformer (D2PT), an innovative approach for effective crowd counting and localization. Specifically, D2PT employs a Transformer-based teacher-student framework that integrates the insights of density-based and head-point-based methods. Furthermore, we introduce feature-aligned knowledge distillation, formulating a collaborative training approach that enhances the performance of both density estimation and point map prediction. Optimized with multiple loss functions, D2PT achieves state-of-the-art performance across five crowd counting datasets, demonstrating its robustness and effectiveness for intricate crowd counting and localization challenges.