We study the precise counting of Helicobacter Pylori (HP), which is important for diagnosis of gastric cancer. The crowd counting technique is adapted for a precise quantitative analysis. The challenge of training an HP counting model lies in scarcity of labels. We use a DCGAN for the generative modelling of HP morphology and perform high-fidelity data augmentation. The comparative results show our method outperforms the object detection and semantic segmentation baselines. The proposed framework is potential useful in quantitative analysis of other bacteria in histology images. The dataset is available at https://cyxhello.github.io/HPCDataset/.