Recently, solving the crowd counting problem under occlusion and complex perspective is a hot but difficult topic. Existing methods mainly constructed counters in parallel perspective, but when facing complex perspective, such as the influences of height difference and heavy occlusions, they fail to get good accuracy. To alleviate these problems, this work proposes a novel and interesting framework NOOMP (Need Only One More Point) for perspective adaptation crowd counting task in complex nature scenes. Firstly, this work considers that the common scenes in our daily life usually have the height difference, which brings complex perspective to crowd counting. So, a new labeled method, Absolute-geometry Gaussian Generation is proposed, which only needs one more point for each person in image and gets better accuracy. Secondly, the NOOMP framework consists of meta-learning structure and uses the few-shot way to train the counting model, which can implement the perspective adaptation effective and solve the problem of high label cost. Thirdly, for fitting the characteristic of few-shot learning, this work proposes a new Multi-head Parallel Network (MPNet) for NOOMP. The feature of crowd is extracted by MPNet, which is a hybrid structure composed of shallow network and deep network. This network can save the features of shallow network and the deeper network effectively, which makes MPNet performs well in NOOMP. In addition, this work collects a new dataset, named Multiple Height Differences in Mall (MHDM) for NOOMP, which contains images of different views and height differences from shopping malls and supermarkets. Experiments based on MHDM and other benchmarks show that the NOOMP has good performances in model accuracy and works well for solving perspective change problem.