Many tasks in image processing, e.g. image segmentation, surface reconstruction, are naturally expressed as energy minimization problems, in which the free variables are shapes, such as curves in 2d or surfaces in 3d. This approach is very popular due to its intuitiveness and the ﬂexibility to easily incorporate data ﬁdelity, geometric regularization and statistical prior terms. However, carrying out the actual minimization in an efficient and reliable manner requires overcoming many technical challenges. In this work, we introduce a Python toolbox that implements a diverse collection of shape energies for image processing, and state-of-the-art optimization methods to compute their solutions. This toolbox is built on NumPy and SciPy and is offered to the scientific community is a free open source package.