In the Python world, NumPy arrays are the standard representation for numerical data. Here, we show how these arrays enable efficient implementation of numerical computations in a high-level language. Overall, three techniques are applied to improve performance: vectorizing calculations, avoiding copying data in memory, and minimizing operation counts. We first present the NumPy array structure, then show how to use it for efficient computation, and finally how to share array data with other libraries.

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A crucial behavior for assistive robots that operate in unstructured domestic settings is the ability to efficiently reconstruct the 3D geometry of novel objects at run time using no a priori knowledge of the object. This geometric information is critical for the robot to plan grasping and other manipulation maneuvers, and it would be impractical to employ database driven or other prior knowledge based schemes since the number and variety of objects that system may be tasked to manipulate are large.We have developed a robot vision algorithm capable of reconstructing the 3D geometry of a novel object using only three images of the object captured from a monocular camera in an eye-in-hand configuration. The reconstructions are sufficiently accurate approximations such that the system can use the recovered model to plan grasping and manipulation maneuvers. The three images are captured from disparate locations and the object of interest segmented from the background and converted to a silhouette. The three silhouettes are used to approximate the surface of the object in the form of a point cloud. The accuracy of the approximation is then refined by regressing an 11 parameter superquadric to the cloud of points. The 11 parameters of the recovered superquadric then serve as the model of the object.The entire system is implemented in Python and Python related projects. Image processing tasks are performed with NumPy arrays making use of Cython for performance critical tasks. Camera calibration and image segmentation utilize the Python bindings to the OpenCV library which are available in the scikits.image project. The non-linear constrained optimization uses the fmin_l_bfgs_b algorithm in scipy.optimize. The algorithm was first vetted in a simulation environment built on top of Enthought Traits and Mayavi.The hardware implementation utilizes the Python OpenOPC project to communicate with and control a Kuka KR 6/2 six axis industrial manipulator. Images are captured via an Axis 207MW wireless network camera by issuing cgi requests to the camera with the urllib2 module. The image data is converted from JPEG to RGB raster format with the Python Imaging Library. The core algorithm runs as a server on a standalone machine and is accessed using the XML-RPC protocol. Not including the time required for the robot to capture the images, the entire reconstruction process is executed, on average, in 300 milliseconds.