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Python Imaging Library is a free and open-source additional library for the Python programming language that adds support for opening, manipulating, and saving many different image file formats. It is available for Windows, Mac OS X and Linux. The latest version of PIL is 1.1.7, was released in September 2009 and supports Python 1.5.2–2.7. [3]
Python supports normal floating point numbers, which are created when a dot is used in a literal (e.g. 1.1), when an integer and a floating point number are used in an expression, or as a result of some mathematical operations ("true division" via the / operator, or exponentiation with a negative exponent).
Although spreadsheets like Excel, Open Office Calc, or Google Sheets don't provide a clamping function directly, the same effect can be achieved by using functions like MAX & MIN together, by MEDIAN, [8] [9] or with cell function macros. [10] When attempting to do a clamp where the input is an array, other methods must be used. [11]
The term closure is often used as a synonym for anonymous function, though strictly, an anonymous function is a function literal without a name, while a closure is an instance of a function, a value, whose non-local variables have been bound either to values or to storage locations (depending on the language; see the lexical environment section below).
A sample UML class and sequence diagram for the Command design pattern. [3]In the above UML class diagram, the Invoker class doesn't implement a request directly. Instead, Invoker refers to the Command interface to perform a request (command.execute()), which makes the Invoker independent of how the request is performed.
Any or none of these options may be specified to control the size of the image. In the case of images with captions, if the image is already smaller than the requested size, then the image retains its original size (it is not enlarged). In the case of images without captions, the image will be enlarged or reduced to match the requested size.
Perspective-n-Point [1] is the problem of estimating the pose of a calibrated camera given a set of n 3D points in the world and their corresponding 2D projections in the image. The camera pose consists of 6 degrees-of-freedom (DOF) which are made up of the rotation (roll, pitch, and yaw) and 3D translation of the camera with respect to the world.
In the case of row vectors, this works exactly the other way around. The multiplication now takes place from the left as v o u t = v i n ∗ M {\displaystyle v_{out}=v_{in}*M} with 1x4-row vectors and the concatenation is M = R x ∗ T x {\displaystyle M=R_{x}*T_{x}} when we also first rotate and then move.