On this page |
Overview ¶
The code in this example defines a simple class that wraps functionality in hou
to make it easier to build compositing networks in Houdini by calling methods on the class. For example:
>>> import comp # Load in $HFS/houdini/pic/default.pic and convert it to a jpeg, writing the # output to the current directory. >>> comp.readFile("default.pic").writeFile("default.jpg") # Load in a sequence of images (from $HFS/houdini/pic), brighten them, # and convert them to jpegs. >>> comp.readFile("butterfly$F.pic").bright(1.8).writeFile("butterfly$F.jpg") # Load in default.pic, brighten it, composite it over a gray background, # and write it to out.pic. >>> comp.readFile("default.pic").bright(1.2).over( ... comp.constant(0.3, 0.3, 0.3)).writeFile("out.pic") ...
As you call the methods on the object returned by comp.readFile
, they COP nodes, set parameters on them, and wire the nodes together.
Tip
With some simple extensions to this example, you could create a Python equivalent of Houdini’s icomposite program.
Implementation ¶
You can save the source code below to under $HOUDINIPATH/pythonX.Xlibs
. See where to put Python code for more information.
import hou """ This module lets you create and evaluate a compositing network simply by writing an expression describing the compositing operations to perform. With some simple extensions to this example, you can create a Pythonic equivalent of Houdini's icomposite program. For example, you can write: import comp comp.readFile("default.pic").bright(1.2).over(comp.constant(0.3, 0.3, 0.3) ).writeFile("out.pic") and Houdini will build a composite network that loads the default.pic image, brightens it, composites it over a constant, and writes out the result to out.pic. Note that this module supports compositing over a sequence of images: simply use a time-dependent expression (like $F) in the input and output image names. If you use this module from a graphical Houdini session, you can inspect the compositing networks it creates. """ def test(): """This function creates a simple test case that evaluates the following: comp.readFile("default.pic").bright(1.2).over( comp.constant(0.3, 0.3, 0.3)).writeFile("out.pic") """ readFile("default.pic").bright(1.2).over(constant(0.3, 0.3, 0.3) ).writeFile("out.pic") class _Image: """This image class wraps a COP node and exposes image operations via methods that simply create COP nodes and return a new image wrapping that node. """ def __init__(self, node): # The node parameter is a COP node. The user of this module will # create images with the readFile and constant methods, and construct # _Image objects directly. self.node = node def __createNode(self, type): # Create and return a COP node of the specified type in the current # network. return self.node.parent().createNode(type) def bright(self, amount): """Brighten the image, returning a new image.""" n = self.__createNode("bright") n.setFirstInput(self.node) n.parm("bright").set(amount) return _Image(n) def over(self, image): """Composite this image over the specified one, returning a new image.""" n = self.__createNode("over") n.setFirstInput(self.node) n.setInput(1, image.node) return _Image(n) def writeFile(self, file_name): """Write this image to a file or file sequence.""" n = self.__createNode("rop_comp") n.setFirstInput(self.node) n.parm("copoutput").set(file_name) self.node.parent().layoutChildren() # If we're called from a standard Python shell or hython, actually # write out the file. if hou.applicationName() == 'hbatch': n.render() def __network(): # This internal function just returns the COP network. For this example, # it simply hard-codes a particular network. return hou.node("/img/comp1") or hou.node("/img").createNode("img", "comp1") _lastResolution = None def readFile(file_name): """Return an image object corresponding to a file or file sequence.""" n = __network().createNode("file") n.parm("filename1").set(file_name) # Remember the image resolution. If we later create a constant color, # we'll use this resolution. global _lastResolution _lastResolution = (n.xRes(), n.yRes()) return _Image(n) def constant(r, g, b, a=1.0): """Return an image that's a constant color. The size of the image will be the same as the size of the last file read in.""" n = __network().createNode("color") n.parmTuple("color").set((r, g, b, a)) if _lastResolution is not None: n.parm("overridesize").set(True) n.parmTuple("size").set(_lastResolution) return _Image(n)