Pyblish Search Engine

0_README.md

Pyblish Search

Demonstration of plug-ins for Pyblish being exposed to a search engine via customisable keywords.

How it works

Each plug-in is associated with a series of keywords, the keywords are then browsed via plain-text search.

class MyPlugin(...):
   tags = ["keyword1", "keyword2"]

The discovered plug-ins are then dynamically added to Pyblish at run-time such that publishing occurs relative to what was searched for.

For example, the search term might include "games character geometry" which would mine the collection of plug-ins available for any that matched any of the included tags, "games", "character" or "geometry".

How it is useful

Picture having access to 1500 validations, some developed by peers within your company and some provided externally through a cloud-service. You have a number of criterias that your asset must fulfill, but aren't interested in the technicalities of how to actually validate anything; you just care about what's right.

From here, you could conjure up a series of keywords relevant to your requirements and pass it off to the search engine. It would then ensure that every validation passes prior to the asset being shipped.

Limitations

With this naive approach, keywords are bound to be quite specific.

For example, you might want to validate for the height of an asset. Keywords may include "<=1.5m" or ">=200m" in which case these exact keywords must exist to facilitate this exact requirement.

What would be better, is for there to be a single "MaximumHeight" validation that could be customised to whichever height was sought after.

configurator.py

import pyblish.api
import pyblish.util

disk = {}


class ValidateMeshDensity(pyblish.api.Validator):
    tags = ["geometry", "character", "film"]

    def process(self, instance):
        assert True


class ValidateEntropy(pyblish.api.Validator):
    tags = ["geometry", "character", "film", "game", "tv"]

    def process(self, instance):
        assert True


class ValidateUVOverlap(pyblish.api.Validator):
    tags = ["geometry", "uv", "film", "game", "tv"]

    def process(self, instance):
        assert True


class ValidateDefaultPose(pyblish.api.Validator):
    tags = ["rigging", "animation"]

    def process(self, instance):
        assert True


class CollectInstances(pyblish.api.Collector):
    def process(self, context):
        context.create_instance("MyInstance", family="rig")


class ExtractCharacter(pyblish.api.Extractor):
    def process(self, instance):
        disk["data"] = True


# Database of all available validators
validators = [
    ValidateMeshDensity,
    ValidateEntropy,
    ValidateUVOverlap,
    ValidateDefaultPose,
]


def search(queries):
    """Search engine, based on tags

    Arguments:
        queries (list): Individual tags, e.g. "film" and "games"

    """

    return list(
        v for v in validators
        if any(t in v.tags for t in queries)
    )


def setup(query):
    """Register plug-ins based on search query

    Arguments:
        query (str): Space-separated search query, e.g. "film games"

    """

    validators = search(queries=query.split())
    pyblish.api.deregister_all_plugins()
    for plugin in validators + [CollectInstances, ExtractCharacter]:
        pyblish.api.register_plugin(plugin)


setup("geometry rigging")

context = pyblish.util.publish()
for result in context.data("results"):
    if result["instance"] is None:
        continue

    print("{instance.id} + {plugin.id} = {success}".format(**result))

# MyInstance + ValidateUVOverlap = True
# MyInstance + ValidateMeshDensity = True
# MyInstance + ValidateEntropy = True
# MyInstance + ValidateDefaultPose = True
# MyInstance + ExtractCharacter = True