117 lines
4.8 KiB
Python
117 lines
4.8 KiB
Python
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"""
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This file implements an avatar studio
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"""
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import py_avataaars as pa
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from py_avataaars import PyAvataaar as Avatar
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import pandas as pd
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import numpy as np
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import transport
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from transport import providers
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from enum import Enum
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import io
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import base64
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import copy
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# _map = {'eye type':pa.EyesType,'frame style':pa.AvatarStyle,'race':pa.SkinColor,'hair color':pa.HairColor,'facial hair':pa.FacialHairType, 'facial hair color':pa.HairColor,'hair dress':pa.TopType,'mouth':pa.MouthType,'nose':pa.NoseType,'eyebrows':pa.EyebrowType }
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# _vmap= {'eye type':'eye_type','frame style':'style','race':'skin_color','hair color':'hair_color','facial hair color':'facial_hair_color','facial hair':'facial_hair_type','hair dress':'top_type','eyebrows':'eye_brow','nose':'nose_type','mouth':'mouth_type'}
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# _omap = {'eye_type':pa.EyesType,'style':pa.AvatarStyle}
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_df = [['eye type','eye_type',pa.EyesType],['frame style','style',pa.AvatarStyle],['hair color','hair_color',pa.HairColor],['race','skin_color',pa.SkinColor],['facial hair','facial_hair_type',pa.FacialHairType],
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['facial hair color','facial_hair_color',pa.Color],['mouth','mouth_type',pa.MouthType],['hat color','hat_color',pa.Color],['accessory','accessories_type',pa.AccessoriesType],['nose','nose_type',pa.NoseType],['eyebrows','eyebrow_type',pa.EyebrowType],
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['hair dress','top_type',pa.TopType],['clothes', 'clothe_type', pa.ClotheType],['clothe color','clothe_color', pa.Color],['clothe graphics','clothe_graphic_type',pa.ClotheGraphicType]
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]
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_df = pd.DataFrame(_df,columns=['label','variable','values'])
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_df.to_csv('/home/steve/me.avatar.csv',index=False)
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def _parameters():
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_out = {'basic':{},'face':{},'clothes':{}}
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for _index in np.arange(_df.shape[0]) :
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row = _df.iloc[_index]
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key = row['label']
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if key in ['race','nose','mouth','eyebrows','eye type'] :
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_id = 'basic'
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elif 'clothe' in key or key=='accessory':
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_id = 'clothes'
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else:
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_id = 'face'
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if len( list(row['values'])) < 2 :
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continue
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_out[_id][key] = {'values': [{'name':_item.name.replace('_', ' '),'value':_item.value} for _item in row['values']],'variable':row['variable']}
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return _out
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def cast(_args) :
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_params = {}
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for key in _args :
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value = int(_args[key])
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_info = _df[_df.variable == key].copy()
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if _info.shape[0] > 0 :
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_params[key] = list(_info['values'].tolist()[0])[value]
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# _params[key] = _info['values'].tolist()[value]
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return _params
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# def _xparameters() :
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# """
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# This function returns parameters to be used within an HTML context
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# """
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# _orgout = {'basic':{},'extended':{}}
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# _out = {}
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# for _key in _map :
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# # _out[_key] = {'class':_map[_key].__name__,'values':[],"variable":_vmap[_key]}
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# # _out[_key]['values'] = [{'name':_item.name.replace('_', ' '),'value':_item.value} for _item in _map[_key]]
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# if _key == 'nose' :
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# continue
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# if _key in ['race','nose','mouth','eyebrows','eye type'] :
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# _out = _orgout['basic']
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# else:
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# _out = _orgout['extended']
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# _out[_key] = {'class':_map[_key].__name__,'values':[],"variable":_vmap[_key]}
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# _out[_key]['values'] = [{'name':_item.name.replace('_', ' '),'value':_item.value} for _item in _map[_key]]
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# # return _out
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# return _orgout
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def _build (_args):
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"""
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This function builds the avatar with a set of arguments
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"""
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_args = cast(_args)
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if _args :
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_avatar = Avatar(**_args)
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_stream = _avatar.render_png()
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_stream =io.BytesIO(_stream)
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_stream = base64.encodebytes(_stream.getvalue()).decode('ascii')
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else:
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_stream = None
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return _stream
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def _save(_args):
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"""
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This function will save the data/candidate to the database
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:_args {alias,email,stream}
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"""
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writer = transport.factory.instance(provider=providers.MONGO,context='write',db='genomix',doc='participants')
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reader = transport.factory.instance(provider=providers.MONGO,context='read',db='genomix',doc='participants')
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#
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# Let us make sure the candidate doesn't exist
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_df = reader.read(mongo={"find":"participants","filter":{"alias":_args['alias']},"projection":{"_id":0}})
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_info = None
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try:
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if _df.shape[0] == 0 :
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writer.write(_args)
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else:
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writer.set(_args)
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_info = "1"
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except Exception as e:
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pass
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return _info
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def _participants ():
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reader = transport.factory.instance(provider=providers.MONGO,context='read',db='genomix',doc='participants')
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_df = reader.read(mongo={"find":"participants","limit":10,"projection":{"_id":0}})
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return _df.to_dict(orient='records')
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