175 lines
7.1 KiB
Python
175 lines
7.1 KiB
Python
import json
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import pdb
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import numpy as np
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from sklearn.preprocessing import OneHotEncoder
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import itertools
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SHAPES = {
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# walls
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'square': 0,
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# objects
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'heart': 1, 'clove_tree': 2, 'moon': 3, 'wine': 4, 'double_dia': 5,
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'flag': 6, 'capsule': 7, 'vase': 8, 'curved_triangle': 9, 'spoon': 10,
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'medal': 11, # inaccessible goal
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# home
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'home': 12,
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# agent
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'pentagon': 13, 'clove': 14, 'kite': 15,
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# key
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'triangle0': 16, 'triangle180': 16, 'triangle90': 16, 'triangle270': 16,
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# lock
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'triangle_slot0': 17, 'triangle_slot180': 17, 'triangle_slot90': 17, 'triangle_slot270': 17
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}
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ENTITIES = {
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'agent': 0 , 'walls': 1, 'fuse_walls': 2, 'key': 3,
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'blocking': 4, 'home': 5, 'objects': 6, 'pin': 7, 'lock': 8
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}
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# =============================== GridPbject class ===============================
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class GridObject():
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"object is specified by its location"
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def __init__(self, x, y, object_type, attributes=[]):
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self.x = x
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self.y = y
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self.type = object_type
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self.attributes = attributes
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@property
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def pos(self):
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return np.array([self.x, self.y])
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@property
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def name(self):
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return {'type': str(self.type),
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'x': str(self.x),
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'y': str(self.y),
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'color': self.attributes[0],
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'shape': self.attributes[1]}
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# =============================== Helper functions ===============================
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def type2index(key):
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for name, idx in ENTITIES.items():
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if name == key:
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return idx
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def find_shape(shape_string, print_shape=False):
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try:
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shape = shape_string.split('/')[-1].split('.')[0]
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except:
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shape = shape_string.split('.')[0]
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if print_shape: print(shape)
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for name, idx in SHAPES.items():
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if name == shape:
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return idx
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def parse_objects(frame):
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"""
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x and y are computed differently from walls and objects
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for walls x, y = obj[0][0] + obj[1][0]/2, obj[0][1] + obj[1][1]/2
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for objects x, y = obj[0][0] + obj[1], obj[0][1] + obj[1]
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:param obj:
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:return: GridObject
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"""
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shape_onehot_encoder = OneHotEncoder(sparse=False)
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shape_onehot_encoder.fit([[i] for i in range(len(SHAPES)-6)])
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type_onehot_encoder = OneHotEncoder(sparse=False)
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type_onehot_encoder.fit([[i] for i in range(len(ENTITIES))])
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# remove duplicate walls
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frame['walls'].sort()
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frame['walls'] = list(k for k, _ in itertools.groupby(frame['walls']))
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# remove boundary walls
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frame['walls'] = [w for w in frame['walls'] if (w[0][0] != 0 and w[0][0] != 180 and w[0][1] != 0 and w[0][1] != 180)]
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# remove duplicate fuse_walls
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frame['fuse_walls'].sort()
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frame['fuse_walls'] = list(k for k, _ in itertools.groupby(frame['fuse_walls']))
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grid_objs = []
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assert 'agent' in frame.keys()
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for key in frame.keys():
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#print(key)
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if key == 'size':
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continue
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obj = frame[key]
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if obj == []:
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#print(key, 'skipped')
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continue
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obj_type = type2index(key)
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obj_type = type_onehot_encoder.transform([[obj_type]])
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if key == 'walls':
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for wall in obj:
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x, y = wall[0][0] + wall[1][0]/2, wall[0][1] + wall[1][1]/2
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#x, y = (wall[0][0] + wall[1][0]/2)/200, (wall[0][1] + wall[1][1]/2)/200 if u use this you need to change relations.py!!!
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color = [0, 0, 0] if key == 'walls' else [80, 146, 56]
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#color = [c / 255 for c in color]
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shape = 0
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assert shape in SHAPES.values(), 'Shape not found'
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shape = shape_onehot_encoder.transform([[shape]])[0]
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grid_obj = GridObject(x=x, y=y, object_type=obj_type, attributes=[color, shape])
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grid_objs.append(grid_obj)
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elif key == 'fuse_walls':
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# resample green barriers
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obj = [obj[i] for i in range(len(obj)) if (obj[i][0][0] % 20 == 0 and obj[i][0][1] % 20 == 0)]
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for wall in obj:
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x, y = wall[0][0] + wall[1][0]/2, wall[0][1] + wall[1][1]/2
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#x, y = (wall[0][0] + wall[1][0]/2)/200, (wall[0][1] + wall[1][1]/2)/200 if u use this you need to change relations.py!!!
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color = [80, 146, 56]
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#color = [c / 255 for c in color]
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shape = 0
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assert shape in SHAPES.values(), 'Shape not found'
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shape = shape_onehot_encoder.transform([[shape]])[0]
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grid_obj = GridObject(x=x, y=y, object_type=obj_type, attributes=[color, shape])
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grid_objs.append(grid_obj)
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elif key == 'objects':
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for ob in obj:
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x, y = ob[0][0] + ob[1], ob[0][1] + ob[1]
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color = ob[-1]
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#color = [c / 255 for c in color]
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shape = find_shape(ob[2], print_shape=False)
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assert shape in SHAPES.values(), 'Shape not found'
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shape = shape_onehot_encoder.transform([[shape]])[0]
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grid_obj = GridObject(x=x, y=y, object_type=obj_type, attributes=[color, shape])
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grid_objs.append(grid_obj)
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elif key == 'key':
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obj = obj[0]
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x, y = obj[0][0] + obj[1], obj[0][1] + obj[1]
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color = obj[-1]
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#color = [c / 255 for c in color]
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shape = find_shape(obj[2], print_shape=False)
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assert shape in SHAPES.values(), 'Shape not found'
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shape = shape_onehot_encoder.transform([[shape]])[0]
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grid_obj = GridObject(x=x, y=y, object_type=obj_type, attributes=[color, shape])
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grid_objs.append(grid_obj)
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elif key == 'lock':
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obj = obj[0]
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x, y = obj[0][0] + obj[1], obj[0][1] + obj[1]
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color = obj[-1]
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#color = [c / 255 for c in color]
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shape = find_shape(obj[2], print_shape=False)
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assert shape in SHAPES.values(), 'Shape not found'
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shape = shape_onehot_encoder.transform([[shape]])[0]
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grid_obj = GridObject(x=x, y=y, object_type=obj_type, attributes=[color, shape])
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grid_objs.append(grid_obj)
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else:
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try:
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x, y = obj[0][0] + obj[1], obj[0][1] + obj[1]
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color = obj[-1]
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#color = [c / 255 for c in color]
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shape = find_shape(obj[2], print_shape=False)
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assert shape in SHAPES.values(), 'Shape not found'
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shape = shape_onehot_encoder.transform([[shape]])[0]
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except:
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# [[[x, y], extension, shape, color]] in some cases in instrumental_no_barrier (bib_evaluation_1_1)
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x, y = obj[0][0][0] + obj[0][1], obj[0][0][1] + obj[0][1]
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color = obj[0][-1]
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#color = [c / 255 for c in color]
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assert len(color) == 3
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shape = find_shape(obj[0][2], print_shape=False)
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assert shape in SHAPES.values(), 'Shape not found'
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shape = shape_onehot_encoder.transform([[shape]])[0]
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grid_obj = GridObject(x=x, y=y, object_type=obj_type, attributes=[color, shape])
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grid_objs.append(grid_obj)
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return grid_objs
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