##### https://github.com/brownvc/deep-synth

Tip revision:

**b800e11290b763b58e7d3b30329769a7b77cd12a**authored by**kwang-ether**on**14 June 2019, 23:53:57 UTC****remove csv** Tip revision:

**b800e11** livingroom.py

```
from data.house import *
from data.dataset import DatasetFilter
from data.object_data import ObjectCategories
from .global_category_filter import *
import utils
"""
Living room filter
"""
def livingroom_filter(version, source):
data_dir = utils.get_data_root_dir()
with open(f"{data_dir}/{source}/coarse_categories_frequency", "r") as f:
coarse_categories_frequency = ([s[:-1] for s in f.readlines()])
coarse_categories_frequency = [s.split(" ") for s in coarse_categories_frequency]
coarse_categories_frequency = dict([(a,int(b)) for (a,b) in coarse_categories_frequency])
category_map = ObjectCategories()
if version == "final":
filtered, rejected, door_window = GlobalCategoryFilter.get_filter()
with open(f"{data_dir}/{source}/final_categories_frequency", "r") as f:
frequency = ([s[:-1] for s in f.readlines()])
frequency = [s.split(" ") for s in frequency]
frequency = dict([(a,int(b)) for (a,b) in frequency])
def node_criteria(node, room):
category = category_map.get_final_category(node.modelId)
if category in filtered: return False
return True
def room_criteria(room, house):
node_count = 0
for node in room.nodes:
category = category_map.get_final_category(node.modelId)
if category in rejected:
return False
if not category in door_window:
node_count += 1
t = np.asarray(node.transform).reshape((4,4)).transpose()
a = t[0][0]
b = t[0][2]
c = t[2][0]
d = t[2][2]
xscale = (a**2 + c**2)**0.5
yscale = (b**2 + d**2)**0.5
zscale = t[1][1]
if not 0.8<xscale<1.2: #Reject rooms where any object is scaled by too much
return False
if not 0.8<yscale<1.2:
return False
if not 0.8<zscale<1.2:
return False
if frequency[category] < 100: return False
if node_count < 4 or node_count > 20: return False
return True
else:
raise NotImplementedError
dataset_f = DatasetFilter(room_filters = [room_criteria], node_filters = [node_criteria])
return dataset_f
```