def __init__(self,
traj_dir,
house_id=args.house_id,
traj_id=None,
load_graph=True):
self.house_id = house_id
self.traj_dir = traj_dir
if house_id != None:
self.create_env(args.config_path, house_id, args.render_width,
args.render_height)
# Contains methods for calculataing distances, room location, etc
self.hp = HouseParse(dataDir=args.data_dir)
# Loads house graph and generates shortest paths
self.utils = House3DUtils(
self.env,
rotation_sensitivity=45,
target_obj_conn_map_dir=False,
# Changed load_graph method to use pickle directly and Graph(g) initialisation;
# self.graph.load(path) left the graph empty!
build_graph=load_graph,
graph_dir=args.graph_dir)
self.house = {}
self.current_room = None
self.env_coors = None
self.traj_id = None
if traj_id != None:
self.update_trajectory(traj_id)
parser.add_argument('--HouseApiDir', default='../pyutils/House3D', help='house3d api dir')
parser.add_argument('--cacheDir', default='../cache/question-gen-outputs', help='directory for saving generated questions')
parser.add_argument('--outputJson', default='questions_from_engine_v2.json', help='output json file')
parser.add_argument('--object_counts_by_room_file', default='env_lists/800env_object_counts_by_room.json', help='roomTp to objT to cnt')
parser.add_argument('--env_obj_colors_file', default='env_lists/env_obj_colors_v2.json', help='obj to color mapping')
args = parser.parse_args()
# load splits
splits = json.load(open(osp.join(args.dataDir, 'eqa_v1', args.dataJson), 'r'))['splits']
for split, hids in splits.items():
print('There are %s %s house_ids.' % (len(hids), split))
house_ids = [hid for split, hids in splits.items() for hid in hids]
print('There are in all %s house_ids.' % len(house_ids))
# HouseParse and QA-engine
Hp = HouseParse(dataDir=osp.join(args.dataDir, 'SUNCGdata'),
objrenderPath=osp.join(args.HouseApiDir, 'House3D'))
E = Engine(args.object_counts_by_room_file, args.env_obj_colors_file)
# # try one house
# hid = splits['train'][2]
# Hp.parse(hid); E.cacheHouse(Hp)
# qns = E.executeFn(E.template_defs['room_size_compare'])
# pprint(qns)
# SAVE QUESTIONS TO A JSON FILE
T = ['object_dist_compare', 'object_color_compare', 'object_size_compare', 'room_size_compare']
# T = E.template_defs.keys()
num_envs = len(house_ids)
idx, all_qns = 0, []
empty_envs = []
for i in tqdm(range(num_envs)):
class TrajectoryGenerator():
def __init__(self,
traj_dir,
house_id=args.house_id,
traj_id=None,
load_graph=True):
self.house_id = house_id
self.traj_dir = traj_dir
if house_id != None:
self.create_env(args.config_path, house_id, args.render_width,
args.render_height)
# Contains methods for calculataing distances, room location, etc
self.hp = HouseParse(dataDir=args.data_dir)
# Loads house graph and generates shortest paths
self.utils = House3DUtils(
self.env,
rotation_sensitivity=45,
target_obj_conn_map_dir=False,
# Changed load_graph method to use pickle directly and Graph(g) initialisation;
# self.graph.load(path) left the graph empty!
build_graph=load_graph,
graph_dir=args.graph_dir)
self.house = {}
self.current_room = None
self.env_coors = None
self.traj_id = None
if traj_id != None:
self.update_trajectory(traj_id)
"""
Initialize environment for a given house.
"""
def create_env(self,
config_path,
house_id,
render_width=args.render_width,
render_height=args.render_height):
api = objrender.RenderAPIThread(w=render_width,
h=render_height,
device=0)
cfg = load_config(config_path)
self.env = Environment(api, house_id, cfg)
"""
Load given trajectory from file.
"""
def update_trajectory(self, traj_id):
self.traj_id = traj_id
load_path = os.path.join(self.traj_dir, self.house_id + '.npy')
self.env_coors = np.load(load_path)[traj_id]
# Add look-arounds when entering rooms
print(
'Preprocessing trajectory for room views (90 degrees left and right)'
)
self.house = {}
self.add_180s_to_trajectory()
"""
Update the agent's position.
"""
def update_env(self, new_pos):
self.env.cam.pos.x = new_pos[0]
self.env.cam.pos.y = 1.2
self.env.cam.pos.z = new_pos[2]
self.env.cam.yaw = new_pos[3]
self.env.cam.updateDirection()
"""
Preprocesses trajectory by adding frames where the agent looks around when entering a room.
"""
def add_180s_to_trajectory(self):
# Index house rooms first
self.build_rooms_and_objects_description()
new_coors = []
for i in range(len(self.env_coors)):
self.update_env(self.env_coors[i])
new_coors.append(self.env_coors[i])
# Entered a new room, look around
if self.update_current_room(self.env_coors[i]):
init_yaw = new_coors[-1][3]
new_coor = new_coors[-1]
# Look around (left, right) up to 90 degrees in increments of 30
yaw_adds = [1, 1, 1, -1, -1, -1, -1, -1, -1, 1, 1, 1]
for yaw_add in yaw_adds:
new_coor = (new_coor[0], new_coor[1], new_coor[2],
new_coor[3] + 30 * yaw_add)
new_coors.append(new_coor)
self.env_coors = new_coors
"""
Render the trajectory step by step.
"""
def render_and_save_trajectory(self, frame_dir):
self.env.set_render_mode(RenderMode.RGB)
s = self.env_coors[0]
d = self.env_coors[-1]
assert os.path.exists(
frame_dir), 'Can\'t save frames, non-existent directory!'
filename = '%s/%s_%04d.mp4' % (frame_dir, self.house_id, self.traj_id)
print('Generating', filename)
video = cv2.VideoWriter(filename, cv2.VideoWriter_fourcc(*'mp4v'),
args.fps,
(args.render_width, args.render_height))
for i in range(len(self.env_coors)):
self.update_env(self.env_coors[i])
img = np.array(self.env.render(), copy=False)
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
# Write video frame
video.write(img)
cv2.destroyAllWindows()
video.release()
"""
Render the agent's current view in a given mode.
"""
def render_frame(self, mode=RenderMode.RGB):
self.env.set_render_mode(mode)
img = self.env.render()
if mode in [RenderMode.SEMANTIC, RenderMode.RGB]:
img = np.array(img, copy=False, dtype=np.int32)
return img
elif mode == RenderMode.DEPTH:
img = img[:, :, 0]
img = np.array(img, copy=False, dtype=np.float32)
return img
else:
return None
"""
Returns a set of all the simple room types in the given list, with the following changes:
- guest_room -> bedroom
- toilet -> bathroom
"""
@staticmethod
def get_room_types(types):
room_types = types
if 'toilet' in room_types:
room_types.remove('toilet')
if not 'bathroom' in room_types:
room_types.append('bathroom')
if 'guest_room' in room_types:
room_types.remove('guest_room')
if not 'bedroom' in room_types:
room_types.append('bedroom')
return list(sorted(room_types))
"""
Trajectory/question generation requires frame-by-frame semantic processing and establishing
object/room attributes. As we have access to the ground truth information, we can just lookup
the properties in the dict that this method returns.
"""
def build_rooms_and_objects_description(self):
obj_id_to_color = json.load(open(args.obj_color_path))
room_unique_id = 0
for room in self.utils.rooms:
# Add room type to dict
room_types = TrajectoryGenerator.get_room_types(room['type'])
room_type = '|'.join(room_types)
if not room_type in self.house:
self.house[room_type] = {
'room_list': [],
'count': 0,
'been_here_count': 0
}
self.house[room_type]['count'] += 1
# Prepare property container for room
room_unique_id += 1
room_desc = {
'been_here': False,
'room_type': room_type,
'bbox': room['bbox'],
'objects': {},
'room_id': room_type + str(room_unique_id)
}
objects_in_room = self.get_object_objects_in_room(room)
for obj in objects_in_room:
if not obj['coarse_class'] in constants.query_objects:
continue
# Add object type to dict
obj_type = obj['coarse_class']
if not obj_type in room_desc['objects']:
room_desc['objects'][obj_type] = {
'obj_list': [],
'count': 0,
'seen_count': 0
}
room_desc['objects'][obj_type]['count'] += 1
# Prepare property container for object
color = None
node = '.0_' + obj['id'][2:]
if self.house_id + node in obj_id_to_color:
color = obj_id_to_color[self.house_id + node]
obj_desc = {
'node': node,
'bbox': obj['bbox'],
'color': color,
'seen': False,
'room_location': room_type,
'obj_type': obj_type,
'room_id': room_type + str(room_unique_id)
}
room_desc['objects'][obj_type]['obj_list'].append(obj_desc)
self.house[room_type]['room_list'].append(room_desc)
"""
Index all objects in the given room.
"""
def get_object_objects_in_room(self, room):
return [
self.utils.objects['0_' + str(node)] for node in room['nodes']
if '0_' + str(node) in self.utils.objects and self.utils.objects[
'0_' + str(node)]['coarse_class'] in constants.query_objects
]
"""
Generate a shortest path between random locations in room1 and room2.
"""
def generate_random_path(self, room1, room2):
# Disabled assert in getRandomLocation for ALLOWED_TARGET_ROOM_TYPES
c1 = self.env.house.getRandomLocation(
room1, return_grid_loc=True, mixedTp=True) + (0, )
c2 = self.env.house.getRandomLocation(
room2, return_grid_loc=True, mixedTp=True) + (0, )
print('Generating random path from %s to %s' % (room1, room2), c1, c2)
start = time.time()
path = self.utils.compute_shortest_path(c1, c2)
print(time.time() - start, 'seconds to generate path of length',
len(path.nodes))
return path
"""
Turn grid path into a trajectory inside the house.
"""
def get_graph_to_env_coors(self, path):
env_coors = []
for node in path.nodes:
to_coor = self.env.house.to_coor(node[0], node[1])
env_coor = (to_coor[0], self.env.house.robotHei, to_coor[1],
node[2])
env_coors.append(env_coor)
return env_coors
"""
Returns whether the agent entered a new room.
"""
def update_current_room(self, agent_pos):
agent_new_pos_obj = TrajectoryGenerator.get_agent_pos_obj(agent_pos)
if self.current_room == None or \
not (self.hp.isContained(self.current_room, agent_new_pos_obj, axis=0) and \
self.hp.isContained(self.current_room, agent_new_pos_obj, axis=2)):
# New room
for room_type in self.house:
for room in self.house[room_type]['room_list']:
if self.hp.isContained(room, agent_new_pos_obj, axis=0) and \
self.hp.isContained(room, agent_new_pos_obj, axis=2):
self.current_room = room
# print("New room entered:", self.current_room['room_id'])
self.current_room['been_here'] = True
self.house[self.current_room['room_type']][
'been_here_count'] += 1
return True
return False
"""
Given a door node, finds at most two rooms which are on either side of the door and adds this
information to the door object.
"""
def find_adjacent_rooms_for_door(self, door_node):
door_obj = self.doors[door_node]
# One adjacent room is the room which the door belongs to
door_obj['adjacent_rooms'] = [door_obj['room_id']]
# If the door doesn't belong to the room we're currently in, append the latter to the list
# as the other adjacent room
if door_obj['room_id'] != self.current_room['room_id']:
door_obj['adjacent_rooms'].append(self.current_room['room_id'])
return
# Looking for the second adjacent room
for room_type in self.house:
for room_obj in self.house[room_type]['room_list']:
if room_obj['room_id'] == door_obj['room_id']:
continue
if self.hp.isContained(room_obj, door_obj, axis=0) or\
self.hp.isContained(room_obj, door_obj, axis=2):
# Found the other adjacent room
door_obj['adjacent_rooms'].append(room_obj['room_id'])
return
"""
Given a list of objects in the current view (type->count, approx_depths) and the ground truth
information, mark doors that were seen. Unlike match_seen_to_ground_truth(), this method tries
to find seen doors in the entire house. (Uses approximation when computing distances, not 100%
accurate.)
"""
def match_seen_to_doors(self, objs_in_frame, agent_pos):
if 'door' not in objs_in_frame:
return []
agent_pos_obj = TrajectoryGenerator.get_agent_pos_obj(agent_pos)
doors_seen = []
count = objs_in_frame['door']['count']
depths = objs_in_frame['door']['depths']
for i in range(count):
curr_depth = depths[i]
for room_type in self.house:
for room in self.house[room_type]['room_list']:
if not 'door' in room['objects']:
continue
door_objs_in_room = room['objects']['door']['obj_list']
for door_obj in door_objs_in_room:
# Distance from agent to bbox centre of door
coord_bbox = list((np.array(door_obj['bbox']['min']) +\
np.array(door_obj['bbox']['max'])) / 2)
bbox_centre = {
'bbox': {
'min': coord_bbox,
'max': coord_bbox
}
}
true_dist1 = self.hp.getClosestDistance(
agent_pos_obj, bbox_centre)
# Default distance computation
true_dist2 = self.hp.getClosestDistance(
agent_pos_obj, door_obj)
# Check if the door object corresponds to the door seen
if isclose(curr_depth, true_dist1, rtol=0.25) or\
isclose(curr_depth, true_dist2, rtol=0.25):
doors_seen.append(door_obj)
break
return doors_seen
"""
Given a list of objects in the current view (type->count, approx_depths) and the ground truth
information, mark objects that were seen. (Uses approximation when computing distances, not 100%
accurate.)
"""
def match_seen_to_ground_truth(self, objs_in_frame, agent_pos):
if self.current_room == None:
return []
agent_pos_obj = TrajectoryGenerator.get_agent_pos_obj(agent_pos)
obj_nodes_seen = []
for obj_type in objs_in_frame:
count = objs_in_frame[obj_type]['count']
depths = objs_in_frame[obj_type]['depths']
for i in range(count):
if not obj_type in self.current_room['objects']:
continue
curr_depth = depths[i]
for j in range(
self.current_room['objects'][obj_type]['count']):
# An object is usually visible across multiple frames - only mark it once
if not self.current_room['objects'][obj_type]['obj_list'][
j]['seen']:
# Get distance between bboxes of agent and object
coord1 = list((np.array(
self.current_room['objects'][obj_type]['obj_list'][j]['bbox']['min']) +\
np.array(
self.current_room['objects'][obj_type]['obj_list'][j]['bbox']['max']))
/ 2)
bbox_centre = {'bbox': {'min': coord1, 'max': coord1}}
true_dist1 = self.hp.getClosestDistance(
agent_pos_obj, bbox_centre)
true_dist2 = self.hp.getClosestDistance(
agent_pos_obj, self.current_room['objects']
[obj_type]['obj_list'][j])
# Check if the object in view is in the room
if isclose(curr_depth, true_dist1, rtol=0.25) or\
isclose(curr_depth, true_dist2, rtol=0.25):
obj_nodes_seen.append(self.current_room['objects']
[obj_type]['obj_list'][j])
self.current_room['objects'][obj_type]['obj_list'][
j]['seen'] = True
self.current_room['objects'][obj_type][
'seen_count'] += 1
break
return obj_nodes_seen
"""
Generate a trajectory and gather seen rooms and objects. Optionally returns objects in all video
frames corresponding to the trajectory.
"""
def generate_trajectory_and_seen_items(self,
frame_dir=None,
compute_seen_doors=False,
return_objects_in_frames=False):
self.build_rooms_and_objects_description()
if frame_dir:
self.render_and_save_trajectory(frame_dir)
rgb_to_obj = TrajectoryGenerator.get_semantic_to_object_mapping(
args.csv_path)
self.current_room = None
if return_objects_in_frames:
objects_in_frames = []
# Parse frames
start = time.time()
for c in range(len(self.env_coors)):
# Update agent position in the environment
self.update_env(self.env_coors[c])
semantic_img = self.render_frame(mode=RenderMode.SEMANTIC)
depth_img = self.render_frame(mode=RenderMode.DEPTH)
# Mark current room as visited
self.update_current_room(self.env_coors[c])
# Get objects types and approximate depths from current frame
objs_in_frame = TrajectoryGenerator.get_objects_in_frame(
semantic_img, rgb_to_obj, depth_img)
if return_objects_in_frames:
objects_in_frames.append(list(objs_in_frame.keys()))
# Mark objects in ground truth room that correspond to current view
seen_nodes = self.match_seen_to_ground_truth(
objs_in_frame, self.env_coors[c])
if compute_seen_doors:
# Store objects corresponding to all seen doors (in the entire house)
seen_doors = self.match_seen_to_doors(objs_in_frame,
self.env_coors[c])
for door in seen_doors:
if door['node'] in self.doors:
continue
# See which rooms are on both sides of the new door
self.doors[door['node']] = door
self.find_adjacent_rooms_for_door(door['node'])
print(time.time() - start, 'seconds to process',
str(len(self.env_coors)), 'frames.')
if return_objects_in_frames:
return objects_in_frames
"""
Returns a list of the valid room types in the house.
"""
def get_all_valid_room_types(self):
return [
x for x in self.env.house.all_roomTypes
if TrajectoryGenerator.valid_room_type(x)
]
"""
Get pairs of nearby objects for a visited room with marked objects.
"""
def get_nearby_object_pairs(self, room_desc):
assert room_desc['been_here'], 'This room has not been visited!'
obj_container = []
for obj_type in room_desc['objects']:
cnt_type = 0
for obj_entry in room_desc['objects'][obj_type]['obj_list']:
# Make sure the object was seen on the trajectory
if not obj_entry['seen']:
continue
cnt_type += 1
obj = ItemInfo(name=obj_type + str(cnt_type), meta=obj_entry)
obj_container.append(obj)
if len(obj_container) > 0:
return self.hp.getNearbyPairs(obj_container,
hthreshold=args.h_threshold,
vthreshold=args.v_threshold)
return {'on': [], 'next_to': []}
"""
Returns a dict with keys ['on', 'next_to'] and values as list of tuples (obj1, obj2, dist)
showing spatial relationships between objects.
"""
def get_all_nearby_object_pairs(self):
all_pairs = {'on': [], 'next_to': []}
for room_type in self.house:
for room_obj in self.house[room_type]['room_list']:
# Only look at visited rooms
if room_obj['been_here']:
pairs = self.get_nearby_object_pairs(room_obj)
for rel in ['on', 'next_to']:
all_pairs[rel] += pairs[rel]
return all_pairs
"""
Get the list of all objects (either on the trajectory or in the entire house).
"""
def get_all_objects(self,
include_unseen_objects=False,
include_objects_in_all_rooms=False):
obj_list = []
for room_type in self.house:
for room in self.house[room_type]['room_list']:
if not room['been_here'] and not include_objects_in_all_rooms:
continue
for obj_type in room['objects']:
for obj in room['objects'][obj_type]['obj_list']:
if obj['seen'] or include_unseen_objects:
obj_list.append(obj)
return obj_list
"""
Get the list of all rooms (either on the trajectory or in the entire house). Does not include
object list for rooms.
"""
def get_all_rooms(self, include_unseen_rooms=False):
room_list = []
for room_type in self.house:
for room in self.house[room_type]['room_list']:
if room['been_here'] or include_unseen_rooms:
# Don't include object list
room_list.append({
'been_here': True,
'room_type': room_type,
'bbox': room['bbox'],
'room_id': room['room_id']
})
return room_list
"""
Return agent's current position inside an object with 'bbox' attribute. Useful for calling
HouseParse methods.
"""
@staticmethod
def get_agent_pos_obj(agent_pos):
agent_new_pos_obj = {
'bbox': {
'min': agent_pos[0:3],
'max': agent_pos[0:3],
},
}
return agent_new_pos_obj
"""
Given a depth map and an image with numbered disjoint regions corresponding to a single object
type, return an __approximate__ depth for each one of them.
"""
@staticmethod
def get_approx_depths_for_object_type(depth_img, labeled_objs_img,
num_objs):
approx_depths = []
for i in range(num_objs):
first_idx = next(idx
for idx, val in np.ndenumerate(labeled_objs_img)
if val == i + 1)
approx_depths.append(depth_img[first_idx] / 255.0 * 20.0)
return approx_depths
"""
Extract objects from a frame.
object_type -> (num_objects, object_depths)
"""
@staticmethod
def get_objects_in_frame(semantic_img, rgb_to_obj, depth_img):
label_img = TrajectoryGenerator.rgb_to_int_image(semantic_img)
labels = np.unique(label_img)
objs_in_frame = {}
# Process information about each unique type of object in the current frame
only_curr_obj_img = np.zeros(shape=label_img.shape, dtype=np.int32)
for i in range(len(labels)):
# "Paint" on a background only the objects of the current type
only_curr_obj_img[:, :] = 0
only_curr_obj_img[np.where(label_img == labels[i])] = 1
# Find number of occurrences of object in frame (might be misleading, occlusions or
# several objects overlapping e.g. chairs)
s = [[1, 1, 1], [1, 1, 1], [1, 1, 1]]
num_objs = label(only_curr_obj_img, output=only_curr_obj_img)
# Find semantic color of object and convert it to object type
first_idx = next(idx
for idx, val in np.ndenumerate(only_curr_obj_img)
if val == 1)
rgb = (semantic_img[first_idx[0], first_idx[1],
0], semantic_img[first_idx[0], first_idx[1],
1], semantic_img[first_idx[0],
first_idx[1],
2])
obj_name = rgb_to_obj[rgb]
# Check if we want to ask questions about the object type
if not obj_name in constants.query_objects:
continue
objs_in_frame[obj_name] = {}
# Get number of objects of this type in the frame
objs_in_frame[obj_name]['count'] = num_objs
# Get approximate depths
depths = TrajectoryGenerator.get_approx_depths_for_object_type(
depth_img, only_curr_obj_img, num_objs)
objs_in_frame[obj_name]['depths'] = depths
return objs_in_frame
"""
Open CSV category file to map semantic frames to sets of objects.
"""
@staticmethod
def get_semantic_to_object_mapping(path):
f = open(path, newline="")
reader = csv.DictReader(f)
all_objects_dict = {}
for line in reader:
all_objects_dict[(int(line['r']), int(line['g']),
int(line['b']))] = line['name']
return all_objects_dict
"""
Map RGB values in an image to integers: (r,g,b) -> (256^2 * r + 256 * g + b).
"""
@staticmethod
def rgb_to_int_image(img):
out = np.zeros(shape=(img.shape[0], img.shape[1]), dtype=np.int32)
out = (img[:, :, 0] << 16) | (img[:, :, 1] << 8) | (img[:, :, 2])
return out
"""
Blacklist some room types.
"""
@staticmethod
def valid_room_type(room_type):
return len(room_type) > 0 and\
all([not tp.lower() in constants.exclude_rooms for tp in room_type])
default="data/working/questions.json")
parser.add_argument(
"-dataDir",
help="Directory that has the SUNCG dataset",
default="/path/to/suncg/")
parser.add_argument(
"-numEnvs", help="No. of environments to read", default=False)
args = parser.parse_args()
data = json.load(open(args.inputJson, 'r'))
house_ids = list(data.keys())
num_envs = len(house_ids)
if args.numEnvs != False:
num_envs = int(args.numEnvs)
Hp = HouseParse(dataDir=args.dataDir)
E = Engine()
# SAVE QUESTIONS TO A JSON FILE
#
# -- STARTS HERE
idx, all_qns = 1, []
empty_envs = []
for i in tqdm(range(num_envs)):
Hp.parse(house_ids[i])
num_qns_for_house = 0
for j in E.template_defs:
if j == 'global_object_count': continue
E.cacheHouse(Hp)
qns = E.executeFn(E.template_defs[j])
import argparse
import os
import os.path as osp
import sys
import random
import time
import json
from tqdm import tqdm
from house_parse import HouseParse
this_dir = osp.dirname(__file__)
eqa_v1_file = osp.join(this_dir, '../data/eqa_v1/eqa_v1.json')
hid_to_qns = json.load(open(eqa_v1_file))['questions']
# get house info, as well as id_to_cat
dataDir = osp.join(this_dir, '../data')
HouseApiDir = osp.join(this_dir, '../pyutils/House3D')
Hp = HouseParse(dataDir=osp.join(dataDir, 'SUNCGdata'),
objrenderPath=osp.join(HouseApiDir, 'House3D'))
id_to_cat = {}
for hid in tqdm(hid_to_qns.keys()):
Hp.parse(hid)
for obj_id, obj in Hp.objects.items():
id_to_cat[str(hid) + '.' + str(obj_id)] = obj['fine_class']
output_file = osp.join(this_dir,
'../cache/question-gen-outputs/object_id_to_cat.json')
with open(output_file, 'w') as f:
json.dump(id_to_cat, f)
default="data/working/questions.json")
parser.add_argument("-dataDir",
help="Directory that has the SUNCG dataset",
default="/path/to/suncg/")
parser.add_argument("-numEnvs",
help="No. of environments to read",
default=False)
args = parser.parse_args()
data = json.load(open(args.inputJson, 'r'))
house_ids = list(data.keys())
num_envs = len(house_ids)
if args.numEnvs != False:
num_envs = int(args.numEnvs)
Hp = HouseParse(dataDir=args.dataDir)
E = Engine()
# SAVE QUESTIONS TO A JSON FILE
#
# -- STARTS HERE
idx, all_qns = 1, []
empty_envs = []
for i in tqdm(range(num_envs)):
Hp.parse(house_ids[i])
num_qns_for_house = 0
for j in E.template_defs:
if j == 'global_object_count': continue
E.cacheHouse(Hp)
qns = E.executeFn(E.template_defs[j])