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import torch
from torch.utils.data import Dataset, DataLoader
import torch.utils.data.distributed
import os
import cv2
import json
from S3DLoader.s3d_utils import *
from S3DLoader.poly_utils import *
class S3DLoader(object):
def __init__(self, args, mode, generate_input_candidates=False):
self.mode = mode
self.seed = 8978
np.random.seed(seed=self.seed)
if hasattr(args, 'network_mode'):
self.function_mode = args.network_mode
else:
self.function_mode = "S"
if hasattr(args, 'batch_size'):
self.batch_size = args.batch_size
else:
self.batch_size = 1
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print('Selected device is:', device)
self.device = device
if mode == 'train':
self.dataset = self.create_dataset(args, mode, generate_input_candidates)
self.augment = True
self.data = DataLoader(self.dataset, self.batch_size,
drop_last=True,
collate_fn=self.collate_fn,
shuffle=True)
self.sample_n = len(self.dataset)
elif mode == 'online_eval' or mode == 'test':
self.dataset = self.create_dataset(args, mode, generate_input_candidates)
self.augment = False
# self.batch_size = 4
self.sample_n = len(self.dataset)
self.data = DataLoader(self.dataset, self.batch_size,
drop_last=True,
collate_fn=self.collate_fn)
elif mode == 'test':
self.dataset = self.create_dataset(args, mode)
self.augment = False
self.batch_size = 1
self.sample_n = 20
self.data = DataLoader(self.dataset, self.batch_size,
num_workers=1,
drop_last=True,
collate_fn=self.collate_fn)
# elif mode == 'test':
# self.dataset = self.create_dataset(args, mode)
# self.augment = False
#
# self.data = DataLoader(self.dataset,
# 1,
# shuffle=False,
# num_workers=1)
# self.sample_n = 20
else:
print('mode should be one of \'train, test, online_eval\'. Got {}'.format(mode))
def collate_fn(self, samples):
# wall_maps = [torch.tensor(s["wall_map"][None,:,:,None], device=self.device) for s in samples]
room_maps = [torch.tensor(s["room_map"][None,:,:,None], device=self.device) for s in samples]
input_maps = [torch.tensor(s["input_map"][None], device=self.device) for s in samples]
scores = [torch.tensor(s["score"][None], device=self.device) for s in samples]
torch_sample = {}
torch_sample["room_map"] = torch.cat(room_maps, dim=0)
# torch_sample["wall_map"] = torch.cat(wall_maps, dim=0)
torch_sample["input_map"] = torch.cat(input_maps, dim=0)
torch_sample["score"] = torch.cat(scores, dim=0)
for key, value in torch_sample.items():
assert torch.all(torch_sample[key] == torch_sample[key])
assert torch.all(torch.logical_not(torch.isinf(torch_sample[key])))
return torch_sample
def create_dataset(self, args, mode, generate_input_candidates):
#dataset_path = "../Structured3D/montefloor_data"
self.args = args
dataset_path = args.dataset_path
if mode == "train":
scenes_path = os.path.join(dataset_path, "train")
dataset = S3DDataset(args, scenes_path, None,
num_scenes=3000, generate_input_candidates=generate_input_candidates, mode=mode)
elif mode == "online_eval":
scenes_path = os.path.join(dataset_path, "val")
dataset = S3DDataset(args, scenes_path, None,
num_scenes=250, generate_input_candidates=generate_input_candidates, mode=mode)
elif mode == "test":
scenes_path = os.path.join(dataset_path, "test")
# scenes_path = os.path.join(dataset_path, "val")
dataset = S3DDataset(args, scenes_path, None,
num_scenes=250, generate_input_candidates=generate_input_candidates, mode=mode)
return dataset
def load_sample(self, sample_batch):
"""
Identity function. Everything is already loaded in Dataset class for Structured 3D
:param sample_batch:
:return:
"""
return sample_batch
class S3DDataset(Dataset):
def __init__(self, options, scenes_path, score_gen, num_scenes, generate_input_candidates, mode):
print("Creating Structured3D Dataset with %d scenes..." % num_scenes)
self.options = options
self.score_gen = None
self.mode = mode
self.scenes_path = scenes_path
self.floor_data_folder_name = ""
self.scenes_list = os.listdir(scenes_path)
self.scenes_list.sort()
inv_scenes = ["scene_01155", "scene_01852", "scene_01192", "scene_01816"]
self.scenes_list = [s for s in self.scenes_list if s not in inv_scenes]
self.scenes_list = self.scenes_list[:num_scenes]
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.device = device
self.gen_input_candidates = generate_input_candidates
def __getitem__(self, item):
scene_name = self.scenes_list[item]
sample = self.load_scene(scene_name)
return sample
def __len__(self):
return len(self.scenes_list)
def load_density_map(self, sp):
"""
Load density map
:param sp:
:return:
"""
density_path = os.path.join(sp, self.floor_data_folder_name, "density.png")
density_map = cv2.imread(density_path, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH) / 255.
if self.gen_input_candidates:
thresh = np.maximum(np.random.random(), 0.8)
density_map = np.minimum(density_map, thresh) / thresh
if self.mode != "test":
pow = np.random.random()
pow = (1.5 - 1.) * (pow - 1) + 1.5
density_map = density_map ** pow
return density_map.astype(np.float32)
def load_annotation(self, sp):
"""
Load annotation dict
:param sp:
:return:
:rtype: dict
"""
anno_path = os.path.join(sp, self.floor_data_folder_name, "annotation_3d.json")
with open(anno_path, "r") as f:
anno_dict = json.load(f)
return anno_dict
def load_scene(self, scene_name):
"""
Load scene
:param scene_name:
:return:
"""
def cvt_tmp_sample_to_torch():
torch_sample = {}
room_map = torch.tensor(np.array(sample['room_map']), device=self.device)[None]
# room_map = kornia.morphology.dilation(room_map[:, None], kernel=torch.ones((5, 5), device=self.device))[:,0]
torch_sample['room_map'] = room_map
if 'input_map' in sample.keys():
torch_sample['input_map'] = torch.tensor(np.array(sample['input_map']), device=self.device)[None]
torch_sample['cand_inst'] = torch.tensor(np.array(sample['cand_inst']), device=self.device)[None]
torch_sample['cand_confidence'] = torch.tensor(np.array(sample['cand_confidence']), device=self.device)[
None]
else:
torch_sample['density_map'] = torch.tensor(np.array(sample['density_map']), device=self.device)[None]
torch_sample['wall_map'] = torch.tensor(np.array(sample['wall_map']), device=self.device)[None]
# torch_sample['room_map'] = torch.tensor(np.array(sample['room_map']), device=self.device)[None]
torch_sample['polygons_list'] = [torch.tensor(poly, device=self.device)[None] for poly in sample['polygons_list']]
return torch_sample
sp = os.path.join(self.scenes_path, scene_name)
sample = {}
sample["scene_name"] = scene_name
scene_anno = self.load_annotation(sp)
# density_map = torch.tensor(np.array(density_map))[None]
density_map = self.load_density_map(sp)
self.generate_room_map(sample, scene_anno, density_map)
sample['density_map'] = density_map
# import pdb; pdb.set_trace()
for key, value in sample.items():
assert np.all(value == value), "%s contains NaN" % key
# import matplotlib.pyplot as plt
# plt.figure()
# plt.subplot(131)
# plt.title(scene_name)
# plt.imshow(density_map)
# plt.subplot(132)
# plt.imshow(sample["room_map"])
# plt.subplot(133)
# # plt.imshow(sample["input_map"][:,:,1])
# # plt.imshow(sample["cand_inst"][:,:,0])
# plt.show()
return sample
def generate_room_map(self, sample, annos, density_map):
"""
:param density_map:
:param sample:
:param annos:
:return:
"""
h, w = density_map.shape
polys = parse_floor_plan_polys(annos)
room_map, polygons_list = generate_floorplan(annos, polys, h, w, ignore_types=['outwall', 'door', 'window'], constant_color=False, shuffle=self.gen_input_candidates)
room_map = cv2.dilate(room_map, np.ones((5,5)))
wall_map, _ = generate_floorplan(annos, polys, h, w, ignore_types=[], include_types=['outwall'], constant_color=True)
wall_map *= (room_map == 0)
sample['room_map'] = room_map.astype(np.float32)
sample['wall_map'] = wall_map.astype(np.float32)
sample['polygons_list'] = polygons_list
def generate_density(self, points, width=256, height=256):
image_res_tensor = torch.tensor([width, height], device=self.device).reshape(1, 1, 2)
coordinates = torch.round(points[:, :, :2] * image_res_tensor)
coordinates = torch.minimum(torch.maximum(coordinates, torch.zeros_like(image_res_tensor)),
image_res_tensor - 1).type(torch.cuda.LongTensor)
density = torch.zeros((self.batch_size, height, width), dtype=torch.float, device=self.device)
for i in range(self.batch_size):
unique_coordinates, counts = torch.unique(coordinates[i], return_counts=True, dim=0)
density[i, unique_coordinates[:, 1], unique_coordinates[:, 0]] = counts.type(torch.cuda.FloatTensor)
density[i] = density[i] / torch.max(density[i])
return density
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