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create_splits.py
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import random
import sys
import os
import argparse
import itertools
import csv
from easydict import EasyDict as edict
S2D3D_Area2Split =\
{
"area1": "train",
"area2": "train",
"area4": "train",
"area6": "train",
"area3": "val",
"area5a": "test",
"area5b": "test",
}
M3D_Hash2Split =\
{
"ed1e790a785e4b74b895e41682b2ae88": "test",
"2797de2c1a00404faa63b1c722809d0c": "test",
"a48996e5b4d64ddca7ebae1e8058bb75": "test",
"3086b4adcdf0410380493d08fa18185d": "test",
"0151156dd8254b07a241a2ffaf0451d4": "test",
"95d83883728b4cf089a7338117dafda0": "test",
"ef0f394cf59549f2a46b3df2a8ba4620": "test",
"d4f8bb56230e4695860deffe751adf20": "test",
"e440fec9930c40f780cc2dc1bae19799": "test",
"3e02d80e4b4048abb1befdf84ede89d6": "test",
"331000ad2a204322916c92f197fbe6cb": "test",
"3f6062eeed474a6691e1116032b09fc7": "test",
"6cbebf6c7e6647edabb53546e5200c5c": "test",
"0b724f78b3c04feeb3e744945517073d": "test",
"0b217f59904d4bdf85d35da2cab96347": "test",
"380914818d0f4dfa88035fe57f994130": "test",
"fa5f164b48f043c6b2b0bb9e8631a482": "test",
"72e0512c082d4895b68c82b35884ff98": "test",
"bed1a77d92d64f5cbbaaae4feed64ec1": "train",
"b94039b4eb8947bdb9ff5719d9173eae": "train",
"2e84c97e728d46babd3270f4e1a0ae3a": "train",
"04eb2788768d40a38d35d876a02e9624": "train",
"0c334eaabb844eaaad049cbbb2e0a4f2": "train",
"9c6b166997ae486f96d011c1de0b3427": "train",
"d7a2911178dd48e89d6a23afb09cbc11": "train",
"da8b0a1818094c3590080b6fbcc5d161": "train",
"f04f5cfeb7f34d03a05e6a62fe5bd572": "train",
"2fe17a1527c5496ba74a942d442147ab": "train",
"caef338e1683434ba3a471ead89008cc": "train",
"85cef4a4c3c244479c56e56d9a723ad2": "train",
"0b414c5246f64cc3a1f71251e28eb07d": "train",
"9f4011217225489ead8fff22ff0b1e15": "train",
"e996abcc45ad411fa7f406025fcf2a63": "train",
"1bfc8a70a5a048b28425bdc93f15539c": "train",
"387b4cca2ac3488da561a13d0b2561f2": "train",
"ead4af2db2b44bc08477dee83ce9b1e1": "train",
"698437e8ee11434d86a39226e4bd97b7": "train",
"7b285021f3114c4cb66675cbd139cd17": "train",
"a1c9e2f1618b46bf9c6d9ffee64ad5da": "train",
"22ea79f774cd4381b6fd9700495e7162": "train",
"80997e9c2f234974bc7227adcceec83d": "train",
"a253d4a0a2af436e848e57af525a6133": "train",
"6c53b7897e774853845a0086306880d7": "train",
"7e39d733f3134410949894d49d072a39": "train",
"ef342b3b24fb413b97bff722de0acb23": "train",
"b9116f2d4e0a44178d14fe804de4e518": "train",
"04d3f2105168491db767ad1fe7bc39df": "train",
"2e7560fb87394e69bfe7462470cff2cd": "train",
"579e0103ce5b4823a6276f2a943e0576": "train",
"886ece56bf7e436e8d5365cbfec29a44": "train",
"8caaade0a587493ca329937a41be44fc": "train",
"ce4dcbb88c474cc1a0d1a3768062ec5d": "train",
"a74d64d3f8d94500816467e5d936db10": "train",
"e399b36ee7c94d8886179197335aebb0": "train",
"c391c42c70d84a7abe10263925a03acc": "train",
"8d41b897141546e5a209d39bd7fbd449": "train",
"65a6bb9bce044fa8bc0c1865820930be": "train",
"1f638a3819a544669350d1d56688aad3": "train",
"f46e2dd75698487ba7838224d9acf3a1": "train",
"fbf6d32ff0e044e88355076d502e160b": "train",
"e2ed48cedbd04eb1b33b935df4d78911": "train",
"975b8a35009841e6aaec4a0124a3e2ff": "train",
"cc84e75d262344ed8ade6f0e086cc6a0": "train",
"0656f1bc96024777a6247e601a2131ed": "train",
"cd658a17893f455198018c3f37b3b4a9": "train",
"eb00de2714da4edba8fcd867924c2a27": "train",
"edb61af9bebd428aa21a59c4b2597b20": "train",
"ae48a43f548144fb8e82c32d4b64148e": "train",
"bdf5a25b5dc14e85ba3c70b3cb0635eb": "train",
"022cea327b744abe87758faf883425da": "train",
"ec72ed7d211541abbdf96faee1d049e3": "train",
"b24264da8ac84505872a0cbebdc0ea0d": "train",
"d2974dbf53904bb0a907b4b1de0c177b": "train",
"b2f1bf0a0de54856b1d9c8816633c0bb": "train",
"ef3600fd356e4df3afe10e6e382e5f18": "train",
"59e3dc4b2b5848b6a55eb9bc98a42f43": "train",
"602971d3594745e6b1ae71d0a1c6fde6": "train",
"b693ef1b45de41a6a51bdbf5ee631907": "train",
"e9510fcbae554d6cb8136a7274521ff3": "train",
"7812e14df5e746388ff6cfe8b043950a": "val",
"9266ab00ab6744348efa7afe13b3db9f": "val",
"f9aeabd92a05469badd3c6324dc35a55": "val",
"0685d2c5313948bd94e920b5b9e1a7b2": "val",
"9f2deaf4cf954d7aa43ce5dc70e7abbe": "val",
"e0166dba74ee42fd80fccc26fe3f02c8": "val",
"a2577698031844e7a5982c8ee0fecdeb": "val",
"bd8722e710f14c949259a02ae1a51dee": "val",
"eef1d4cc7acb4e2db42b22a2177e7236": "val",
"2aa12c3747f948b8bf9df281ec784627": "val",
"a641c3f4647545a2a4f5c50f5f5fbb57": "val",
}
def parse_arguments(args):
desc = (
"3D60 dataset splits generator."
)
parser = argparse.ArgumentParser(description=desc)
# paths
parser.add_argument("--suncg_path", type=str,\
default=argparse.SUPPRESS,\
help="Path to the rendered data of SunCG")
parser.add_argument("--s2d3d_path", type=str,\
default=argparse.SUPPRESS,\
help="Path to the rendered data of Stanford2D3D")
parser.add_argument("--m3d_path", type=str,\
default=argparse.SUPPRESS,\
help="Path to the rendered data of Matterport3D")
parser.add_argument("--outliers_path", type=str,\
default=".\\splits\\", \
help="The path where the outliers files will be read from.")
# model
parser.add_argument("--name", type=str, default="3D60",\
help="Output file name that will be prefixed to the saved files (with corresponding _train, _test and _val suffix).")
return parser.parse_known_args(args)
def create_m3d_splits(m3d_path):
m3d_splits = edict({
'train': {},
'val': {},
'test': {},
})
for k, v in M3D_Hash2Split.items():
m3d_splits[v][k] = edict()
for rendered_image in os.listdir(m3d_path):
for k in M3D_Hash2Split.keys():
if k in rendered_image:
pose_id = rendered_image.split("_")[0]
if pose_id not in m3d_splits[M3D_Hash2Split[k]][k]:
m3d_splits[M3D_Hash2Split[k]][k][pose_id] = list()
m3d_splits[M3D_Hash2Split[k]][k][pose_id].append(\
os.path.join(m3d_path, rendered_image))
break
return m3d_splits
def create_s2d3d_splits(s2d3d_path):
s2d3d_splits = edict({
'train': {},
'val': {},
'test': {},
})
for k, v in S2D3D_Area2Split.items():
s2d3d_splits[v][k] = edict()
for area in os.listdir(s2d3d_path):
for rendered_image in os.listdir(os.path.join(s2d3d_path, area)):
pose_id = rendered_image.split("_")[0]
if pose_id not in s2d3d_splits[S2D3D_Area2Split[area]][area]:
s2d3d_splits[S2D3D_Area2Split[area]][area][pose_id] = list()
s2d3d_splits[S2D3D_Area2Split[area]][area][pose_id].append(
os.path.join(s2d3d_path, area, rendered_image))
return s2d3d_splits
def create_suncg_splits(suncg_path):
suncg_splits = edict({
'train': {},
'val': {},
'test': {},
})
train_percentage = 0.7
validation_percentage = 0.1
test_percentage = 0.2
for rendered_image in os.listdir(suncg_path):
scene_id = rendered_image.split("_")[0]
if scene_id not in suncg_splits['train']\
and scene_id not in suncg_splits['test']\
and scene_id not in suncg_splits['val']:
rng = random.random()
if rng <= train_percentage:
if scene_id not in suncg_splits['train']:
suncg_splits['train'][scene_id] = edict()
suncg_splits['train'][scene_id]['0'] = list()
suncg_splits['train'][scene_id]['0'].append(
os.path.join(suncg_path, rendered_image)
)
elif rng >= (1-test_percentage):
if scene_id not in suncg_splits['test']:
suncg_splits['test'][scene_id] = edict()
suncg_splits['test'][scene_id]['0'] = list()
suncg_splits['test'][scene_id]['0'].append(
os.path.join(suncg_path, rendered_image)
)
else:
if scene_id not in suncg_splits['val']:
suncg_splits['val'][scene_id] = edict()
suncg_splits['val'][scene_id]['0'] = list()
suncg_splits['val'][scene_id]['0'].append(
os.path.join(suncg_path, rendered_image)
)
else:
if scene_id in suncg_splits['train']:
suncg_splits['train'][scene_id]['0'].append(
os.path.join(suncg_path, rendered_image)
)
elif scene_id in suncg_splits['test']:
suncg_splits['test'][scene_id]['0'].append(
os.path.join(suncg_path, rendered_image)
)
elif scene_id in suncg_splits['val']:
suncg_splits['val'][scene_id]['0'].append(
os.path.join(suncg_path, rendered_image)
)
else:
print("An error has occured with SunCG scene id: {}".format(scene_id))
return suncg_splits
def dump_splits(splits, args, outliers):
split_names = ['train', 'test', 'val']
for name in split_names:
with open(os.path.join(args.outliers_path, "{}_{}.txt".format(args.name, name)), 'w') as out:
for split in splits:
for scene_key, scene_list in split[name].items():
for pose_key, pose_files in scene_list.items():
sorted_split = sorted(pose_files)
filenames_sorted_split = [os.path.basename(s) for s in sorted_split]
if outliers.isdisjoint(filenames_sorted_split):
line = " ".join(sorted_split)
out.writelines([line, "\n"])
def create_outliers_list(outliers_files_list):
outliers = []
for of in outliers_files_list:
with open(of, mode='r') as outlier_file:
csv_reader = csv.reader(outlier_file)
next(csv_reader, None) # skip the headers
for row in csv_reader:
outliers.extend([row[0]])
return set(outliers)
if __name__ == "__main__":
args, unknown = parse_arguments(sys.argv)
splits = list()
''' Matterport3D '''
if 'm3d_path' in args:
m3d_splits = create_m3d_splits(args.m3d_path)
splits.append(m3d_splits)
''' Stanford2D3D '''
if 's2d3d_path' in args:
s2d3d_splits = create_s2d3d_splits(args.s2d3d_path)
splits.append(s2d3d_splits)
''' SunCG '''
if 'suncg_path' in args:
suncg_splits = create_suncg_splits(args.suncg_path)
splits.append(suncg_splits)
outliers = create_outliers_list([
os.path.join(args.outliers_path, 'm3d_outliers.csv'),
os.path.join(args.outliers_path, 's2d3d_outliers.csv'),
os.path.join(args.outliers_path, 'scg_outliers.csv')])
if len(splits) > 0:
dump_splits(splits, args, outliers)