def __init__(self, options, split, random=True):
self.options = options
self.random = random
dataFolder = '../../Data/ScanNet/'
self.scenes = []
self.sceneImageIndices = []
with open(dataFolder + '/ScanNet/Tasks/Benchmark/scannetv1_' + split + '.txt') as f:
for line in f:
scene_id = line.strip()
scenePath = dataFolder + '/scans/' + scene_id
if not os.path.exists(scenePath + '/' + scene_id + '.txt') or len(glob.glob(scenePath + '/annotation/segmentation/*')) == 0:
continue
scene = ScanNetScene(options, scenePath, scene_id)
self.scenes.append(scene)
self.sceneImageIndices += [[len(self.scenes) - 1, imageIndex] for imageIndex in range(len(scene.imagePaths))]
continue
pass
#np.savetxt(dataFolder + '/image_list_' + split + '.txt', imagePaths, fmt='%s')
#imagePaths = np.loadtxt(dataFolder + '/image_list_' + split + '.txt', fmt='%s')
print(('num images', len(self.sceneImageIndices)))
np.random.shuffle(self.sceneImageIndices)
numImages = options.numTrainingImages if split == 'train' else options.numTestingImages
if numImages > 0:
self.sceneImageIndices = self.sceneImageIndices[:numImages]
pass
return
def __init__(self, config, split, random=True, evaluation=False):
self.random = random
self.dataFolder = config.dataFolder
self.split = split
self.eval = evaluation
self.scenes = []
self.loadClassMap()
planenet_scene_ids_val = np.load('datasets/scene_ids_val.npy')
planenet_scene_ids_val = {
scene_id.decode('utf-8'): True
for scene_id in planenet_scene_ids_val
}
with open(self.dataFolder + '/ScanNet/Tasks/Benchmark/scannetv1_' +
split + '.txt') as f:
for line in f:
scene_id = line.strip()
if split == 'test':
## Remove scenes which are in PlaneNet's training set for fair comparison
if scene_id not in planenet_scene_ids_val:
continue
pass
scenePath = self.dataFolder + '/scans/' + scene_id
if not os.path.exists(scenePath + '/' + scene_id +
'.txt') or not os.path.exists(
scenePath +
'/annotation/planes.npy'):
continue
scene = ScanNetScene(config, scenePath, scene_id,
self.confident_labels, self.layout_labels)
if len(scene) < 100:
continue
self.scenes.append(scene)
continue
pass
self.transform = tf.Compose([
tf.ToTensor(),
tf.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
if random:
t = int(time.time() * 1000000)
np.random.seed(((t & 0xff000000) >> 24) + ((t & 0x00ff0000) >> 8) +
((t & 0x0000ff00) << 8) + ((t & 0x000000ff) << 24))
else:
np.random.seed(0)
pass
print(f'num of scenes {len(self.scenes)}')
self.tmp_img_index = np.load('./datasets/img_id_list.npy').astype(
np.int)
def __init__(self,
options,
config,
split,
random=True,
loadNeighborImage=False,
load_semantics=False,
load_boundary=False):
self.options = options
self.config = config
self.split = split
self.random = random
self.dataFolder = options.dataFolder
self.scenes = []
self.sceneImageIndices = []
self.loadClassMap()
planenet_scene_ids_val = np.load('datasets/scene_ids_val.npy')
planenet_scene_ids_val = {
scene_id.decode('utf-8'): True
for scene_id in planenet_scene_ids_val
}
with open(self.dataFolder + '/ScanNet/Tasks/Benchmark/scannetv1_' +
split + '.txt') as f:
for line in f:
scene_id = line.strip()
if split == 'test':
## Remove scenes which are in PlaneNet's training set for fair comparison
if scene_id not in planenet_scene_ids_val:
continue
pass
scenePath = self.dataFolder + '/scans/' + scene_id
if not os.path.exists(scenePath + '/' + scene_id +
'.txt') or not os.path.exists(
scenePath +
'/annotation/planes.npy'):
continue
scene = ScanNetScene(options,
scenePath,
scene_id,
self.confident_labels,
self.layout_labels,
load_semantics=load_semantics,
load_boundary=load_boundary)
self.scenes.append(scene)
self.sceneImageIndices += [[
len(self.scenes) - 1, imageIndex
] for imageIndex in range(len(scene.imagePaths))]
continue
pass
if random:
t = int(time.time() * 1000000)
np.random.seed(((t & 0xff000000) >> 24) + ((t & 0x00ff0000) >> 8) +
((t & 0x0000ff00) << 8) + ((t & 0x000000ff) << 24))
else:
np.random.seed(0)
pass
np.random.shuffle(self.sceneImageIndices)
self.invalid_indices = {}
with open(self.dataFolder + '/invalid_indices_' + split + '.txt',
'r') as f:
for line in f:
tokens = line.split(' ')
if len(tokens) == 3:
assert (int(tokens[2]) < 10000)
invalid_index = int(tokens[1]) * 10000 + int(tokens[2])
if invalid_index not in self.invalid_indices:
self.invalid_indices[invalid_index] = True
pass
pass
continue
pass
self.sceneImageIndices = [
[sceneIndex, imageIndex]
for sceneIndex, imageIndex in self.sceneImageIndices
if (sceneIndex * 10000 + imageIndex) not in self.invalid_indices
]
print('num images', len(self.sceneImageIndices))
self.anchors = utils.generate_pyramid_anchors(config.RPN_ANCHOR_SCALES,
config.RPN_ANCHOR_RATIOS,
config.BACKBONE_SHAPES,
config.BACKBONE_STRIDES,
config.RPN_ANCHOR_STRIDE)
self.loadNeighborImage = loadNeighborImage
return
def __init__(self, config, split, random=True, evaluation=False):
self.random = random
self.dataFolder = config.dataFolder
self.split = split
self.eval = evaluation
self.scenes = []
self.sceneImageIndices = []
self.loadClassMap()
planenet_scene_ids_val = np.load('datasets/scene_ids_val.npy')
planenet_scene_ids_val = {
scene_id.decode('utf-8'): True
for scene_id in planenet_scene_ids_val
}
with open(self.dataFolder + '/ScanNet/Tasks/Benchmark/scannetv1_' +
split + '.txt') as f:
for line in f:
scene_id = line.strip()
if split == 'test':
## Remove scenes which are in PlaneNet's training set for fair comparison
if scene_id not in planenet_scene_ids_val:
continue
pass
scenePath = self.dataFolder + '/scans/' + scene_id
if not os.path.exists(scenePath + '/' + scene_id +
'.txt') or not os.path.exists(
scenePath +
'/annotation/planes.npy'):
continue
scene = ScanNetScene(config, scenePath, scene_id,
self.confident_labels, self.layout_labels)
self.scenes.append(scene)
self.sceneImageIndices += [[
len(self.scenes) - 1, imageIndex
] for imageIndex in range(len(scene.imagePaths))]
continue
pass
self.transform = tf.Compose([
tf.ToTensor(),
tf.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
if random:
t = int(time.time() * 1000000)
np.random.seed(((t & 0xff000000) >> 24) + ((t & 0x00ff0000) >> 8) +
((t & 0x0000ff00) << 8) + ((t & 0x000000ff) << 24))
else:
np.random.seed(0)
pass
np.random.shuffle(self.sceneImageIndices)
self.invalid_indices = {}
with open(self.dataFolder + '/invalid_indices_' + split + '.txt',
'r') as f:
for line in f:
tokens = line.split(' ')
if len(tokens) == 3:
assert (int(tokens[2]) < 10000)
invalid_index = int(tokens[1]) * 10000 + int(tokens[2])
if invalid_index not in self.invalid_indices:
self.invalid_indices[invalid_index] = True
pass
pass
continue
pass
self.sceneImageIndices = [
[sceneIndex, imageIndex]
for sceneIndex, imageIndex in self.sceneImageIndices
if (sceneIndex * 10000 + imageIndex) not in self.invalid_indices
]
print('num images', len(self.sceneImageIndices))
self.save_png = False
self.return_prcnn = False
self.tmp_img_index = np.load('./datasets/img_id_list.npy').astype(
np.int)
self.test_ids = np.loadtxt('./datasets/test_ids.txt', dtype=np.int)