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extract_posed_images.py
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extract_posed_images.py
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# Modified from https://github.com/ScanNet/ScanNet/blob/master/SensReader/python/SensorData.py # noqa
import os
import struct
import zlib
from argparse import ArgumentParser
from functools import partial
import imageio
import mmcv
import numpy as np
COMPRESSION_TYPE_COLOR = {-1: 'unknown', 0: 'raw', 1: 'png', 2: 'jpeg'}
COMPRESSION_TYPE_DEPTH = {
-1: 'unknown',
0: 'raw_ushort',
1: 'zlib_ushort',
2: 'occi_ushort'
}
class RGBDFrame:
"""Class for single ScanNet RGB-D image processing."""
def load(self, file_handle):
self.camera_to_world = np.asarray(
struct.unpack('f' * 16, file_handle.read(16 * 4)),
dtype=np.float32).reshape(4, 4)
self.timestamp_color = struct.unpack('Q', file_handle.read(8))[0]
self.timestamp_depth = struct.unpack('Q', file_handle.read(8))[0]
self.color_size_bytes = struct.unpack('Q', file_handle.read(8))[0]
self.depth_size_bytes = struct.unpack('Q', file_handle.read(8))[0]
self.color_data = b''.join(
struct.unpack('c' * self.color_size_bytes,
file_handle.read(self.color_size_bytes)))
self.depth_data = b''.join(
struct.unpack('c' * self.depth_size_bytes,
file_handle.read(self.depth_size_bytes)))
def decompress_depth(self, compression_type):
assert compression_type == 'zlib_ushort'
return zlib.decompress(self.depth_data)
def decompress_color(self, compression_type):
assert compression_type == 'jpeg'
return imageio.imread(self.color_data)
class SensorData:
"""Class for single ScanNet scene processing.
Single scene file contains multiple RGB-D images.
"""
def __init__(self, filename, limit):
self.version = 4
self.load(filename, limit)
def load(self, filename, limit):
with open(filename, 'rb') as f:
version = struct.unpack('I', f.read(4))[0]
assert self.version == version
strlen = struct.unpack('Q', f.read(8))[0]
self.sensor_name = b''.join(
struct.unpack('c' * strlen, f.read(strlen)))
self.intrinsic_color = np.asarray(
struct.unpack('f' * 16, f.read(16 * 4)),
dtype=np.float32).reshape(4, 4)
self.extrinsic_color = np.asarray(
struct.unpack('f' * 16, f.read(16 * 4)),
dtype=np.float32).reshape(4, 4)
self.intrinsic_depth = np.asarray(
struct.unpack('f' * 16, f.read(16 * 4)),
dtype=np.float32).reshape(4, 4)
self.extrinsic_depth = np.asarray(
struct.unpack('f' * 16, f.read(16 * 4)),
dtype=np.float32).reshape(4, 4)
self.color_compression_type = COMPRESSION_TYPE_COLOR[struct.unpack(
'i', f.read(4))[0]]
self.depth_compression_type = COMPRESSION_TYPE_DEPTH[struct.unpack(
'i', f.read(4))[0]]
self.color_width = struct.unpack('I', f.read(4))[0]
self.color_height = struct.unpack('I', f.read(4))[0]
self.depth_width = struct.unpack('I', f.read(4))[0]
self.depth_height = struct.unpack('I', f.read(4))[0]
self.depth_shift = struct.unpack('f', f.read(4))[0]
num_frames = struct.unpack('Q', f.read(8))[0]
self.frames = []
if limit > 0 and limit < num_frames:
index = np.random.choice(
np.arange(num_frames), limit, replace=False).tolist()
else:
index = list(range(num_frames))
for i in range(num_frames):
frame = RGBDFrame()
frame.load(f)
if i in index:
self.frames.append(frame)
def export_depth_images(self, output_path):
if not os.path.exists(output_path):
os.makedirs(output_path)
for f in range(len(self.frames)):
depth_data = self.frames[f].decompress_depth(
self.depth_compression_type)
depth = np.fromstring(
depth_data, dtype=np.uint16).reshape(self.depth_height,
self.depth_width)
imageio.imwrite(
os.path.join(output_path,
self.index_to_str(f) + '.png'), depth)
def export_color_images(self, output_path):
if not os.path.exists(output_path):
os.makedirs(output_path)
for f in range(len(self.frames)):
color = self.frames[f].decompress_color(
self.color_compression_type)
imageio.imwrite(
os.path.join(output_path,
self.index_to_str(f) + '.jpg'), color)
@staticmethod
def index_to_str(index):
return str(index).zfill(5)
@staticmethod
def save_mat_to_file(matrix, filename):
with open(filename, 'w') as f:
for line in matrix:
np.savetxt(f, line[np.newaxis], fmt='%f')
def export_poses(self, output_path):
if not os.path.exists(output_path):
os.makedirs(output_path)
for f in range(len(self.frames)):
self.save_mat_to_file(
self.frames[f].camera_to_world,
os.path.join(output_path,
self.index_to_str(f) + '.txt'))
def export_intrinsics(self, output_path):
if not os.path.exists(output_path):
os.makedirs(output_path)
self.save_mat_to_file(self.intrinsic_color,
os.path.join(output_path, 'intrinsic.txt'))
def process_scene(path, limit, idx):
"""Process single ScanNet scene.
Extract RGB images, poses and camera intrinsics.
"""
data = SensorData(os.path.join(path, idx, f'{idx}.sens'), limit)
output_path = os.path.join('posed_images', idx)
data.export_color_images(output_path)
data.export_intrinsics(output_path)
data.export_poses(output_path)
def process_directory(path, limit, nproc):
print(f'processing {path}')
mmcv.track_parallel_progress(
func=partial(process_scene, path, limit),
tasks=os.listdir(path),
nproc=nproc)
if __name__ == '__main__':
parser = ArgumentParser()
parser.add_argument('--max-images-per-scene', type=int, default=300)
parser.add_argument('--nproc', type=int, default=8)
args = parser.parse_args()
# process train and val scenes
if os.path.exists('scans'):
process_directory('scans', args.max_images_per_scene, args.nproc)
# process test scenes
if os.path.exists('scans_test'):
process_directory('scans_test', args.max_images_per_scene, args.nproc)