https://github.com/hu-zhiyu/ARCM
Tip revision: ccd2205446fc22786852cc1a1c12bbc5448e8c0a authored by 胡智宇 on 22 May 2020, 13:27:05 UTC
update_2020_05_22
update_2020_05_22
Tip revision: ccd2205
indoor3d_util.pyc
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Modified from: https://github.com/charlesq34/pointnet/blob/master/sem_seg/indoor3d_util.py
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� n d | GHt � d S( s Convert original dataset files to data_label file (each line is XYZRGBLG).
We aggregated all the points from each instance in the room.
Args:
anno_path: path to annotations. e.g. Area_1/office_2/Annotations/
out_filename: path to save collected points and labels (each line is XYZRGBLG)
file_format: txt or numpy, determines what file format to save.
Returns:
None
Note:
the points are shifted before save, the most negative point is now at origin.
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� d S( s For visualization of a room from data_label file,
input_filename: each line is X Y Z R G B L
out_filename: OBJ filename,
visualize input file by coloring point with label color
easy_view: only visualize furnitures and floor
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if N < num_sample, we will randomly duplicate samples.
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| d � � qWt j | d � t j | d � t j | d � f S( s� Prepare block training data.
Args:
data: N x 6 numpy array, 012 are XYZ in meters, 345 are RGB in [0,1]
assumes the data is shifted (min point is origin) and aligned
(aligned with XYZ axis)
label: N size uint8 numpy array from 0-12
num_point: int, how many points to sample in each block
block_size: float, physical size of the block in meters
stride: float, stride for block sweeping
random_sample: bool, if True, we will randomly sample blocks in the room
sample_num: int, if random sample, how many blocks to sample
[default: room area]
sample_aug: if random sample, how much aug
Returns:
block_datas: K x num_point x 6 np array of XYZRGB, RGB is in [0,1]
block_labels: K x num_point x 1 np array of uint8 labels
TODO: for this version, blocking is in fixed, non-overlapping pattern.
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Args:
data: N x 6 numpy array, 012 are XYZ in meters, 345 are RGB in [0,1]
assumes the data is shifted (min point is origin) and
aligned (aligned with XYZ axis)
label: N size uint8 numpy array from 0-12
sample_num_point: int, how many points to sample in each sample
Returns:
sample_datas: K x sample_num_point x 9
numpy array of XYZRGBX'Y'Z', RGB is in [0,1]
sample_labels: K x sample_num_point x 1 np array of uint8 labels
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for each block centralize XYZ, add normalized XYZ as 678 channels
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d f f � q�W| j � d S( s$ Compute bounding boxes from each instance in original dataset files on
one room. **We assume the bbox is aligned with XYZ coordinate.**
Args:
anno_path: path to annotations. e.g. Area_1/office_2/Annotations/
out_filename: path to save instance bounding boxes for that room.
each line is x1 y1 z1 x2 y2 z2 label,
where (x1,y1,z1) is the point on the diagonal closer to origin
Returns:
None
Note:
room points are shifted, the most negative point is now at origin.
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Args:
input_filename: each line is x1 y1 z1 x2 y2 z2 label
out_filename_prefix: OBJ filename prefix,
visualize object by g_label2color
easy_view: if True, only visualize furniture and floor
Returns:
output a list of OBJ file and MTL files with the same prefix
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Args:
input_filename: each line is x1 y1 z1 x2 y2 z2 label
out_filename_prefix: OBJ filename prefix,
visualize object by g_label2color
easy_view: if True, only visualize furniture and floor
permute: if not None, permute XYZ for rendering, e.g. [0 2 1]
center: if True, move obj to have zero origin
Returns:
output a list of OBJ file and MTL files with the same prefix
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� qW| j � n/ | d k r�t j | | � n d | GHt � d S( s� Compute bounding boxes from each instance in original dataset files on
one room. **We assume the bbox is aligned with XYZ coordinate.**
Save both the point XYZRGB and the bounding box for the point's
parent element.
Args:
anno_path: path to annotations. e.g. Area_1/office_2/Annotations/
out_filename: path to save instance bounding boxes for each point,
plus the point's XYZRGBL
each line is XYZRGBL offsetX offsetY offsetZ a b c,
where cx = X+offsetX, cy=X+offsetY, cz=Z+offsetZ
where (cx,cy,cz) is center of the box, a,b,c are distances from center
to the surfaces of the box, i.e. x1 = cx-a, x2 = cx+a, y1=cy-b etc.
file_format: output file format, txt or numpy
Returns:
None
Note:
room points are shifted, the most negative point is now at origin.
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