Comments (4)
It seem that the data is different from ours.
Let use camera_e7c078fec2414967b99f65c847b9d38a_office_11_frame_equirectangular_domain_
in area_5a
to check the consistency.
For the target depth map:
from imageio import imread
depth = imread('data/stanford2D3D/area_5a/depth/camera_e7c078fec2414967b99f65c847b9d38a_office_11_frame_equirectangular_domain_depth.png')
print(depth.min(), d.max(), d[d!=65535].max())
It output 356 65535 3156
For the input rgb:
from imageio import imread
rgb = imread('data/stanford2D3D/area_5a/rgb/camera_e7c078fec2414967b99f65c847b9d38a_office_11_frame_equirectangular_domain_rgb.png')
print((rgb==0).all(-1).mean())
It output 0.2767658233642578
.
from hohonet.
Hi thanks for reply, there is my result:
>>> from imageio import imread
>>> depth = imread('/data1/lhw/workspace/HoHoNet/data/stanford2D3D/area_5a/depth/camera_e7c078fec2414967b99f65c847b9d38a_office_11_frame_equirectangular_domain_depth.png')
>>> print(depth.min(), depth.max(), depth[depth!=65535].max())
0 65535 65534
>>> rgb = imread('/data1/lhw/workspace/HoHoNet/data/stanford2D3D/area_5a/rgb/camera_e7c078fec2414967b99f65c847b9d38a_office_11_frame_equirectangular_domain_rgb.png')
>>> print((rgb==0).all(-1).mean())
0.0
Different from your result, can you upload your datasets just for depth train and valid? Thanks very much! Have a good day.
from hohonet.
I think I don't have the right to distribute Stanford2d3d dataset.
I download the data from the official site here (http://buildingparser.stanford.edu/dataset.html).
The official RGBs show black in the north-/south-pole region.
But from your
>>> print((rgb==0).all(-1).mean())
0.0
It seem that you use the restiched one?
from hohonet.
您好,非常感谢您的解答,但是我还是有一些问题我按照您的https://github.com/sunset1995/HoHoNet/blob/master/README_prepare_data_s2d3d.md
准备的方法,生成对应的文件。
from imageio import imread
rgb = imread('/home/lmolhw/workspace/HoHoNet/Data_test/Stanford/area_5a/rgb/camera_e7c078fec2414967b99f65c847b9d38a_office_11_frame_equirectangular_domain_rgb.png')
print((rgb==0).all(-1).mean())
depth = imread('/home/lmolhw/workspace/HoHoNet/Data_test/Stanford/area_5a/depth/camera_e7c078fec2414967b99f65c847b9d38a_office_11_frame_equirectangular_domain_depth.png')
print(depth.min(), depth.max(), depth[depth!=65535].max())
0.25
0 65535 65534
另外还有几个问题:
- 这个数据的prepare对模型的训练结果会有影响么?
- 每个模型大概训练了多少个epoch呢?
- 在test时,(BiFuse's stitching and setting)是什么?
- 训练集和测试集是如何选择的呢?我看原文里面写的是:5用作测试,其余用作训练?
- 或者如果方便,可否提供百度网盘关于depth的训练、测试数据下载~我将非常感激!
期待您的回复~祝好~
from hohonet.
Related Issues (15)
- What is the order of the segmentation labels provided in the segmentation notebook?
- Semantic segmentation on Matterport3D dataset
- Training on image size other than 512x1024?
- for perspective images HOT 1
- why i get so bad sem result ??
- How to get the correct depth?
- how to cut wall, floor, ceil images in particularly
- The CKPT file has become invalid.
- How to calculate the weights of the CrossEntropyLoss, i.e. data/s2d3d_sem/label13_weight.pth? HOT 2
- Where is BiFuse's stitching HOT 3
- Request for another paper "Indoor Panorama Planar 3D Reconstruction via Divide and Conquer" HOT 4
- Spherical coordinate system used in codebase HOT 3
- The unit of the depth graph HOT 3
- Ground Truth same as HorizonNet? HOT 1
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