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import torch
import numpy as np
from torchsparse import SparseTensor
from torchsparse.utils import sparse_collate_fn, sparse_quantize
from plyfile import PlyData, PlyElement
import os
def init_image_coor(height, width, u0=None, v0=None):
u0 = width / 2.0 if u0 is None else u0
v0 = height / 2.0 if v0 is None else v0
x_row = np.arange(0, width)
x = np.tile(x_row, (height, 1))
x = x.astype(np.float32)
u_u0 = x - u0
y_col = np.arange(0, height)
y = np.tile(y_col, (width, 1)).T
y = y.astype(np.float32)
v_v0 = y - v0
return u_u0, v_v0
def depth_to_pcd(depth, u_u0, v_v0, f, invalid_value=0):
mask_invalid = depth <= invalid_value
depth[mask_invalid] = 0.0
x = u_u0 / f * depth
y = v_v0 / f * depth
z = depth
pcd = np.stack([x, y, z], axis=2)
return pcd, ~mask_invalid
def pcd_to_sparsetensor(pcd, mask_valid, voxel_size=0.01, num_points=100000):
pcd_valid = pcd[mask_valid]
block_ = pcd_valid
block = np.zeros_like(block_)
block[:, :3] = block_[:, :3]
pc_ = np.round(block_[:, :3] / voxel_size)
pc_ -= pc_.min(0, keepdims=1)
feat_ = block
# transfer point cloud to voxels
inds = sparse_quantize(pc_,
feat_,
return_index=True,
return_invs=False)
if len(inds) > num_points:
inds = np.random.choice(inds, num_points, replace=False)
pc = pc_[inds]
feat = feat_[inds]
lidar = SparseTensor(feat, pc)
feed_dict = [{'lidar': lidar}]
inputs = sparse_collate_fn(feed_dict)
return inputs
def pcd_uv_to_sparsetensor(pcd, u_u0, v_v0, mask_valid, f= 500.0, voxel_size=0.01, mask_side=None, num_points=100000):
if mask_side is not None:
mask_valid = mask_valid & mask_side
pcd_valid = pcd[mask_valid]
u_u0_valid = u_u0[mask_valid][:, np.newaxis] / f
v_v0_valid = v_v0[mask_valid][:, np.newaxis] / f
block_ = np.concatenate([pcd_valid, u_u0_valid, v_v0_valid], axis=1)
block = np.zeros_like(block_)
block[:, :] = block_[:, :]
pc_ = np.round(block_[:, :3] / voxel_size)
pc_ -= pc_.min(0, keepdims=1)
feat_ = block
# transfer point cloud to voxels
inds = sparse_quantize(pc_,
feat_,
return_index=True,
return_invs=False)
if len(inds) > num_points:
inds = np.random.choice(inds, num_points, replace=False)
pc = pc_[inds]
feat = feat_[inds]
lidar = SparseTensor(feat, pc)
feed_dict = [{'lidar': lidar}]
inputs = sparse_collate_fn(feed_dict)
return inputs
def refine_focal_one_step(depth, focal, model, u0, v0):
# reconstruct PCD from depth
u_u0, v_v0 = init_image_coor(depth.shape[0], depth.shape[1], u0=u0, v0=v0)
pcd, mask_valid = depth_to_pcd(depth, u_u0, v_v0, f=focal, invalid_value=0)
# input for the voxelnet
feed_dict = pcd_uv_to_sparsetensor(pcd, u_u0, v_v0, mask_valid, f=focal, voxel_size=0.005, mask_side=None)
inputs = feed_dict['lidar'].cuda()
outputs = model(inputs)
return outputs
def refine_shift_one_step(depth_wshift, model, focal, u0, v0):
# reconstruct PCD from depth
u_u0, v_v0 = init_image_coor(depth_wshift.shape[0], depth_wshift.shape[1], u0=u0, v0=v0)
pcd_wshift, mask_valid = depth_to_pcd(depth_wshift, u_u0, v_v0, f=focal, invalid_value=0)
# input for the voxelnet
feed_dict = pcd_to_sparsetensor(pcd_wshift, mask_valid, voxel_size=0.01)
inputs = feed_dict['lidar'].cuda()
outputs = model(inputs)
return outputs
def refine_focal(depth, focal, model, u0, v0):
last_scale = 1
focal_tmp = np.copy(focal)
for i in range(1):
scale = refine_focal_one_step(depth, focal_tmp, model, u0, v0)
focal_tmp = focal_tmp / scale.item()
last_scale = last_scale * scale
return torch.tensor([[last_scale]])
def refine_shift(depth_wshift, model, focal, u0, v0):
depth_wshift_tmp = np.copy(depth_wshift)
last_shift = 0
for i in range(1):
shift = refine_shift_one_step(depth_wshift_tmp, model, focal, u0, v0)
shift = shift if shift.item() < 0.7 else torch.tensor([[0.7]])
depth_wshift_tmp -= shift.item()
last_shift += shift.item()
return torch.tensor([[last_shift]])
def reconstruct_3D(depth, f):
"""
Reconstruct depth to 3D pointcloud with the provided focal length.
Return:
pcd: N X 3 array, point cloud
"""
cu = depth.shape[1] / 2
cv = depth.shape[0] / 2
width = depth.shape[1]
height = depth.shape[0]
row = np.arange(0, width, 1)
u = np.array([row for i in np.arange(height)])
col = np.arange(0, height, 1)
v = np.array([col for i in np.arange(width)])
v = v.transpose(1, 0)
if f > 1e5:
print('Infinit focal length!!!')
x = u - cu
y = v - cv
z = depth / depth.max() * x.max()
else:
x = (u - cu) * depth / f
y = (v - cv) * depth / f
z = depth
x = np.reshape(x, (width * height, 1)).astype(float)
y = np.reshape(y, (width * height, 1)).astype(float)
z = np.reshape(z, (width * height, 1)).astype(float)
pcd = np.concatenate((x, y, z), axis=1)
pcd = pcd.astype(int)
return pcd
def save_point_cloud(pcd, rgb, filename, binary=True):
"""Save an RGB point cloud as a PLY file.
:paras
@pcd: Nx3 matrix, the XYZ coordinates
@rgb: NX3 matrix, the rgb colors for each 3D point
"""
assert pcd.shape[0] == rgb.shape[0]
if rgb is None:
gray_concat = np.tile(np.array([128], dtype=np.uint8), (pcd.shape[0], 3))
points_3d = np.hstack((pcd, gray_concat))
else:
points_3d = np.hstack((pcd, rgb))
python_types = (float, float, float, int, int, int)
npy_types = [('x', 'f4'), ('y', 'f4'), ('z', 'f4'), ('red', 'u1'), ('green', 'u1'),
('blue', 'u1')]
if binary is True:
# Format into NumPy structured array
vertices = []
for row_idx in range(points_3d.shape[0]):
cur_point = points_3d[row_idx]
vertices.append(tuple(dtype(point) for dtype, point in zip(python_types, cur_point)))
vertices_array = np.array(vertices, dtype=npy_types)
el = PlyElement.describe(vertices_array, 'vertex')
# Write
PlyData([el]).write(filename)
else:
x = np.squeeze(points_3d[:, 0])
y = np.squeeze(points_3d[:, 1])
z = np.squeeze(points_3d[:, 2])
r = np.squeeze(points_3d[:, 3])
g = np.squeeze(points_3d[:, 4])
b = np.squeeze(points_3d[:, 5])
ply_head = 'ply\n' \
'format ascii 1.0\n' \
'element vertex %d\n' \
'property float x\n' \
'property float y\n' \
'property float z\n' \
'property uchar red\n' \
'property uchar green\n' \
'property uchar blue\n' \
'end_header' % r.shape[0]
# ---- Save ply data to disk
np.savetxt(filename, np.column_stack((x, y, z, r, g, b)), fmt="%d %d %d %d %d %d", header=ply_head, comments='')
def reconstruct_depth(depth, rgb, dir, pcd_name, focal):
"""
para disp: disparity, [h, w]
para rgb: rgb image, [h, w, 3], in rgb format
"""
rgb = np.squeeze(rgb)
depth = np.squeeze(depth)
mask = depth < 1e-8
depth[mask] = 0
depth = depth / depth.max() * 10000
pcd = reconstruct_3D(depth, f=focal)
rgb_n = np.reshape(rgb, (-1, 3))
save_point_cloud(pcd, rgb_n, os.path.join(dir, pcd_name + '.ply'))
def recover_metric_depth(pred, gt):
if type(pred).__module__ == torch.__name__:
pred = pred.cpu().numpy()
if type(gt).__module__ == torch.__name__:
gt = gt.cpu().numpy()
gt = gt.squeeze()
pred = pred.squeeze()
mask = (gt > 1e-8) & (pred > 1e-8)
gt_mask = gt[mask]
pred_mask = pred[mask]
a, b = np.polyfit(pred_mask, gt_mask, deg=1)
pred_metric = a * pred + b
return pred_metric
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