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"""
Author: Luigi Piccinelli
Licensed under the CC-BY NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/)
"""
import os
import numpy as np
from PIL import Image
import matplotlib.cm
import wandb
import torch
from unidepth.utils.misc import ssi_helper
def colorize(
value: np.ndarray, vmin: float = None, vmax: float = None, cmap: str = "magma_r"
):
# if already RGB, do nothing
if value.ndim > 2:
if value.shape[-1] > 1:
return value
value = value[..., 0]
invalid_mask = value < 0.0001
# normalize
vmin = value.min() if vmin is None else vmin
vmax = value.max() if vmax is None else vmax
value = (value - vmin) / (vmax - vmin) # vmin..vmax
# set color
cmapper = matplotlib.cm.get_cmap(cmap)
value = cmapper(value, bytes=True) # (nxmx4)
value[invalid_mask] = 0
img = value[..., :3]
return img
def image_grid(imgs: list[np.ndarray], rows: int, cols: int) -> np.ndarray:
if not len(imgs):
return None
assert len(imgs) == rows * cols
h, w = imgs[0].shape[:2]
grid = Image.new("RGB", size=(cols * w, rows * h))
for i, img in enumerate(imgs):
grid.paste(
Image.fromarray(img.astype(np.uint8)).resize(
(w, h), resample=Image.BILINEAR
),
box=(i % cols * w, i // cols * h),
)
return np.array(grid)
def get_pointcloud_from_rgbd(
image: np.array,
depth: np.array,
mask: np.ndarray,
intrinsic_matrix: np.array,
extrinsic_matrix: np.array = None,
):
depth = np.array(depth).squeeze()
mask = np.array(mask).squeeze()
# Mask the depth array
masked_depth = np.ma.masked_where(mask == False, depth)
# masked_depth = np.ma.masked_greater(masked_depth, 8000)
# Create idx array
idxs = np.indices(masked_depth.shape)
u_idxs = idxs[1]
v_idxs = idxs[0]
# Get only non-masked depth and idxs
z = masked_depth[~masked_depth.mask]
compressed_u_idxs = u_idxs[~masked_depth.mask]
compressed_v_idxs = v_idxs[~masked_depth.mask]
image = np.stack(
[image[..., i][~masked_depth.mask] for i in range(image.shape[-1])], axis=-1
)
# Calculate local position of each point
# Apply vectorized math to depth using compressed arrays
cx = intrinsic_matrix[0, 2]
fx = intrinsic_matrix[0, 0]
x = (compressed_u_idxs - cx) * z / fx
cy = intrinsic_matrix[1, 2]
fy = intrinsic_matrix[1, 1]
# Flip y as we want +y pointing up not down
y = -((compressed_v_idxs - cy) * z / fy)
# # Apply camera_matrix to pointcloud as to get the pointcloud in world coords
# if extrinsic_matrix is not None:
# # Calculate camera pose from extrinsic matrix
# camera_matrix = np.linalg.inv(extrinsic_matrix)
# # Create homogenous array of vectors by adding 4th entry of 1
# # At the same time flip z as for eye space the camera is looking down the -z axis
# w = np.ones(z.shape)
# x_y_z_eye_hom = np.vstack((x, y, -z, w))
# # Transform the points from eye space to world space
# x_y_z_world = np.dot(camera_matrix, x_y_z_eye_hom)[:3]
# return x_y_z_world.T
# else:
x_y_z_local = np.stack((x, y, z), axis=-1)
return np.concatenate([x_y_z_local, image], axis=-1)
def save_file_ply(xyz, rgb, pc_file):
if rgb.max() < 1.001:
rgb = rgb * 255.0
rgb = rgb.astype(np.uint8)
# print(rgb)
with open(pc_file, "w") as f:
# headers
f.writelines(
[
"ply\n" "format ascii 1.0\n",
"element vertex {}\n".format(xyz.shape[0]),
"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\n",
]
)
for i in range(xyz.shape[0]):
str_v = "{:10.6f} {:10.6f} {:10.6f} {:d} {:d} {:d}\n".format(
xyz[i, 0], xyz[i, 1], xyz[i, 2], rgb[i, 0], rgb[i, 1], rgb[i, 2]
)
f.write(str_v)
# really awful fct... FIXME
def log_train_artifacts(rgbs, gts, preds, ds_name, step, infos={}):
rgbs = [
(127.5 * (rgb + 1))
.clip(0, 255)
.to(torch.uint8)
.cpu()
.detach()
.permute(1, 2, 0)
.numpy()
for rgb in rgbs
]
new_gts, new_preds = [], []
if len(gts) > 0:
for i, gt in enumerate(gts):
scale, shift = ssi_helper(
gts[i][gts[i] > 0].cpu().detach(), preds[i][gts[i] > 0].cpu().detach()
)
gt = gts[i].cpu().detach().squeeze().numpy()
pred = (preds[i].cpu().detach() * scale + shift).squeeze().numpy()
vmin = gt[gt > 0].min() if (gt > 0).any() else 0.0
vmax = gt.max() if (gt > 0).any() else 0.1
new_gts.append(colorize(gt, vmin=vmin, vmax=vmax))
new_preds.append(colorize(pred, vmin=vmin, vmax=vmax))
gts, preds = new_gts, new_preds
else:
preds = [
colorize(pred.cpu().detach().squeeze().numpy(), 0.0, 80.0)
for i, pred in enumerate(preds)
]
num_additional, additionals = 0, []
for name, info in infos.items():
num_additional += 1
if info.shape[1] == 3:
additionals.extend(
[
(127.5 * (x + 1))
.clip(0, 255)
.to(torch.uint8)
.cpu()
.detach()
.permute(1, 2, 0)
.numpy()
for x in info[:4]
]
)
else:
additionals.extend(
[
colorize(x.cpu().detach().squeeze().numpy())
for i, x in enumerate(info[:4])
]
)
num_rows = 2 + int(len(gts) > 0) + num_additional
artifacts_grid = image_grid(
[*rgbs, *gts, *preds, *additionals], num_rows, len(rgbs)
)
try:
wandb.log({f"{ds_name}_training": [wandb.Image(artifacts_grid)]}, step=step)
except:
Image.fromarray(artifacts_grid).save(
os.path.join(os.environ["HOME"], "Workspace", f"art_grid{step}.png")
)
print("Logging training images failed")
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