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from copy import deepcopy
import cv2
import numpy as np
import torch
import torch.nn as nn
import roma
from copy import deepcopy
import tqdm
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.pyplot as plt
from matplotlib.backends.backend_agg import FigureCanvasAgg
from scipy.spatial.transform import Rotation
from PIL import Image
import imageio.v2 as iio
from matplotlib.figure import Figure
def save_focals(cam_dict, path):
# convert focal to txt
focals = cam_dict["focal"]
np.savetxt(path, focals, fmt="%.6f")
return focals
def save_intrinsics(cam_dict, path):
K_raw = np.eye(3)[None].repeat(len(cam_dict["focal"]), axis=0)
K_raw[:, 0, 0] = cam_dict["focal"]
K_raw[:, 1, 1] = cam_dict["focal"]
K_raw[:, :2, 2] = cam_dict["pp"]
K = K_raw.reshape(-1, 9)
np.savetxt(path, K, fmt="%.6f")
return K_raw
def save_conf_maps(conf, path):
for i, c in enumerate(conf):
np.save(f"{path}/conf_{i}.npy", c.detach().cpu().numpy())
return conf
def save_rgb_imgs(colors, path):
imgs = colors
for i, img in enumerate(imgs):
# convert from rgb to bgr
iio.imwrite(
f"{path}/frame_{i:04d}.jpg", (img.cpu().numpy() * 255).astype(np.uint8)
)
return imgs
def save_depth_maps(pts3ds_self, path, conf_self=None):
depth_maps = torch.stack([pts3d_self[..., -1] for pts3d_self in pts3ds_self], 0)
min_depth = depth_maps.min() # float(torch.quantile(out, 0.01))
max_depth = depth_maps.max() # float(torch.quantile(out, 0.99))
colored_depth = colorize(
depth_maps,
cmap_name="Spectral_r",
range=(min_depth, max_depth),
append_cbar=True,
)
images = []
if conf_self is not None:
conf_selfs = torch.concat(conf_self, 0)
min_conf = torch.log(conf_selfs.min()) # float(torch.quantile(out, 0.01))
max_conf = torch.log(conf_selfs.max()) # float(torch.quantile(out, 0.99))
colored_conf = colorize(
torch.log(conf_selfs),
cmap_name="jet",
range=(min_conf, max_conf),
append_cbar=True,
)
for i, depth_map in enumerate(colored_depth):
# Apply color map to depth map
img_path = f"{path}/frame_{(i):04d}.png"
if conf_self is None:
to_save = (depth_map * 255).detach().cpu().numpy().astype(np.uint8)
else:
to_save = torch.cat([depth_map, colored_conf[i]], dim=1)
to_save = (to_save * 255).detach().cpu().numpy().astype(np.uint8)
iio.imwrite(img_path, to_save)
images.append(Image.open(img_path))
np.save(f"{path}/frame_{(i):04d}.npy", depth_maps[i].detach().cpu().numpy())
# comment this as it may fail sometimes
# images[0].save(f'{path}/_depth_maps.gif', save_all=True, append_images=images[1:], duration=100, loop=0)
return depth_maps
def get_vertical_colorbar(h, vmin, vmax, cmap_name="jet", label=None, cbar_precision=2):
"""
:param w: pixels
:param h: pixels
:param vmin: min value
:param vmax: max value
:param cmap_name:
:param label
:return:
"""
fig = Figure(figsize=(2, 8), dpi=100)
fig.subplots_adjust(right=1.5)
canvas = FigureCanvasAgg(fig)
# Do some plotting.
ax = fig.add_subplot(111)
cmap = cm.get_cmap(cmap_name)
norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
tick_cnt = 6
tick_loc = np.linspace(vmin, vmax, tick_cnt)
cb1 = mpl.colorbar.ColorbarBase(
ax, cmap=cmap, norm=norm, ticks=tick_loc, orientation="vertical"
)
tick_label = [str(np.round(x, cbar_precision)) for x in tick_loc]
if cbar_precision == 0:
tick_label = [x[:-2] for x in tick_label]
cb1.set_ticklabels(tick_label)
cb1.ax.tick_params(labelsize=18, rotation=0)
if label is not None:
cb1.set_label(label)
# fig.tight_layout()
canvas.draw()
s, (width, height) = canvas.print_to_buffer()
im = np.frombuffer(s, np.uint8).reshape((height, width, 4))
im = im[:, :, :3].astype(np.float32) / 255.0
if h != im.shape[0]:
w = int(im.shape[1] / im.shape[0] * h)
im = cv2.resize(im, (w, h), interpolation=cv2.INTER_AREA)
return im
def colorize_np(
x,
cmap_name="jet",
mask=None,
range=None,
append_cbar=False,
cbar_in_image=False,
cbar_precision=2,
):
"""
turn a grayscale image into a color image
:param x: input grayscale, [H, W]
:param cmap_name: the colorization method
:param mask: the mask image, [H, W]
:param range: the range for scaling, automatic if None, [min, max]
:param append_cbar: if append the color bar
:param cbar_in_image: put the color bar inside the image to keep the output image the same size as the input image
:return: colorized image, [H, W]
"""
if range is not None:
vmin, vmax = range
elif mask is not None:
# vmin, vmax = np.percentile(x[mask], (2, 100))
vmin = np.min(x[mask][np.nonzero(x[mask])])
vmax = np.max(x[mask])
# vmin = vmin - np.abs(vmin) * 0.01
x[np.logical_not(mask)] = vmin
# print(vmin, vmax)
else:
vmin, vmax = np.percentile(x, (1, 100))
vmax += 1e-6
x = np.clip(x, vmin, vmax)
x = (x - vmin) / (vmax - vmin)
# x = np.clip(x, 0., 1.)
cmap = cm.get_cmap(cmap_name)
x_new = cmap(x)[:, :, :3]
if mask is not None:
mask = np.float32(mask[:, :, np.newaxis])
x_new = x_new * mask + np.ones_like(x_new) * (1.0 - mask)
cbar = get_vertical_colorbar(
h=x.shape[0],
vmin=vmin,
vmax=vmax,
cmap_name=cmap_name,
cbar_precision=cbar_precision,
)
if append_cbar:
if cbar_in_image:
x_new[:, -cbar.shape[1] :, :] = cbar
else:
x_new = np.concatenate(
(x_new, np.zeros_like(x_new[:, :5, :]), cbar), axis=1
)
return x_new
else:
return x_new
# tensor
def colorize(
x, cmap_name="jet", mask=None, range=None, append_cbar=False, cbar_in_image=False
):
"""
turn a grayscale image into a color image
:param x: torch.Tensor, grayscale image, [H, W] or [B, H, W]
:param mask: torch.Tensor or None, mask image, [H, W] or [B, H, W] or None
"""
device = x.device
x = x.cpu().numpy()
if mask is not None:
mask = mask.cpu().numpy() > 0.99
kernel = np.ones((3, 3), np.uint8)
if x.ndim == 2:
x = x[None]
if mask is not None:
mask = mask[None]
out = []
for x_ in x:
if mask is not None:
mask = cv2.erode(mask.astype(np.uint8), kernel, iterations=1).astype(bool)
x_ = colorize_np(x_, cmap_name, mask, range, append_cbar, cbar_in_image)
out.append(torch.from_numpy(x_).to(device).float())
out = torch.stack(out).squeeze(0)
return out
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