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utils.py
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# Copyright Niantic 2019. Patent Pending. All rights reserved.
#
# This software is licensed under the terms of the Monodepth2 licence
# which allows for non-commercial use only, the full terms of which are made
# available in the LICENSE file.
from __future__ import absolute_import, division, print_function
import os
import numpy as np
import hashlib
import zipfile
from six.moves import urllib
# relative_poses: [N, 4, 4]
def relative2absolute(relative_poses):
N = relative_poses.shape[0]
absolute_poses = np.zeros((N+1, 4, 4))
absolute_poses[0] = np.eye(4)
for i in range(1, N+1):
absolute_poses[i] = absolute_poses[i-1] @ np.linalg.inv(relative_poses[i-1])
return absolute_poses
def umeyama_alignment(x, y, with_scale=False):
"""
Computes the least squares solution parameters of an Sim(m) matrix
that minimizes the distance between a set of registered points.
Umeyama, Shinji: Least-squares estimation of transformation parameters
between two point patterns. IEEE PAMI, 1991
:param x: mxn matrix of points, m = dimension, n = nr. of data points
:param y: mxn matrix of points, m = dimension, n = nr. of data points
:param with_scale: set to True to align also the scale (default: 1.0 scale)
:return: r, t, c - rotation matrix, translation vector and scale factor
"""
if x.shape != y.shape:
assert False, "x.shape not equal to y.shape"
# m = dimension, n = nr. of data points
m, n = x.shape
# means, eq. 34 and 35
mean_x = x.mean(axis=1)
mean_y = y.mean(axis=1)
# variance, eq. 36
# "transpose" for column subtraction
sigma_x = 1.0 / n * (np.linalg.norm(x - mean_x[:, np.newaxis])**2)
# covariance matrix, eq. 38
outer_sum = np.zeros((m, m))
for i in range(n):
outer_sum += np.outer((y[:, i] - mean_y), (x[:, i] - mean_x))
cov_xy = np.multiply(1.0 / n, outer_sum)
# SVD (text betw. eq. 38 and 39)
u, d, v = np.linalg.svd(cov_xy)
# S matrix, eq. 43
s = np.eye(m)
if np.linalg.det(u) * np.linalg.det(v) < 0.0:
# Ensure a RHS coordinate system (Kabsch algorithm).
s[m - 1, m - 1] = -1
# rotation, eq. 40
r = u.dot(s).dot(v)
# scale & translation, eq. 42 and 41
c = 1 / sigma_x * np.trace(np.diag(d).dot(s)) if with_scale else 1.0
t = mean_y - np.multiply(c, r.dot(mean_x))
return r, t, c
def readlines(filename):
"""Read all the lines in a text file and return as a list
"""
with open(filename, 'r') as f:
lines = f.read().splitlines()
return lines
def normalize_image(x):
"""Rescale image pixels to span range [0, 1]
"""
ma = float(x.max().cpu().data)
mi = float(x.min().cpu().data)
d = ma - mi if ma != mi else 1e5
return (x - mi) / d
def sec_to_hm(t):
"""Convert time in seconds to time in hours, minutes and seconds
e.g. 10239 -> (2, 50, 39)
"""
t = int(t)
s = t % 60
t //= 60
m = t % 60
t //= 60
return t, m, s
def sec_to_hm_str(t):
"""Convert time in seconds to a nice string
e.g. 10239 -> '02h50m39s'
"""
h, m, s = sec_to_hm(t)
return "{:02d}h{:02d}m{:02d}s".format(h, m, s)
def download_model_if_doesnt_exist(model_name):
"""If pretrained kitti model doesn't exist, download and unzip it
"""
# values are tuples of (<google cloud URL>, <md5 checksum>)
download_paths = {
"mono_640x192":
("https://storage.googleapis.com/niantic-lon-static/research/monodepth2/mono_640x192.zip",
"a964b8356e08a02d009609d9e3928f7c"),
"stereo_640x192":
("https://storage.googleapis.com/niantic-lon-static/research/monodepth2/stereo_640x192.zip",
"3dfb76bcff0786e4ec07ac00f658dd07"),
"mono+stereo_640x192":
("https://storage.googleapis.com/niantic-lon-static/research/monodepth2/mono%2Bstereo_640x192.zip",
"c024d69012485ed05d7eaa9617a96b81"),
"mono_no_pt_640x192":
("https://storage.googleapis.com/niantic-lon-static/research/monodepth2/mono_no_pt_640x192.zip",
"9c2f071e35027c895a4728358ffc913a"),
"stereo_no_pt_640x192":
("https://storage.googleapis.com/niantic-lon-static/research/monodepth2/stereo_no_pt_640x192.zip",
"41ec2de112905f85541ac33a854742d1"),
"mono+stereo_no_pt_640x192":
("https://storage.googleapis.com/niantic-lon-static/research/monodepth2/mono%2Bstereo_no_pt_640x192.zip",
"46c3b824f541d143a45c37df65fbab0a"),
"mono_1024x320":
("https://storage.googleapis.com/niantic-lon-static/research/monodepth2/mono_1024x320.zip",
"0ab0766efdfeea89a0d9ea8ba90e1e63"),
"stereo_1024x320":
("https://storage.googleapis.com/niantic-lon-static/research/monodepth2/stereo_1024x320.zip",
"afc2f2126d70cf3fdf26b550898b501a"),
"mono+stereo_1024x320":
("https://storage.googleapis.com/niantic-lon-static/research/monodepth2/mono%2Bstereo_1024x320.zip",
"cdc5fc9b23513c07d5b19235d9ef08f7"),
}
if not os.path.exists("models"):
os.makedirs("models")
model_path = os.path.join("models", model_name)
def check_file_matches_md5(checksum, fpath):
if not os.path.exists(fpath):
return False
with open(fpath, 'rb') as f:
current_md5checksum = hashlib.md5(f.read()).hexdigest()
return current_md5checksum == checksum
# see if we have the model already downloaded...
if not os.path.exists(os.path.join(model_path, "encoder.pth")):
model_url, required_md5checksum = download_paths[model_name]
if not check_file_matches_md5(required_md5checksum, model_path + ".zip"):
print("-> Downloading pretrained model to {}".format(model_path + ".zip"))
urllib.request.urlretrieve(model_url, model_path + ".zip")
if not check_file_matches_md5(required_md5checksum, model_path + ".zip"):
print(" Failed to download a file which matches the checksum - quitting")
quit()
print(" Unzipping model...")
with zipfile.ZipFile(model_path + ".zip", 'r') as f:
f.extractall(model_path)
print(" Model unzipped to {}".format(model_path))