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utils.py
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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 hashlib
import zipfile
import numpy as np
import torch
from six.moves import urllib
import matplotlib.pyplot as plt
import PIL.Image as pil
import math
from labels import *
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))
def set_axes_equal(ax):
'''Make axes of 3D plot have equal scale so that spheres appear as spheres,
cubes as cubes, etc.. This is one possible solution to Matplotlib's
ax.set_aspect('equal') and ax.axis('equal') not working for 3D.
Input
ax: a matplotlib axis, e.g., as output from plt.gca().
'''
x_limits = ax.get_xlim3d()
y_limits = ax.get_ylim3d()
z_limits = ax.get_zlim3d()
x_range = abs(x_limits[1] - x_limits[0])
x_middle = np.mean(x_limits)
y_range = abs(y_limits[1] - y_limits[0])
y_middle = np.mean(y_limits)
z_range = abs(z_limits[1] - z_limits[0])
z_middle = np.mean(z_limits)
# The plot bounding box is a sphere in the sense of the infinity
# norm, hence I call half the max range the plot radius.
plot_radius = 0.5*max([x_range, y_range, z_range])
ax.set_xlim3d([x_middle - plot_radius, x_middle + plot_radius])
ax.set_ylim3d([y_middle - plot_radius, y_middle + plot_radius])
ax.set_zlim3d([z_middle - plot_radius, z_middle + plot_radius])
def reconstruct3dPts(depthval, ix, iy, intrinsic, extrinsic):
recon3Pts = np.stack((ix * depthval, iy * depthval, depthval, depthval / depthval), axis=1)
recon3Pts = (np.linalg.inv(intrinsic @ extrinsic) @ recon3Pts.T).T
return recon3Pts
def project3dPts(pts3d, intrinsic, extrinsic, isDepth = False):
pts2d = (intrinsic @ extrinsic @ pts3d.T).T
pts2d[:, 0] = pts2d[:, 0] / pts2d[:, 2]
pts2d[:, 1] = pts2d[:, 1] / pts2d[:, 2]
depth = pts2d[:, 2]
pts2d = pts2d[:, 0:2]
if not isDepth:
return pts2d
else:
return pts2d, depth
def con_sin(angle):
return math.cos(angle), math.sin(angle)
def angle2matrix(pitch, roll, yaw):
cy, sy = con_sin(yaw)
cr, sr = con_sin(roll)
cp, sp = con_sin(pitch)
ex = [
[cy * cp, cy * sp * sr - sy * cr, cy * sp * cr + sy * sr],
[sy * cp, sy * sp * sr + cy * cr, sy * sp * cr - cy * sr],
[-sp, cp * sr, cp * cr]
]
return np.array(ex)
def get_gaussian_kernel_weights(kernel_size=3, sigma=2):
# Create a x, y coordinate grid of shape (kernel_size, kernel_size, 2)
x_coord = torch.arange(kernel_size)
x_grid = x_coord.repeat(kernel_size).view(kernel_size, kernel_size)
y_grid = x_grid.t()
xy_grid = torch.stack([x_grid, y_grid], dim=-1).float()
mean = (kernel_size - 1) / 2.
variance = sigma ** 2.
# Calculate the 2-dimensional gaussian kernel which is
# the product of two gaussian distributions for two different
# variables (in this case called x and y)
gaussian_kernel = (1. / (2. * math.pi * variance)) * \
torch.exp(
-torch.sum((xy_grid - mean) ** 2., dim=-1) / \
(2 * variance)
)
# Make sure sum of values in gaussian kernel equals 1.
gaussian_kernel = gaussian_kernel / torch.sum(gaussian_kernel)
# Reshape to 2d depthwise convolutional weight
gaussian_kernel = gaussian_kernel.view(1, 1, kernel_size, kernel_size)
return gaussian_kernel
def tensor2rgb(tensor, ind):
slice = (tensor[ind, :, :, :].permute(1,2,0).detach().contiguous().cpu().numpy() * 255).astype(np.uint8)
return pil.fromarray(slice)
def tensor2semantic(tensor, ind, isGt = False):
slice = tensor[ind, :, :, :]
slice = slice[0,:,:].detach().cpu().numpy()
return visualize_semantic(slice)
def visualize_semantic(img_inds):
# please input numpy array
size = [img_inds.shape[1], img_inds.shape[0]]
background = name2label['unlabeled'].color
labelImg = np.array(pil.new("RGB", size, background))
for id in trainId2label.keys():
if id >= 0:
label = trainId2label[id].name
else:
label = 'unlabeled'
color = name2label[label].color
mask = img_inds == id
labelImg[mask, :] = color
return pil.fromarray(labelImg)
def tensor2disp(tensor, ind, vmax = None, percentile = None):
slice = tensor[ind, 0, :, :].detach().cpu().numpy()
if percentile is None:
percentile = 90
if vmax is None:
vmax = np.percentile(slice, percentile)
slice = slice / vmax
cm = plt.get_cmap('magma')
slice = (cm(slice) * 255).astype(np.uint8)
return pil.fromarray(slice[:,:,0:3])
def float2uint8(img):
return (img * 4.3 * 255).astype(np.uint8)
def uint82float(img):
return (img).astype(np.float) / 4.3 / 255
def cvtPNG2Arr(png):
sr = 10000
png = np.array(png)
h = png[:,:,0]
e = png[:,:,1]
l = png[:,:,2]
arrs = h.astype(np.float32) * 256 * 256 + e.astype(np.float32) * 256 + l.astype(np.float32)
arr = arrs / sr
return arr