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utils_various.py
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utils_various.py
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import shutil
import os
import math
import numpy as np
from numpy import array as npa
import matplotlib as mpl
colab_drive_dir = '/content/gdrive/My Drive'
if os.path.exists(colab_drive_dir):
homeDir = colab_drive_dir
else:
homeDir = os.path.curdir
print(homeDir)
originalSPEED_dir = homeDir + '/speed'
mySPEED_dir = homeDir + '/speed_MS'
mySPEED_RoI_dir = homeDir + '/speed_MS_roi'
earthBGStart_ID = 7500
RENDERING_TAG = '_rendering'
# rgb_color_sequence = [plt.rcParams['axes.prop_cycle'].by_key()['color'][0],
# 'darkred',
# 'olivedrab']
rgb_color_sequence = ['#249cd8', # B
'#e2415a', # R
'#92d849'] # G
# Create custom colormaps with: hex_to_rgb(), rgb_to_dec(), get_continuous_cmap()
def hex_to_rgb(value):
"""
Converts hex to rgb colours
value: string of 6 characters representing a hex colour.
Returns: list length 3 of RGB values
"""
value = value.strip("#") # removes hash symbol if present
lv = len(value)
return tuple(int(value[i:i + lv // 3], 16) for i in range(0, lv, lv // 3))
def rgb_to_dec(value):
"""
Converts rgb to decimal colours (i.e. divides each value by 256)
value: list (length 3) of RGB values
Returns: list (length 3) of decimal values
"""
return [v/256 for v in value]
def get_continuous_cmap(hex_list, float_list=None):
""" creates and returns a color map that can be used in heat map figures.
If float_list is not provided, colour map graduates linearly between each color in hex_list.
If float_list is provided, each color in hex_list is mapped to the respective location in float_list.
Parameters
----------
hex_list: list of hex code strings
float_list: list of floats between 0 and 1, same length as hex_list. Must start with 0 and end with 1.
Returns
----------
colormap
"""
rgb_list = [rgb_to_dec(hex_to_rgb(i)) for i in hex_list]
if float_list:
pass
else:
float_list = list(np.linspace(0, 1, len(rgb_list)))
cdict = dict()
for num, col in enumerate(['red', 'green', 'blue']):
col_list = [[float_list[i], rgb_list[i][num], rgb_list[i][num]] for i in range(len(float_list))]
cdict[col] = col_list
cmp = mpl.colors.LinearSegmentedColormap('my_cmp', segmentdata=cdict, N=256)
return cmp
rgb_cmap = get_continuous_cmap(hex_list= [rgb_color_sequence[i] for i in [1,0,2]] ) # RBG order
rb_cmap = get_continuous_cmap(hex_list= [rgb_color_sequence[i] for i in [0,1]] )
def createDirectory(myDir):
if os.path.exists(myDir):
shutil.rmtree(myDir)
os.mkdir(myDir)
setOrder = ['train', 'dev', 'test']
def get_coords_from_landmark_label(landmarks, point_label):
is_my_label_here = [point['label'] == point_label for point in landmarks]
my_label_idx = [idx for idx, myBool in enumerate(is_my_label_here) if myBool][0]
return landmarks[my_label_idx]['r_B']
class Wireframe:
""""
Utility class that defines landmarks and coordinates
of other points from the wireframe model.
"""
# Define 11 landmark points (body coordinates)
landmarks = [
{'label': 'B1', 'r_B': [-0.37, 0.304, 0]},
{'label': 'B2', 'r_B': [-0.37, -0.264, 0]},
{'label': 'B3', 'r_B': [0.37, -0.264, 0]},
{'label': 'B4', 'r_B': [0.37, 0.304, 0]},
{'label': 'S1', 'r_B': [-0.37, 0.385, 0.3215]},
{'label': 'S2', 'r_B': [-0.37, -0.385, 0.3215]},
{'label': 'S3', 'r_B': [0.37, -0.38, 0.3215]},
{'label': 'S4', 'r_B': [0.37, 0.385, 0.3215]},
{'label': 'A1', 'r_B': [-0.5427, 0.4877, 0.2535]},
{'label': 'A2', 'r_B': [0.3050, -0.5790, 0.2515]},
{'label': 'A3', 'r_B': [0.5427, 0.4877, 0.2591]}
]
landmark_mat = np.column_stack( [point['r_B'] for point in landmarks] )
# Top of the main body (not used as landmarks)
topMainBody = [
{'label': 'T1', 'r_B': [-0.37, 0.304, 0.305]},
{'label': 'T2', 'r_B': [-0.37, -0.264, 0.305]},
{'label': 'T3', 'r_B': [0.37, -0.264, 0.305]},
{'label': 'T4', 'r_B': [0.37, 0.304, 0.305]}
]
topMainBody_mat = np.column_stack( [point['r_B'] for point in topMainBody] )
# Antenna clamps
antClamps = [
{'label': 'Ac1', 'r_B': [-0.23, 0.3, 0.2535]},
{'label': 'Ac2', 'r_B': [0.31, -0.26, 0.2515]},
{'label': 'Ac3', 'r_B': [0.23, 0.3, 0.2591]}
]
antClamps_mat = np.column_stack( [point['r_B'] for point in antClamps] )
body_center = [0, 0, (0+get_coords_from_landmark_label(landmarks, 'S1')[2])/2]
# We compute L_c as the diagonal length of the solar panel
k1 = 1.05 # a constant empirically tuned, by testing on outliers
charact_length = k1 * np.linalg.norm( npa(get_coords_from_landmark_label(landmarks, 'S1'))
-
npa(get_coords_from_landmark_label(landmarks, 'S3')) )
# reference points @ body frame (for drawing axes)
p_axes = np.array([[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]])
BB_enlarge = .1 # i.e. 10% larger than minimum rectangle
max_ROI_size = 320 # [px]
class Camera:
"""" Utility class for accessing camera parameters. """
fx = 0.0176 # focal length [m]
fy = 0.0176 # focal length [m]
nu = 1920 # no. horizontal pixels
nv = 1200 # no. of vertical pixels
ppx = 5.86e-6 # horizontal pixel pitch [m/px]
ppy = ppx # vertical pixel pitch [m/px]
fpx = fx / ppx # horizontal focal length [px]
fpy = fy / ppy # vertical focal length [px]
k = [[fpx, 0, nu / 2], # camera intrinsic matrix
[0, fpy, nv / 2],
[0, 0, 1]]
K = npa(k)
# angular size of the camera's FoV, considering the diagonal aperture [deg]
FOV_diagonal_deg = 180*math.pi * 2 * math.atan( ppx * math.sqrt(nu**2 + nv**2) / (2*fx) )
def project3Dto2D(dcm_CB, t_CB, r_B_mat):
""" Projecting points to image frame.
q_CB: quaternion representing rotation: camera_frame --> Tango princ. axes
t_CB: camera2body_translation
r_B_mat: body coordinates of SC points (stacked column by column)
"""
points_body = np.concatenate( ( r_B_mat, np.ones((1,r_B_mat.shape[1])) ), axis=0 )
# transformation to camera frame
pose_mat = np.hstack( ( dcm_CB.T, np.expand_dims(t_CB, 1) ) )
p_cam = np.dot(pose_mat, points_body)
# getting homogeneous coordinates
points_camera_frame = p_cam / p_cam[2]
# projection to image plane
points_image_plane = Camera.K.dot(points_camera_frame)
x, y = (points_image_plane[0], points_image_plane[1])
return x, y
def quat2dcm(q):
""" Convert quaternion [q4 q1 q2 q3] to Direction Cosine Matrix. """
# normalizing quaternion
q = q/np.linalg.norm(q)
q0 = q[0]
q1 = q[1]
q2 = q[2]
q3 = q[3]
dcm = np.zeros((3, 3))
dcm[0, 0] = 2 * q0**2 - 1 + 2 * q1**2
dcm[1, 1] = 2 * q0**2 - 1 + 2 * q2**2
dcm[2, 2] = 2 * q0**2 - 1 + 2 * q3**2
dcm[0, 1] = 2 * q1 * q2 + 2 * q0 * q3
dcm[0, 2] = 2 * q1 * q3 - 2 * q0 * q2
dcm[1, 0] = 2 * q1 * q2 - 2 * q0 * q3
dcm[1, 2] = 2 * q2 * q3 + 2 * q0 * q1
dcm[2, 0] = 2 * q1 * q3 + 2 * q0 * q2
dcm[2, 1] = 2 * q2 * q3 - 2 * q0 * q1
return dcm
def dcm2quat(dcm):
"""
Based on the method described here:
http://www.euclideanspace.com/maths/geometry/rotations/conversions/matrixToQuaternion/
"""
if dcm[2, 2] < 0:
if dcm[0, 0] > dcm[1, 1]:
t = 1 + dcm[0, 0] - dcm[1, 1] - dcm[2, 2]
q = [dcm[1, 2] - dcm[2, 1], t, dcm[0, 1] + dcm[1, 0], dcm[2, 0] + dcm[0, 2]]
else:
t = 1 - dcm[0, 0] + dcm[1, 1] - dcm[2, 2]
q = [dcm[2, 0] - dcm[0, 2], dcm[0, 1] + dcm[1, 0], t, dcm[1, 2] + dcm[2, 1]]
else:
if dcm[0, 0] < -dcm[1, 1]:
t = 1 - dcm[0, 0] - dcm[1, 1] + dcm[2, 2]
q = [dcm[0, 1] - dcm[1, 0], dcm[2, 0] + dcm[0, 2], dcm[1, 2] + dcm[2, 1], t]
else:
t = 1 + dcm[0, 0] + dcm[1, 1] + dcm[2, 2]
q = [t, dcm[1, 2] - dcm[2, 1], dcm[2, 0] - dcm[0, 2], dcm[0, 1] - dcm[1, 0]]
q = np.array(q)
q *= 0.5 / math.sqrt(t)
return q
# def euler2dcm(euler):
# R_x = npa([[1, 0, 0],
# [0, math.cos(euler[0]), math.sin(euler[0])],
# [0, -math.sin(euler[0]), math.cos(euler[0])]
# ])
#
# R_y = npa([[math.cos(euler[1]), 0, math.sin(euler[1])],
# [0, 1, 0],
# [math.sin(euler[1]), 0, math.cos(euler[1])]
# ])
#
# R_z = npa([[math.cos(euler[2]), -math.sin(euler[2]), 0],
# [-math.sin(euler[2]), math.cos(euler[2]), 0],
# [0, 0, 1]
# ])
#
# dcm = np.dot(R_z, np.dot(R_y, R_x))
#
# return dcm
def dcm2euler(dcm):
"""
Converts Direction Cosine Matrix to corresponding Euler angles representation.
th_x, th_y, th_z [deg] are computed as the rotation angles
about the x,y,x axes, respectively, whose sign is given by the SCREW rule
"""
# assert(isRotationMatrix(dcm))
# N.B. I used math instead of numpy since it is a little faster
sy = math.sqrt(dcm[0, 0] * dcm[0, 0] + dcm[1, 0] * dcm[1, 0])
singular = sy < 1e-6
if not singular:
th_x = math.atan2( dcm[2, 1], dcm[2, 2])
th_y = math.atan2(-dcm[2, 0], sy)
th_z = math.atan2( dcm[1, 0], dcm[0, 0])
else:
th_x = math.atan2(-dcm[1, 2], dcm[1, 1])
th_y = math.atan2(-dcm[2, 0], sy)
th_z = 0
return -npa([th_x, th_y, th_z]) * 180/math.pi # [deg]