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m2bk.py
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m2bk.py
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import os
import math
import numpy as np
import cv2 as cv
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from mpl_toolkits.mplot3d import Axes3D
class DatasetHandler:
def __init__(self):
# Define number of frames
self.num_frames = 52
# Set up paths
root_dir_path = os.path.dirname(os.path.realpath(__file__))
self.image_dir = os.path.join(root_dir_path, 'data/rgb')
self.depth_dir = os.path.join(root_dir_path, 'data/depth')
# Set up data holders
self.images = []
self.images_rgb = []
self.depth_maps = []
self.k = np.array([[640, 0, 640],
[0, 480, 480],
[0, 0, 1]], dtype=np.float32)
# Read first frame
self.read_frame()
print("\r" + ' '*20 + "\r", end='')
def read_frame(self):
self._read_depth()
self._read_image()
def _read_image(self):
for i in range(1, self.num_frames + 1):
zeroes = "0" * (5 - len(str(i)))
im_name = "{0}/frame_{1}{2}.png".format(self.image_dir, zeroes, str(i))
self.images.append(cv.imread(im_name, flags=0))
self.images_rgb.append(cv.imread(im_name)[:, :, ::-1])
print ("Data loading: {0}%".format(int((i + self.num_frames) / (self.num_frames * 2 - 1) * 100)), end="\r")
def _read_depth(self):
for i in range(1, self.num_frames + 1):
zeroes = "0" * (5 - len(str(i)))
depth_name = "{0}/frame_{1}{2}.dat".format(self.depth_dir, zeroes, str(i))
depth = np.loadtxt(
depth_name,
delimiter=',',
dtype=np.float64) * 1000.0
self.depth_maps.append(depth)
print ("Data loading: {0}%".format(int(i / (self.num_frames * 2 - 1) * 100)), end="\r")
def visualize_camera_movement(image1, image1_points, image2, image2_points, is_show_img_after_move=False):
image1 = image1.copy()
image2 = image2.copy()
for i in range(0, len(image1_points)):
# Coordinates of a point on t frame
p1 = (int(image1_points[i][0]), int(image1_points[i][1]))
# Coordinates of the same point on t+1 frame
p2 = (int(image2_points[i][0]), int(image2_points[i][1]))
cv.circle(image1, p1, 5, (0, 255, 0), 1)
cv.arrowedLine(image1, p1, p2, (0, 255, 0), 1)
cv.circle(image1, p2, 5, (255, 0, 0), 1)
if is_show_img_after_move:
cv.circle(image2, p2, 5, (255, 0, 0), 1)
if is_show_img_after_move:
return image2
else:
return image1
def visualize_trajectory(trajectory):
# Unpack X Y Z each trajectory point
locX = []
locY = []
locZ = []
# This values are required for keeping equal scale on each plot.
# matplotlib equal axis may be somewhat confusing in some situations because of its various scale on
# different axis on multiple plots
max = -math.inf
min = math.inf
# Needed for better visualisation
maxY = -math.inf
minY = math.inf
for i in range(0, trajectory.shape[1]):
current_pos = trajectory[:, i]
locX.append(current_pos.item(0))
locY.append(current_pos.item(1))
locZ.append(current_pos.item(2))
if np.amax(current_pos) > max:
max = np.amax(current_pos)
if np.amin(current_pos) < min:
min = np.amin(current_pos)
if current_pos.item(1) > maxY:
maxY = current_pos.item(1)
if current_pos.item(1) < minY:
minY = current_pos.item(1)
auxY_line = locY[0] + locY[-1]
if max > 0 and min > 0:
minY = auxY_line - (max - min) / 2
maxY = auxY_line + (max - min) / 2
elif max < 0 and min < 0:
minY = auxY_line + (min - max) / 2
maxY = auxY_line - (min - max) / 2
else:
minY = auxY_line - (max - min) / 2
maxY = auxY_line + (max - min) / 2
# Set styles
mpl.rc("figure", facecolor="white")
plt.style.use("seaborn-whitegrid")
# Plot the figure
fig = plt.figure(figsize=(8, 6), dpi=100)
gspec = gridspec.GridSpec(3, 3)
ZY_plt = plt.subplot(gspec[0, 1:])
YX_plt = plt.subplot(gspec[1:, 0])
traj_main_plt = plt.subplot(gspec[1:, 1:])
D3_plt = plt.subplot(gspec[0, 0], projection='3d')
# Actual trajectory plotting ZX
toffset = 1.06
traj_main_plt.set_title("Autonomous vehicle trajectory (Z, X)", y=toffset)
traj_main_plt.set_title("Trajectory (Z, X)", y=1)
traj_main_plt.plot(locZ, locX, ".-", label="Trajectory", zorder=1, linewidth=1, markersize=4)
traj_main_plt.set_xlabel("Z")
# traj_main_plt.axes.yaxis.set_ticklabels([])
# Plot reference lines
traj_main_plt.plot([locZ[0], locZ[-1]], [locX[0], locX[-1]], "--", label="Auxiliary line", zorder=0, linewidth=1)
# Plot camera initial location
traj_main_plt.scatter([0], [0], s=8, c="red", label="Start location", zorder=2)
traj_main_plt.set_xlim([min, max])
traj_main_plt.set_ylim([min, max])
traj_main_plt.legend(loc=1, title="Legend", borderaxespad=0., fontsize="medium", frameon=True)
# Plot ZY
# ZY_plt.set_title("Z Y", y=toffset)
ZY_plt.set_ylabel("Y", labelpad=-4)
ZY_plt.axes.xaxis.set_ticklabels([])
ZY_plt.plot(locZ, locY, ".-", linewidth=1, markersize=4, zorder=0)
ZY_plt.plot([locZ[0], locZ[-1]], [(locY[0] + locY[-1]) / 2, (locY[0] + locY[-1]) / 2], "--", linewidth=1, zorder=1)
ZY_plt.scatter([0], [0], s=8, c="red", label="Start location", zorder=2)
ZY_plt.set_xlim([min, max])
ZY_plt.set_ylim([minY, maxY])
# Plot YX
# YX_plt.set_title("Y X", y=toffset)
YX_plt.set_ylabel("X")
YX_plt.set_xlabel("Y")
YX_plt.plot(locY, locX, ".-", linewidth=1, markersize=4, zorder=0)
YX_plt.plot([(locY[0] + locY[-1]) / 2, (locY[0] + locY[-1]) / 2], [locX[0], locX[-1]], "--", linewidth=1, zorder=1)
YX_plt.scatter([0], [0], s=8, c="red", label="Start location", zorder=2)
YX_plt.set_xlim([minY, maxY])
YX_plt.set_ylim([min, max])
# Plot 3D
D3_plt.set_title("3D trajectory", y=toffset)
D3_plt.plot3D(locX, locZ, locY, zorder=0)
D3_plt.scatter(0, 0, 0, s=8, c="red", zorder=1)
D3_plt.set_xlim3d(min, max)
D3_plt.set_ylim3d(min, max)
D3_plt.set_zlim3d(min, max)
D3_plt.tick_params(direction='out', pad=-2)
D3_plt.set_xlabel("X", labelpad=0)
D3_plt.set_ylabel("Z", labelpad=0)
D3_plt.set_zlabel("Y", labelpad=-2)
# plt.axis('equal')
D3_plt.view_init(45, azim=30)
plt.tight_layout()
plt.show()