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a37f5d3 f506594 a37f5d3 f506594 a37f5d3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 | import os
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import sys
sys.path.append(os.path.abspath(".")) # one level up
import numpy as np
import cv2
import open3d as o3d
from scipy.spatial.transform import Rotation
from utils.lidar import PointCloud
from utils.camera import ImageData
import utils.utils as utils
from natsort import natsorted
hf_app = True
if hf_app:
from huggingface_hub import snapshot_download
cmap = plt.get_cmap("jet")
LABEL_UNKNOWN = -1
# User parameters
location = 'Cambogan'
sequence = '20250811_113017'
condition = 'flooded'
camera_pos = 'front'
root_directory = f"/data/FRED/{condition}/KITTI-style"
if (not os.path.exists(root_directory)) and (hf_app):
snapshot_download(
repo_id="CMalone-Jupiter/FRED",
repo_type="dataset",
local_dir="/data/FRED",
allow_patterns=f"{condition}/KITTI-style/{location}_{sequence}/**",
token=os.environ.get("HF_TOKEN")
)
############ Define filenames and directories ####################################
image_dir = f"{root_directory}/{location}_{sequence}/{camera_pos}-imgs/"
label_dir = f"{root_directory}/{location}_{sequence}/{camera_pos}-labels/"
lidar_dir = f"{root_directory}/{location}_{sequence}/ouster/"
utm_dir = f"{root_directory}/{location}_{sequence}/utm/"
img_calib_file = f"./camera_calib.txt"
lidar_calib_file = f"./calib.txt"
timestamps = [filename.split('.png')[0] for filename in natsorted(os.listdir(image_dir)) if os.path.isfile(image_dir+filename)]
groundplane_eqn = tuple(np.loadtxt(f"{root_directory}/{location}_{sequence}/ground_plane_eqn.txt"))
a, b, c, d = groundplane_eqn
fig, ax = plt.subplots(figsize=(12.8, 8))
idx = [183]
def show_image(i):
ax.clear()
if i >= len(timestamps):
plt.close(fig)
return
image_timestamp = timestamps[i]
try:
image_filename = f"{image_dir}/{image_timestamp}.png"
label_filename = f"{label_dir}/{image_timestamp}.png"
lidar_filename, utm_filename = utils.get_corr_files(image_timestamp, [lidar_dir, utm_dir])
image = ImageData(image_filename, img_calib_file, label_filename)
pointcloud = PointCloud(lidar_filename, lidar_calib_file)
point_cam, distances_cam, intensities_cam, all_points_cam, valid_cam = pointcloud.points_ouster_to_cam() #, beam_id, azimuth
img_vis, uv, valid_img = image.project_points(all_points_cam, intensities_cam, cmap, valid_cam) #, beam_id, azimuth
valid_semantic = valid_cam & valid_img
# Assign semantics
semantic_labels = utils.assign_semantic_labels(
pointcloud.points[:, :3],
uv,
valid_semantic,
image.label_img,
interp_flags=None,
unknown_label=3
)
print(f"Max x distance: {pointcloud.points[semantic_labels==0,0].max()}")
ground_filter = pointcloud.ground_semantic == 0
inlier_filter = pointcloud.ground_inlier == 1
img_vis, uv, valid_img = image.project_points(all_points_cam, semantic_labels, cmap, valid_cam) #, beam_id, azimuth
ax.imshow(img_vis[:, :, ::-1])
ax.set_title(f"{image_timestamp}.png")
ax.axis("off")
fig.canvas.draw()
except Exception as e:
print(f"Could not project pointcloud onto {image_timestamp}.png: {e}")
idx[0] += 1
show_image(idx[0]) # skip bad one
def on_key(event):
if event.key in [' ', 'right']: # space or right arrow
idx[0] += 1
show_image(idx[0])
elif event.key in [' ', 'left']: # space or right arrow
if idx[0] > 0:
idx[0] -= 1
show_image(idx[0])
elif event.key in ['q', 'escape']: # q or Esc → quit
plt.close(fig)
fig.canvas.mpl_connect('key_press_event', on_key)
show_image(idx[0])
plt.show() |