Spaces:
Sleeping
Sleeping
File size: 3,813 Bytes
a37f5d3 f506594 a37f5d3 f506594 a37f5d3 4490536 a37f5d3 4490536 a37f5d3 4490536 a37f5d3 f506594 4490536 a37f5d3 4490536 a37f5d3 4490536 a37f5d3 4490536 a37f5d3 4490536 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 | 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 = [160]
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)
pointcloud.points, pointcloud.ground_semantic, pointcloud.ground_inlier = pointcloud.destagger() #pointcloud.points, pointcloud.ground_semantic, pointcloud.ground_inlier
groundplane_eqn = utils.fit_height_field_linear(pointcloud.points[pointcloud.ground_semantic==0,:3])
pointcloud.points, interp_flags = utils.complete_cloud(pointcloud.points, groundplane_eqn)
pointcloud.points = pointcloud.points[interp_flags]
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, colour_norm=255) #, beam_id, azimuth
ax.imshow(img_vis[:,:,::-1])
ax.set_title(f"{i+1}/{len(timestamps)} — {image_timestamp}.png\n(close window or press any key to continue)")
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)
# while idx[0] < len(timestamps):
fig.canvas.mpl_connect('key_press_event', on_key)
show_image(idx[0])
plt.show()
print(f"Finished all pointclouds") |