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configs/dry/cambogan_20250812_122101.yaml

@@ -2,5 +2,5 @@ location: "Cambogan"
 sequence: "20250812_122101"
 condition: "dry"
 camera_pos: "front"
-root: "../Datasets/FRED/"
+root: "/data/FRED/"
 img_calib_file: "./camera_calib.txt"

configs/dry/cambogan_20250812_122339.yaml

@@ -2,5 +2,5 @@ location: "Cambogan"
 sequence: "20250812_122339"
 condition: "dry"
 camera_pos: "front"
-root: "../Datasets/FRED/"
+root: "/data/FRED/"
 img_calib_file: "./camera_calib.txt"

configs/dry/cambogan_20250812_122621.yaml

@@ -2,5 +2,5 @@ location: "Cambogan"
 sequence: "20250812_122621"
 condition: "dry"
 camera_pos: "front"
-root: "../Datasets/FRED/"
+root: "/data/FRED/"
 img_calib_file: "./camera_calib.txt"

configs/dry/dairycreek_20250812_122954.yaml

@@ -2,5 +2,5 @@ location: "Dairy-Creek"
 sequence: "20250812_122954"
 condition: "dry"
 camera_pos: "front"
-root: "../Datasets/FRED/"
+root: "/data/FRED/"
 img_calib_file: "./camera_calib.txt"

configs/dry/dairycreek_20250812_123312.yaml

@@ -2,5 +2,5 @@ location: "Dairy-Creek"
 sequence: "20250812_120856"
 condition: "dry"
 camera_pos: "front"
-root: "../Datasets/FRED/"
+root: "/data/FRED/"
 img_calib_file: "./camera_calib.txt"

configs/dry/holmview_20250812_120100.yaml

@@ -2,5 +2,5 @@ location: "Holmview"
 sequence: "20250812_120100"
 condition: "dry"
 camera_pos: "front"
-root: "../Datasets/FRED/"
+root: "/data/FRED/"
 img_calib_file: "./camera_calib.txt"

configs/dry/holmview_20250812_120856.yaml

@@ -2,5 +2,5 @@ location: "Holmview"
 sequence: "20250812_120100"
 condition: "dry"
 camera_pos: "front"
-root: "../Datasets/FRED/"
+root: "/data/FRED/"
 img_calib_file: "./camera_calib.txt"

configs/dry/pullenvale_20250812_134316.yaml

@@ -2,5 +2,5 @@ location: "Pullenvale"
 sequence: "20250812_134316"
 condition: "dry"
 camera_pos: "front"
-root: "../Datasets/FRED/"
+root: "/data/FRED/"
 img_calib_file: "./camera_calib.txt"

configs/dry/pullenvale_20250812_134524.yaml

@@ -2,5 +2,5 @@ location: "Pullenvale"
 sequence: "20250812_134524"
 condition: "dry"
 camera_pos: "front"
-root: "../Datasets/FRED/"
+root: "/data/FRED/"
 img_calib_file: "./camera_calib.txt"

configs/flooded/cambogan.yaml

@@ -2,5 +2,5 @@ location: "Cambogan"
 sequence: "20250811_113017"
 condition: "flooded"
 camera_pos: "front"
-root: "../Datasets/FRED/"
+root: "/data/FRED/"
 img_calib_file: "./camera_calib.txt"

configs/flooded/dairycreek.yaml

@@ -2,5 +2,5 @@ location: "DairyCreek"
 sequence: "20250811_103318"
 condition: "flooded"
 camera_pos: "front"
-root: "../Datasets/FRED/"
+root: "/data/FRED/"
 img_calib_file: "./camera_calib.txt"

configs/flooded/holmview.yaml

@@ -2,5 +2,5 @@ location: "Holmview"
 sequence: "20250820_130327"
 condition: "flooded"
 camera_pos: "front"
-root: "../Datasets/FRED/"
+root: "/data/FRED/"
 img_calib_file: "./camera_calib.txt"

configs/flooded/mountcotton.yaml

@@ -2,5 +2,5 @@ location: "Mount-Cotton"
 sequence: "20241217_113410"
 condition: "flooded"
 camera_pos: "front"
-root: "../Datasets/FRED/"
+root: "/data/FRED/"
 img_calib_file: "./camera_calib.txt"

configs/flooded/pullenvale.yaml

@@ -2,5 +2,5 @@ location: "Pullenvale"
 sequence: "20250916_124105"
 condition: "flooded"
 camera_pos: "front"
-root: "../Datasets/FRED/"
+root: "/data/FRED/"
 img_calib_file: "./camera_calib.txt"

lidar_postprocessing/create_pointcloud_labels.py

@@ -12,20 +12,29 @@ 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'
-# location = 'Holmview'
-# sequence = '20250820_130327'
-# location = 'Mount-Cotton'
-# sequence = '20241217_113410'
 condition = 'flooded'
 camera_pos = 'front'
-root_directory = f"../Datasets/FRED/{condition}/KITTI-style"
-# 01000000
+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 ####################################
 
@@ -41,10 +50,8 @@ timestamps = [filename.split('.png')[0] for filename in natsorted(os.listdir(ima
 groundplane_eqn = tuple(np.loadtxt(f"{root_directory}/{location}_{sequence}/ground_plane_eqn.txt"))
 a, b, c, d = groundplane_eqn
 
-# timestamps.sort()
 
 fig, ax = plt.subplots(figsize=(12.8, 8))
-# idx = [0]  # mutable index
 idx = [183]
 
 def show_image(i):
@@ -83,43 +90,9 @@ def show_image(i):
 
         img_vis, uv, valid_img = image.project_points(all_points_cam, semantic_labels, cmap, valid_cam) #, beam_id, azimuth
 
-        # filtered_points = pointcloud.points[(semantic_labels==0) & (abs(pointcloud.points[:,1]) < 1),:]
-        # max_lookahead = filtered_points[:,0].max()
-        # far_points = filtered_points[filtered_points[:,0]==max_lookahead,:]
-
-        # if far_points.shape[0] > 1:
-        #     far_point = far_points[abs(far_points[:,1]) == abs(far_points[:,1]).min(),:]
-        # else:
-        #     far_point = far_points
-        # far_point_cam, far_point_distnace, far_point_intensity = pointcloud.select_points_ouster_to_cam(far_point)
-        # far_pixel = image.get_image_coords(far_point_cam)
-
-        # if far_pixel is not None and len(far_pixel) > 0:
-        #     u, v = far_pixel[0]  # pixel coordinates
-
-        # h, w = img_vis.shape[:2]
-        # bottom_center = (w // 2, h)
-
-        # ax.plot(
-        #     [bottom_center[0], u],
-        #     [bottom_center[1], v],
-        #     color="lime",
-        #     linewidth=2
-        # )
-        # ax.text(
-        #     u,
-        #     v - 10,
-        #     f"{far_point[0,0]:.2f}",
-        #     color="lime",
-        #     fontsize=12,
-        #     ha="center",
-        #     bbox=dict(facecolor="black", alpha=0.6, edgecolor="none")
-        # )
-
         ax.imshow(img_vis[:, :, ::-1])
         ax.set_title(f"{image_timestamp}.png")
         ax.axis("off")
-        # plt.savefig('paper_figures/labelled_pointcloud.pdf', format="pdf", bbox_inches='tight')
         fig.canvas.draw()
 
     except Exception as e:

lidar_postprocessing/fill_pointcloud_gaps.py

@@ -12,20 +12,29 @@ 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'
-# location = 'Holmview'
-# sequence = '20250820_130327'
-# location = 'Mount-Cotton'
-# sequence = '20241217_113410'
 condition = 'flooded'
 camera_pos = 'front'
-root_directory = f"C:/Users/conno/Documents/data/FRED/{condition}/KITTI-style/" #f"../Datasets/FRED/{condition}/KITTI-style"
-# 01000000
+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 ####################################
 
@@ -41,12 +50,9 @@ timestamps = [filename.split('.png')[0] for filename in natsorted(os.listdir(ima
 groundplane_eqn = tuple(np.loadtxt(f"{root_directory}/{location}_{sequence}/ground_plane_eqn.txt"))
 a, b, c, d = groundplane_eqn
 
-# timestamps.sort()
 
 fig, ax = plt.subplots(figsize=(12.8, 8))
 
-# idx = [0]  # mutable index
-# idx = [0]
 idx = [160]
 
 def show_image(i):
@@ -62,10 +68,7 @@ def show_image(i):
 
         image = ImageData(image_filename, img_calib_file, label_filename)
         pointcloud = PointCloud(lidar_filename, lidar_calib_file)
-        # print(f"Number of points in pointcloud: {pointcloud.points.shape}")
-        # # print(f"Nan's in pointcloud? {np.any( == np.nan)}")
-        # print(f"Number of zeroed points: {np.sum(np.all(pointcloud.points == 0, axis=1))}")
-        # print(f"invalid points in ground plane: {np.sum(pointcloud.ground_semantic[np.all(pointcloud.points == 0, axis=1)]==0)}")
+
         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)
@@ -74,20 +77,7 @@ def show_image(i):
 
         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
-        # semantic_labels = interp_flags.astype(int) + 1
-
-        # labels_norm = semantic_labels.astype(np.float64) / semantic_labels.max()
-
-        # colors = np.stack(
-        #     (labels_norm, np.zeros(labels_norm.shape[0]), np.zeros(labels_norm.shape[0])),
-        #     axis=1
-        # )  # shape (N, 3)
-
-        # pcd = o3d.geometry.PointCloud()
-        # pcd.points = o3d.utility.Vector3dVector(pointcloud.points[:,:3])
 
-        # pcd.colors = o3d.utility.Vector3dVector(colors)
-        # o3d.visualization.draw_geometries([pcd,])
         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")

lidar_postprocessing/project_water_label.py

@@ -12,19 +12,28 @@ 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")
 
 # User parameters
 location = 'Cambogan'
 sequence = '20250811_113017'
-# location = 'Holmview'
-# sequence = '20250820_130327'
-# location = 'Mount-Cotton'
-# sequence = '20241217_113410'
 condition = 'flooded'
 camera_pos = 'front'
-root_directory = f"C:/Users/conno/Documents/data/FRED/{condition}/KITTI-style/" # f"D:/Datasets/FRED/{condition}/KITTI-style"
-# 01000000
+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 ####################################
 
@@ -37,7 +46,6 @@ 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)]
 
-# timestamps.sort()
 
 fig, ax = plt.subplots(figsize=(12.8, 8))
 idx = [0]  # mutable index

localisation/VPR_eval-all.py

@@ -14,6 +14,12 @@ from natsort import natsorted, index_natsorted
 import torch
 from tqdm import tqdm
 
+# Toggle the following boolean to False if not using HuggingFace App
+hf_app = True
+
+if hf_app:
+    from huggingface_hub import snapshot_download
+
 ################## set device based on cuda availability #################
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
@@ -99,15 +105,8 @@ dist_tolerance = 10 # metres
 # qry_idx = 4
 
 # User parameters
-# vpr_desc = 'cosplace'
-# location = 'Holmview'
-# location = 'Cambogan'
 
 ################ Reference filenames and directories #################################
-# ref_sequence = '20250812_120100'
-# 20250812_120856
-# ref_sequence = '20250812_122339'
-# 20250812_122621
 ref_condition = 'dry'
 ref_camera_pos = 'front'
 
@@ -160,14 +159,7 @@ for vpr_desc in vpr_descs:
     for qry_set in qry_sets:
 
         ################ Query filenames and directories #################################
-        # qry_sequence = '20250820_130327'
-        # qry_sequence = '20250812_120856'
-        ###
-        # qry_sequence = '20250811_113017'
-        # qry_sequence = '20250812_122621'
         qry_condition = 'flooded'
-        # qry_sequence = '20250812_122339'
-        # qry_condition = 'dry'
         qry_camera_pos = 'front'
         qry_root_directory = f"../Datasets/FRED/{qry_condition}/KITTI-style"
         qry_vpr_root = f"../Datasets/FRED/vpr_ftrs/{qry_condition}/KITTI-style"
@@ -192,12 +184,6 @@ for vpr_desc in vpr_descs:
             qry_utm_timestamp = utils.get_corr_files(qry_image_timestamp, [qry_utm_dir,])
             qry_utm = np.loadtxt(qry_utm_timestamp)
 
-            # print(f"Number of queries: {len(qry_timestamps)}")
-            # print(f"Number of references{ {len(ref_timestamps)}}")
-            # print(f"Matche  {mInds[qry_idx]}")
-            # print(natsorted(os.listdir(qry_image_dir)))
-            # print(os.listdir(qry_image_dir))
-
             diffs = ref_utms - qry_utm           # shape (N, 2)
             qry_dists = np.linalg.norm(diffs, axis=1)   # shape (N,)
             if qry_dists.min() > dist_tolerance:
@@ -205,9 +191,6 @@ for vpr_desc in vpr_descs:
             else:
                 valid_qry += 1
 
-
-            # ref_utm_timestamp = utils.get_corr_files(ref_timestamps[int(mInds[qry_idx])], [ref_utm_dir,])
-            # ref_utm = np.loadtxt(ref_utm_timestamp)
             ref_utm = np.loadtxt(ref_utm_filenames[int(mInds[qry_idx])])
 
             diff = ref_utm - qry_utm           # shape (N, 2)

localisation/groundtruth_utm_checker-all.py

@@ -12,29 +12,50 @@ from utils.camera import ImageData
 import utils.utils as utils
 from natsort import natsorted
 
+# Toggle the following boolean to False if not using HuggingFace App
+hf_app = True
+
+if hf_app:
+    from huggingface_hub import snapshot_download
+
 cmap = plt.get_cmap("jet")
 
 # User parameters
-# location = 'Holmview'
 location = 'Cambogan'
 
 ################ Query filenames and directories #################################
-# qry_sequence = '20250820_130327'
 qry_sequence = '20250811_113017'
 qry_condition = 'flooded'
 qry_camera_pos = 'front'
-qry_root_directory = f"D:/Datasets/FRED/{qry_condition}/KITTI-style"
+qry_root_directory = f"/data/FRED/{qry_condition}/KITTI-style"
+
+if (not os.path.exists(qry_root_directory)) and (hf_app):
+    snapshot_download(
+        repo_id="CMalone-Jupiter/FRED",
+        repo_type="dataset",
+        local_dir="/data/FRED",
+        allow_patterns=f"{qry_condition}/KITTI-style/{location}_{qry_sequence}/**",
+        token=os.environ.get("HF_TOKEN")
+    )
 
 qry_image_dir = f"{qry_root_directory}/{location}_{qry_sequence}/{qry_camera_pos}-imgs/"
 qry_utm_dir = f"{qry_root_directory}/{location}_{qry_sequence}/utm/"
 qry_timestamps = [filename.split('.png')[0] for filename in natsorted(os.listdir(qry_image_dir)) if os.path.isfile(qry_image_dir+filename)]
 
 ################ Reference filenames and directories #################################
-# ref_sequence = '20250812_120100'
 ref_sequence = '20250812_122339'
 ref_condition = 'dry'
 ref_camera_pos = 'front'
-ref_root_directory = f"D:/Datasets/FRED/{ref_condition}/KITTI-style"
+ref_root_directory = f"/data/FRED/{ref_condition}/KITTI-style"
+
+if (not os.path.exists(ref_root_directory)) and (hf_app):
+    snapshot_download(
+        repo_id="CMalone-Jupiter/FRED",
+        repo_type="dataset",
+        local_dir="/data/FRED",
+        allow_patterns=f"{ref_condition}/KITTI-style/{location}_{ref_sequence}/**",
+        token=os.environ.get("HF_TOKEN")
+    )
 
 ref_image_dir = f"{ref_root_directory}/{location}_{ref_sequence}/{ref_camera_pos}-imgs/"
 ref_utm_dir = f"{ref_root_directory}/{location}_{ref_sequence}/utm/"
@@ -48,8 +69,6 @@ dist_tolerance = 10 # metres
 
 
 fig, ax = plt.subplots(1, 2, figsize=(19.4, 6))
-# idx = [0]  # mutable index
-# idx = [183]
 idx = [130]-
 
 def show_image(i):
@@ -59,7 +78,6 @@ def show_image(i):
         plt.close(fig)
         return
     qry_image_timestamp = qry_timestamps[i]
-    # try:
     qry_image_filename = f"{qry_image_dir}/{qry_image_timestamp}.png"
     qry_utm_timestamp = utils.get_corr_files(qry_image_timestamp, [qry_utm_dir,])
     qry_utm = np.loadtxt(qry_utm_timestamp)
@@ -94,7 +112,6 @@ def show_image(i):
         ax[1].set_title(f"No Match (min dist={closest_dist:.2f}m)")
 
     ax[1].axis("off")
-    # plt.savefig('paper_figures/localization_check.pdf', format="pdf", bbox_inches='tight')
     fig.canvas.draw()
 
 def on_key(event):

segmentation/evaluate-predictions.py

@@ -16,6 +16,12 @@ import json
 import yaml  # pip install pyyaml
 from tqdm import tqdm
 
+# Toggle the following boolean to False if not using HuggingFace App
+hf_app = True
+
+if hf_app:
+    from huggingface_hub import snapshot_download
+
 cmap = plt.get_cmap("jet")
 
 # ---------------- Argument Parsing ---------------- #
@@ -27,7 +33,7 @@ parser.add_argument("--location", type=str, default="Cambogan", help="Location n
 parser.add_argument("--sequence", type=str, default="20250811_113017", help="Sequence ID (e.g., 20250811_113017)")
 parser.add_argument("--condition", type=str, default="flooded", help="Condition (e.g., flooded)")
 parser.add_argument("--camera_pos", type=str, default="front", help="Camera position (e.g., front)")
-parser.add_argument("--root", type=str, default="../Datasets/FRED/", help="Root dataset directory (e.g., ../Datasets/FRED/)")
+parser.add_argument("--root", type=str, default="/data/FRED/", help="Root dataset directory (e.g., ../Datasets/FRED/)")
 parser.add_argument("--masks", type=str, required=True, help="Where predicted masks are saved")
 parser.add_argument("--img_calib_file", type=str, default="./camera_calib.txt", help="Path to camera calibration file (e.g., ./camera_calib.txt)")
 parser.add_argument('--vis', action='store_true', help="Store visual comparisons of the predictions and labels")
@@ -68,19 +74,14 @@ else:
     root_directory = f"{args.root}/{args.condition}/KITTI-style"
     img_calib_file = args.img_calib_file
 
-# # User parameters
-# # location = 'Cambogan'
-# # sequence = '20250811_113017'
-# # location = 'Holmview'
-# # sequence = '20250820_130327'
-# # location = 'Pullenvale'
-# # sequence = '20250916_124105'
-# location = 'Mount-Cotton'
-# sequence = '20241217_113410'
-# condition = 'flooded'
-# camera_pos = 'front'
-# root_directory = f"../Datasets/FRED/{condition}/KITTI-style"
-# # 01000000
+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 ####################################
 
@@ -93,7 +94,6 @@ mask_filenames = [filename for filename in natsorted(os.listdir(args.masks)) if
 
 fig, ax = plt.subplots(figsize=(12.8, 8))
 idx = 0  # mutable index
-# idx = 183  # mutable index
 
 def load_image_data(i):
     image_timestamp = timestamps[i]
@@ -105,7 +105,6 @@ def load_image_data(i):
 
         return water_label.astype(int)
 
-        # label_mask = np.any(image.colour_label != image.semantic_classes['other'], axis=-1)
     except Exception as e:
         print(f"Could not show label for {image_timestamp}.png: {e}")
 
@@ -171,21 +170,13 @@ iou_scores = []
 for i in tqdm(range(idx, len(timestamps))):
 
     img_label = load_image_data(i)
-    # print(mask_filenames[i])
     pred_label = (cv2.cvtColor(cv2.imread(args.masks+mask_filenames[i]), cv2.COLOR_BGR2GRAY)/255).astype(int)
 
-    # cv2.imshow('pred', cv2.resize(cv2.cvtColor(cv2.imread(args.masks+mask_filenames[i]), cv2.COLOR_BGR2GRAY), (640, 480)))
-    # cv2.waitKey(0)
-
-    # print(f"label shape: {img_label.shape}, label values: {np.unique(img_label)}")
-    # print(f"pred shape: {pred_label.shape}, pred values: {np.unique(pred_label)}")
-    # break
     iou = calculate_iou(img_label, pred_label)
     iou_scores.append(iou)
     if args.vis:
         os.makedirs(args.output, exist_ok=True)
         save_mask_comparison(img_label, pred_label, f"{args.output}/{timestamps[i]}.png", iou)
 
-    # break
 
 print(f"Mean IOU for water predictions: {np.mean(np.array(iou_scores))}")

segmentation/show_labels-all.py

@@ -15,6 +15,12 @@ from natsort import natsorted
 import json
 import yaml  # pip install pyyaml
 
+# Toggle the following boolean to False if not using HuggingFace App
+hf_app = True
+
+if hf_app:
+    from huggingface_hub import snapshot_download
+
 cmap = plt.get_cmap("jet")
 
 # ---------------- Argument Parsing ---------------- #
@@ -26,7 +32,7 @@ parser.add_argument("--location", type=str, default="Cambogan", help="Location n
 parser.add_argument("--sequence", type=str, default="20250811_113017", help="Sequence ID (e.g., 20250811_113017)")
 parser.add_argument("--condition", type=str, default="flooded", help="Condition (e.g., flooded)")
 parser.add_argument("--camera_pos", type=str, default="front", help="Camera position (e.g., front)")
-parser.add_argument("--root", type=str, default="D:/Datasets/FRED/", help="Root dataset directory (e.g., D:/Datasets/FRED/)")
+parser.add_argument("--root", type=str, default="/data/FRED/", help="Root dataset directory (e.g., D:/Datasets/FRED/)")
 parser.add_argument("--img_calib_file", type=str, default="./camera_calib.txt", help="Path to camera calibration file (e.g., ./camera_calib.txt)")
 
 args = parser.parse_args()
@@ -64,19 +70,14 @@ else:
     root_directory = f"{args.root}/{args.condition}/KITTI-style"
     img_calib_file = args.img_calib_file
 
-# # User parameters
-# # location = 'Cambogan'
-# # sequence = '20250811_113017'
-# # location = 'Holmview'
-# # sequence = '20250820_130327'
-# # location = 'Pullenvale'
-# # sequence = '20250916_124105'
-# location = 'Mount-Cotton'
-# sequence = '20241217_113410'
-# condition = 'flooded'
-# camera_pos = 'front'
-# root_directory = f"D:/Datasets/FRED/{condition}/KITTI-style"
-# # 01000000
+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 ####################################
 
@@ -86,9 +87,7 @@ img_calib_file = f"./camera_calib.txt"
 timestamps = [filename.split('.png')[0] for filename in natsorted(os.listdir(image_dir)) if os.path.isfile(image_dir+filename)]
 
 fig, ax = plt.subplots(figsize=(12.8, 8))
-# idx = [0]  # mutable index
-# idx = [183]  # mutable index
-idx = [130]
+idx = [0]  # mutable index
 
 def show_image(i):
     ax.clear()
@@ -111,7 +110,6 @@ def show_image(i):
         ax.imshow(overlay_img[:, :, ::-1])
         ax.set_title(f"{image_timestamp}.png")
         ax.axis("off")
-        # plt.savefig('paper_figures/semantic_image_labels.pdf', format="pdf", bbox_inches='tight')
         fig.canvas.draw()
     except Exception as e:
         print(f"Could not show label for {image_timestamp}.png: {e}")

visualisation/points2image-all.py

@@ -12,26 +12,29 @@ from utils.camera import ImageData
 import utils.utils as utils
 from natsort import natsorted
 
+# Toggle the following boolean to False if not using HuggingFace App
+hf_app = True
+
+if hf_app:
+    from huggingface_hub import snapshot_download
+
 cmap = plt.get_cmap("jet")
 
 # User parameters
-# location = 'Cambogan'
-# sequence = '20250811_113017'
-# sequence = '20250812_122339' #'20250812_122101'
-# location = 'Holmview'
-# sequence = '20250820_130327'
-# location = 'Mount-Cotton'
-# sequence = '20241217_113410'
-# location   = 'Pullenvale'
-# sequence   = '20250916_124105'
-location     = 'DairyCreek'
-sequence     = '20250811_103318'
+location = 'Cambogan'
+sequence = '20250811_113017'
 condition = 'flooded'
-# condition = 'dry'
 camera_pos = 'front'
-# root_directory = f"C:/Users/conno/Documents/data/FRED/{condition}/KITTI-style/" #f"D:/Datasets/FRED/{condition}/KITTI-style"
-root_directory = f"U:/Research/Projects/KVFPRA9190/FRED/{condition}/KITTI-style" 
-# 01000000
+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 ####################################
 
@@ -44,12 +47,9 @@ 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)]
 
-# timestamps.sort()
 
 fig, ax = plt.subplots(figsize=(12.8, 8))
-# idx = [0]  # mutable index
-# idx = [183]
-idx = [200]
+idx = [0]  # mutable index
 
 def show_image(i):
     ax.clear()
@@ -75,7 +75,6 @@ def show_image(i):
         ax.imshow(img_vis[:, :, ::-1])
         ax.set_title(f"{image_timestamp}.png")
         ax.axis("off")
-        # plt.savefig('paper_figures/CADRRAS/projected_pointcloud_distance_flooded.svg', format="svg", bbox_inches='tight')
         fig.canvas.draw()
     except Exception as e:
         print(f"Could not project pointcloud onto {image_timestamp}.png: {e}")