split subter implementation (training + inference)

Deep-SAD-PyTorch/flake.nix

@@ -10,11 +10,18 @@
     };
   };
 
-  outputs = { self, nixpkgs, flake-utils, poetry2nix }:
-    flake-utils.lib.eachDefaultSystem (system:
+  outputs =
+    {
+      self,
+      nixpkgs,
+      flake-utils,
+      poetry2nix,
+    }:
+    flake-utils.lib.eachDefaultSystem (
+      system:
       let
         # see https://github.com/nix-community/poetry2nix/tree/master#api for more functions and examples.
-        pkgs = import nixpkgs{
+        pkgs = import nixpkgs {
           inherit system;
           config.allowUnfree = true;
           config.cudaSupport = true;
@@ -43,7 +50,11 @@
         };
 
         devShells.poetry = pkgs.mkShell {
-          packages = [ pkgs.poetry pkgs.python311 ];
+          packages = [
+            pkgs.poetry
+            pkgs.python311
+          ];
         };
-      });
+      }
+    );
 }

Deep-SAD-PyTorch/src/datasets/main.py

@@ -1,9 +1,10 @@
-from .mnist import MNIST_Dataset
-from .elpv import ELPV_Dataset
-from .subter import SubTer_Dataset
-from .fmnist import FashionMNIST_Dataset
 from .cifar10 import CIFAR10_Dataset
+from .elpv import ELPV_Dataset
+from .fmnist import FashionMNIST_Dataset
+from .mnist import MNIST_Dataset
 from .odds import ODDSADDataset
+from .subter import SubTer_Dataset
+from .subtersplit import SubTerSplit_Dataset
 
 
 def load_dataset(
@@ -24,6 +25,7 @@ def load_dataset(
         "mnist",
         "elpv",
         "subter",
+        "subtersplit",
         "fmnist",
         "cifar10",
         "arrhythmia",
@@ -46,6 +48,15 @@ def load_dataset(
             inference=inference,
         )
 
+    if dataset_name == "subtersplit":
+        dataset = SubTerSplit_Dataset(
+            root=data_path,
+            ratio_known_normal=ratio_known_normal,
+            ratio_known_outlier=ratio_known_outlier,
+            ratio_pollution=ratio_pollution,
+            inference=inference,
+        )
+
     if dataset_name == "elpv":
         dataset = ELPV_Dataset(
             root=data_path,

Deep-SAD-PyTorch/src/datasets/subter.py

@@ -1,22 +1,21 @@
-from torch.utils.data import Subset
-from PIL import Image
-from torch.utils.data.dataset import ConcatDataset
-from torchvision.datasets import VisionDataset
-from base.torchvision_dataset import TorchvisionDataset
-from .preprocessing import create_semisupervised_setting
+import logging
+import random
+from pathlib import Path
 from typing import Callable, Optional
 
-import logging
+import numpy as np
 import torch
 import torchvision.transforms as transforms
-import random
-import numpy as np
+from base.torchvision_dataset import TorchvisionDataset
+from PIL import Image
+from torch.utils.data import Subset
+from torch.utils.data.dataset import ConcatDataset
+from torchvision.datasets import VisionDataset
 
-from pathlib import Path
+from .preprocessing import create_semisupervised_setting
 
 
 class SubTer_Dataset(TorchvisionDataset):
-
     def __init__(
         self,
         root: str,
@@ -31,6 +30,7 @@ class SubTer_Dataset(TorchvisionDataset):
         self.n_classes = 2  # 0: normal, 1: outlier
         self.normal_classes = tuple([0])
         self.outlier_classes = tuple([1])
+        self.inference_set = None
 
         # MNIST preprocessing: feature scaling to [0, 1]
         # FIXME understand mnist feature scaling and check if it or other preprocessing is necessary for elpv
@@ -78,7 +78,6 @@ class SubTer_Dataset(TorchvisionDataset):
 
 
 class SubTerTraining(VisionDataset):
-
     def __init__(
         self,
         root: str,
@@ -95,10 +94,18 @@ class SubTerTraining(VisionDataset):
 
         experiments_data = []
         experiments_targets = []
+        validation_files = []
+        experiment_files = []
 
         for experiment_file in Path(root).iterdir():
+            if experiment_file.is_dir() and experiment_file.name == "validation":
+                for validation_file in experiment_file.iterdir():
+                    if validation_file.suffix != ".npy":
+                        continue
+                    validation_files.append(experiment_file)
             if experiment_file.suffix != ".npy":
                 continue
+            experiment_files.append(experiment_file)
             experiment_data = np.load(experiment_file)
             # experiment_data = np.lib.format.open_memmap(experiment_file, mode='r+')
             experiment_targets = (
@@ -109,6 +116,26 @@ class SubTerTraining(VisionDataset):
             experiments_data.append(experiment_data)
             experiments_targets.append(experiment_targets)
 
+        filtered_validation_files = []
+        for validation_file in validation_files:
+            validation_file_name = validation_file.name
+            file_exists_in_experiments = any(
+                experiment_file.name == validation_file_name
+                for experiment_file in experiment_files
+            )
+            if not file_exists_in_experiments:
+                filtered_validation_files.append(validation_file)
+        validation_files = filtered_validation_files
+
+        logger = logging.getLogger()
+
+        logger.info(
+            f"Train/Test experiments: {[experiment_file.name for experiment_file in experiment_files]}"
+        )
+        logger.info(
+            f"Validation experiments: {[validation_file.name for validation_file in validation_files]}"
+        )
+
         lidar_projections = np.concatenate(experiments_data)
         smoke_presence = np.concatenate(experiments_targets)
 
@@ -166,7 +193,6 @@ class SubTerTraining(VisionDataset):
 
 
 class SubTerInference(VisionDataset):
-
     def __init__(
         self,
         root: str,

Deep-SAD-PyTorch/src/datasets/subtersplit.py

@@ -0,0 +1,263 @@
+import logging
+from pathlib import Path
+from typing import Callable, Optional
+
+import numpy as np
+import torch
+import torchvision.transforms as transforms
+from base.torchvision_dataset import TorchvisionDataset
+from PIL import Image
+from torch.utils.data import Subset
+from torchvision.datasets import VisionDataset
+
+from .preprocessing import create_semisupervised_setting
+
+
+class SubTerSplit_Dataset(TorchvisionDataset):
+    def __init__(
+        self,
+        root: str,
+        ratio_known_normal: float = 0.0,
+        ratio_known_outlier: float = 0.0,
+        ratio_pollution: float = 0.0,
+        inference: bool = False,
+    ):
+        super().__init__(root)
+
+        # Define normal and outlier classes
+        self.n_classes = 2  # 0: normal, 1: outlier
+        self.normal_classes = tuple([0])
+        self.outlier_classes = tuple([1])
+        self.inference_set = None
+
+        # MNIST preprocessing: feature scaling to [0, 1]
+        # FIXME understand mnist feature scaling and check if it or other preprocessing is necessary for elpv
+        transform = transforms.ToTensor()
+        target_transform = transforms.Lambda(lambda x: int(x in self.outlier_classes))
+
+        if inference:
+            self.inference_set = SubTerSplitInference(
+                root=self.root,
+                transform=transform,
+            )
+        else:
+            # Get train set
+            train_set = SubTerSplitTraining(
+                root=self.root,
+                transform=transform,
+                target_transform=target_transform,
+                train=True,
+            )
+
+            # Create semi-supervised setting
+            idx, _, semi_targets = create_semisupervised_setting(
+                train_set.targets.cpu().data.numpy(),
+                self.normal_classes,
+                self.outlier_classes,
+                self.outlier_classes,
+                ratio_known_normal,
+                ratio_known_outlier,
+                ratio_pollution,
+            )
+            train_set.semi_targets[idx] = torch.tensor(
+                np.array(semi_targets, dtype=np.int8)
+            )  # set respective semi-supervised labels
+
+            # Subset train_set to semi-supervised setup
+            self.train_set = Subset(train_set, idx)
+
+            # Get test set
+            self.test_set = SubTerSplitTraining(
+                root=self.root,
+                train=False,
+                transform=transform,
+                target_transform=target_transform,
+            )
+
+
+def split_array_into_subarrays(array, split_height, split_width):
+    original_shape = array.shape
+    height, width = original_shape[-2], original_shape[-1]
+    assert height % split_height == 0, "The height is not divisible by the split_height"
+    assert width % split_width == 0, "The width is not divisible by the split_width"
+    num_splits_height = height // split_height
+    num_splits_width = width // split_width
+    reshaped_array = array.reshape(
+        -1, num_splits_height, split_height, num_splits_width, split_width
+    )
+    transposed_array = reshaped_array.transpose(0, 1, 3, 2, 4)
+    final_array = transposed_array.reshape(-1, split_height, split_width)
+    return final_array
+
+
+class SubTerSplitTraining(VisionDataset):
+    def __init__(
+        self,
+        root: str,
+        transforms: Optional[Callable] = None,
+        transform: Optional[Callable] = None,
+        target_transform: Optional[Callable] = None,
+        train=False,
+        split=0.7,
+        seed=0,
+        height=16,
+        width=256,
+    ):
+        super(SubTerSplitTraining, self).__init__(
+            root, transforms, transform, target_transform
+        )
+
+        experiments_data = []
+        experiments_targets = []
+        validation_files = []
+        experiment_files = []
+
+        logger = logging.getLogger()
+
+        for experiment_file in Path(root).iterdir():
+            if experiment_file.is_dir() and experiment_file.name == "validation":
+                for validation_file in experiment_file.iterdir():
+                    if validation_file.suffix != ".npy":
+                        continue
+                    validation_files.append(experiment_file)
+            if experiment_file.suffix != ".npy":
+                continue
+            experiment_files.append(experiment_file)
+            experiment_data = np.load(experiment_file)
+
+            if (
+                experiment_data.shape[1] % height != 0
+                or experiment_data.shape[2] % width != 0
+            ):
+                logger.error(
+                    f"Experiment {experiment_file.name} has shape {experiment_data.shape} which is not divisible by {height}x{width}"
+                )
+            experiment_data = split_array_into_subarrays(experiment_data, height, width)
+
+            # experiment_data = np.lib.format.open_memmap(experiment_file, mode='r+')
+            experiment_targets = (
+                np.ones(experiment_data.shape[0], dtype=np.int8)
+                if "smoke" in experiment_file.name
+                else np.zeros(experiment_data.shape[0], dtype=np.int8)
+            )
+            experiments_data.append(experiment_data)
+            experiments_targets.append(experiment_targets)
+
+        filtered_validation_files = []
+        for validation_file in validation_files:
+            validation_file_name = validation_file.name
+            file_exists_in_experiments = any(
+                experiment_file.name == validation_file_name
+                for experiment_file in experiment_files
+            )
+            if not file_exists_in_experiments:
+                filtered_validation_files.append(validation_file)
+        validation_files = filtered_validation_files
+
+        logger.info(
+            f"Train/Test experiments: {[experiment_file.name for experiment_file in experiment_files]}"
+        )
+        logger.info(
+            f"Validation experiments: {[validation_file.name for validation_file in validation_files]}"
+        )
+
+        lidar_projections = np.concatenate(experiments_data)
+        smoke_presence = np.concatenate(experiments_targets)
+
+        np.random.seed(seed)
+
+        shuffled_indices = np.random.permutation(lidar_projections.shape[0])
+        shuffled_lidar_projections = lidar_projections[shuffled_indices]
+        shuffled_smoke_presence = smoke_presence[shuffled_indices]
+
+        split_idx = int(split * shuffled_lidar_projections.shape[0])
+
+        if train:
+            self.data = shuffled_lidar_projections[:split_idx]
+            self.targets = shuffled_smoke_presence[:split_idx]
+
+        else:
+            self.data = shuffled_lidar_projections[split_idx:]
+            self.targets = shuffled_smoke_presence[split_idx:]
+
+        self.data = np.nan_to_num(self.data)
+
+        self.data = torch.tensor(self.data)
+        self.targets = torch.tensor(self.targets, dtype=torch.int8)
+
+        self.semi_targets = torch.zeros_like(self.targets, dtype=torch.int8)
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, index):
+        """Override the original method of the MNIST class.
+        Args:
+            index (int): Index
+
+        Returns:
+            tuple: (image, target, semi_target, index)
+        """
+        img, target, semi_target = (
+            self.data[index],
+            int(self.targets[index]),
+            int(self.semi_targets[index]),
+        )
+
+        # doing this so that it is consistent with all other datasets
+        # to return a PIL Image
+        img = Image.fromarray(img.numpy(), mode="F")
+
+        if self.transform is not None:
+            img = self.transform(img)
+
+        if self.target_transform is not None:
+            target = self.target_transform(target)
+
+        return img, target, semi_target, index
+
+
+class SubTerSplitInference(VisionDataset):
+    def __init__(
+        self,
+        root: str,
+        transforms: Optional[Callable] = None,
+        transform: Optional[Callable] = None,
+    ):
+        super(SubTerSplitInference, self).__init__(root, transforms, transform)
+        logger = logging.getLogger()
+
+        self.experiment_file_path = Path(root)
+
+        if not self.experiment_file_path.is_file():
+            logger.error(
+                "For inference the data path has to be a single experiment file!"
+            )
+            raise Exception("Inference data is not a loadable file!")
+
+        self.data = np.load(self.experiment_file_path)
+        self.data = split_array_into_subarrays(self.data, 16, 256)
+        self.data = np.nan_to_num(self.data)
+        self.data = torch.tensor(self.data)
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, index):
+        """Override the original method of the MNIST class.
+        Args:
+            index (int): Index
+
+        Returns:
+            tuple: (image, index)
+        """
+        img = self.data[index]
+
+        # doing this so that it is consistent with all other datasets
+        # to return a PIL Image
+        img = Image.fromarray(img.numpy(), mode="F")
+
+        if self.transform is not None:
+            img = self.transform(img)
+
+        return img, index

Deep-SAD-PyTorch/src/main.py

@@ -1,14 +1,14 @@
-import click
-import torch
 import logging
 import random
-import numpy as np
 from pathlib import Path
 
+import click
+import numpy as np
+import torch
+from datasets.main import load_dataset
+from DeepSAD import DeepSAD
 from utils.config import Config
 from utils.visualization.plot_images_grid import plot_images_grid
-from DeepSAD import DeepSAD
-from datasets.main import load_dataset
 
 
 ################################################################################
@@ -31,6 +31,7 @@ from datasets.main import load_dataset
             "mnist",
             "elpv",
             "subter",
+            "subtersplit",
             "fmnist",
             "cifar10",
             "arrhythmia",
@@ -49,6 +50,7 @@ from datasets.main import load_dataset
             "mnist_LeNet",
             "elpv_LeNet",
             "subter_LeNet",
+            "subter_LeNet_Split",
             "fmnist_LeNet",
             "cifar10_LeNet",
             "arrhythmia_mlp",
@@ -315,7 +317,6 @@ def main(
     logger.info("Number of dataloader workers: %d" % n_jobs_dataloader)
 
     if action == "train":
-
         # Load data
         dataset = load_dataset(
             dataset_name,
@@ -413,7 +414,6 @@ def main(
         ]  # from lowest to highest score
 
         if dataset_name in ("mnist", "fmnist", "cifar10", "elpv"):
-
             if dataset_name in ("mnist", "fmnist", "elpv"):
                 X_all_low = dataset.test_set.data[idx_all_sorted[:32], ...].unsqueeze(1)
                 X_all_high = dataset.test_set.data[idx_all_sorted[-32:], ...].unsqueeze(

Deep-SAD-PyTorch/src/networks/main.py

@@ -1,11 +1,12 @@
-from .mnist_LeNet import MNIST_LeNet, MNIST_LeNet_Autoencoder
-from .elpv_LeNet import ELPV_LeNet, ELPV_LeNet_Autoencoder
-from .subter_LeNet import SubTer_LeNet, SubTer_LeNet_Autoencoder
-from .fmnist_LeNet import FashionMNIST_LeNet, FashionMNIST_LeNet_Autoencoder
 from .cifar10_LeNet import CIFAR10_LeNet, CIFAR10_LeNet_Autoencoder
-from .mlp import MLP, MLP_Autoencoder
-from .vae import VariationalAutoencoder
 from .dgm import DeepGenerativeModel, StackedDeepGenerativeModel
+from .elpv_LeNet import ELPV_LeNet, ELPV_LeNet_Autoencoder
+from .fmnist_LeNet import FashionMNIST_LeNet, FashionMNIST_LeNet_Autoencoder
+from .mlp import MLP, MLP_Autoencoder
+from .mnist_LeNet import MNIST_LeNet, MNIST_LeNet_Autoencoder
+from .subter_LeNet import SubTer_LeNet, SubTer_LeNet_Autoencoder
+from .subter_LeNet_Split import SubTer_LeNet_Split, SubTer_LeNet_Split_Autoencoder
+from .vae import VariationalAutoencoder
 
 
 def build_network(net_name, ae_net=None):
@@ -15,6 +16,7 @@ def build_network(net_name, ae_net=None):
         "mnist_LeNet",
         "elpv_LeNet",
         "subter_LeNet",
+        "subter_LeNet_Split",
         "mnist_DGM_M2",
         "mnist_DGM_M1M2",
         "fmnist_LeNet",
@@ -46,6 +48,9 @@ def build_network(net_name, ae_net=None):
     if net_name == "subter_LeNet":
         net = SubTer_LeNet()
 
+    if net_name == "subter_LeNet_Split":
+        net = SubTer_LeNet_Split()
+
     if net_name == "elpv_LeNet":
         net = ELPV_LeNet()
 
@@ -130,6 +135,7 @@ def build_autoencoder(net_name):
     implemented_networks = (
         "elpv_LeNet",
         "subter_LeNet",
+        "subter_LeNet_Split",
         "mnist_LeNet",
         "mnist_DGM_M1M2",
         "fmnist_LeNet",
@@ -154,6 +160,9 @@ def build_autoencoder(net_name):
     if net_name == "subter_LeNet":
         ae_net = SubTer_LeNet_Autoencoder()
 
+    if net_name == "subter_LeNet_Split":
+        ae_net = SubTer_LeNet_Split_Autoencoder()
+
     if net_name == "elpv_LeNet":
         ae_net = ELPV_LeNet_Autoencoder()
 

Deep-SAD-PyTorch/src/networks/subter_LeNet_Split.py

@@ -0,0 +1,66 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from base.base_net import BaseNet
+
+
+class SubTer_LeNet_Split(BaseNet):
+    def __init__(self, rep_dim=256):
+        super().__init__()
+
+        self.rep_dim = rep_dim
+        self.pool = nn.MaxPool2d(2, 2)
+
+        self.conv1 = nn.Conv2d(1, 8, 5, bias=False, padding=2)
+        self.bn1 = nn.BatchNorm2d(8, eps=1e-04, affine=False)
+        self.conv2 = nn.Conv2d(8, 4, 5, bias=False, padding=2)
+        self.bn2 = nn.BatchNorm2d(4, eps=1e-04, affine=False)
+        self.fc1 = nn.Linear(4 * 64 * 4, self.rep_dim, bias=False)
+
+    def forward(self, x):
+        x = x.view(-1, 1, 16, 256)
+        x = self.conv1(x)
+        x = self.pool(F.leaky_relu(self.bn1(x)))
+        x = self.conv2(x)
+        x = self.pool(F.leaky_relu(self.bn2(x)))
+        x = x.view(int(x.size(0)), -1)
+        x = self.fc1(x)
+        return x
+
+
+class SubTer_LeNet_Split_Decoder(BaseNet):
+    def __init__(self, rep_dim=256):
+        super().__init__()
+
+        self.rep_dim = rep_dim
+
+        # Decoder network
+        self.fc3 = nn.Linear(self.rep_dim, 4 * 64 * 4, bias=False)
+        self.bn3 = nn.BatchNorm2d(4, eps=1e-04, affine=False)
+        self.deconv1 = nn.ConvTranspose2d(4, 8, 5, bias=False, padding=2)
+        self.bn4 = nn.BatchNorm2d(8, eps=1e-04, affine=False)
+        self.deconv2 = nn.ConvTranspose2d(8, 1, 5, bias=False, padding=2)
+
+    def forward(self, x):
+        x = self.fc3(x)
+        x = x.view(int(x.size(0)), 4, 4, 64)
+        x = F.interpolate(F.leaky_relu(self.bn3(x)), scale_factor=2)
+        x = self.deconv1(x)
+        x = F.interpolate(F.leaky_relu(self.bn4(x)), scale_factor=2)
+        x = self.deconv2(x)
+        x = torch.sigmoid(x)
+        return x
+
+
+class SubTer_LeNet_Split_Autoencoder(BaseNet):
+    def __init__(self, rep_dim=256):
+        super().__init__()
+
+        self.rep_dim = rep_dim
+        self.encoder = SubTer_LeNet_Split(rep_dim=rep_dim)
+        self.decoder = SubTer_LeNet_Split_Decoder(rep_dim=rep_dim)
+
+    def forward(self, x):
+        x = self.encoder(x)
+        x = self.decoder(x)
+        return x

tools/poetry.lock

@@ -1435,6 +1435,21 @@ toolz = "*"
 [package.extras]
 complete = ["blosc", "numpy (>=1.20.0)", "pandas (>=1.3)", "pyzmq"]
 
+[[package]]
+name = "pathvalidate"
+version = "3.2.0"
+description = "pathvalidate is a Python library to sanitize/validate a string such as filenames/file-paths/etc."
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "pathvalidate-3.2.0-py3-none-any.whl", hash = "sha256:cc593caa6299b22b37f228148257997e2fa850eea2daf7e4cc9205cef6908dee"},
+    {file = "pathvalidate-3.2.0.tar.gz", hash = "sha256:5e8378cf6712bff67fbe7a8307d99fa8c1a0cb28aa477056f8fc374f0dff24ad"},
+]
+
+[package.extras]
+docs = ["Sphinx (>=2.4)", "sphinx-rtd-theme (>=1.2.2)", "urllib3 (<2)"]
+test = ["Faker (>=1.0.8)", "allpairspy (>=2)", "click (>=6.2)", "pytest (>=6.0.1)", "pytest-discord (>=0.1.4)", "pytest-md-report (>=0.4.1)"]
+
 [[package]]
 name = "pillow"
 version = "10.3.0"
@@ -2416,4 +2431,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.11"
-content-hash = "607634c6605566c6bec22c1e8f41d056d2920de45108297193592fe57b10507b"
+content-hash = "cd1f01135a813fbbc06da686dfa67b0981d26ccb3038f7d0e4a17359326f2c8d"

tools/pyproject.toml

@@ -19,6 +19,7 @@ matplotlib = "^3.8.4"
 dask = "^2024.4.2"
 dask-expr = "^1.1.3"
 pandas = "^2.2.2"
+pathvalidate = "^3.2.0"
 
 [build-system]
 requires = ["poetry-core"]

tools/render2d.py

@@ -1,18 +1,22 @@
-from configargparse import (
-    ArgParser,
-    YAMLConfigFileParser,
-    ArgumentDefaultsRawHelpFormatter,
-)
-from sys import exit
-from pathlib import Path
-from pointcloudset import Dataset
-from rich.progress import track
-from pandas import DataFrame
-from PIL import Image
 from math import pi
-from typing import Optional
+from multiprocessing import Pool
+from pathlib import Path
+from sys import exit
+from typing import Optional, Tuple
+
 import matplotlib
 import numpy as np
+from configargparse import (
+    ArgParser,
+    ArgumentDefaultsRawHelpFormatter,
+    YAMLConfigFileParser,
+)
+from pandas import DataFrame
+from pathvalidate import sanitize_filename
+from PIL import Image
+from pointcloudset import Dataset
+from rich.progress import track
+from rosbags.highlevel import AnyReader
 
 matplotlib.use("Agg")
 import matplotlib.pyplot as plt
@@ -20,16 +24,44 @@ import matplotlib.pyplot as plt
 from util import (
     angle,
     angle_width,
-    positive_int,
-    load_dataset,
-    existing_path,
-    create_video_from_images,
     calculate_average_frame_rate,
+    create_video_from_images,
+    existing_path,
     get_colormap_with_special_missing_color,
+    load_dataset,
+    positive_int,
 )
 
 
-def crop_lidar_data_to_roi(
+def save_image_topics(
+    bag_file_path: Path, output_folder_path: Path, topic_names: list[str]
+) -> list[Path]:
+    with AnyReader([bag_file_path]) as reader:
+        topic_paths = []
+        for topic_name in topic_names:
+            connections = [x for x in reader.connections if x.topic == topic_name]
+            frame_count = 0
+            topic_output_folder_path = output_folder_path / sanitize_filename(
+                topic_name
+            )
+            topic_output_folder_path.mkdir(exist_ok=True, parents=True)
+            topic_paths.append(topic_output_folder_path)
+            for connection, timestamp, rawdata in reader.messages(
+                connections=connections
+            ):
+                img_msg = reader.deserialize(rawdata, connection.msgtype)
+
+                img_data = np.frombuffer(
+                    img_msg.data,
+                    dtype=np.uint8 if "8" in img_msg.encoding else np.uint16,
+                ).reshape((img_msg.height, img_msg.width))
+                img = Image.fromarray(img_data, mode="L")
+                frame_count += 1
+                img.save(topic_output_folder_path / f"frame_{frame_count:04d}.png")
+    return topic_paths
+
+
+def crop_projection_data_to_roi(
     data: DataFrame,
     roi_angle_start: float,
     roi_angle_width: float,
@@ -83,103 +115,196 @@ def create_2d_projection(
     tmp_file_path.unlink()
 
 
-def create_projection_data(
-    dataset: Dataset,
-    output_path: Path,
-    colormap_name: str,
-    missing_data_color: str,
-    reverse_colormap: bool,
-    horizontal_resolution: int,
-    vertical_scale: int,
-    horizontal_scale: int,
-    roi_angle_start: float,
-    roi_angle_width: float,
-    render_images: bool,
-) -> (np.ndarray, Optional[list[Path]]):
-    rendered_images = []
-    converted_lidar_frames = []
-
-    for i, pc in track(
-        enumerate(dataset, 1), description="Creating projections...", total=len(dataset)
-    ):
-        vertical_resolution = int(pc.data["ring"].max() + 1)
-
-        # Angle calculation implementation
-
-        # projected_data = pc.data.copy()
-        # projected_data["arctan"] = np.arctan2(projected_data["y"], projected_data["x"])
-        # projected_data["arctan_normalized"] = 0.5 * (projected_data["arctan"] / pi + 1.0)
-        # projected_data["arctan_scaled"] = projected_data["arctan_normalized"] * horizontal_resolution
-        # #projected_data["horizontal_position"] = np.floor(projected_data["arctan_scaled"])
-        # projected_data["horizontal_position"] = np.round(projected_data["arctan_scaled"])
-        # projected_data["normalized_range"] = 1 / np.sqrt(
-        #     projected_data["x"] ** 2 + projected_data["y"] ** 2 + projected_data["z"] ** 2
-        # )
-        # duplicates = projected_data[projected_data.duplicated(subset=['ring', 'horizontal_position'], keep=False)].sort_values(by=['ring', 'horizontal_position'])
-        # sorted = projected_data.sort_values(by=['ring', 'horizontal_position'])
-
-        # FIXME: following pivot fails due to duplicates in the data, some points (x, y) are mapped to the same pixel in the projection, have to decide how to handles
-        #        these cases
-
-        # projected_image_data =  projected_data.pivot(
-        #     index="ring", columns="horizontal_position", values="normalized_range"
-        # )
-        # projected_image_data = projected_image_data.reindex(columns=range(horizontal_resolution), fill_value=0)
-
-        # projected_image_data, output_horizontal_resolution = crop_lidar_data_to_roi(
-        #     projected_image_data, roi_angle_start, roi_angle_width, horizontal_resolution
-        # )
-
-        # create_2d_projection(
-        #     projected_image_data,
-        #     output_path / f"frame_{i:04d}_projection.png",
-        #     output_path / "tmp.png",
-        #     colormap_name,
-        #     missing_data_color,
-        #     reverse_colormap,
-        #     horizontal_resolution=output_horizontal_resolution * horizontal_scale,
-        #     vertical_resolution=vertical_resolution * vertical_scale,
-        # )
+def process_frame(args) -> Tuple[int, np.ndarray, Optional[Path]]:
+    (
+        i,
+        pc,
+        output_path,
+        colormap_name,
+        missing_data_color,
+        reverse_colormap,
+        vertical_resolution,
+        horizontal_resolution,
+        vertical_scale,
+        horizontal_scale,
+        roi_angle_start,
+        roi_angle_width,
+        render_images,
+    ) = args
 
     lidar_data = pc.data.copy()
+
     lidar_data["horizontal_position"] = (
         lidar_data["original_id"] % horizontal_resolution
     )
+    lidar_data["horizontal_position_yaw_f"] = (
+        0.5
+        * horizontal_resolution
+        * (np.arctan2(lidar_data["y"], lidar_data["x"]) / pi + 1.0)
+    )
+    lidar_data["horizontal_position_yaw"] = np.floor(
+        lidar_data["horizontal_position_yaw_f"]
+    )
+    lidar_data["vertical_position"] = np.floor(
+        lidar_data["original_id"] / horizontal_resolution
+    )
+    # fov = 32 * pi / 180
+    # fov_down = 17 * pi / 180
+    fov = 31.76 * pi / 180
+    fov_down = 17.3 * pi / 180
+    lidar_data["vertical_angle"] = np.arcsin(
+        lidar_data["z"]
+        / np.sqrt(lidar_data["x"] ** 2 + lidar_data["y"] ** 2 + lidar_data["z"] ** 2)
+    )
+    lidar_data["vertical_angle_degree"] = lidar_data["vertical_angle"] * 180 / pi
+
+    lidar_data["vertical_position_pitch_f"] = vertical_resolution * (
+        1 - ((lidar_data["vertical_angle"] + fov_down) / fov)
+    )
+    lidar_data["vertical_position_pitch"] = np.floor(
+        lidar_data["vertical_position_pitch_f"]
+    )
+
+    duplicates = lidar_data[
+        lidar_data.duplicated(
+            subset=["vertical_position_pitch", "horizontal_position_yaw"],
+            keep=False,
+        )
+    ].sort_values(by=["vertical_position_pitch", "horizontal_position_yaw"])
+
     lidar_data["normalized_range"] = 1 / np.sqrt(
         lidar_data["x"] ** 2 + lidar_data["y"] ** 2 + lidar_data["z"] ** 2
     )
-        lidar_data = lidar_data.pivot(
-            index="ring", columns="horizontal_position", values="normalized_range"
+    projection_data = lidar_data.pivot(
+        index="vertical_position_pitch",
+        columns="horizontal_position_yaw",
+        values="normalized_range",
     )
-        lidar_data = lidar_data.reindex(
+    projection_data = projection_data.reindex(
         columns=range(horizontal_resolution), fill_value=0
     )
-        lidar_data = lidar_data.reindex(index=range(vertical_resolution), fill_value=0)
-        lidar_data, output_horizontal_resolution = crop_lidar_data_to_roi(
-            lidar_data, roi_angle_start, roi_angle_width, horizontal_resolution
+    projection_data = projection_data.reindex(
+        index=range(vertical_resolution), fill_value=0
+    )
+    projection_data, output_horizontal_resolution = crop_projection_data_to_roi(
+        projection_data, roi_angle_start, roi_angle_width, horizontal_resolution
     )
 
-        converted_lidar_frames.append(lidar_data.to_numpy())
     if render_images:
         image_path = create_2d_projection(
-                lidar_data,
+            projection_data,
             output_path / f"frame_{i:04d}.png",
-                output_path / "tmp.png",
+            output_path / f"tmp_{i:04d}.png",
             colormap_name,
             missing_data_color,
             reverse_colormap,
             horizontal_resolution=output_horizontal_resolution * horizontal_scale,
             vertical_resolution=vertical_resolution * vertical_scale,
         )
+    else:
+        image_path = None
 
-            rendered_images.append(image_path)
+    return (
+        i,
+        projection_data.to_numpy(),
+        image_path,
+        lidar_data["vertical_position_pitch_f"].min(),
+        lidar_data["vertical_position_pitch_f"].max(),
+    )
+
+
+# Adjusted to use a generator for args_list
+def create_projection_data(
+    dataset: Dataset,
+    output_path: Path,
+    colormap_name: str,
+    missing_data_color: str,
+    reverse_colormap: bool,
+    vertical_resolution: int,
+    horizontal_resolution: int,
+    vertical_scale: int,
+    horizontal_scale: int,
+    roi_angle_start: float,
+    roi_angle_width: float,
+    render_images: bool,
+) -> Tuple[np.ndarray, Optional[list[Path]]]:
+    rendered_images = []
+    converted_lidar_frames = []
+
+    # Generator for args_list
+    # def args_generator():
+    #     for i, pc in enumerate(dataset, 1):
+    #         yield (
+    #             i,
+    #             pc,
+    #             output_path,
+    #             colormap_name,
+    #             missing_data_color,
+    #             reverse_colormap,
+    #             vertical_resolution,
+    #             horizontal_resolution,
+    #             vertical_scale,
+    #             horizontal_scale,
+    #             roi_angle_start,
+    #             roi_angle_width,
+    #             render_images,
+    #         )
+
+    # results = []
+
+    # with Pool() as pool:
+    #     results_gen = pool.imap(process_frame, args_generator())
+    #     for result in track(
+    #         results_gen, description="Processing...", total=len(dataset)
+    #     ):
+    #         results.append(result)
+
+    # results.sort(key=lambda x: x[0])
+
+    for i, pc in track(
+        enumerate(dataset, 1), description="Processing...", total=len(dataset)
+    ):
+        results = process_frame(
+            (
+                i,
+                pc,
+                output_path,
+                colormap_name,
+                missing_data_color,
+                reverse_colormap,
+                vertical_resolution,
+                horizontal_resolution,
+                vertical_scale,
+                horizontal_scale,
+                roi_angle_start,
+                roi_angle_width,
+                render_images,
+            )
+        )
+        converted_lidar_frames.append(results[1])
+        if render_images:
+            rendered_images.append(results[2])
+
+    # min_all = 100
+    # max_all = 0
+
+    # for _, data, img_path, min_frame, max_frame in results:
+    #     converted_lidar_frames.append(data)
+    #     if img_path:
+    #         rendered_images.append(img_path)
+    #     if min_frame < min_all:
+    #         min_all = min_frame
+    #     if max_frame > max_all:
+    #         max_all = max_frame
+
+    # print(f"{min_all=}, {max_all=}")
 
     projection_data = np.stack(converted_lidar_frames, axis=0)
 
     if render_images:
-        return rendered_images, projection_data
+        return projection_data, rendered_images
     else:
-        return projection_data
+        return (projection_data,)
 
 
 def main() -> int:
@@ -204,6 +329,12 @@ def main() -> int:
         type=str,
         help="topic in the ros/mcap bag file containing the point cloud data",
     )
+    parser.add_argument(
+        "--image-topics",
+        default=[],
+        nargs="+",
+        help="topics in the ros/mcap bag file containing the image data",
+    )
     parser.add_argument(
         "--output-path",
         default=Path("./output"),
@@ -248,11 +379,17 @@ def main() -> int:
         type=bool,
         help="if colormap should be reversed",
     )
+    parser.add_argument(
+        "--vertical-resolution",
+        default=32,
+        type=positive_int,
+        help="number of vertical lidar data point rows",
+    )
     parser.add_argument(
         "--horizontal-resolution",
         default=2048,
         type=positive_int,
-        help="number of horizontal lidar data points",
+        help="number of horizontal lidar data point columns",
     )
     parser.add_argument(
         "--vertical-scale",
@@ -296,6 +433,7 @@ def main() -> int:
     dataset = load_dataset(args.input_experiment_path, args.pointcloud_topic)
 
     images = []
+    topic_paths = []
 
     if not args.output_no_images or not args.output_no_video:
         if not args.force_generation and all(
@@ -312,6 +450,7 @@ def main() -> int:
                 args.colormap_name,
                 args.missing_data_color,
                 args.reverse_colormap,
+                args.vertical_resolution,
                 args.horizontal_resolution,
                 args.vertical_scale,
                 args.horizontal_scale,
@@ -319,6 +458,10 @@ def main() -> int:
                 args.roi_angle_width,
                 render_images=True,
             )
+            if args.image_topics:
+                topic_paths = save_image_topics(
+                    args.input_experiment_path, output_path, args.image_topics
+                )
 
     output_numpy_path = (output_path / args.input_experiment_path.stem).with_suffix(
         ".npy"
@@ -336,6 +479,7 @@ def main() -> int:
                     args.colormap_name,
                     args.missing_data_color,
                     args.reverse_colormap,
+                    args.vertical_resolution,
                     args.horizontal_resolution,
                     args.vertical_scale,
                     args.horizontal_scale,
@@ -358,11 +502,19 @@ def main() -> int:
                 f"Skipping video generation for {args.input_experiment_path} as {output_path / args.input_experiment_path.stem}.mp4 already exists"
             )
         else:
+            frame_rate = calculate_average_frame_rate(dataset)
             input_images_pattern = f"{tmp_path}/frame_%04d.png"
             create_video_from_images(
                 input_images_pattern,
                 (output_path / args.input_experiment_path.stem).with_suffix(".mp4"),
-                calculate_average_frame_rate(dataset),
+                frame_rate,
+            )
+            for topic_path in topic_paths:
+                input_images_pattern = f"{topic_path}/frame_%04d.png"
+                create_video_from_images(
+                    input_images_pattern,
+                    (output_path / topic_path.stem).with_suffix(".mp4"),
+                    frame_rate,
                 )
 
     if args.output_no_images:

tools/util.py

@@ -1,11 +1,11 @@
-from pointcloudset import Dataset
-from pointcloudset.io.dataset.ros import dataset_from_ros
-from pathlib import Path
 from argparse import ArgumentTypeError
-from subprocess import run
 from datetime import timedelta
-from matplotlib.colors import Colormap
+from pathlib import Path
+from subprocess import run
+
 from matplotlib import colormaps
+from matplotlib.colors import Colormap
+from pointcloudset import Dataset
 
 
 def load_dataset(