euler-preprocess 1.0.1

Physics-based preprocessing (fog, etc.) for RGB+depth datasets

euler-preprocess

Physics-based preprocessing transforms for multi-modal RGB+depth datasets. Built on top of euler-loading and ds-crawler.

Available transforms:

Command	Description
euler-preprocess fog	Synthetic fog via the Koschmieder atmospheric scattering model
euler-preprocess sky-depth	Override depth values in sky regions with a constant
euler-preprocess radial	Convert planar (z-buffer) depth to radial (Euclidean) depth

Installation

uv pip install "euler-preprocess[gpu,progress] @ git+https://github.com/d-rothen/euler-fog"

Usage

euler-preprocess fog       -c configs/example_dataset_config.json
euler-preprocess sky-depth -c configs/sky_depth_dataset_config.json
euler-preprocess radial    -c configs/radial_dataset_config.json

Configuration

Every subcommand takes a dataset config JSON that points to the input data and a transform config. Each modality path must be a directory indexed by ds-crawler with an euler_loading property that specifies the loader and function. This allows euler-loading to auto-select the correct dataset-specific loader.

Dataset Config

{
  "transform_config_path": "configs/run1.json",
  "output_path": "/path/to/output",
  "modalities": {
    "rgb": "/path/to/rgb",
    "depth": "/path/to/depth",
    "semantic_segmentation": "/path/to/classSegmentation"
  },
  "hierarchical_modalities": {
    "intrinsics": "/path/to/intrinsics"
  }
}

Field	Description
transform_config_path	Path to the transform-specific config (see below). fog_config_path is also accepted for backward compatibility.
output_path	Directory where outputs are written.
modalities	Regular modalities that participate in sample-ID intersection. Which modalities are required depends on the transform (see table below).
hierarchical_modalities	Per-scene data (e.g. intrinsics). Loaded once per scene and cached.

Required modalities per transform:

Transform	modalities	hierarchical_modalities
fog	rgb, depth, semantic_segmentation	— (intrinsics optional)
sky-depth	depth, semantic_segmentation	—
radial	depth	intrinsics

---

Fog Transform

Fog Config

Controls the fog simulation.

{
  "airlight": "from_sky",
  "seed": 1337,
  "depth_scale": 1.0,
  "resize_depth": true,
  "contrast_threshold": 0.05,
  "device": "cpu",
  "gpu_batch_size": 4,
  "selection": { ... },
  "models": { ... }
}

Field	Description
airlight	Required. Airlight estimation method: "from_sky" (mean sky colour), "dcp" (dark channel prior), or "dcp_heuristic" (DCP with median heuristic).
seed	Random seed for reproducibility. null for non-deterministic.
depth_scale	Multiplier applied to depth values after loading.
resize_depth	Resize the depth map to match the RGB resolution (bilinear).
contrast_threshold	Threshold C_t used in the visibility-to-attenuation conversion (default 0.05).
device	"cpu", "cuda", "mps", or "gpu" (alias for cuda).
gpu_batch_size	Batch size when running on GPU. Uniform-model samples are batched; heterogeneous samples are processed individually.

Fog Model

The core equation is the Koschmieder model (atmospheric scattering):

I_fog(x) = I(x) * t(x)  +  L_s * (1 - t(x))

where:

• I(x) is the original RGB colour at pixel x
• t(x) = exp(-k * d(x)) is the transmittance, which falls exponentially with depth d and attenuation coefficient k
• L_s is the atmospheric light (airlight), i.e. the colour of the fog/sky light scattered towards the camera
• k is derived from a meteorological visibility distance V: k = -ln(C_t) / V

Distant objects are attenuated more (t approaches 0) and replaced by airlight, just as in real fog.

How Each Modality is Used

RGB — The clean scene image. Normalised to float32 in [0, 1]. This is the I(x) term in the fog equation -- it gets blended with the airlight according to transmittance.

Depth — A per-pixel depth map in metres. Provides the d(x) term in the transmittance calculation t(x) = exp(-k * d(x)). Pixels with greater depth receive more fog. Invalid values (NaN, inf, negative) are clamped to zero (treated as infinitely close, receiving no fog).

Semantic Segmentation — A per-pixel semantic segmentation map from which a boolean sky mask is derived, loaded via euler-loading's dataset-specific semantic_segmentation loader. The sky mask is used for airlight estimation when the airlight method is "from_sky": the mean RGB of all sky pixels in the clean image is used as the airlight colour L_s.

Intrinsics (optional) — When present, planar (z-buffer) depth is converted to radial (Euclidean) depth before fog is applied.

Airlight Estimation

The airlight config key selects how the atmospheric light L_s is estimated:

Method	Description
from_sky	Mean RGB of sky pixels in the clean image. Falls back to white [1, 1, 1] when no sky pixels exist.
dcp	Dark Channel Prior — selects the brightest pixel (by channel sum) among the top 0.1% darkest-channel pixels.
dcp_heuristic	DCP with median heuristic — selects the pixel closest to the median intensity (BT.601 grayscale) among the top 0.1% darkest-channel pixels.

GPU-native implementations (DCPAirlightTorch, DCPHeuristicAirlightTorch) are used automatically when running on GPU.

Model Selection

Each image is assigned a fog model via the selection block:

"selection": {
  "mode": "weighted",
  "weights": {
    "uniform": 1.0,
    "heterogeneous_k": 0.0,
    "heterogeneous_ls": 0.0,
    "heterogeneous_k_ls": 0.0
  }
}

• fixed mode: always use a single named model.
• weighted mode: randomly select a model per image according to normalised weights.

Four models are available:

Model	Description
uniform	Constant k and L_s. Standard homogeneous fog.
heterogeneous_k	Spatially-varying k, constant L_s. Simulates patchy fog / fog banks.
heterogeneous_ls	Constant k, spatially-varying L_s. Simulates scattered-light colour variation.
heterogeneous_k_ls	Both k and L_s vary spatially. Most expressive model.

Visibility Distribution

Each model specifies a visibility_m distribution from which a visibility distance (in metres) is sampled per image:

dist	Parameters	Description
constant	value	Fixed value.
uniform	min, max	Uniform random in range.
normal	mean, std, optional min/max	Gaussian, optionally clamped.
lognormal	mean, sigma, optional min/max	Log-normal.
choice	values, optional weights	Discrete weighted choice.

The sampled visibility V is converted to the attenuation coefficient: k = -ln(C_t) / V.

Heterogeneous Noise Fields

Both k_hetero and ls_hetero use Perlin FBM (fractional Brownian motion) to generate spatially-varying factor fields:

"k_hetero": {
  "scales": "auto",
  "min_scale": 2,
  "max_scale": null,
  "min_factor": 0.0,
  "max_factor": 1.0,
  "normalize_to_mean": true
}

The noise field (values in [0, 1]) is mapped to a factor field: factor(x) = min_factor + (max_factor - min_factor) * noise(x). When normalize_to_mean is true, the factor field is rescaled so its spatial mean equals 1.0, preserving the overall fog density while introducing spatial variation.

Parameter	Effect
min_factor / max_factor	Range of the multiplicative factor.
normalize_to_mean	Rescale factors so the image-wide mean equals the base value. Recommended for k_hetero.
scales / min_scale / max_scale	Control spatial frequency content.

Fog Output

Foggy images are saved as PNG files organised by model name:

<output_path>/
  uniform/
    beta_0.0374_airlight_0.353_0.784_1_rgb_00000.png
    config.json
  heterogeneous_k/
    ...

---

Sky-Depth Transform

Overrides depth values in sky regions with a configurable constant. Useful for datasets where sky depth is encoded as zero or infinity and needs to be normalised to a large finite value.

Sky-Depth Config

{
  "sky_depth_value": 1000.0
}

Field	Description
sky_depth_value	Depth value assigned to all sky pixels. Defaults to 1000.0.

Sky-Depth Output

Depth maps are saved as .npy float32 files preserving the original directory hierarchy.

---

Radial Transform

Converts planar (z-buffer) depth to radial (Euclidean) depth using camera intrinsics. For each pixel (u, v):

d_radial(u, v) = d_planar(u, v) * sqrt(((u - cx)/fx)^2 + ((v - cy)/fy)^2 + 1)

Radial Config

{}

No special parameters are required. The transform reads intrinsics from the intrinsics hierarchical modality.

Radial Output

Depth maps are saved as .npy float32 files preserving the original directory hierarchy.