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#!/usr/bin/env python3
"""
benchmark_sieve.py

Benchmark sieving, polygonizing, and simplifying GeoTIFFs using two methods:
  - "gdal": subprocess calls to gdal_sieve.py, gdal_polygonize.py, and ogr2ogr
  - "python": pure Python using rasterio.features, shapely, and fiona
"""

import os

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class RCFSegmentationFeatures(RCF):
    def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
        """Forward pass of the RCF model.

        Args:
            x: a tensor with shape (C, H, W)
            y: a tensor with shape (H, W)

        Returns:
            a tensor of size (``self.num_features``)

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def check_if_scene_is_cloudy_at_box(item: pystac.Item, box: shapely.geometry.Polygon):
    """Uses the S2 Scene Classification Layer (SCL) to determine if a S2 L2A scene is cloudy at a given bbox.

    Args:
        item (pystac.Item): The S2 L2A item to check
        box (shapely.geometry.Polygon): The geometry to check (should be EPSG:4326)

    Returns:
        float: The fraction of the box that is classified as "Cloud medium probability" + "Cloud high probability"
    """

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import torch

class RunningStatsButFast(torch.nn.Module):
    def __init__(self, shape, dims):
        """Initializes the RunningStatsButFast method.
        
        A PyTorch module that can be put on the GPU and calculate the multidimensional
        mean and variance of inputs online in a numerically stable way. This is useful
        for calculating the channel-wise mean and variance of a big dataset because you
        don't have to load the entire dataset into memory.

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# Requires the `segment-geospatial` package https://samgeo.gishub.org/
import argparse
import os

import cv2
import numpy as np
import rasterio
import rasterio.features
import rasterio.transform
import rasterio.windows