sen2_statistics.py

import os
import rasterio
import numpy as np
from tqdm import tqdm

USED_BANDS = (1, 2, 3, 8, 11, 12)

def get_category(use: str, data_root: str):
    """Get file paths for specified data type"""
    if use == 'gt':
        data_dir = f"{data_root}/GT"
    elif use == 's2':
        data_dir = f"{data_root}/S2"
    else:
        raise ValueError(f"Invalid use type: {use}. Must be 'gt' or 's2'")
    
    aoi_configs = [
        ("AOI01", "DEL", "pleiades"),
        ("AOI02", "DEL", "planet"),
        ("AOI03", "DEL", "planet"),
        ("AOI05", "DEL", "sentinel2"),
        ("AOI07", "GRA", "planet"),
    ]
    
    def read_include(path: str) -> list[str]:
        if not os.path.exists(path):
            return []
        with open(path, 'r') as file:
            return [line.strip() for line in file if line.strip()]
    
    result = {}
    for aoi_id, product_type, satellite in aoi_configs:
        aoi_num = aoi_id[-2:]
        aoi_dir = f"{data_dir}/EMSR507_{aoi_id}_{product_type}_PRODUCT"
        include_path = f"{aoi_dir}/include.txt"
        include_items = read_include(include_path)
        tif_files = [
            f"{aoi_dir}/EMSR507_{aoi_id}_{product_type}_PRODUCT_{item}.tif"
            for item in include_items
        ]
        key = f"tif{aoi_num}_{'gt' if use == 'gt' else 's2'}"
        result[key] = tif_files
    
    return result

def calculate_s2_statistics(data_root: str, used_bands=USED_BANDS):
    print("Getting S2 file paths...")
    s2_dict = get_category(use='s2', data_root=data_root)
    
    all_files = []
    for key, file_list in s2_dict.items():
        all_files.extend(file_list)
    
    all_files = [f for f in all_files if os.path.exists(f)]
    
    band_indices = [b - 1 for b in used_bands]
    num_bands = len(band_indices)
    
    count = 0
    mean = np.zeros(num_bands, dtype=np.float64)
    m2 = np.zeros(num_bands, dtype=np.float64)
    min_vals = np.full(num_bands, np.inf, dtype=np.float64)
    max_vals = np.full(num_bands, -np.inf, dtype=np.float64)
    
    for file_path in tqdm(all_files, desc="Computing statistics"):
        try:
            with rasterio.open(file_path) as src:
                data = src.read()
                
                if data.shape[0] < max(used_bands):
                    print(f"\nWarning: {os.path.basename(file_path)} has only {data.shape[0]} bands, skipping...")
                    continue
                
                selected_bands = data[band_indices, :, :]
                num_pixels = selected_bands.shape[1] * selected_bands.shape[2]
                pixels = selected_bands.reshape(num_bands, -1)
                
                for i in range(num_pixels):
                    pixel_values = pixels[:, i]
                    
                    if np.any(np.isnan(pixel_values)) or np.any(np.isinf(pixel_values)):
                        continue
                    
                    count += 1
                    delta = pixel_values - mean
                    mean += delta / count
                    delta2 = pixel_values - mean
                    m2 += delta * delta2
                    
                    min_vals = np.minimum(min_vals, pixel_values)
                    max_vals = np.maximum(max_vals, pixel_values)
                    
        except Exception as e:
            print(f"\nError processing {os.path.basename(file_path)}: {e}")
            continue
    
    if count == 0:
        raise ValueError("No valid pixels found in dataset!")
    
    variance = m2 / count
    std = np.sqrt(variance)
    
    mean_normalized = (mean - min_vals) / (max_vals - min_vals)
    std_normalized = std / (max_vals - min_vals)
    
    print("\nFormatted for code:")
    print(f"MEANS = [{', '.join([f'{m:.8f}' for m in mean_normalized])}]")
    print(f"STDS  = [{', '.join([f'{s:.8f}' for s in std_normalized])}]")
    
    return mean_normalized.tolist(), std_normalized.tolist()

if __name__ == "__main__":
    data_root = "./datasets/Timor_ML4FLood"
    means, stds = calculate_s2_statistics(data_root)