Datasets:

paeslemesa
/

matchgeodem

import os
from pathlib import Path
import shutil
import random
from datetime import datetime

try:
    import rasterio
    from rasterio.windows import from_bounds
except ImportError:
    raise ImportError("This script requires rasterio. Install it with: pip install rasterio")

areas = {'ATA_MV', 'BRA_SP', 'CHN_WS', 'ESP_EH', 'FIN_LM', 'GER_BN', 'IDN_SV', 
         'KAZ_AC', 'KSA_WA', 'NAM_HF', 'NZL_KP', 'PHL_TA', 'USA_GC'}

data_path = Path("/home/sabrina/Documents/Tese/05_Dataset/MatchGeo-DEM-v1/data/")
tiny_path = Path("/home/sabrina/Documents/Tese/05_Dataset/MatchGeo-DEM-v1-tiny/data/")

# Configuration
SEED = 42
N = 5              # <-- n x n tiles contiguous subset
TILE_SIZE = 333    # Expected tile dimension in pixels
INCLUDE_ANNOTATIONS = True

random.seed(SEED)


def build_tile_grid(tiles_src):
    """
    Reads all tiles and builds a 2D grid based on their top-left geographic corners.
    Returns (grid, n_rows, n_cols) where grid[r][c] is a Path or None.
    """
    tile_info = []
    for tile_path in tiles_src.iterdir():
        if tile_path.suffix != '.tif':
            continue
        try:
            with rasterio.open(tile_path) as src:
                if src.width != TILE_SIZE or src.height != TILE_SIZE:
                    print(f"   ⚠️ {tile_path.name} is {src.width}x{src.height}, "
                          f"expected {TILE_SIZE}x{TILE_SIZE}")
                # Top-left corner in geo coordinates
                x, y = src.transform * (0, 0)
                tile_info.append((y, x, tile_path))
        except Exception as e:
            print(f"   ⚠️ Error reading {tile_path.name}: {e}")
            continue

    if not tile_info:
        return None, 0, 0

    # Pixel size from first tile to set clustering tolerance
    with rasterio.open(tile_info[0][2]) as src:
        pixel_size = max(abs(src.transform.a), abs(src.transform.e))

    # Tolerance: half the expected geo-distance between adjacent tile origins
    tol = TILE_SIZE * pixel_size * 0.5

    ys = [t[0] for t in tile_info]
    xs = [t[1] for t in tile_info]

    # Unique Y coordinates = rows (top to bottom, descending)
    unique_ys = []
    for y in sorted(ys, reverse=True):
        if not unique_ys or abs(y - unique_ys[-1]) > tol:
            unique_ys.append(y)

    # Unique X coordinates = columns (left to right, ascending)
    unique_xs = []
    for x in sorted(xs):
        if not unique_xs or abs(x - unique_xs[-1]) > tol:
            unique_xs.append(x)

    n_rows = len(unique_ys)
    n_cols = len(unique_xs)

    # Place each tile in the grid
    grid = [[None for _ in range(n_cols)] for _ in range(n_rows)]
    for y, x, path in tile_info:
        row_idx = min(range(n_rows), key=lambda i: abs(y - unique_ys[i]))
        col_idx = min(range(n_cols), key=lambda i: abs(x - unique_xs[i]))
        grid[row_idx][col_idx] = path

    return grid, n_rows, n_cols


# ------------------------------------------------------------------
print("=" * 80)
print("MatchGeo-DEM Tiny Dataset Generator — n×n Tile Subset")
print(f"Subset size: {N}x{N} tiles ({N*TILE_SIZE}x{N*TILE_SIZE} pixels)")
print(f"Output: {tiny_path}")
print("=" * 80)

total_copied = 0
total_size = 0

for location in sorted(areas):
    src_dir = data_path / location
    dst_dir = tiny_path / location

    if not src_dir.exists():
        print(f"\n⚠️  {location}: Source not found, skipping")
        continue

    dst_dir.mkdir(parents=True, exist_ok=True)
    print(f"\n📁 {location}:")

    # ------------------------------------------------------------------
    # 1. Build tile grid and select random n×n window
    # ------------------------------------------------------------------
    tiles_src = src_dir / "tiles"
    if not tiles_src.exists():
        print(f"   ⚠️ Tiles directory not found, skipping")
        continue

    grid, n_rows, n_cols = build_tile_grid(tiles_src)
    if grid is None:
        print(f"   ⚠️ No valid tiles found, skipping")
        continue

    print(f"   📐 Grid: {n_rows} rows × {n_cols} cols")

    # Clamp window to actual grid size
    win_h = min(N, n_rows)
    win_w = min(N, n_cols)
    max_row = n_rows - win_h
    max_col = n_cols - win_w

    start_row = random.randint(0, max_row) if max_row > 0 else 0
    start_col = random.randint(0, max_col) if max_col > 0 else 0
    end_row = start_row + win_h
    end_col = start_col + win_w

    if win_h < N or win_w < N:
        print(f"   ℹ️ Grid smaller than {N}x{N}; using {win_h}x{win_w} window")
    else:
        print(f"   🎯 Window: rows {start_row}-{end_row-1}, cols {start_col}-{end_col-1}")

    # Collect selected tiles
    selected_tiles = []
    for r in range(start_row, end_row):
        for c in range(start_col, end_col):
            if grid[r][c] is not None:
                selected_tiles.append(grid[r][c])

    if not selected_tiles:
        print(f"   ⚠️ No tiles in selected window, skipping")
        continue

    # ------------------------------------------------------------------
    # 2. Copy selected tiles
    # ------------------------------------------------------------------
    tiles_dst = dst_dir / "tiles"
    tiles_dst.mkdir(parents=True, exist_ok=True)
    selected_names = set()

    for tile_path in selected_tiles:
        dst = tiles_dst / tile_path.name
        shutil.copy2(tile_path, dst)
        total_size += tile_path.stat().st_size
        selected_names.add(tile_path.stem)

    total_copied += len(selected_tiles)
    expected = win_h * win_w
    if len(selected_tiles) < expected:
        print(f"   ✅ Tiles: {len(selected_tiles)}/{expected} copied (incomplete grid)")
    else:
        print(f"   ✅ Tiles: {len(selected_tiles)}/{expected} copied")

    # ------------------------------------------------------------------
    # 3. Crop merged DEM to the exact bounds of selected tiles
    # ------------------------------------------------------------------
    merged_src = src_dir / f"{location}.tif"
    merged_dst = dst_dir / f"{location}.tif"

    if merged_src.exists():
        # Union of selected tile bounds
        left = float('inf')
        bottom = float('inf')
        right = float('-inf')
        top = float('-inf')

        for tile_path in selected_tiles:
            with rasterio.open(tile_path) as src:
                b = src.bounds
                left = min(left, b.left)
                bottom = min(bottom, b.bottom)
                right = max(right, b.right)
                top = max(top, b.top)

        with rasterio.open(merged_src) as src:
            window = from_bounds(left, bottom, right, top, src.transform)
            window = window.round_lengths().round_offsets()

            profile = src.profile.copy()
            profile.update({
                'height': int(window.height),
                'width': int(window.width),
                'transform': src.window_transform(window)
            })

            with rasterio.open(merged_dst, 'w', **profile) as dst:
                dst.write(src.read(window=window))

        size = merged_dst.stat().st_size
        total_size += size
        print(f"   ✅ Cropped DEM: {size/1024/1024:.1f} MB "
              f"({int(window.width)}x{int(window.height)} px)")
    else:
        print(f"   ⚠️ Merged DEM not found")

    # ------------------------------------------------------------------
    # 4. Copy metadata (omit geojsons that no longer describe the subset)
    # ------------------------------------------------------------------
    for meta_file in [f"{location}_metadata.json", f"{location}.qmd"]:
        src = src_dir / meta_file
        dst = dst_dir / meta_file
        if src.exists():
            shutil.copy2(src, dst)
    print(f"   ✅ Metadata copied (extent/tiles geojsons omitted)")

    # ------------------------------------------------------------------
    # 5. Copy annotations for selected tiles only
    # ------------------------------------------------------------------
    anno_src = src_dir / "annotations"
    anno_dst = dst_dir / "annotations"

    if INCLUDE_ANNOTATIONS and anno_src.exists():
        anno_dst.mkdir(parents=True, exist_ok=True)
        copied_anno = 0

        for anno_file in anno_src.iterdir():
            if anno_file.suffix == '.json' and anno_file.stem in selected_names:
                dst = anno_dst / anno_file.name
                shutil.copy2(anno_file, dst)
                copied_anno += 1

        print(f"   ✅ Annotations: {copied_anno} copied")

# ------------------------------------------------------------------
# Summary
# ------------------------------------------------------------------
total_mb = total_size / (1024 * 1024)
total_gb = total_size / (1024 * 1024 * 1024)

print(f"\n{'='*80}")
print(f"📊 TINY DATASET SUMMARY:")
print(f"   Subset size: up to {N}x{N} tiles ({N*TILE_SIZE}x{N*TILE_SIZE} pixels)")
print(f"   Total tiles copied: {total_copied}")
print(f"   Total size: {total_mb:.1f} MB ({total_gb:.2f} GB)")
print(f"   Output path: {tiny_path}")
print(f"{'='*80}")

# Create README
tiny_readme = tiny_path.parent / "README.txt"
with open(tiny_readme, 'w') as f:
    f.write(f"""MatchGeo-DEM Tiny Dataset
=========================

This is a SPATIAL SUBSET of the full MatchGeo-DEM dataset.
Each city was cropped to a random contiguous {N}x{N} tile window.
Each tile is {TILE_SIZE}x{TILE_SIZE} pixels.
Total subset size per city: up to {N*TILE_SIZE}x{N*TILE_SIZE} pixels.

Configuration:
- Tiles per city: up to {N}x{N} = {N*N} tiles
- Tile size: {TILE_SIZE}x{TILE_SIZE} pixels
- Random seed: {SEED}
- Total tiles: {total_copied}
- Total size: {total_mb:.1f} MB

NOTE: The original _extent.geojson and _tiles.geojson files were omitted
because they no longer describe the cropped subset. Regenerate them from
the cropped DEM if your pipeline requires them.

For the full dataset, see:
https://doi.org/10.5281/zenodo.19339008

Last generated: {datetime.now().strftime('%Y-%m-%d')}
""")

print(f"\n✅ Tiny README saved to: {tiny_readme}")