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#!/usr/bin/env python3
"""
Utilities for loading and manipulating HDF5 dataset optimized for ML.
Features:
- Fast extraction by class, temporal period
- Create temporal sequences for LSTM/Transformer
- Automatic data normalization
- Filter by metadata (angle, resolution, etc.)
- Extract sliding windows for learning
- Support filtering by mask values
Learning scenarios:
1. Temporal stacking classification (group k-fold)
2. Temporal prediction LSTM (time series)
3. Domain adaptation HH vs HV
4. Domain adaptation PAZ vs TerraSAR-X
"""
import h5py
import numpy as np
import json
from typing import List, Dict, Tuple, Optional, Union
from tqdm import tqdm
from joblib import Parallel, delayed
class MLDatasetLoader:
"""Class to efficiently load the optimized HDF5 dataset with window extraction"""
def __init__(self, hdf5_path: str):
"""
Args:
hdf5_path: Path to HDF5 file
"""
self.hdf5_path = hdf5_path
self.file = None
self._load_metadata()
def _load_metadata(self):
"""Load metadata in memory for fast access"""
with h5py.File(self.hdf5_path, 'r') as f:
meta = f['metadata']
self.classes = json.loads(meta.attrs['classes'])
self.n_groups = meta.attrs['n_total_groups']
self.nodata = meta.attrs['nodata_value']
# Charger les index
self.class_index = {}
for class_name in f['index/by_class'].keys():
entries_json = f[f'index/by_class/{class_name}'].attrs['entries_json']
self.class_index[class_name] = json.loads(entries_json)
temp_ranges_json = f['index/temporal_ranges'].attrs['ranges_json']
self.temporal_ranges = json.loads(temp_ranges_json)
def __enter__(self):
"""Context manager entry"""
self.file = h5py.File(self.hdf5_path, 'r')
return self
def __exit__(self, exc_type, exc_val, exc_tb):
"""Context manager exit"""
if self.file:
self.file.close()
def get_group_info(self, group_name: str) -> Dict:
"""Get information for a group"""
with h5py.File(self.hdf5_path, 'r') as f:
if group_name not in f['data']:
raise ValueError(f"Group {group_name} not found")
group = f['data'][group_name]
return {
'class': group.attrs['class'],
'latitude': group.attrs['latitude'],
'longitude': group.attrs['longitude'],
'elevation': group.attrs['elevation'],
'orientation': group.attrs['orientation'],
'slope': group.attrs['slope'],
'orbits': list(group.keys())
}
def extract_windows(
self,
image: np.ndarray,
mask: np.ndarray,
window_size: int,
stride: Optional[int] = None,
max_mask_value: int = 3,
max_mask_percentage: float = 100.0,
min_valid_percentage: float = 50.0,
skip_optim_offset: bool = False
) -> Tuple[np.ndarray, np.ndarray, List[Tuple[int, int]]]:
"""
Extract windows from an image with mask filtering.
Automatically optimize starting positions to maximize the number of valid windows.
Args:
image: Image (H, W) or (H, W, C) or (H, W, T) or (H, W, C, T)
mask: Mask (H, W) or (H, W, T)
window_size: Window size (square)
stride: Stride step (if None, = window_size for non-overlapping)
max_mask_value: Maximum accepted mask value (0, 1, 2, 3)
max_mask_percentage: Max percentage of pixels with mask > max_mask_value
min_valid_percentage: Min percentage of valid pixels (non nodata)
skip_optim_offset: If True, skip offset optimization and use (0, 0)
Returns:
windows: Array of extracted windows
window_masks: Array of corresponding masks
positions: List of (y, x) positions for each window
"""
if stride is None:
stride = window_size
# Handle dimensions
if image.ndim == 2: # (H, W)
h, w = image.shape
has_channels = False
has_time = False
elif image.ndim == 3: # (H, W, C) ou (H, W, T)
h, w, c = image.shape
has_channels = True
has_time = False
elif image.ndim == 4: # (H, W, C, T)
h, w, c, t = image.shape
has_channels = True
has_time = True
if mask.ndim == 2:
mask_has_time = False
elif mask.ndim == 3:
mask_has_time = True
# =====================================================================
# OPTIMIZATION: Find the best starting offset (start_y, start_x)
# to maximize the number of valid windows
# =====================================================================
if skip_optim_offset:
# Skip optimization and use (0, 0) directly
best_start_y = 0
best_start_x = 0
best_count = -1 # Unknown count when skipping optimization
else:
def count_valid_windows(start_y, start_x):
"""Count the number of valid windows for a given offset"""
count = 0
for y in range(start_y, h - window_size + 1, stride):
for x in range(start_x, w - window_size + 1, stride):
# Extract window for testing
if mask_has_time:
window_mask = mask[y:y+window_size, x:x+window_size, :]
else:
window_mask = mask[y:y+window_size, x:x+window_size]
# Check mask criterion
if mask_has_time:
bad_pixels = np.any(window_mask > max_mask_value, axis=-1)
else:
bad_pixels = window_mask > max_mask_value
bad_percentage = (np.sum(bad_pixels) / (window_size * window_size)) * 100.0
# Check nodata
if image.ndim == 2:
window = image[y:y+window_size, x:x+window_size]
elif image.ndim == 3:
window = image[y:y+window_size, x:x+window_size, :]
elif image.ndim == 4:
window = image[y:y+window_size, x:x+window_size, :, :]
# Check nodata and NaN (handle both invalid value types)
if has_time:
# Check if pixels are nodata OR NaN
is_invalid = (window == self.nodata) | np.isnan(window)
valid_pixels = np.all(~is_invalid, axis=-1)
if has_channels:
valid_pixels = np.all(valid_pixels, axis=-1)
valid_percentage = (np.sum(valid_pixels) / (window_size * window_size)) * 100.0
else:
if has_channels:
is_invalid = (window == self.nodata) | np.isnan(window)
valid_pixels = np.all(~is_invalid, axis=-1)
else:
is_invalid = (window == self.nodata) | np.isnan(window)
valid_pixels = ~is_invalid
valid_percentage = (np.sum(valid_pixels) / (window_size * window_size)) * 100.0
# Count if criteria are met
if bad_percentage <= max_mask_percentage and valid_percentage >= min_valid_percentage:
count += 1
return count
# Test all possible offsets (0 to stride-1 for each dimension)
# OPTIMIZATION: Parallelization with joblib
best_count = 0
best_start_y = 0
best_start_x = 0
# Limit search to stride (or window_size if non-overlapping)
max_offset = min(stride, window_size)
# Create list of all offsets to test
offsets_to_test = []
for start_y in range(max_offset):
for start_x in range(max_offset):
if start_y + window_size <= h and start_x + window_size <= w:
offsets_to_test.append((start_y, start_x))
# Calculate in parallel the number of valid windows for each offset
counts = Parallel(n_jobs=-1)(
delayed(count_valid_windows)(start_y, start_x)
for start_y, start_x in tqdm(offsets_to_test, desc="Optimizing offset", leave=False)
)
# Find the best offset
if len(counts) > 0:
best_idx = np.argmax(counts)
best_start_y, best_start_x = offsets_to_test[best_idx]
best_count = counts[best_idx]
# =====================================================================
# EXTRACTION with the best offset found
# =====================================================================
windows = []
window_masks = []
positions = []
# Sweep the image with optimal offset
for y in range(best_start_y, h - window_size + 1, stride):
for x in range(best_start_x, w - window_size + 1, stride):
# Extract window
if image.ndim == 2:
window = image[y:y+window_size, x:x+window_size]
elif image.ndim == 3:
window = image[y:y+window_size, x:x+window_size, :]
elif image.ndim == 4:
window = image[y:y+window_size, x:x+window_size, :, :]
if mask_has_time:
window_mask = mask[y:y+window_size, x:x+window_size, :]
else:
window_mask = mask[y:y+window_size, x:x+window_size]
# Check mask criterion
# Pixels with mask > max_mask_value
if mask_has_time:
bad_pixels = np.any(window_mask > max_mask_value, axis=-1)
else:
bad_pixels = window_mask > max_mask_value
bad_percentage = (np.sum(bad_pixels) / (window_size * window_size)) * 100.0
# Check nodata and NaN (handle both invalid value types)
if has_time:
# Check if pixels are nodata OR NaN
is_invalid = (window == self.nodata) | np.isnan(window)
valid_pixels = np.all(~is_invalid, axis=-1)
if has_channels:
valid_pixels = np.all(valid_pixels, axis=-1)
valid_percentage = (np.sum(valid_pixels) / (window_size * window_size)) * 100.0
else:
if has_channels:
is_invalid = (window == self.nodata) | np.isnan(window)
valid_pixels = np.all(~is_invalid, axis=-1)
else:
is_invalid = (window == self.nodata) | np.isnan(window)
valid_pixels = ~is_invalid
valid_percentage = (np.sum(valid_pixels) / (window_size * window_size)) * 100.0
# Accept window if criteria are met (use strict inequalities for determinism)
if bad_percentage <= max_mask_percentage and valid_percentage >= min_valid_percentage:
# Ensure float32 type and keep NaN values as-is
window = window.astype(np.float32)
windows.append(window)
window_masks.append(window_mask)
positions.append((y, x))
if len(windows) == 0:
return None, None, []
return np.array(windows), np.array(window_masks), positions
def load_data(
self,
group_name: str,
orbit: str = 'DSC',
polarisation: Union[str, List[str]] = 'HH',
start_date: Optional[str] = None,
end_date: Optional[str] = None,
normalize: bool = False,
remove_nodata: bool = True,
scale_type: str = 'intensity'
) -> Dict:
"""
Load data for a specific group.
Args:
group_name: Group name (e.g. 'ABL001')
orbit: 'ASC' or 'DSC'
polarisation: 'HH', 'HV' or ['HH', 'HV'] for dual-pol
start_date: Start date (format 'YYYYMMDD')
end_date: End date (format 'YYYYMMDD')
normalize: If True, normalize with pre-calculated stats
remove_nodata: If True, replace nodata with NaN
scale_type: 'intensity' (default), 'amplitude' (data**0.5), or 'log10' (log10 scale)
Returns:
Dict containing: images, masks, timestamps, metadata
"""
with h5py.File(self.hdf5_path, 'r') as f:
# Support dual-pol
if isinstance(polarisation, list):
# Dual-pol: load HH and HV with aligned timestamps
data_list = []
for pol in polarisation:
path = f'data/{group_name}/{orbit}/{pol}'
if path not in f:
raise ValueError(f"Path {path} not found in dataset")
data_list.append(f[path])
# Trouver les timestamps communs
timestamps_hh = data_list[0]['timestamps'][:]
timestamps_hv = data_list[1]['timestamps'][:]
# Intersection of timestamps
common_ts = np.intersect1d(timestamps_hh, timestamps_hv)
if len(common_ts) == 0:
raise ValueError(f"No common timestamps between HH and HV for {group_name}")
# Filter by dates if specified
if start_date or end_date:
mask_ts = np.ones(len(common_ts), dtype=bool)
if start_date:
mask_ts &= common_ts >= start_date.encode('utf-8')
if end_date:
mask_ts &= common_ts <= end_date.encode('utf-8')
common_ts = common_ts[mask_ts]
if len(common_ts) == 0:
raise ValueError(f"No data in specified date range")
# Load data for common timestamps
images_list = []
masks_list = []
angles_list = []
# First, determine minimum dimensions
min_h, min_w = None, None
for pol, data_pol in zip(polarisation, data_list):
ts_pol = data_pol['timestamps'][:]
indices = [np.where(ts_pol == ts)[0][0] for ts in common_ts]
img_pol = data_pol['images'][:, :, indices]
h, w, t = img_pol.shape
if min_h is None:
min_h, min_w = h, w
else:
min_h = min(min_h, h)
min_w = min(min_w, w)
# Load and crop
for pol, data_pol in zip(polarisation, data_list):
# Find indices
ts_pol = data_pol['timestamps'][:]
indices = [np.where(ts_pol == ts)[0][0] for ts in common_ts]
# Load and crop to min_h, min_w
img_pol = data_pol['images'][:min_h, :min_w, indices]
mask_pol = data_pol['masks'][:min_h, :min_w, indices]
images_list.append(img_pol)
masks_list.append(mask_pol)
if pol == polarisation[0]: # Only once
angles_list = data_pol['angles_incidence'][:][indices]
# Stack in last dimension: (H, W, T, 2)
images = np.stack(images_list, axis=-1)
# Mask: take max between HH and HV
masks = np.maximum(masks_list[0], masks_list[1])
timestamps = common_ts
angles = angles_list
metadata = {
'polarisation': polarisation,
'dual_pol': True
}
else:
# Single pol
path = f'data/{group_name}/{orbit}/{polarisation}'
if path not in f:
raise ValueError(f"Path {path} not found in dataset")
pol_data = f[path]
# Load data
images = pol_data['images'][:]
masks = pol_data['masks'][:]
timestamps = pol_data['timestamps'][:]
angles = pol_data['angles_incidence'][:]
# Filter by dates
if start_date or end_date:
mask_ts = np.ones(len(timestamps), dtype=bool)
if start_date:
mask_ts &= timestamps >= start_date.encode('utf-8')
if end_date:
mask_ts &= timestamps <= end_date.encode('utf-8')
if not np.any(mask_ts):
raise ValueError(f"No data in specified date range")
images = images[:, :, mask_ts]
masks = masks[:, :, mask_ts]
timestamps = timestamps[mask_ts]
angles = angles[mask_ts]
metadata = {
'mean': pol_data.attrs['stat_mean'],
'std': pol_data.attrs['stat_std'],
'min': pol_data.attrs['stat_min'],
'max': pol_data.attrs['stat_max'],
'n_samples': pol_data.attrs['n_timestamps'],
'polarisation': polarisation,
'dual_pol': False
}
# Replace nodata BEFORE applying transformations
if remove_nodata:
images = np.where(images == self.nodata, np.nan, images)
# Apply scale transformation
if scale_type == 'amplitude':
# Use sqrt and handle NaN properly
images_transformed = np.where(images >= 0, np.sqrt(images), np.nan)
images = images_transformed.astype(np.float32)
elif scale_type == 'log10':
# Handle both NaN and non-positive values
images = np.where(images > 0, np.log10(images), np.nan)
# If 'intensity', keep as is (default)
if normalize and not isinstance(polarisation, list):
mean = metadata['mean']
std = metadata['std']
if std > 0:
images = (images - mean) / std
return {
'images': images,
'masks': masks,
'timestamps': [t.decode('utf-8') for t in timestamps],
'angles_incidence': angles,
'metadata': metadata,
'group': group_name,
'orbit': orbit
}
def get_groups_by_class(self, class_name: str) -> List[str]:
"""Return the list of groups for a given class"""
if class_name not in self.class_index:
return []
return [entry['group'] for entry in self.class_index[class_name]]
def get_all_groups_with_classes(self) -> Dict[str, str]:
"""Return a dictionary {group_name: class_name}"""
group_to_class = {}
for class_name in self.classes:
for group in self.get_groups_by_class(class_name):
group_to_class[group] = class_name
return group_to_class
def get_statistics_summary(self) -> Dict:
"""Return a summary of dataset statistics"""
stats = {
'by_class': {},
'global': {
'n_groups': self.n_groups,
'n_classes': len(self.classes),
}
}
# Stats by class
for class_name in self.classes:
groups = self.get_groups_by_class(class_name)
stats['by_class'][class_name] = {
'n_groups': len(groups),
'groups': groups
}
return stats
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