File size: 3,031 Bytes
80e070e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | # D:\RockGlacier_Project\harmonize_dataset.py
import os
import glob
import rasterio
from rasterio.windows import Window
import numpy as np
import pandas as pd
FEATURE_DIR = r"D:\RockGlacier_Project\features"
MASK_DIR = r"D:\RockGlacier_Project\masks"
H_FEATURE_DIR = r"D:\RockGlacier_Project\harmonized_features"
H_MASK_DIR = r"D:\RockGlacier_Project\harmonized_masks"
os.makedirs(H_FEATURE_DIR, exist_ok=True)
os.makedirs(H_MASK_DIR, exist_ok=True)
feature_files = sorted(glob.glob(os.path.join(FEATURE_DIR, 'feature_*.tif')))
mask_files = sorted(glob.glob(os.path.join(MASK_DIR, 'mask_*.tif')))
def get_idx(filepath):
return os.path.basename(filepath).split('_')[1].split('.')[0]
mask_lookup = {get_idx(p): p for p in mask_files}
results = []
print(f"Found {len(feature_files)} feature files and {len(mask_files)} mask files.")
print("Starting harmonization process...")
for f_path in feature_files:
idx = get_idx(f_path)
m_path = mask_lookup.get(idx)
if not m_path:
continue
with rasterio.open(f_path) as f_src, rasterio.open(m_path) as m_src:
# Determine the minimum common shape
min_h = min(f_src.shape[0], m_src.shape[0])
min_w = min(f_src.shape[1], m_src.shape[1])
f_window = Window(0, 0, min_w, min_h)
m_window = Window(0, 0, min_w, min_h)
f_data = f_src.read(window=f_window)
m_data = m_src.read(1, window=m_window)
f_meta = f_src.meta.copy()
m_meta = m_src.meta.copy()
# Calculate new transform for the window
f_transform = f_src.window_transform(f_window)
f_meta.update({
'height': min_h,
'width': min_w,
'transform': f_transform
})
# Enforce exact match for mask meta based on feature
m_meta.update({
'height': min_h,
'width': min_w,
'transform': f_transform
})
out_f = os.path.join(H_FEATURE_DIR, f"feature_{idx}.tif")
out_m = os.path.join(H_MASK_DIR, f"mask_{idx}.tif")
with rasterio.open(out_f, 'w', **f_meta) as dest:
dest.write(f_data)
with rasterio.open(out_m, 'w', **m_meta) as dest:
dest.write(m_data, 1)
results.append({
'idx': idx,
'orig_f_shape': f_src.shape,
'orig_m_shape': m_src.shape,
'harmonized_shape': (min_h, min_w),
'CRS_match': f_src.crs == m_src.crs
})
df = pd.DataFrame(results)
print("\n--- Harmonization Complete ---")
print(f"Total processed pairs: {len(df)}")
mismatch_count = len(df[df['orig_f_shape'] != df['orig_m_shape']])
print(f"Pairs that required cropping: {mismatch_count}")
print("\nSample metadata summary:")
print(df.head())
summary_csv = os.path.join("D:\\RockGlacier_Project", "harmonization_summary.csv")
df.to_csv(summary_csv, index=False)
print(f"\nFull metadata summary saved to {summary_csv}")
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