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1908732 1a71d19 1908732 1a71d19 1908732 | 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 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 | import torch
import rasterio
from PIL import Image, ImageDraw, ImageFont
import os
import numpy as np
class InMemoryDataset(torch.utils.data.Dataset):
def __init__(self, data_list, preprocess_func):
self.data_list = data_list
self.preprocess_func = preprocess_func
def __getitem__(self, i):
return self.preprocess_func(self.data_list[i])
def __len__(self):
return len(self.data_list)
INPUT_SIZE = 224
PATCH_SIZE = 224
STRIDE = 224
root = 'datasets/WorldFloodsv2'
test_path_s2 = f'{root}/train/S2/'
test_path_labels = f'{root}/train/gt/'
extension = '.tif'
timor_leste_events = {
"EMSR507_AOI01_DEL_PRODUCT": "Pleiades-1A-1B",
"EMSR507_AOI02_DEL_PRODUCT": "PlanetScope",
"EMSR507_AOI03_DEL_PRODUCT": "PlanetScope",
"EMSR507_AOI05_DEL_PRODUCT": "Sentinel-2",
"EMSR507_AOI07_GRA_PRODUCT": "PlanetScope"
}
files_s2 = [(f"{test_path_s2}{event_id}{extension}", satellite)
for event_id, satellite in timor_leste_events.items()]
files_gt = [(f"{test_path_labels}{event_id}{extension}", satellite)
for event_id, satellite in timor_leste_events.items()]
output_root_s2 = "./datasets/Timor_Processed/S2"
os.makedirs(output_root_s2, exist_ok=True)
output_root_gt = "./datasets/Timor_Processed/GT"
os.makedirs(output_root_gt, exist_ok=True)
def sliding_window_crop(image, window_size=PATCH_SIZE, stride=STRIDE):
C, H, W = image.shape
patches = []
for y in range(0, H, stride):
for x in range(0, W, stride):
y_end = min(y + window_size, H)
x_end = min(x + window_size, W)
y_start = max(y_end - window_size, 0)
x_start = max(x_end - window_size, 0)
patch = image[:, y_start:y_end, x_start:x_end]
patches.append(patch)
return patches
def read_tif_as_tensor(tif_path):
with rasterio.open(tif_path) as src:
img = src.read() # shape: (bands, H, W)
img = torch.from_numpy(img).float()
return img
def save_patch_as_tif(patch_tensor, output_path):
patch_np = patch_tensor.numpy()
with rasterio.open(
output_path,
'w',
driver='GTiff',
height=patch_np.shape[1],
width=patch_np.shape[2],
count=patch_np.shape[0],
dtype=patch_np.dtype
) as dst:
dst.write(patch_np)
def plot_patches(patches, cols=5, save_path=None, is_label=False):
rows = (len(patches) + cols - 1) // cols
patch_images = []
font = ImageFont.load_default()
for idx, patch in enumerate(patches):
if is_label:
# Labels assumed to be single-channel
patch_np = patch[0].numpy()
patch_np = ((patch_np - patch_np.min()) / (patch_np.max() - patch_np.min() + 1e-8) * 255).astype(np.uint8)
img = Image.fromarray(patch_np).convert("L")
else:
# RGB visualization for images
patch_np = patch[:3].numpy()
patch_np = (patch_np - patch_np.min()) / (patch_np.max() - patch_np.min() + 1e-8) * 255
patch_np = patch_np.transpose(1,2,0).astype(np.uint8)
img = Image.fromarray(patch_np)
draw = ImageDraw.Draw(img)
draw.text((5,5), str(idx), fill=(255,0,0), font=font)
patch_images.append(img)
width, height = patch_images[0].size
grid_img = Image.new('RGB' if not is_label else 'L', (cols * width, rows * height), color=(255,255,255) if not is_label else 255)
for i, img in enumerate(patch_images):
row = i // cols
col = i % cols
grid_img.paste(img, (col*width, row*height))
if save_path:
grid_img.save(save_path)
# Class color map: 0=invalid, 1=land, 2=flood, 3=permanent water
CLASS_COLORS = {
0: (0, 0, 0), # black for invalid/no data
1: (34, 139, 34), # green for flood (gt)
2: (0, 0, 255), # blue for cloud (gt)
}
def plot_label_patches(label_patches, cols=5, save_path=None):
rows = (len(label_patches) + cols - 1) // cols
patch_images = []
font = ImageFont.load_default()
for idx, patch in enumerate(label_patches):
patch_np = patch[0].numpy().astype(int) # assume single channel
H, W = patch_np.shape
color_img = np.zeros((H, W, 3), dtype=np.uint8)
for cls, color in CLASS_COLORS.items():
color_img[patch_np == cls] = color
img = Image.fromarray(color_img)
draw = ImageDraw.Draw(img)
draw.text((5,5), str(idx), fill=(255,0,0), font=font)
patch_images.append(img)
width, height = patch_images[0].size
grid_img = Image.new('RGB', (cols * width, rows * height), color=(255,255,255))
for i, img in enumerate(patch_images):
row = i // cols
col = i % cols
grid_img.paste(img, (col*width, row*height))
if save_path:
grid_img.save(save_path)
for tif_path, satellite in files_s2:
print(f"Processing {tif_path} ({satellite})...")
img_tensor = read_tif_as_tensor(tif_path)
patches = sliding_window_crop(img_tensor, PATCH_SIZE, STRIDE)
base_name = os.path.splitext(os.path.basename(tif_path))[0]
patch_output_dir = os.path.join(output_root_s2, base_name)
os.makedirs(patch_output_dir, exist_ok=True)
# Save image patches
for idx, patch in enumerate(patches):
patch_name = f"{base_name}_{idx}.tif"
save_patch_as_tif(patch, os.path.join(patch_output_dir, patch_name))
# # Plot image patches
# plot_save_path = os.path.join(patch_output_dir, f"{base_name}_grid.png")
# plot_patches(patches, save_path=plot_save_path)
# # If labels exist in a corresponding folder
# label_path = tif_path.replace('/S2/', '/gt/') # assuming label folder structure
# if os.path.exists(label_path):
# label_tensor = read_tif_as_tensor(label_path)
# label_patches = sliding_window_crop(label_tensor, PATCH_SIZE, STRIDE)
# plot_label_path = os.path.join(patch_output_dir, f"{base_name}_labels_grid.png")
# plot_label_patches(label_patches, save_path=plot_label_path)
for tif_path, satellite in files_gt:
print(f"Processing {tif_path} ({satellite})...")
img_tensor = read_tif_as_tensor(tif_path)
patches = sliding_window_crop(img_tensor, PATCH_SIZE, STRIDE)
base_name = os.path.splitext(os.path.basename(tif_path))[0]
patch_output_dir = os.path.join(output_root_gt, base_name)
os.makedirs(patch_output_dir, exist_ok=True)
# Save image patches
for idx, patch in enumerate(patches):
patch_name = f"{base_name}_{idx}.tif"
save_patch_as_tif(patch, os.path.join(patch_output_dir, patch_name))
# # Plot image patches
# plot_save_path = os.path.join(patch_output_dir, f"{base_name}_grid.png")
# plot_patches(patches, save_path=plot_save_path)
# # If labels exist in a corresponding folder
# label_path = tif_path.replace('/S2/', '/gt/') # assuming label folder structure
# if os.path.exists(label_path):
# label_tensor = read_tif_as_tensor(label_path)
# label_patches = sliding_window_crop(label_tensor, PATCH_SIZE, STRIDE)
# plot_label_path = os.path.join(patch_output_dir, f"{base_name}_labels_grid.png")
# plot_label_patches(label_patches, save_path=plot_label_path)
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