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)