Duplicate from XavierJiezou/cloud-adapter-datasets

Co-authored-by: XavierJiezou <XavierJiezou@users.noreply.huggingface.co>

.gitattributes

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+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
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+*.jpg filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+license: cc-by-nc-4.0
+task_categories:
+- image-segmentation
+language:
+- en
+tags:
+- Cloud Detection
+- Cloud Segmentation
+- Remote Sensing Images
+- Satellite Images
+- HRC-WHU
+- CloudSEN12-High
+- GF12MS-WHU
+- L8-Biome
+---
+
+# Cloud-Adapter-Datasets
+
+This dataset card aims to describe the datasets used in the [Cloud-Adapter](https://github.com/XavierJiezou/cloud-adapter), a collection of high-resolution satellite images and semantic segmentation masks for cloud detection and related tasks.
+
+## Install
+
+```bash
+pip install huggingface-hub
+```
+
+## Usage
+
+```bash
+# Step 1: Download datasets
+huggingface-cli download --repo-type dataset XavierJiezou/cloud-adapter-datasets --local-dir data --include hrc_whu.zip 
+huggingface-cli download --repo-type dataset XavierJiezou/cloud-adapter-datasets --local-dir data --include gf12ms_whu_gf1.zip
+huggingface-cli download --repo-type dataset XavierJiezou/cloud-adapter-datasets --local-dir data --include gf12ms_whu_gf2.zip
+huggingface-cli download --repo-type dataset XavierJiezou/cloud-adapter-datasets --local-dir data --include cloudsen12_high_l1c.zip
+huggingface-cli download --repo-type dataset XavierJiezou/cloud-adapter-datasets --local-dir data --include cloudsen12_high_l2a.zip
+huggingface-cli download --repo-type dataset XavierJiezou/cloud-adapter-datasets --local-dir data --include l8_biome.zip
+
+# Step 2: Extract datasets
+unzip hrc_whu.zip -d hrc_whu
+unzip gf12ms_whu_gf1.zip -d gf12ms_whu_gf1
+unzip gf12ms_whu_gf2.zip -d gf12ms_whu_gf2
+unzip cloudsen12_high_l1c.zip -d cloudsen12_high_l1c
+unzip cloudsen12_high_l2a.zip -d cloudsen12_high_l2a
+unzip l8_biome.zip -d l8_biome
+```
+
+## Example
+
+```python
+import os
+import zipfile
+from huggingface_hub import hf_hub_download
+
+# Define the dataset repository
+repo_id = "XavierJiezou/Cloud-Adapter"
+# Select the zip file of the dataset to download
+zip_files = [
+    "hrc_whu.zip",
+    # "gf12ms_whu_gf1.zip",
+    # "gf12ms_whu_gf2.zip",
+    # "cloudsen12_high_l1c.zip",
+    # "cloudsen12_high_l2a.zip",
+    # "l8_biome.zip",
+]
+
+# Define a directory to extract the datasets
+output_dir = "cloud_adapter_paper_data"
+
+# Ensure the output directory exists
+os.makedirs(output_dir, exist_ok=True)
+
+# Step 1: Download and extract each ZIP file
+for zip_file in zip_files:
+    print(f"Downloading {zip_file}...")
+    # Download the ZIP file from Hugging Face Hub
+    zip_path = hf_hub_download(repo_id=repo_id, filename=zip_file, repo_type="dataset")
+    
+    # Extract the ZIP file
+    extract_path = os.path.join(output_dir, zip_file.replace(".zip", ""))
+    with zipfile.ZipFile(zip_path, "r") as zip_ref:
+        print(f"Extracting {zip_file} to {extract_path}...")
+        zip_ref.extractall(extract_path)
+
+# Step 2: Explore the extracted datasets
+# Example: Load and display the contents of the "hrc_whu" dataset
+dataset_path = os.path.join(output_dir, "hrc_whu")
+train_images_path = os.path.join(dataset_path, "img_dir", "train")
+train_annotations_path = os.path.join(dataset_path, "ann_dir", "train")
+
+# Display some files in the training set
+print("Training Images:", os.listdir(train_images_path)[:5])
+print("Training Annotations:", os.listdir(train_annotations_path)[:5])
+
+# Example: Load and display an image and its annotation
+from PIL import Image
+
+# Load an example image and annotation
+image_path = os.path.join(train_images_path, os.listdir(train_images_path)[0])
+annotation_path = os.path.join(train_annotations_path, os.listdir(train_annotations_path)[0])
+
+# Open and display the image
+image = Image.open(image_path)
+annotation = Image.open(annotation_path)
+
+print("Displaying the image...")
+image.show()
+
+print("Displaying the annotation...")
+annotation.show()
+```
+
+## Source Data
+
+- hrc_whu: https://github.com/dr-lizhiwei/HRC_WHU
+- gf12ms_whu: https://github.com/whu-ZSC/GF1-GF2MS-WHU
+- cloudsen12_high: https://huggingface.co/datasets/csaybar/CloudSEN12-high
+- l8_biome: https://landsat.usgs.gov/landsat-8-cloud-cover-assessment-validation-data
+
+## Citation
+
+```bib
+@article{hrc_whu,
+title = {Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors},
+journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
+volume = {150},
+pages = {197-212},
+year = {2019},
+author = {Zhiwei Li and Huanfeng Shen and Qing Cheng and Yuhao Liu and Shucheng You and Zongyi He},
+}
+
+@article{gf12ms_whu,
+  author={Zhu, Shaocong and Li, Zhiwei and Shen, Huanfeng},
+  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
+  title={Transferring Deep Models for Cloud Detection in Multisensor Images via Weakly Supervised Learning}, 
+  year={2024},
+  volume={62},
+  pages={1-18},
+}
+
+@article{cloudsen12_high,
+  title={CloudSEN12, a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2},
+  author={Aybar, Cesar and Ysuhuaylas, Luis and Loja, Jhomira and Gonzales, Karen and Herrera, Fernando and Bautista, Lesly and Yali, Roy and Flores, Angie and Diaz, Lissette and Cuenca, Nicole and others},
+  journal={Scientific data},
+  volume={9},
+  number={1},
+  pages={782},
+  year={2022},
+}
+
+@article{l8_biome,
+    title = {Cloud detection algorithm comparison and validation for operational Landsat data products},
+    journal = {Remote Sensing of Environment},
+    volume = {194},
+    pages = {379-390},
+    year = {2017},
+    author = {Steve Foga and Pat L. Scaramuzza and Song Guo and Zhe Zhu and Ronald D. Dilley and Tim Beckmann and Gail L. Schmidt and John L. Dwyer and M. {Joseph Hughes} and Brady Laue}
+}
+```
+
+## Contact
+
+For questions, please contact Xavier Jiezou at xuechaozou (at) foxmail (dot) com.

cloudsen12_high_l1c.zip

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cloudsen12_high_l2a.zip

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gf12ms_whu_gf1.zip

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gf12ms_whu_gf2.zip

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give_colors_to_mask.py

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+import os
+import numpy as np
+from PIL import Image
+from tqdm import tqdm
+from concurrent.futures import ThreadPoolExecutor
+
+# Define the function to retrieve the color palette for a given dataset
+def get_palette(dataset_name: str):
+    if dataset_name in ["cloudsen12_high_l1c", "cloudsen12_high_l2a"]:
+        return [79, 253, 199, 77, 2, 115, 251, 255, 41, 221, 53, 223]
+    if dataset_name == "l8_biome":
+        return [79, 253, 199, 221, 53, 223, 251, 255, 41, 77, 2, 115]
+    if dataset_name in ["gf12ms_whu_gf1", "gf12ms_whu_gf2", "hrc_whu"]:
+        return [79, 253, 199, 77, 2, 115]
+    raise Exception("dataset_name not supported")
+
+# Function to apply the color palette to a mask
+def give_colors_to_mask(mask: np.ndarray, colors=None) -> np.ndarray:
+    """Convert a mask to a colorized version using the specified palette."""
+    im = Image.fromarray(mask.astype(np.uint8)).convert("P")
+    im.putpalette(colors)
+    return im
+
+# Function to process a single file
+def process_file(file_path, palette):
+    try:
+        # Load the mask
+        mask = np.array(Image.open(file_path))
+
+        # Apply the color palette
+        colored_mask = give_colors_to_mask(mask, palette)
+
+        # Save the colored mask, overwriting the original file
+        colored_mask.save(file_path)
+        return True
+    except Exception as e:
+        print(f"Error processing {file_path}: {e}")
+        return False
+
+# Main processing function for a dataset
+def process_dataset(dataset_name, base_root, progress_bar):
+    ann_dir = os.path.join(base_root, dataset_name, "ann_dir")
+    if not os.path.exists(ann_dir):
+        print(f"Annotation directory does not exist for {dataset_name}: {ann_dir}")
+        return
+
+    # Get the color palette for this dataset
+    palette = get_palette(dataset_name)
+
+    # Gather all files to process
+    files_to_process = []
+    for split in ["train", "val", "test"]:
+        split_dir = os.path.join(ann_dir, split)
+        if not os.path.exists(split_dir):
+            print(f"Split directory does not exist for {dataset_name}: {split_dir}")
+            continue
+
+        # Add all png files in the directory to the list
+        for file_name in os.listdir(split_dir):
+            if file_name.endswith(".png"):
+                files_to_process.append(os.path.join(split_dir, file_name))
+
+    # Multi-threaded processing
+    with ThreadPoolExecutor() as executor:
+        results = list(tqdm(
+            executor.map(lambda f: process_file(f, palette), files_to_process),
+            total=len(files_to_process),
+            desc=f"Processing {dataset_name}",
+            leave=False
+        ))
+
+    # Update the progress bar
+    progress_bar.update(len(files_to_process))
+
+    print(f"{dataset_name}: Processed {sum(results)} files out of {len(files_to_process)}.")
+
+# Define the root directory and datasets
+base_root = "data"  # Replace with your datasets' root directory
+dataset_names = [
+    "cloudsen12_high_l1c",
+    "cloudsen12_high_l2a",
+    "gf12ms_whu_gf1",
+    "gf12ms_whu_gf2",
+    "hrc_whu",
+    "l8_biome"
+]
+
+# Main script
+if __name__ == "__main__":
+    # Calculate total number of files for all datasets
+    total_files = 0
+    for dataset_name in dataset_names:
+        ann_dir = os.path.join(base_root, dataset_name, "ann_dir")
+        for split in ["train", "val", "test"]:
+            split_dir = os.path.join(ann_dir, split)
+            if os.path.exists(split_dir):
+                total_files += len([f for f in os.listdir(split_dir) if f.endswith(".png")])
+
+    # Create a progress bar
+    with tqdm(total=total_files, desc="Overall Progress") as progress_bar:
+        for dataset_name in dataset_names:
+            process_dataset(dataset_name, base_root, progress_bar)

hrc_whu.zip

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+size 115524658

l8_biome.zip

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upload_zip_to_hub.py

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+import os
+import zipfile
+from huggingface_hub import HfApi, HfFolder
+
+# Define the root directory containing all datasets
+base_root = "data"  # Replace with the directory containing all datasets
+dataset_repo = "XavierJiezou/Cloud-Adapter"  # Hugging Face repository name
+dataset_names = [
+    "hrc_whu",
+    "gf12ms_whu_gf1",
+    "gf12ms_whu_gf2",
+    "cloudsen12_high_l1c",
+    "cloudsen12_high_l2a",
+    "l8_biome",
+]
+
+# Function to create a ZIP file for a dataset directory
+def create_zip(dataset_path, output_path):
+    """
+    Compress a dataset directory into a ZIP file.
+
+    Args:
+        dataset_path (str): Path to the dataset directory.
+        output_path (str): Path to save the ZIP file.
+    """
+    with zipfile.ZipFile(output_path, 'w', zipfile.ZIP_DEFLATED) as zipf:
+        for root, _, files in os.walk(dataset_path):
+            for file in files:
+                file_path = os.path.join(root, file)
+                arcname = os.path.relpath(file_path, dataset_path)
+                zipf.write(file_path, arcname)
+    print(f"Compressed {dataset_path} into {output_path}")
+
+# Function to upload ZIP files to Hugging Face Hub
+def upload_zip_to_hub(dataset_name, zip_path, repo_name):
+    """
+    Upload a ZIP file to a Hugging Face repository.
+
+    Args:
+        dataset_name (str): Name of the dataset (used as a file identifier).
+        zip_path (str): Path to the ZIP file.
+        repo_name (str): Hugging Face repository name.
+    """
+    api = HfApi()
+    token = HfFolder.get_token()
+    file_name = f"{dataset_name}.zip"
+    api.upload_file(
+        path_or_fileobj=zip_path,
+        path_in_repo=file_name,
+        repo_id=repo_name,
+        repo_type="dataset",
+        token=token,
+    )
+    print(f"Uploaded {file_name} to {repo_name}")
+
+# Main script
+if __name__ == "__main__":
+    for dataset_name in dataset_names:
+        dataset_path = os.path.join(base_root, dataset_name)
+        if not os.path.exists(dataset_path):
+            print(f"Dataset directory does not exist: {dataset_path}")
+            continue
+        
+        # Create ZIP file
+        zip_path = f"{dataset_name}.zip"
+        create_zip(dataset_path, zip_path)
+        
+        # Upload ZIP file to Hugging Face Hub
+        upload_zip_to_hub(dataset_name, zip_path, dataset_repo)