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import os |
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import requests |
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import time |
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import zipfile |
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import glob |
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from hashlib import md5 |
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import concurrent.futures |
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base_url = "https://huggingface.co/datasets/imageomics/KABR/resolve/main/KABR" |
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""" |
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To extend the dataset, add additional animals and parts ranges to the list and dictionary below. |
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""" |
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animals = ["giraffes", "zebras_grevys", "zebras_plains"] |
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animal_parts_range = { |
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"giraffes": ("aa", "ad"), |
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"zebras_grevys": ("aa", "am"), |
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"zebras_plains": ("aa", "al"), |
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} |
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dataset_prefix = "dataset/image/" |
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static_files = [ |
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"README.txt", |
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"annotation/classes.json", |
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"annotation/distribution.xlsx", |
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"annotation/train.csv", |
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"annotation/val.csv", |
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"configs/I3D.yaml", |
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"configs/SLOWFAST.yaml", |
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"configs/X3D.yaml", |
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"dataset/image2video.py", |
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"dataset/image2visual.py", |
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] |
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def generate_part_files(animal, start, end): |
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start_a, start_b = ord(start[0]), ord(start[1]) |
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end_a, end_b = ord(end[0]), ord(end[1]) |
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return [ |
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f"{dataset_prefix}{animal}_part_{chr(a)}{chr(b)}" |
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for a in range(start_a, end_a + 1) |
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for b in range(start_b, end_b + 1) |
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] |
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part_files = [ |
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part |
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for animal, (start, end) in animal_parts_range.items() |
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for part in generate_part_files(animal, start, end) |
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] |
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archive_md5_files = [f"{dataset_prefix}{animal}_md5.txt" for animal in animals] |
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files = static_files + archive_md5_files + part_files |
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def progress_bar(iteration, total, message, bar_length=50): |
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progress = (iteration / total) |
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bar = '=' * int(round(progress * bar_length) - 1) |
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spaces = ' ' * (bar_length - len(bar)) |
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message = f'{message:<100}' |
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print(f'[{bar + spaces}] {int(progress * 100)}% {message}', end='\r', flush=True) |
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if iteration == total: |
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print() |
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save_dir = "KABR_files" |
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print(f"Downloading the Kenyan Animal Behavior Recognition (KABR) dataset ...") |
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total = len(files) |
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for i, file_path in enumerate(files): |
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save_path = os.path.join(save_dir, file_path) |
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if os.path.exists(save_path): |
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print(f"File {save_path} already exists. Skipping download.") |
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continue |
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full_url = f"{base_url}/{file_path}" |
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os.makedirs(os.path.join(save_dir, os.path.dirname(file_path)), exist_ok=True) |
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response = requests.get(full_url) |
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with open(save_path, 'wb') as file: |
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file.write(response.content) |
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progress_bar(i+1, total, f"downloaded: {save_path}") |
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print("Download of repository contents completed.") |
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print(f"Concatenating split files into a full archive for {animals} ...") |
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def concatenate_files(animal): |
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print(f"Concatenating files for {animal} ...") |
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part_files_pattern = f"{save_dir}/dataset/image/{animal}_part_*" |
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part_files = sorted(glob.glob(part_files_pattern)) |
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if part_files: |
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with open(f"{save_dir}/dataset/image/{animal}.zip", 'wb') as f_out: |
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for f_name in part_files: |
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with open(f_name, 'rb') as f_in: |
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CHUNK_SIZE = 8*1024*1024 |
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for chunk in iter(lambda: f_in.read(CHUNK_SIZE), b""): |
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f_out.write(chunk) |
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os.remove(f_name) |
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print(f"Archive for {animal} concatenated.") |
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else: |
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print(f"No part files found for {animal}.") |
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with concurrent.futures.ThreadPoolExecutor() as executor: |
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executor.map(concatenate_files, animals) |
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def compute_md5(file_path): |
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hasher = md5() |
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with open(file_path, 'rb') as f: |
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CHUNK_SIZE = 8*1024*1024 |
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for chunk in iter(lambda: f.read(CHUNK_SIZE), b""): |
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hasher.update(chunk) |
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return hasher.hexdigest() |
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def verify_and_extract(animal): |
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print(f"Confirming data integrity for {animal}.zip ...") |
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zip_md5 = compute_md5(f"{save_dir}/dataset/image/{animal}.zip") |
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with open(f"{save_dir}/dataset/image/{animal}_md5.txt", 'r') as file: |
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expected_md5 = file.read().strip().split()[0] |
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if zip_md5 == expected_md5: |
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print(f"MD5 sum for {animal}.zip is correct.") |
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print(f"Extracting {animal}.zip ...") |
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with zipfile.ZipFile(f"{save_dir}/dataset/image/{animal}.zip", 'r') as zip_ref: |
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zip_ref.extractall(f"{save_dir}/dataset/image/") |
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print(f"{animal}.zip extracted.") |
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print(f"Cleaning up for {animal} ...") |
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os.remove(f"{save_dir}/dataset/image/{animal}.zip") |
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os.remove(f"{save_dir}/dataset/image/{animal}_md5.txt") |
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else: |
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print(f"MD5 sum for {animal}.zip is incorrect. Expected: {expected_md5}, but got: {zip_md5}.") |
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print("There may be data corruption. Please try to download and reconstruct the data again or reach out to the corresponding authors for assistance.") |
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with concurrent.futures.ThreadPoolExecutor() as executor: |
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executor.map(verify_and_extract, animals) |
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print("Download script finished.") |
