import os import requests import time import zipfile import glob from hashlib import md5 import concurrent.futures base_url = "https://huggingface.co/datasets/imageomics/KABR/resolve/main/KABR" """ To extend the dataset, add additional animals and parts ranges to the list and dictionary below. """ animals = ["giraffes", "zebras_grevys", "zebras_plains"] animal_parts_range = { "giraffes": ("aa", "ad"), "zebras_grevys": ("aa", "am"), "zebras_plains": ("aa", "al"), } dataset_prefix = "dataset/image/" # Define the static files that are not dependent on the animals list static_files = [ "README.txt", "annotation/classes.json", "annotation/distribution.xlsx", "annotation/train.csv", "annotation/val.csv", "configs/I3D.yaml", "configs/SLOWFAST.yaml", "configs/X3D.yaml", "dataset/image2video.py", "dataset/image2visual.py", ] def generate_part_files(animal, start, end): start_a, start_b = ord(start[0]), ord(start[1]) end_a, end_b = ord(end[0]), ord(end[1]) return [ f"{dataset_prefix}{animal}_part_{chr(a)}{chr(b)}" for a in range(start_a, end_a + 1) for b in range(start_b, end_b + 1) ] # Generate the part files for each animal part_files = [ part for animal, (start, end) in animal_parts_range.items() for part in generate_part_files(animal, start, end) ] archive_md5_files = [f"{dataset_prefix}{animal}_md5.txt" for animal in animals] files = static_files + archive_md5_files + part_files def progress_bar(iteration, total, message, bar_length=50): progress = (iteration / total) bar = '=' * int(round(progress * bar_length) - 1) spaces = ' ' * (bar_length - len(bar)) message = f'{message:<100}' print(f'[{bar + spaces}] {int(progress * 100)}% {message}', end='\r', flush=True) if iteration == total: print() # Directory to save files save_dir = "KABR_files" # Loop through each relative file path print(f"Downloading the Kenyan Animal Behavior Recognition (KABR) dataset ...") total = len(files) for i, file_path in enumerate(files): # Construct the full URL save_path = os.path.join(save_dir, file_path) if os.path.exists(save_path): print(f"File {save_path} already exists. Skipping download.") continue full_url = f"{base_url}/{file_path}" # Create the necessary directories based on the file path os.makedirs(os.path.join(save_dir, os.path.dirname(file_path)), exist_ok=True) # Download the file and save it with the preserved file path response = requests.get(full_url) with open(save_path, 'wb') as file: file.write(response.content) progress_bar(i+1, total, f"downloaded: {save_path}") print("Download of repository contents completed.") print(f"Concatenating split files into a full archive for {animals} ...") def concatenate_files(animal): print(f"Concatenating files for {animal} ...") part_files_pattern = f"{save_dir}/dataset/image/{animal}_part_*" part_files = sorted(glob.glob(part_files_pattern)) if part_files: with open(f"{save_dir}/dataset/image/{animal}.zip", 'wb') as f_out: for f_name in part_files: with open(f_name, 'rb') as f_in: f_out.write(f_in.read()) print(f"Archive for {animal} concatenated.") else: print(f"No part files found for {animal}.") with concurrent.futures.ThreadPoolExecutor() as executor: executor.map(concatenate_files, animals) def verify_and_extract(animal): print(f"Confirming data integrity for {animal}.zip ...") with open(f"{save_dir}/dataset/image/{animal}.zip", 'rb') as f: zip_md5 = md5(f.read()).hexdigest() with open(f"{save_dir}/dataset/image/{animal}_md5.txt", 'r') as file: expected_md5 = file.read().strip().split()[0] if zip_md5 == expected_md5: print(f"MD5 sum for {animal}.zip is correct.") print(f"Extracting {animal}.zip ...") with zipfile.ZipFile(f"{save_dir}/dataset/image/{animal}.zip", 'r') as zip_ref: zip_ref.extractall(f"{save_dir}/dataset/image/") print(f"{animal}.zip extracted.") print(f"Cleaning up for {animal} ...") os.remove(f"{save_dir}/dataset/image/{animal}.zip") os.remove(f"{save_dir}/dataset/image/{animal}_md5.txt") else: print(f"MD5 sum for {animal}.zip is incorrect. Expected: {expected_md5}, but got: {zip_md5}.") print("There may be data corruption. Please try to download and reconstruct the data again or reach out to the corresponding authors for assistance.") with concurrent.futures.ThreadPoolExecutor() as executor: executor.map(verify_and_extract, animals) print("Download and setup complete.")