download.py

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:
                    # Read and write in chunks
                    CHUNK_SIZE = 8*1024*1024 # 8MB
                    for chunk in iter(lambda: f_in.read(CHUNK_SIZE), b""):
                        f_out.write(chunk) 
                # Delete part files as they are concatenated              
                os.remove(f_name)
        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 compute_md5(file_path):
    hasher = md5()
    with open(file_path, 'rb') as f:
        CHUNK_SIZE = 8*1024*1024 # 8MB
        for chunk in iter(lambda: f.read(CHUNK_SIZE), b""):
            hasher.update(chunk)
    return hasher.hexdigest()
    
def verify_and_extract(animal):
    print(f"Confirming data integrity for {animal}.zip ...")
    zip_md5 = compute_md5(f"{save_dir}/dataset/image/{animal}.zip")
    
    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 script finished.")