maalrron · January 13, 2025 17:19 · WAUthethird · Jan 2, 2025 · SkillipEvolver · Jan 4, 2025
diff --git a/Dl_ms_assets_readme.md b/Dl_ms_assets_readme.md
diff --git a/dlMegascans.py b/dlMegascans.py
 import os
 import json
 import concurrent.futures
 import requests
 import time

 # Define the token, download path, and target category
 token = "paste token here"
 download_path = "path/to/where/you/want/the/zips/to/go"   # Update with the correct path to the directory
 json_file_path = "path/to/json/ms_asset_categories.json"  # Update with the correct path to the file, not the directory
 cache_file_path = "path/to/cache.txt"

 target_category = "3D plants/grass/lawn"




 # Function to normalize category strings
 def normalize_category(category):
    return category.strip().lower().rstrip('s')

 # Function to load asset categories from JSON file
 def load_asset_categories(json_file):
    with open(json_file, 'r') as f:
        return json.load(f)

 # Function to convert categories to a space-separated string
 def categories_to_string(categories):
    result = []
    if isinstance(categories, dict):
        for key, value in categories.items():
            if isinstance(value, dict) and value:
                subcategories = categories_to_string(value)
                if subcategories:
                    result.append(f"{key} {subcategories}")
                else:
                    result.append(key)
            elif isinstance(value, list):
                for item in value:
                    result.append(normalize_category(item))
            else:
                result.append(normalize_category(key))
    return " ".join(result)

 # Load the asset categories from the JSON file
 asset_categories_dict = load_asset_categories(json_file_path)

 # Print what it's trying to match against
 print(f"Trying to match against target category: {target_category}")

 def get_asset_download_id(asset_id):
    url = "https://quixel.com/v1/downloads"
    params = {
        "asset": asset_id,
        "config": {
            "lowerlod_meshes" : True,
            "maxlod" : 100000000,
            "highpoly": False,
            "ztool": False,
            "lowerlod_normals": True,
            "albedo_lods": True,
            "meshMimeType": "application/x-fbx",
            "brushes": False,
            "lods": [29155, 13541, 6289, 2921],
        },
    }
 headers = {
        "X-Api-Key": "your_api_key_here",
        "Authorization": "Bearer " + token,
    }

    print(f"Getting download ID for asset: {asset_id}")
    download_url_response = requests.post(url, headers=headers, json=params)
    if download_url_response.status_code == 200:
        return download_url_response.json()["id"]
    else:
        print(f"Failed to get asset download url for id: {asset_id}")
        print(download_url_response.json())
        return None

 def download_asset(download_id, download_directory):
    os.makedirs(download_directory, exist_ok=True)

    url = f"https://assetdownloads.quixel.com/download/{download_id}?preserveStructure=true&url=https%3A%2F%2Fquixel.com%2Fv1%2Fdownloads"

    print(f"Attempting to download from: {url}")
    
    response = requests.get(url, stream=True)
    
    if response.status_code == 400:
        print(f"Error 400: {response.text}")  # Print the response to see what's causing the issue

    attempt_count = 0
    delay = 5
    max_attempts = 5

    while attempt_count < max_attempts:
        response = requests.get(url, stream=True)
        if response.status_code == 200:
            content_disposition = response.headers.get("Content-Disposition")
            filename = content_disposition.split("filename=")[-1].strip('"') if content_disposition else download_id
            file_path = os.path.join(download_directory, filename)

            print(f"Downloading file: {file_path}")

            try:
                with open(file_path, "wb") as file:
                    for chunk in response.iter_content(chunk_size=8192):
                        file.write(chunk)
                print(f"File downloaded successfully: {file_path}")
                return True
            except requests.exceptions.ChunkedEncodingError as e:
                print(f"Error during download: {e}")
                time.sleep(delay)
                delay += 5
                attempt_count += 1
        else:
            print(f"Failed to download asset {download_id}, status code: {response.status_code}")
            print(f"Response: {response.text}")  # Print the response content for more details
            return False

    print(f"Exceeded maximum retry attempts for asset {download_id}")
    return False

 # Load cached assets
 cached_assets = set()
 if os.path.exists(cache_file_path):
    with open(cache_file_path, "r") as cache_file:
        cached_assets = set(cache_file.read().splitlines())

 # Normalize target category for matching
 normalized_target_categories = [normalize_category(part) for part in target_category.split("/")]

 matching_asset_ids = []
 # Check matches for each asset in the loaded categories
 for asset_id, categories in asset_categories_dict.items():
    # Convert the categories to a single string for matching
    categories_str = categories_to_string(categories)

    # Check if all parts of target_category exist in the categories string
    matches = all(normalize_category(part) in categories_str.lower() for part in normalized_target_categories)

    if matches and asset_id not in cached_assets:
        print(f"Asset ID: {asset_id} matches target category: {target_category}")
        matching_asset_ids.append(asset_id)

 if not matching_asset_ids:
    print("No assets found for the target category.")
    exit()

 # Ask the user for confirmation before downloading
 print(f"{len(matching_asset_ids)} assets found.")
 confirmation = input(f"Do you want to download these {len(matching_asset_ids)} assets? (y/n): ").strip().lower()
 if confirmation != "y":
    print("Download canceled.")
    exit()

 # Function to handle downloading for threading
 def download_asset_with_id(asset_id):
    download_id = get_asset_download_id(asset_id)
    if download_id:
        return download_asset(download_id, download_path)

 # Open the cache file for appending new downloads
 with open(cache_file_path, "a+") as cache_file:
    # Use threading for faster downloading
    with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
        futures = {executor.submit(download_asset_with_id, asset_id): asset_id for asset_id in matching_asset_ids}

        for future in concurrent.futures.as_completed(futures):
            asset_id = futures[future]
            try:
                result = future.result()
                if result:
                    # Add the asset to the cache file after successful download
                    cache_file.write(f"{asset_id}\n")
                    cache_file.flush()
            except Exception as e:
                print(f"Error downloading asset {asset_id}: {e}")
diff --git a/gettoken.js b/gettoken.js
 // Function to get the value of a specific cookie by name
 function getCookie(name) {
  const value = `; ${document.cookie}`;
  const parts = value.split(`; ${name}=`);
  if (parts.length === 2) return parts.pop().split(';').shift();
 }

 // Get the auth cookie
 const authCookie = getCookie('auth');

 // Parse the auth cookie (it should be a JSON string containing the token)
 if (authCookie) {
  try {
    const authData = JSON.parse(decodeURIComponent(authCookie));
    const authToken = authData.token;
    console.log("Auth Token:", authToken);
  } catch (error) {
    console.error("Error parsing auth cookie:", error);
  }
 } else {
  console.error("Auth cookie not found. Please make sure you are logged in.");
 }
	import os
	import json
	import concurrent.futures
	import requests
	import time

	# Define the token, download path, and target category
	token = "paste token here"
	download_path = "path/to/where/you/want/the/zips/to/go" # Update with the correct path to the directory
	json_file_path = "path/to/json/ms_asset_categories.json" # Update with the correct path to the file, not the directory
	cache_file_path = "path/to/cache.txt"

	target_category = "3D plants/grass/lawn"




	# Function to normalize category strings
	def normalize_category(category):
	return category.strip().lower().rstrip('s')

	# Function to load asset categories from JSON file
	def load_asset_categories(json_file):
	with open(json_file, 'r') as f:
	return json.load(f)

	# Function to convert categories to a space-separated string
	def categories_to_string(categories):
	result = []
	if isinstance(categories, dict):
	for key, value in categories.items():
	if isinstance(value, dict) and value:
	subcategories = categories_to_string(value)
	if subcategories:
	result.append(f"{key} {subcategories}")
	else:
	result.append(key)
	elif isinstance(value, list):
	for item in value:
	result.append(normalize_category(item))
	else:
	result.append(normalize_category(key))
	return " ".join(result)

	# Load the asset categories from the JSON file
	asset_categories_dict = load_asset_categories(json_file_path)

	# Print what it's trying to match against
	print(f"Trying to match against target category: {target_category}")

	def get_asset_download_id(asset_id):
	url = "https://quixel.com/v1/downloads"
	params = {
	"asset": asset_id,
	"config": {
	"lowerlod_meshes" : True,
	"maxlod" : 100000000,
	"highpoly": False,
	"ztool": False,
	"lowerlod_normals": True,
	"albedo_lods": True,
	"meshMimeType": "application/x-fbx",
	"brushes": False,
	"lods": [29155, 13541, 6289, 2921],
	},
	}
	headers = {
	"X-Api-Key": "your_api_key_here",
	"Authorization": "Bearer " + token,
	}

	print(f"Getting download ID for asset: {asset_id}")
	download_url_response = requests.post(url, headers=headers, json=params)
	if download_url_response.status_code == 200:
	return download_url_response.json()["id"]
	else:
	print(f"Failed to get asset download url for id: {asset_id}")
	print(download_url_response.json())
	return None

	def download_asset(download_id, download_directory):
	os.makedirs(download_directory, exist_ok=True)

	url = f"https://assetdownloads.quixel.com/download/{download_id}?preserveStructure=true&url=https%3A%2F%2Fquixel.com%2Fv1%2Fdownloads"

	print(f"Attempting to download from: {url}")

	response = requests.get(url, stream=True)

	if response.status_code == 400:
	print(f"Error 400: {response.text}") # Print the response to see what's causing the issue

	attempt_count = 0
	delay = 5
	max_attempts = 5

	while attempt_count < max_attempts:
	response = requests.get(url, stream=True)
	if response.status_code == 200:
	content_disposition = response.headers.get("Content-Disposition")
	filename = content_disposition.split("filename=")[-1].strip('"') if content_disposition else download_id
	file_path = os.path.join(download_directory, filename)

	print(f"Downloading file: {file_path}")

	try:
	with open(file_path, "wb") as file:
	for chunk in response.iter_content(chunk_size=8192):
	file.write(chunk)
	print(f"File downloaded successfully: {file_path}")
	return True
	except requests.exceptions.ChunkedEncodingError as e:
	print(f"Error during download: {e}")
	time.sleep(delay)
	delay += 5
	attempt_count += 1
	else:
	print(f"Failed to download asset {download_id}, status code: {response.status_code}")
	print(f"Response: {response.text}") # Print the response content for more details
	return False

	print(f"Exceeded maximum retry attempts for asset {download_id}")
	return False

	# Load cached assets
	cached_assets = set()
	if os.path.exists(cache_file_path):
	with open(cache_file_path, "r") as cache_file:
	cached_assets = set(cache_file.read().splitlines())

	# Normalize target category for matching
	normalized_target_categories = [normalize_category(part) for part in target_category.split("/")]

	matching_asset_ids = []
	# Check matches for each asset in the loaded categories
	for asset_id, categories in asset_categories_dict.items():
	# Convert the categories to a single string for matching
	categories_str = categories_to_string(categories)

	# Check if all parts of target_category exist in the categories string
	matches = all(normalize_category(part) in categories_str.lower() for part in normalized_target_categories)

	if matches and asset_id not in cached_assets:
	print(f"Asset ID: {asset_id} matches target category: {target_category}")
	matching_asset_ids.append(asset_id)

	if not matching_asset_ids:
	print("No assets found for the target category.")
	exit()

	# Ask the user for confirmation before downloading
	print(f"{len(matching_asset_ids)} assets found.")
	confirmation = input(f"Do you want to download these {len(matching_asset_ids)} assets? (y/n): ").strip().lower()
	if confirmation != "y":
	print("Download canceled.")
	exit()

	# Function to handle downloading for threading
	def download_asset_with_id(asset_id):
	download_id = get_asset_download_id(asset_id)
	if download_id:
	return download_asset(download_id, download_path)

	# Open the cache file for appending new downloads
	with open(cache_file_path, "a+") as cache_file:
	# Use threading for faster downloading
	with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
	futures = {executor.submit(download_asset_with_id, asset_id): asset_id for asset_id in matching_asset_ids}

	for future in concurrent.futures.as_completed(futures):
	asset_id = futures[future]
	try:
	result = future.result()
	if result:
	# Add the asset to the cache file after successful download
	cache_file.write(f"{asset_id}\n")
	cache_file.flush()
	except Exception as e:
	print(f"Error downloading asset {asset_id}: {e}")
	// Function to get the value of a specific cookie by name
	function getCookie(name) {
	const value = `; ${document.cookie}`;
	const parts = value.split(`; ${name}=`);
	if (parts.length === 2) return parts.pop().split(';').shift();
	}

	// Get the auth cookie
	const authCookie = getCookie('auth');

	// Parse the auth cookie (it should be a JSON string containing the token)
	if (authCookie) {
	try {
	const authData = JSON.parse(decodeURIComponent(authCookie));
	const authToken = authData.token;
	console.log("Auth Token:", authToken);
	} catch (error) {
	console.error("Error parsing auth cookie:", error);
	}
	} else {
	console.error("Auth cookie not found. Please make sure you are logged in.");
	}