Automate Sora Watermark Removal - Bulk Processing & API Integration Guide

import requests
import time

# API endpoint and authentication
API_URL = "https://api.sorawatermark.com/v1"
API_KEY = "your_api_key_here"

headers = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json"
}

# Step 1: Upload video
def upload_video(video_path):
    with open(video_path, 'rb') as f:
        files = {'video': f}
        response = requests.post(
            f"{API_URL}/upload",
            headers=headers,
            files=files
        )
    return response.json()['task_id']

# Step 2: Check processing status
def check_status(task_id):
    response = requests.get(
        f"{API_URL}/status/{task_id}",
        headers=headers
    )
    return response.json()

# Step 3: Download cleaned video
def download_video(task_id, output_path):
    response = requests.get(
        f"{API_URL}/download/{task_id}",
        headers=headers,
        stream=True
    )
    with open(output_path, 'wb') as f:
        for chunk in response.iter_content(chunk_size=8192):
            f.write(chunk)

# Complete workflow
task_id = upload_video("input_video.mp4")
print(f"Task ID: {task_id}")

# Poll for completion
while True:
    status = check_status(task_id)
    if status['state'] == 'completed':
        break
    elif status['state'] == 'failed':
        raise Exception(f"Processing failed: {status['error']}")
    time.sleep(5)

download_video(task_id, "output_video.mp4")
print("Video processed successfully!")

headers = {
    "Authorization": f"Bearer {API_KEY}"
}

from requests_oauthlib import OAuth2Session

oauth = OAuth2Session(client_id, token=token)
response = oauth.post(API_URL, data=payload)

import os
from pathlib import Path

def process_directory(input_dir, output_dir):
    """Process all videos in a directory"""
    input_path = Path(input_dir)
    output_path = Path(output_dir)
    output_path.mkdir(exist_ok=True)
    
    video_files = list(input_path.glob("*.mp4"))
    
    for i, video_file in enumerate(video_files, 1):
        print(f"Processing {i}/{len(video_files)}: {video_file.name}")
        
        # Upload and process
        task_id = upload_video(str(video_file))
        
        # Wait for completion
        while check_status(task_id)['state'] != 'completed':
            time.sleep(5)
        
        # Download result
        output_file = output_path / f"clean_{video_file.name}"
        download_video(task_id, str(output_file))
        
        print(f"Completed: {output_file}")

# Usage
process_directory("./input_videos", "./output_videos")

from concurrent.futures import ThreadPoolExecutor, as_completed

def process_single_video(video_path, output_dir):
    """Process a single video"""
    try:
        # Upload
        task_id = upload_video(video_path)
        
        # Wait for completion
        while check_status(task_id)['state'] != 'completed':
            time.sleep(5)
        
        # Download
        output_path = Path(output_dir) / f"clean_{Path(video_path).name}"
        download_video(task_id, str(output_path))
        
        return f"Success: {video_path}"
    except Exception as e:
        return f"Failed: {video_path} - {str(e)}"

def process_directory_parallel(input_dir, output_dir, max_workers=5):
    """Process videos in parallel"""
    video_files = list(Path(input_dir).glob("*.mp4"))
    
    with ThreadPoolExecutor(max_workers=max_workers) as executor:
        futures = {
            executor.submit(process_single_video, str(vf), output_dir): vf 
            for vf in video_files
        }
        
        for future in as_completed(futures):
            result = future.result()
            print(result)

# Process up to 5 videos simultaneously
process_directory_parallel("./input_videos", "./output_videos", max_workers=5)

import queue
import threading

class VideoProcessor:
    def __init__(self, num_workers=3):
        self.queue = queue.Queue()
        self.num_workers = num_workers
        self.results = []
        
    def worker(self):
        """Worker thread for processing videos"""
        while True:
            item = self.queue.get()
            if item is None:
                break
                
            video_path, output_dir = item
            try:
                result = process_single_video(video_path, output_dir)
                self.results.append(result)
            except Exception as e:
                self.results.append(f"Error: {video_path} - {str(e)}")
            finally:
                self.queue.task_done()
    
    def process_batch(self, video_files, output_dir):
        """Process a batch of videos"""
        # Start worker threads
        threads = []
        for _ in range(self.num_workers):
            t = threading.Thread(target=self.worker)
            t.start()
            threads.append(t)
        
        # Add videos to queue
        for video_file in video_files:
            self.queue.put((video_file, output_dir))
        
        # Wait for completion
        self.queue.join()
        
        # Stop workers
        for _ in range(self.num_workers):
            self.queue.put(None)
        for t in threads:
            t.join()
        
        return self.results

# Usage
processor = VideoProcessor(num_workers=3)
video_files = list(Path("./input_videos").glob("*.mp4"))
results = processor.process_batch(video_files, "./output_videos")

# Clone repository
git clone https://github.com/username/sora-watermark-cleaner.git
cd sora-watermark-cleaner

# Install dependencies
pip install -r requirements.txt

# Run batch processing
python batch_process.py --input ./videos --output ./cleaned

# .github/workflows/remove-watermarks.yml
name: Remove Watermarks

on:
  push:
    paths:
      - 'videos/**/*.mp4'

jobs:
  process:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      
      - name: Set up Python
        uses: actions/setup-python@v4
        with:
          python-version: '3.10'
      
      - name: Install dependencies
        run: |
          pip install requests
      
      - name: Process videos
        env:
          API_KEY: ${{ secrets.WATERMARK_API_KEY }}
        run: |
          python scripts/batch_process.py
      
      - name: Upload results
        uses: actions/upload-artifact@v3
        with:
          name: cleaned-videos
          path: output/

import time
from functools import wraps

def retry_on_failure(max_retries=3, delay=5):
    """Decorator for retrying failed operations"""
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            for attempt in range(max_retries):
                try:
                    return func(*args, **kwargs)
                except Exception as e:
                    if attempt == max_retries - 1:
                        raise
                    print(f"Attempt {attempt + 1} failed: {e}. Retrying...")
                    time.sleep(delay)
        return wrapper
    return decorator

@retry_on_failure(max_retries=3)
def upload_video_with_retry(video_path):
    return upload_video(video_path)

from tqdm import tqdm

def process_with_progress(video_files, output_dir):
    """Process videos with progress bar"""
    with tqdm(total=len(video_files), desc="Processing videos") as pbar:
        for video_file in video_files:
            process_single_video(video_file, output_dir)
            pbar.update(1)

from flask import Flask, request

app = Flask(__name__)

@app.route('/webhook', methods=['POST'])
def webhook():
    data = request.json
    task_id = data['task_id']
    status = data['status']
    
    if status == 'completed':
        # Download and process result
        download_video(task_id, f"output/{task_id}.mp4")
        print(f"Task {task_id} completed")
    
    return {'status': 'received'}, 200

if __name__ == '__main__':
    app.run(port=5000)

import hashlib
import json

def get_file_hash(filepath):
    """Calculate file hash for caching"""
    hasher = hashlib.md5()
    with open(filepath, 'rb') as f:
        for chunk in iter(lambda: f.read(4096), b""):
            hasher.update(chunk)
    return hasher.hexdigest()

def process_with_cache(video_path, output_dir, cache_file='cache.json'):
    """Process video with caching"""
    # Load cache
    try:
        with open(cache_file, 'r') as f:
            cache = json.load(f)
    except FileNotFoundError:
        cache = {}
    
    # Check if already processed
    file_hash = get_file_hash(video_path)
    if file_hash in cache:
        print(f"Using cached result for {video_path}")
        return cache[file_hash]
    
    # Process video
    result = process_single_video(video_path, output_dir)
    
    # Update cache
    cache[file_hash] = result
    with open(cache_file, 'w') as f:
        json.dump(cache, f)
    
    return result

import psutil

def process_with_resource_limits(video_files, output_dir):
    """Process videos while monitoring system resources"""
    for video_file in video_files:
        # Check available memory
        memory = psutil.virtual_memory()
        if memory.percent > 90:
            print("Memory usage high, waiting...")
            time.sleep(30)
            continue
        
        # Check CPU usage
        cpu_percent = psutil.cpu_percent(interval=1)
        if cpu_percent > 90:
            print("CPU usage high, waiting...")
            time.sleep(10)
            continue
        
        process_single_video(video_file, output_dir)