Telegram Bot To Remove Watermark From Video May 2026

import cv2
import numpy as np

cap = cv2.VideoCapture(input_path)
out = cv2.VideoWriter(output_path, fourcc, fps, (w, h))
while True:
ret, frame = cap.read()
if not ret: break
mask = np.zeros(frame.shape[:2], np.uint8)
mask[y:y+h, x:x+w] = 255
inpainted = cv2.inpaint(frame, mask, 3, cv2.INPAINT_TELEA)
out.write(inpainted)

Rating: ⭐⭐⭐⭐☆ The Custom Crop Expert. Unlike others that auto-detect corners, this bot allows you to manually select the watermark region via coordinates.

How to Create a Telegram Bot to Remove Watermark from Videos

Are you tired of dealing with annoying watermarks on videos? Do you want to create a Telegram bot that can help users remove watermarks from their videos? In this blog post, we'll show you how to build a Telegram bot that can do just that.

Prerequisites

Before we dive into the code, make sure you have the following:

Step 1: Create a Telegram Bot

To create a Telegram bot, follow these steps:

Step 2: Set up a Python Environment

For this example, we'll use Python 3.8 and the python-telegram-bot library. You can install it using pip:

pip install python-telegram-bot

Step 3: Write the Bot Code

Create a new file called bot.py and add the following code:

import logging
from telegram.ext import Updater, CommandHandler, MessageHandler, Filters
import cv2
import numpy as np
logging.basicConfig(level=logging.INFO)
TOKEN = 'YOUR_API_TOKEN_HERE'
def start(update, context):
    context.bot.send_message(chat_id=update.effective_chat.id, text='Hello! I can help you remove watermarks from videos.')
def remove_watermark(update, context):
    video_file = update.message.video
    video_path = video_file.file_id
# Download the video
    video = context.bot.get_file(video_path)
    video.download('video.mp4')
# Remove watermark using OpenCV
    cap = cv2.VideoCapture('video.mp4')
    fps = cap.get(cv2.CAP_PROP_FPS)
    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    out = cv2.VideoWriter('output.mp4', fourcc, fps, (width, height))
while True:
        ret, frame = cap.read()
        if not ret:
            break
# Remove watermark (assuming it's a rectangle in the top-right corner)
        x, y, w, h = 10, 10, 100, 100  # adjust these values as needed
        frame[y:y+h, x:x+w] = (0, 0, 0)  # black out the watermark area
out.write(frame)
cap.release()
    out.release()
# Send the output video
    context.bot.send_video(chat_id=update.effective_chat.id, video=open('output.mp4', 'rb'))
def main():
    updater = Updater(TOKEN, use_context=True)
dp = updater.dispatcher
dp.add_handler(CommandHandler('start', start))
    dp.add_handler(MessageHandler(Filters.video, remove_watermark))
updater.start_polling()
    updater.idle()
if __name__ == '__main__':
    main()

Step 4: Deploy and Test

Deploy your bot on a server or a cloud platform. You can use a simple Procfile to run the bot on Heroku: telegram bot to remove watermark from video

web: python bot.py

Test your bot by sending a video with a watermark to the bot. The bot should respond with the video without the watermark.

Limitations and Future Improvements

This is a basic example to demonstrate how to create a Telegram bot to remove watermarks from videos. There are many limitations and potential improvements:

The rhythmic click-clacking of Leo’s keyboard was the only sound in the dimly lit apartment, save for the low hum of his server rack. To his neighbors, Leo was just a quiet freelance editor. In the digital underground, however, he was the creator of "VanishNode"—a Telegram bot that had become an urban legend for creators.

It started as a personal challenge. Leo hated how beautiful, archival footage was often buried under bulky, translucent logos that felt like scars on a digital canvas. He spent months training a neural network to not just blur pixels, but to "hallucinate" what lived behind the watermark, rebuilding the texture of clouds, skin, and steel with eerie precision.

One Tuesday, a message popped up in the bot’s admin console. It wasn't a video file; it was a text string from a user named Echo-01.

"I have a clip from 1994. A news station's logo covers my father’s face in the only video I have of him. Your AI is the only thing that can see through it. Please." import cv2 import numpy as np cap = cv2

Leo watched the progress bar as the 480p file uploaded. The watermark was a jagged, neon-blue station ID, slapped right over a man sitting on a porch. With a single command—/process_deep—Leo triggered his most advanced algorithm.

The server fans roared. On his screen, the blue logo began to dissolve. The AI analyzed the surrounding grain, the movement of the wind in the trees, and the shadow of the man’s brow. Frame by frame, the intruder vanished. What remained was a clear view of a man laughing, his eyes finally visible, looking directly into the camera. Leo hit 'Send.'

Minutes later, a simple reply came back: "He’s there. Thank you for giving him back."

Leo leaned back, the glow of the monitor reflecting in his eyes. He knew that by morning, a dozen copyright trolls would be trying to shut his bot down, and he’d have to migrate the code to a new ghost server. But for tonight, the VanishNode wasn't just a tool for bypassing restrictions—it was a time machine.

Telegram bots designed to remove watermarks from videos have evolved from simple downloaders into complex AI-driven tools. These bots typically function as intermediaries, allowing users to send a link or file and receive a "cleaned" version in return

. While they offer unparalleled convenience, they also sit at the intersection of technological innovation, user convenience, and significant ethical and legal challenges. The Mechanism of Removal

Most modern Telegram watermark remover bots rely on two primary methods: Watermark Remover Rating: ⭐⭐⭐⭐☆ The Custom Crop Expert

| Problem | Solution | |--------|----------| | User must know watermark position | Add object detection (YOLO) to find logo | | Video size > 50 MB (Telegram limit) | Compress video before sending back | | Slow processing | Use async queues + background tasks | | Watermark in complex background | Inpainting or AI models |

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