Video Background Removal using Python with Interactive Deployment.

3 min readSep 14, 2022

Image segmentation is a process of separating multiple image segments from an image.

image source:

The most common usage of image segmentation is in Video Conferencing Apps like Zoom, Google meets, Teams, etc. where a person can add various background effects to their videos.

Today we are going to develop a model which will segment a person from the background while replacing the background with an image. The model will be deployed on TrueFoundry for you all to try.

Key takeaways from this blog

  • Using Mediapipe for face segmentation and background removal.
  • Deploying the model in a Gradio app in which we can upload any video or use a webcam for video input.
  • The model will be deployed using TrueFoundry

Let’s begin

We will start by installing the required libraries.


pip install mediapipe mlfoundry servicefoundry gradio

Importing Libraries

import cv2
import mediapipe as mp
import numpy as np
from IPython.display import HTML
from base64 import b64encode
import numpy as np
import cv2
import matplotlib.pyplot as plt
import mlfoundry as mlf
import warnings
import servicefoundry.core as sfy
from servicefoundry import Build, PythonBuild, Service, Resources
warnings.simplefilter(action='ignore', category=Warning)

Previewing Test Video

Previewing Video on Kaggle is not there by default therefore we are going to do a workaround that will embed the video into the page source

def play(filename):
html = ''
video = open(filename,'rb').read()
src = 'data:video/mp4;base64,' + b64encode(video).decode()
html += '<video width=1000 controls autoplay loop><source src="%s" type="video/mp4"></video>' % src
return HTML(html)
play('../input/videos-for-segmentation/Dance - 32938.mp4')

Creating Mediapipe Objects

mp_drawing =
mp_drawing_styles =
mp_face_mesh =
mp_selfie_segmentation =
mp_objectron =

Importing Video using CV2

cap = cv2.VideoCapture('../input/videos-for-segmentation/Dance - 32938.mp4')

Finalizing Frame widths

frame_width = int(cap.get(3))
frame_height = int(cap.get(4))

size = (frame_width, frame_height)
result = cv2.VideoWriter('result.mp4',
30, size)
tik = 0

Segmentation Object

Face mesh Object

Visualizing result


Now our model is ready, we will deploy the model

Login to TrueFoundry

Using our API Key we are going to log in to the platform.


Writing Deployment Script

In this script, we will download the test video and background from Drive and then use our model on those videos

We are going to write the below script to using the “writefile” magic function.

Gathering Requirements

requirements = sfy.gather_requirements("")
requirements['opencv-python-headless'] = ''
requirements['chardet'] = '3.0.4'
requirements['jinja2'] = '3.1.2'
reqs = []
for i, j in enumerate(requirements):
reqs.append('{}=={}'.format(j, requirements[j]))
with open('requirements.txt', 'w') as f:
for line in reqs:

Creating Dockerfile

We are going to use the OpenCV docker image by Jjanzic and then deploy our code.

%%writefile Dockerfile'''FROM jjanzic/docker-python3-opencv:opencv-4.0.0
COPY ./requirements.txt /tmp/
RUN pip install -U pip && pip install -r /tmp/requirements.txt
COPY . ./app
ENTRYPOINT python'''

The directory structure should look like this.



— requirements.txt

— Dockerfile

Creating Service for deploying Model

Here, we will use DockerFileBuild which will use the Dockerfile for image creation and deployment.

We will limit the memory to 2.5 GB and CPU to 3.5 Cores for the model using Resources Class.

service = Service(
ports=[{"port": 8080}],

The model is Deployed here:


The above Code is also present in this Kaggle Notebook


  1. TrueFoundry:
  2. Kaggle Notebook: