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Building a Deep Learning Application using Python

November 1, 2021

In this tutorial, we will build a selfie segmentation model using Mediapipe, Gradio, and Python.

Using a pre-built machine learning model, the selfie segmentation model allows us to quickly and easily strip out the background from photos.



To fully understand this tutorial, you need to be familiar with:

You need to use either Jupyter Notebook or Google Colab. To follow along, please use Google Colab.


The selfie segmentation model allows us to quickly and easily strip out the background from photos using a pre-built machine learning model.

It is based on a modified MobileNetV3. This is a state-of-the-art model for mobile computer vision networks with speeds twice as fast as the MobileNetV2 model.

The model segments an object from an image/video. This allows one to replace backgrounds or apply effects to videos or images.

The model can run in real-time on both smartphones and laptops, and its intended use cases include creating selfie effects and video conferencing.

Installing Mediapipe, Gradio, OpenCV, and Matplotlib

Mediapipe is a free and open-source machine learning (ML) pipeline that provides pre-built ML solutions in Python and other languages.

It provides ML solutions such as face detection, hair segmentation, object detection, and selfie segmentation.

These ML solutions work across Android, iOS, desktop/cloud, web, and IoT devices. We will use Mediapipe to grab the selfie segmentation model.

Gradio is a library that allows us to build stand-alone applications inside of a Jupyter Notebook/Google Colab. In this tutorial, we will integrate our selfie model into the Gradio app.

OpenCV is a widely used Python library that allows one to solve tasks in computer vision. We will use this library to get real-time feed from our webcam.

Matplotlib is a python package used for creating static, animated, and interactive visualizations. We will use it to visualize our images.

pip install gradio mediapipe opencv-python matplotlib

If you’re using Jupyter notebook, make sure to include an exclamation ! before the pip command, as shown below:

!pip install gradio mediapipe opencv-python matplotlib

Now that we’ve installed them, we need to import them into our Colab.

import mediapipe as mp
import cv2
import numpy as np

Since the video feed will be real-time, we use OpenCV to access our computer’s webcam to relay the feed.

cap = cv2.VideoCapture(0)
while cap.isOpened():
    ret, frame =

    cv2.imshow('Webcam feed', frame)

    if cv2.waitKey(10) & 0xFF == ord('q'):

This is the standard way of getting a video feed using OpenCV. The VideoCapture() method is used to create a video capture object.

The number 0 indicates that we are going to use a web camera. If it’s not picking your webcam or getting an error, try changing this number to either a 1 or 2.

Once we’ve captured our video device, we use while to loop continuously from our webcam feed.

These captured feeds are then unpacked and stored in the ret and frame variables. OpenCV’s imshow() method then renders them on the screen with Webcam feed. You can give yours a different name if you so wish.

Applying selfie segmentation using OpenCV

mp_selfie =

The code above instantiates the selfie_segmentation model and saves it in a variable called mp_selfie.

We now go ahead and apply the selfie_segmentation model to the OpenCV feed, as illustrated below:

cap = cv2.VideoCapture(0)
# Create with statement for model 
with mp_selfie.SelfieSegmentation(model_selection=0) as model: 
    while cap.isOpened():
        ret, frame =
        # Apply segmentation
        frame.flags.writeable = False
        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        results = model.process(frame)
        frame.flags.writeable = True

        cv2.imshow('Webcam feed', frame)

        if cv2.waitKey(10) & 0xFF == ord('q'):

This code mp_selfie.SelfieSegmentation(model_selection=0) as model allows us to apply SelfieSegmentation in OpenCV using the variable called model.

By default, OpenCV saves frames in blue-green-red (BGR) format. Don’t be surprised if your image or video feed appears to show your face in blue.

We convert this into the red-green-blue (RGB) standard format using the command, cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) to pass it to our model.

We save this result in a variable called frame. We then pass this frame into our model and save the result in the res variable.

Please note that this isn’t going to be rendered. At least not yet. We will only see the results from the variable res.

To check the results, use the following command:



array([[6.5710924e-28, 5.9139829e-28, 3.2855462e-28, ..., 0.0000000e+00,
        0.0000000e+00, 0.0000000e+00],
       [1.5529340e-22, 1.3976405e-22, 7.7646702e-23, ..., 3.4236475e-31,
        6.8472963e-32, 0.0000000e+00],
       [4.3136934e-22, 3.8823240e-22, 2.1568467e-22, ..., 9.5101311e-31,
        1.9020266e-31, 0.0000000e+00],
       [5.5132825e-17, 4.9619541e-17, 2.7566413e-17, ..., 4.1491491e-19,
        8.2982972e-20, 3.9673376e-35],
       [2.3155784e-16, 2.0840205e-16, 1.1577892e-16, ..., 1.7426278e-18,
        3.4852552e-19, 1.6662817e-34],
       [3.3079694e-16, 2.9771723e-16, 1.6539847e-16, ..., 2.4894664e-18,
        4.9789340e-19, 2.3804025e-34]], dtype=float32)

The above results are the probabilities of a person being in a webcam frame. You can try and check the probabilities that your webcam’s feed will give.

We can now go ahead and process these results.

Processing the results

We begin by installing pyplot and gridspec. pyplot helps us in plotting while gridspec enables us to use grids in our layout.

Gridspec makes it easy to set up subplots.

from matplotlib import pyplot as plt
from matplotlib import gridspec

# Layout
grid = gridspec.GridSpec(1,2)

# Setup axes
ax0 = plt.subplot(grid[0])
ax1 = plt.subplot(grid[1])


Setting up the layout is important as it will determine how our end plot will appear. In our case, we set it to 15px by 15px.

For our grid, we set it to have one row and two columns. This means that we’ll have one image displayed inside these columns.

Next, we create two variables, ax0 and ax1 to store our subplots. It is then passed to OpenCV’s imshow() method to process the frame and segmentation mask.

This should display the results from our baseline image and segmentation mask side by side.

Let’s now apply a mask to exclude all the components which haven’t been highlighted by the segmentation.

background = np.zeros(frame.shape, dtype=np.uint8)
mask = np.stack((res.segmentation_mask,)*3, axis=-1) > 0.5

segmented_image = np.where(mask, frame, background)


In the above code, we have created a black background image using numpy and stored the results in a variable called background.

The code creates an numpy array and populates it with zeros. The frame.shape tells numpy to assign the zeros to our existing image shape.

On the contrary, to get a white background, you could type in np.ones().

Rather than having a segmentation mask with a single channel, we’ll multiply it by three to have the mask applied on all three channels.

Lastly, we return the result with only a 50% probability. The numpy.where() function returns the indices of elements in an input array where the given condition is satisfied.

You can read more about it here.

Let’s now create the Gradio app.

Integrating the selfie model into the Gradio app

To wrap it up, we will integrate the selfie model into a Gradio application. This will enable the model to be accessible on a web user interface.

import gradio as gr

def segment(image): 
    with mp_selfie.SelfieSegmentation(model_selection=0) as model: 
        res = model.process(image)
        mask = np.stack((res.segmentation_mask,)*3, axis=-1) > 0.5 
        return np.where(mask, image, cv2.blur(image, (40,40)))

webcam = gr.inputs.Image(shape=(640, 480), source="webcam")

webapp = gr.interface.Interface(fn=segment, inputs=webcam, outputs="image")


In the above code, we first imported Gradio. We then set up the function, segment that we want Gradio to run on.

The function returns three values, the mask, input image, image, and our background, background.

Please note it’s not a must to have a black background. You could use a background image or you could apply a blur on the background.

To blur, replace the argument, background with cv2.blur() method.

We then specify the webcam as our input to our Gradio app. Finally, we use the launch() method to start the Gradio app.

The complete code for this tutorial is available here.

Wrapping up

In a nutshell, that’s the selfie segmentation application integrated into Gradio. There’s quite a lot of information to grasp, but I hope you get to understand it.

You can, therefore, use the knowledge gained from this article to craft other quality machine learning applications.

Happy coding!

Further reading

Peer Review Contributions by: Willies Ogola