Get Able to Construct Your Own AI Animated Avatar: Step-by-Step Guide

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Creating an AI animated avatar is an important strategy to learn more about computer vision and machine learning, and it could actually even be a fun project to showcase your skills.

On this tutorial, we will likely be creating an AI animated avatar using Python. An AI animated avatar is an animated character that may mimic your facial expressions and movements in real-time using a camera. The avatar will use machine learning algorithms to detect your facial expression and map them to the animated character.

To follow together with this tutorial, you need to have some experience with Python programming language and be acquainted with the fundamentals of computer vision and machine learning. You may also need a working camera attached to your computer.

  1. Install the vital libraries
  2. Collect the information
  3. Train the machine learning algorithm
  4. Create the animation
  5. Control the animation using facial expressions
  6. Refine the animation
  7. Testing

By the tip of this tutorial, you’ll have created an AI animated avatar that may mimic your facial expressions and movements in real-time.

Now, let’s dive into the tutorial :

Step one is to put in the libraries that we’ll use in our project. OpenCV, Dlib, PyAutoGUI, and Pygame are a few of the libraries we’d like.

!pip install opencv-python
!pip install dlib
!pip install pyautogui
!pip install pygame

The second step is to gather data that will likely be used to coach our machine learning algorithm. We will likely be collecting images of our face from different angles and with different expressions.

We will likely be using the OpenCV library to capture the photographs. Let’s start by importing the vital libraries.

import cv2

# Initialize the camera
cam = cv2.VideoCapture(0)

# Create a window to display the camera feed
cv2.namedWindow("Camera Feed")

# Collect the information
while True:
# Read a frame from the camera
ret, frame = cam.read()

# Display the frame within the window
cv2.imshow("Camera Feed", frame)

# Press 'q' to exit the loop and stop collecting data
if cv2.waitKey(1) == ord('q'):
break

# Release the camera and destroy the window
cam.release()
cv2.destroyAllWindows()

This code will open a camera window and display the camera feed. Press ‘q’ to exit the loop and stop collecting data.

Now that we’ve got collected our data, we’d like to coach our machine learning algorithm.

We will likely be using Dlib library for facial landmark detection and OpenCV for data preprocessing.

import dlib
import numpy as np

# Initialize the face detector and landmark predictor
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

# Initialize the array to store the information
data = []

# Collect the information
while True:
# Read a frame from the camera
ret, frame = cam.read()

# Convert the frame to grayscale
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

# Detect faces within the grayscale frame
faces = detector(gray)

# Loop over the faces and detect facial landmarks
for face in faces:
landmarks = predictor(gray, face)

# Extract the x, y coordinates of the facial landmarks
coords = np.zeros((68, 2), dtype=int)
for i in range(68):
coords[i] = (landmarks.part(i).x, landmarks.part(i).y)

# Append the coordinates to the information array
data.append(coords)

# Draw the facial landmarks on the frame
for (x, y) in coords:
cv2.circle(frame, (x, y), 2, (0, 255, 0), -1)

# Display the frame within the window
cv2.imshow("Camera Feed", frame)

# Press 'q' to exit the loop and stop collecting data
if cv2.waitKey(1) == ord('q'):
break

# Release the camera and destroy the window
cam.release()
cv2.destroyAllWindows()

# Convert the information array to a NumPy array and reserve it to a file
data = np.array(data)
np.save("data.npy", data)

This code will detect faces within the camera feed and extract the coordinates of the facial landmarks. It is going to then append the coordinates to the information array and display the facial landmarks on the camera feed. Press ‘q’ to exit the loop and stop collecting data.

Now that we’ve got collected and processed our data, we are able to use it to create our AI animated avatar. We will likely be using Pygame library to create the animation.

import pygame
import numpy as np

# Load the information from the file
data = np.load("data.npy")

# Initialize Pygame
pygame.init()

# Set the size of the window
WINDOW_WIDTH = 640
WINDOW_HEIGHT = 480
screen = pygame.display.set_mode((WINDOW_WIDTH, WINDOW_HEIGHT))

# Set the size of the avatar
AVATAR_WIDTH = 150
AVATAR_HEIGHT = 200

# Load the avatar image and scale it to the suitable size
avatar = pygame.image.load("avatar.png")
avatar = pygame.transform.scale(avatar, (AVATAR_WIDTH, AVATAR_HEIGHT))

# Set the initial position of the avatar
x = WINDOW_WIDTH // 2 - AVATAR_WIDTH // 2
y = WINDOW_HEIGHT // 2 - AVATAR_HEIGHT // 2

# Set the utmost and minimum movement distances
MAX_MOVE = 50
MIN_MOVE = 5

# Set the movement speed
SPEED = 5

# Create a clock to manage the frame rate
clock = pygame.time.Clock()

# Loop over the information and animate the avatar
for i in range(len(data)):
# Clear the screen
screen.fill((255, 255, 255))

# Draw the avatar at the present position
screen.blit(avatar, (x, y))

# Calculate the movement distance
dx = data[i][30][0] - data[i][27][0]
dy = data[i][30][1] - data[i][27][1]
distance = np.sqrt(dx**2 + dy**2)

# Normalize the movement distance
move = (distance - MIN_MOVE) / (MAX_MOVE - MIN_MOVE)
move = min(max(move, 0), 1)

# Calculate the brand new position of the avatar
new_x = x + int(dx * move * SPEED)
new_y = y + int(dy * move * SPEED)

# Set the brand new position of the avatar
x = min(max(new_x, 0), WINDOW_WIDTH - AVATAR_WIDTH)
y = min(max(new_y, 0), WINDOW_HEIGHT - AVATAR_HEIGHT)

# Update the display
pygame.display.flip()

# Control the frame rate
clock.tick(30)

This code will load the information from the file and use it to animate the avatar. It is going to calculate the movement distance based on the position of the nose and mouth landmarks and move the avatar accordingly.

The avatar will move faster or slower depending on the movement distance. It is going to also control the frame rate to make sure smooth animation.

Now that we’ve got our animation, we are able to control it using our facial expressions. We will likely be using the PyAutoGUI library to manage the movement of the avatar based on our facial expressions.

import pygame
import numpy as np
import pyautogui

# Load the information from the file
data = np.load("data.npy")

# Initialize Pygame
pygame.init()

# Set the size of the window
WINDOW_WIDTH = 640
WINDOW_HEIGHT = 480
screen = pygame.display.set_mode((WINDOW_WIDTH, WINDOW_HEIGHT))

# Set the size of the avatar
AVATAR_WIDTH = 150
AVATAR_HEIGHT = 200

# Load the avatar image and scale it to the suitable size
avatar = pygame.image.load("avatar.png")
avatar = pygame.transform.scale(avatar, (AVATAR_WIDTH, AVATAR_HEIGHT))

# Set the initial position of the avatar
x = WINDOW_WIDTH // 2 - AVATAR_WIDTH // 2
y = WINDOW_HEIGHT // 2 - AVATAR_HEIGHT // 2

# Set the utmost and minimum movement distances
MAX_MOVE = 50
MIN_MOVE = 5

# Set the movement speed
SPEED = 5

# Create a clock to manage the frame rate
clock = pygame.time.Clock()

# Loop over the information and animate the avatar
for i in range(len(data)):
# Clear the screen
screen.fill((255, 255, 255))

# Draw the avatar at the present position
screen.blit(avatar, (x, y))

# Calculate the movement distance
dx = data[i][30][0] - data[i][27][0]
dy = data[i][30][1] - data[i][27][1]
distance = np.sqrt(dx**2 + dy**2)

# Normalize the movement distance
move = (distance - MIN_MOVE) / (MAX_MOVE - MIN_MOVE)
move = min(max(move, 0), 1)

# Calculate the brand new position of the avatar
new_x = x + int(dx * move * SPEED)
new_y = y + int(dy * move * SPEED)

# Set the brand new position of the avatar
x = min(max(new_x, 0), WINDOW_WIDTH - AVATAR_WIDTH)
y = min(max(new_y, 0), WINDOW_HEIGHT - AVATAR_HEIGHT)

# Update the display
pygame.display.flip()

# Control the frame rate
clock.tick(30)

# Get the facial features
smile_ratio = (data[i][51][1] - data[i][57][1]) / (data[i][50][0] - data[i][44][0])

# Control the movement of the avatar based on the facial features
if smile_ratio > 0.5:
pyautogui.moveRel(10, 0, duration=0.1)
elif smile_ratio < 0.2:
pyautogui.moveRel(-10, 0, duration=0.1)

This code will load the information from the file and use it to animate the avatar. It is going to calculate the movement distance based on the position of the nose and mouth landmarks and move the avatar accordingly.

The avatar will move faster or slower depending on the movement distance. It is going to also control the frame rate to make sure smooth animation.

As well as, this code will get the facial features using the smile ratio and control the movement of the avatar based on the facial features using PyAutoGUI library. If the smile ratio is larger than 0.5, the avatar will move right, and if it’s lower than 0.2, the avatar will move left.

Now that we’ve got created our AI animated avatar, we are able to test it to see the way it performs. To do that, we’d like to run the script we just created.

Before running the script, be sure that that you’ve all of the vital packages installed, including OpenCV, dlib, Pygame, NumPy, and PyAutoGUI.

Once you’ve installed the required packages, you possibly can run the script using the next command:

python avatar.py

This can start the script and display the animated avatar on the screen. You need to see the avatar move around and make different facial expressions based in your movements.

To regulate the movement of the avatar, you possibly can move your head or make different facial expressions. You may as well use the PyAutoGUI library to manage the movement of the avatar based on the smile ratio.

You’ve got successfully created your individual AI animated avatar using Python. You’ll be able to now customize your avatar by utilizing different images or adding more functionality to it. I hope that you’ve found this tutorial helpful and informative.

Thanks for reading, and

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