Week 2 content of Face Emoji project
Hello! Welcome to Week 2 of our Face-Emoji project. Last week, we set up our development environment and got a basic camera feed working. This week, we're taking a big step forward by implementing face detection using Google's MediaPipe library!
Setting up a Python virtual environment
Understanding face detection and its importance
Introduction to Google's MediaPipe library
How to detect faces in real-time video
Setting up the foundation for future emotion detection
file you'll need:
week2_code.py
MediaPipe is currently only compatible with Python versions up to 3.12.9. Since Python 3.13 is the latest version and many of you might have it installed, we need to manage our Python version carefully. For Windows users, we'll use Miniconda3, which makes Python version management much easier!
Miniconda3 is a minimal installer for Conda, a powerful package and environment management system. It helps you create isolated environments with specific Python versions and packages, making it perfect for our needs.
Download Miniconda3 for Windows: https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe
Install Miniconda3:
Run the installer
Select "Install for all users" if you want it system-wide
Use default options for everything else
Important: At the end, let it initialize Miniconda3
After installation:
Open "Anaconda Powershell Prompt" from your Start Menu
This special prompt is just like your regular terminal but with Conda powers!
Navigate to your project directory: cd path\to\your\face-emoji
Create a new Conda environment with Python 3.12.9:
conda create -n face-emoji python==3.12.9
Activate the environment: conda activate face-emoji
Install required packages: pip install mediapipe opencv-python
When you're done working on the project, you can deactivate the environment: conda deactivate
Some helpful Conda commands to remember:
conda activate face-emoji - Activates your project environment
conda deactivate - Exits the current environment
conda env list - Shows all your Conda environments
conda list - Shows all packages in current environment
Remember: Always activate your environment (conda activate face-emoji) before working on the project!
For Mac users, we'll use Homebrew to install Python 3.12:
Install Python 3.12 using Homebrew: brew install python@3.12
Install MediaPipe using the Python 3.12 pip: python3.12 -m pip install mediapipe opencv-python
That's it! You're ready to go. When running the code, make sure to use python3.12 instead of just python.
MediaPipe is an amazing framework developed by Google that provides ready-to-use ML solutions. It's particularly great for computer vision tasks like face detection, facial landmarks, pose estimation, and much more. We'll be using MediaPipe throughout this project because:
It's highly accurate and fast
Works great in real-time
Has excellent Python support
Provides detailed facial landmarks (which we'll use later!)
Is actively maintained by Google
Has great documentation
Face detection is the first step in any face analysis system. It's different from face recognition - detection just finds where faces are in an image, while recognition tries to identify whose face it is.
What makes MediaPipe special is that it can not only detect faces but also provide detailed information about facial landmarks - 468 points that map to specific facial features! While we won't use these landmarks today, they'll be crucial when we start detecting emotions in the coming weeks.
Let's break down the code we'll be using today:
import cv2
import mediapipe as mp
mp_face_detection = mp.solutions.face_detection
mp_drawing = mp.solutions.drawing_utils
face_detection = mp_face_detection.FaceDetection( model_selection=0, min_detection_confidence=0.5 )
This section imports the necessary libraries and initializes MediaPipe's face detection. The model_selection=0 parameter tells MediaPipe to use the close-range face detection model, which is perfect for webcam use.
This part should look familiar from last week - we're setting up our camera feed.
while True:
ret, frame = cap.read()
rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
results = face_detection.process(rgb_frame)
Here's where the magic happens! We:
Read a frame from the camera
Convert it to RGB (MediaPipe expects RGB, while OpenCV uses BGR)
Process the frame to detect faces
if results.detections:
for detection in results.detections:
mp_drawing.draw_detection(frame, detection)
This code draws boxes around detected faces and includes some key points like eyes and nose. We've also added confidence scores to show how sure the model is about each detection.
How to set up and use Python virtual environments
Using MediaPipe for face detection
Basic concepts of computer vision processing
How to process video frames in real-time
How to visualize detection results
Next week, we'll dive deeper into MediaPipe's capabilities by exploring facial landmarks. These landmarks will be crucial for emotion detection, as they'll help us understand:
Eye positions and shapes
Mouth configurations
Eyebrow positions
Overall facial expressions
The landmarks we'll learn about next week will form the foundation for our emotion detection system. They'll serve as the input features for our machine learning model, which will help us map facial expressions to emotions and, ultimately, to emojis!
Keep experimenting with this week's code - try adjusting the confidence threshold, see how the detection works in different lighting conditions, and get comfortable with the real-time processing concepts. These skills will be essential as we move forward with the project!
See you next week! 😊
