Creating a Finger Counter Using Computer Vision and OpenCV in Python

What is MediaPipe?

MediaPipe is an independent solution set put together by Google to support the application of the machine learning processing path to cross-platform applications. It offers the solution for computer vision, audio signal processing, and any other ML operation within the optimized models.

Key Features of MediaPipe:

1. Pre-built Solutions: It comes with a set of APIs which are more or less ready to use applications such as Face Detection, Hand Tracking, Pose Estimation, Object Detection, and so much more.
2. Real-Time Performance: MediaPipe is optimized for real-time applications and can process input efficiently.
3. Cross-Platform: Works on Android, iOS, web, and desktop platforms, making it versatile for various applications.
4. High Accuracy: Provides highly accurate results due to advanced ML models.
5. Customizability: Developers can build custom ML pipelines by integrating MediaPipe with their own models.

Common Use Cases

1. Hand Tracking: Detecting hand movements or gestures.
2. Pose Estimation: Identifying body poses for fitness apps or animations.
3. Face Mesh: Detailed face mapping for augmented reality (AR).
4. Object Detection: Identifying objects in an image or video.
5. Audio Processing: Speech or sound analysis.

What is OpenCV?

OpenCV is an open-source parallel processing numerical library for image processing and used for computing different lookups. They are used in analyzing images, videos, and real-time data to create other applications such as object detection or image recognition, as well as tracking faces.

Key Features of OpenCV:

1. Image Processing: OpenCV contains classes to work with images, including changing image's size, rotation, applying filters, and converting between different color spaces.
2. Video Processing: This capability supports, in particular, the fixed and sustained reading and writing, or the sequential processing, of a video file (or real-time video stream).
3. Object Detection: Identify faces, eyes, hands and others using available standard models and algorithms.
4. Edge Detection: Determines borders inside a picture which are useful in the study of shapes and objects.
5. Machine Learning Integration: Uses such other ML algorithms as the k-means clustering, Support Vector Machines (SVMs) and the neural networks in order to analyse images and classify them.
6. Multi-Platform Support: Compatible with Window, Linux, MacOS, Android and IOS operating systems.

Some Applications of OpenCV

1. Face Recognition: Used in security and authentication systems.
2. Object Detection: Assists the robot in identification of objects that are important maybe for robotics or surveillance.
3. Augmented Reality: Real-world data being augmented by virtual ones.
4. Medical Imaging: Hospitals The job involves helping with analyzing X-rays, CT scans or MRI scans.
5. Gaming and Animation: Improves the experience of communication in real time environments.

Creating a Finger Counter in Python

We will now start creating a finger counter in Python with the help of Computer Vision and OpenCV:

Code:

Output:

 Detected fingers: 5 

Explanation:

Here's a simple explanation of how the code works:

• Install Libraries: This code then imports required packages that includes OpenCV and media pipe for image processing and hand detection and Matplotlib for result visualization.
• Image Upload: It invites you to bring an image file from your local computer to Google Colab environment. Once the file is uploaded the code then corrects the file name to read the image from the file.
• Hand Detection: In the code segment with the help of MediaPipe, hand detection model is followed and the hand landmarks of a uploaded image is detected. MediaPipe identifies some particular features on the hand such as tips of fingers which will be used in counting fingers.
• Count Fingers: The code defines a function that counts how many fingers are raised based on the landmarks. It checks if the tip of each finger is above its base by comparing their vertical positions (y-coordinates). If the tip is higher than the base, it counts that finger as raised.
• Draw Landmarks: Once the hand landmarks are detected, the code uses the MediaPipe drawing module to display these landmarks and their connections on the image.
• Display Output: Finally, it shows the processed image with landmarks drawn on it and prints the number of detected fingers to the output console.