Setting Up Keypoint Detection on Ubuntu

This guide will walk you through the setup of Keypoint Detection on Ubuntu using TensorFlow, along with an example implementation.

Step 1: Install Prerequisites

Before starting, ensure you have the following installed:

  • Ubuntu 18.04 or later
  • Python 3.6 or later
  • Pip package manager

Update and Upgrade System

sudo apt update && sudo apt upgrade

Install Python and Pip

If Python and Pip are not installed, install them by running:

sudo apt install python3 python3-pip

Step 2: Set Up a Virtual Environment

Setting up a virtual environment helps keep dependencies organized.

python3 -m venv keypoint_env
source keypoint_env/bin/activate

After activating the environment, you should see the virtual environment name in your terminal prompt.

Step 3: Install TensorFlow and Other Dependencies

Install TensorFlow and required libraries for image processing:

pip install tensorflow opencv-python-headless matplotlib

With TensorFlow installed, you can now proceed to set up Keypoint Detection.

Step 4: Download a Pre-trained Model for Keypoint Detection

We will use the MoveNet model from TensorFlow Hub, which is efficient for detecting keypoints in human poses.

pip install tensorflow-hub

Step 5: Keypoint Detection with TensorFlow - Example Code

Below is an example script to detect keypoints using the MoveNet model.

Code

Save the following script as keypoint_detection.py:


import tensorflow as tf
import tensorflow_hub as hub
import numpy as np
import cv2
import matplotlib.pyplot as plt

# Load the MoveNet model
model = hub.load("https://tfhub.dev/google/movenet/singlepose/lightning/4")

def detect_keypoints(image_path):
    # Load and preprocess the image
    image = tf.io.read_file(image_path)
    image = tf.image.decode_jpeg(image)
    image = tf.image.resize_with_pad(image, 192, 192)
    input_image = tf.expand_dims(image, axis=0)
    input_image = tf.cast(input_image, dtype=tf.int32)

    # Run the model
    outputs = model.signatures["serving_default"](input_image)
    keypoints = outputs["output_0"]

    # Extract keypoints and draw them
    keypoints = np.squeeze(keypoints)
    plot_keypoints(image, keypoints)

def plot_keypoints(image, keypoints):
    image = np.array(image)
    h, w, _ = image.shape
    plt.imshow(image)

    for i, point in enumerate(keypoints[0][0:17]):
        y, x, confidence = point
        plt.scatter([x * w], [y * h], c='red', marker='x')
    plt.show()

# Run the function on an image
detect_keypoints("path_to_your_image.jpg")

Step 6: Run the Keypoint Detection Script

To run the script, ensure you are in the same directory where the keypoint_detection.py file is saved and that you have an image ready for testing:

python keypoint_detection.py

Explanation of the Script

  • Model Loading: The MoveNet model is loaded from TensorFlow Hub, ready to detect keypoints for human poses.
  • Image Preprocessing: The image is resized to the required dimensions and prepared for input to the model.
  • Keypoint Extraction: The model outputs keypoints, which are then extracted and visualized on the image.

Troubleshooting

  • TensorFlow Hub Access Issues: Ensure your internet connection is stable, as the model is downloaded from an online source.
  • OpenCV Errors: Make sure opencv-python-headless is installed to handle image processing without requiring a GUI.
  • CUDA Compatibility: If you want GPU acceleration, ensure your TensorFlow installation is compatible with your CUDA and cuDNN versions.

Further Reading and Resources

Conclusion

Setting up Keypoint Detection on Ubuntu is straightforward with TensorFlow and TensorFlow Hub. By using pre-trained models, you can quickly get started with human pose estimation, facial recognition, and more. With this setup, you're ready to explore advanced keypoint detection and apply it to real-world AI applications.