If you’re looking to work with human pose estimation, you might find OpenPose and OpenCV to be a powerful combination to achieve your goals. This blog will guide you step-by-step to implement human pose estimation using OpenPose MobileNet in OpenCV, along with troubleshooting tips.
Getting Started with Pose Estimation
Human pose estimation is like teaching a computer how to understand body language by recognizing the positions of key points on a person’s body. With OpenPose MobileNet, you can efficiently perform this task using less computational power than traditional models.
Prerequisites
- Python installed on your machine.
- OpenCV library for Python.
- Follow the installation guidelines of [OpenCV](https://opencv.org/releases/) and [ildoonet tf-pose-estimation](https://github.com/ildoonettf-pose-estimation/tree/master/models/graph/mobilenet_thin).
Step-by-Step Instructions
1. Set Up Your Script
You can start by cloning the modified OpenPose script from the OpenCV DNN Example repository:
git clone https://github.com/opencv/opencv.git2. Use the Following Commands
Now you can test the pose estimation with either a webcam or an image:
- Test with Webcam:
Run the following command:python openpose.py - Test with an Image:
For testing an image, you can run:python openpose.py --input image.jpg - Adjust Confidence Threshold:
To increase the confidence threshold, use the following:python openpose.py --input image.jpg --thr 0.5
Understanding the Code Modifications
In the world of computer vision, think of the MobileNet model as a compact car designed for efficiency rather than size. While a traditional Caffe model might be a spacious SUV (over 200MB), MobileNet keeps things light at just 7MB. This is crucial for applications that need speed without compromising results.
We modify the `cv.dnn.blobFromImage` function to ensure that we only retrieve necessary data. The line out = out[:, :19, :, :] is analogous to a chef selecting only the freshest vegetables from a garden. While the garden has many options, we focus only on those essential components that contribute to our dish, or in this case, our pose estimation output.
Troubleshooting Tips
If you encounter issues while running the script, consider the following tips:
- Check if all required libraries are installed correctly.
- Ensure that the path to the input image is correct.
- Adjust the camera settings if the webcam does not display any feed.
- If you’re getting low confidence results, try increasing the threshold using the `–thr` option.
- For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
Final Thoughts
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.