The Human Pose Estimator model detects humans and their poses in a given image. The model first detects the humans in the input image and then identifies the body parts, including nose, neck, eyes, shoulders, elbows, wrists, hips, knees, and ankles. Next, each pair of associated body parts is connected by a "pose line"; for example, as the following image shows, a line may connect the left eye to the nose, while another may connect the nose to the neck.
Each pose line is represented by a list [x1, y1, x2, y2], where the first pair of coordinates (x1, y1) is the start point of the line for one body part, while the second pair of coordinates (x2, y2) is the end point of the line for the other associated body part. The pose lines are assembled into full body poses for each of the humans detected in the image.
The model is based on the open source TF implementation of OpenPose model. The code in this repository deploys the model as a web service in a Docker container. This repository was developed as part of the IBM Developer Model Asset Exchange.
| Domain | Application | Industry | Framework | Training Data | Input Data Format |
|---|---|---|---|---|---|
| Vision | Pose Estimation | General | TensorFlow | COCO | Image(RGB) |
- Zhe Cao, Tomas Simon, Shih-En Wei, Yaser Sheikh, "Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields", CVPR 2017.
- TF-Pose-Estimation Github Repository
| Component | License | Link |
|---|---|---|
| This repository | Apache 2.0 | LICENSE |
| Model Weights (MobileNet only) | Apache 2.0 | LICENSE |
| Model Code (3rd party) | Apache 2.0 | LICENSE |
| Test Samples | Various | Samples README |
docker: The Docker command-line interface. Follow the installation instructions for your system.- The minimum recommended resources for this model is 2GB Memory and 1 CPU.
- If you are on x86-64/AMD64, your CPU must support AVX at the minimum.
To run the docker image, which automatically starts the model serving API, run:
$ docker run -it -p 5000:5000 quay.io/codait/max-human-pose-estimator
This will pull a pre-built image from the Quay.io container registry (or use an existing image if already cached locally) and run it. If you'd rather checkout and build the model locally you can follow the run locally steps below.
You can deploy the model-serving microservice on Red Hat OpenShift by following the instructions for the OpenShift web console or the OpenShift Container Platform CLI in this tutorial, specifying quay.io/codait/max-human-pose-estimator as the image name.
You can also deploy the model on Kubernetes using the latest docker image on Quay.
On your Kubernetes cluster, run the following commands:
$ kubectl apply -f https://raw.githubusercontent.com/IBM/MAX-Human-Pose-Estimator/master/max-human-pose-estimator.yaml
The model will be available internally at port 5000, but can also be accessed externally through the NodePort.
Clone this repository locally. In a terminal, run the following command:
$ git clone https://github.com/IBM/MAX-Human-Pose-Estimator
Change directory into the repository base folder:
$ cd MAX-Human-Pose-Estimator
To build the docker image locally, run:
$ docker build -t max-human-pose-estimator .
All required model assets will be downloaded during the build process. Note that currently this docker image is CPU only (we will add support for GPU images later).
To run the docker image, which automatically starts the model serving API, run:
$ docker run -it -p 5000:5000 max-human-pose-estimator
The API server automatically generates an interactive Swagger documentation page. Go to http://localhost:5000 to load it. From there you can explore the API and also create test requests.
Use the model/predict endpoint to load a test image (you can use one of the test images from the samples folder) and get predicted labels for the image from the API.
You can also test it on the command line, for example:
$ curl -F "file=@samples/Pilots.jpg" -XPOST http://localhost:5000/model/predict
You should see a JSON response like that below:
{
"status": "ok",
"predictions": [
{
"human_id": 0,
"pose_lines": [
{
"line": [
444,
269,
392,
269
]
},
...
{
"line": [
439,
195,
464,
197
]
}
],
"body_parts": [
{
"part_id": 0,
"part_name": "Nose",
"score": "0.83899",
"x": 428,
"y": 205
},
...
{
"part_id": 17,
"part_name": "LEar",
"score": "0.81776",
"x": 464,
"y": 197
}
]
},
...
]
}
Once the model server is running, you can see how to use it by walking through the demo notebook. Note the demo requires jupyter, numpy, matplotlib, opencv-python, json, and requests.
Run the following command from the model repo base folder, in a new terminal window (leaving the model server running in the other terminal window):
$ jupyter notebook
This will start the notebook server. You can open the simple demo notebook by clicking on samples/demo.ipynb.
To run the Flask API app in debug mode, edit config.py to set DEBUG = True under the application settings. You will then need to rebuild the docker image (see step 1).
To stop the Docker container, type CTRL + C in your terminal.
If you are interested in contributing to the Model Asset Exchange project or have any queries, please follow the instructions here.