Pose estimation is a computer vision technique for determining the pose of an object in an image.

In this example, you learn how to implement inference code with a ModelZoo model to detect people and their joints in an image.

The source code can be found at PoseEstimation.java.

Follow setup to configure your development environment.

You can find the image used in this example in the project test resource folder: `src/test/resources/pose_soccer.jpg`

Use the following command to run the project:

```
cd examples
./gradlew run -Dmain=ai.djl.examples.inference.cv.PoseEstimation
```

Your output should look like the following:

```
[INFO ] - Pose image has been saved in: build/output/joints-0.png
[INFO ] - Pose image has been saved in: build/output/joints-1.png
[INFO ] - Pose image has been saved in: build/output/joints-2.png
[INFO ] - [
[
{"Joint": {"x"=0.333, "y"=0.063}, "confidence": 0.6940},
{"Joint": {"x"=0.333, "y"=0.031}, "confidence": 0.7182},
{"Joint": {"x"=0.354, "y"=0.047}, "confidence": 0.4949},
{"Joint": {"x"=0.354, "y"=0.047}, "confidence": 0.9011},
{"Joint": {"x"=0.458, "y"=0.031}, "confidence": 0.8790},
{"Joint": {"x"=0.375, "y"=0.172}, "confidence": 0.8546},
{"Joint": {"x"=0.542, "y"=0.156}, "confidence": 0.8659},
{"Joint": {"x"=0.417, "y"=0.313}, "confidence": 0.7731},
{"Joint": {"x"=0.625, "y"=0.328}, "confidence": 0.9211},
{"Joint": {"x"=0.458, "y"=0.500}, "confidence": 0.7541},
{"Joint": {"x"=0.542, "y"=0.359}, "confidence": 0.5837},
{"Joint": {"x"=0.458, "y"=0.469}, "confidence": 0.6387},
{"Joint": {"x"=0.563, "y"=0.469}, "confidence": 0.6686},
{"Joint": {"x"=0.271, "y"=0.703}, "confidence": 0.8583},
{"Joint": {"x"=0.625, "y"=0.719}, "confidence": 0.8233},
{"Joint": {"x"=0.125, "y"=0.969}, "confidence": 0.7007},
{"Joint": {"x"=0.958, "y"=0.844}, "confidence": 0.7480}
],
[
{"Joint": {"x"=0.354, "y"=0.125}, "confidence": 0.8993},
{"Joint": {"x"=0.375, "y"=0.109}, "confidence": 0.9235},
{"Joint": {"x"=0.354, "y"=0.109}, "confidence": 0.8176},
{"Joint": {"x"=0.438, "y"=0.094}, "confidence": 0.9242},
{"Joint": {"x"=0.458, "y"=0.094}, "confidence": 0.6368},
{"Joint": {"x"=0.500, "y"=0.156}, "confidence": 0.8452},
{"Joint": {"x"=0.688, "y"=0.156}, "confidence": 0.6121},
{"Joint": {"x"=0.479, "y"=0.250}, "confidence": 0.9007},
{"Joint": {"x"=0.854, "y"=0.234}, "confidence": 0.7352},
{"Joint": {"x"=0.208, "y"=0.250}, "confidence": 0.7154},
{"Joint": {"x"=0.958, "y"=0.313}, "confidence": 0.5030},
{"Joint": {"x"=0.625, "y"=0.484}, "confidence": 0.6673},
{"Joint": {"x"=0.500, "y"=0.500}, "confidence": 0.7583},
{"Joint": {"x"=0.708, "y"=0.719}, "confidence": 0.7621},
{"Joint": {"x"=0.271, "y"=0.641}, "confidence": 0.8008},
{"Joint": {"x"=0.250, "y"=0.906}, "confidence": 0.8605}
],
[
{"Joint": {"x"=0.271, "y"=0.156}, "confidence": 0.8428},
{"Joint": {"x"=0.292, "y"=0.141}, "confidence": 0.8469},
{"Joint": {"x"=0.271, "y"=0.125}, "confidence": 0.8029},
{"Joint": {"x"=0.333, "y"=0.141}, "confidence": 0.9200},
{"Joint": {"x"=0.354, "y"=0.141}, "confidence": 0.4879},
{"Joint": {"x"=0.542, "y"=0.250}, "confidence": 0.8573},
{"Joint": {"x"=0.292, "y"=0.250}, "confidence": 0.8553},
{"Joint": {"x"=0.771, "y"=0.359}, "confidence": 0.9046},
{"Joint": {"x"=0.167, "y"=0.391}, "confidence": 0.6416},
{"Joint": {"x"=0.854, "y"=0.469}, "confidence": 0.9166},
{"Joint": {"x"=0.188, "y"=0.359}, "confidence": 0.6091},
{"Joint": {"x"=0.458, "y"=0.563}, "confidence": 0.5665},
{"Joint": {"x"=0.375, "y"=0.563}, "confidence": 0.5728},
{"Joint": {"x"=0.146, "y"=0.750}, "confidence": 0.6888},
{"Joint": {"x"=0.667, "y"=0.766}, "confidence": 0.7807},
{"Joint": {"x"=0.000, "y"=0.938}, "confidence": 0.2272},
{"Joint": {"x"=0.396, "y"=0.828}, "confidence": 0.4885}
]]
```

Output images with the detected joints for each person will be saved in the build/output directory: