Pose estimation example¶
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.
Setup guide¶
Follow setup to configure your development environment.
Run pose estimation example¶
Input image file¶
You can find the image used in this example in the project test resource folder: src/test/resources/pose_soccer.jpg
Build the project and run¶
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:
