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DeployableObject Detection in Images
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By IBM Developer Staff | Published December 12, 2018
Artificial intelligenceVisionImage Feature ExtractionObject Detection in Images
This 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. The pose lines are assembled into full body poses for each of the humans detected in the image. The model is based on the TF implementation of OpenPose model.
This model can be deployed using the following mechanisms:
docker run -it -p 5000:5000 codait/max-human-pose-estimator
kubectl apply -f https://raw.githubusercontent.com/IBM/MAX-Human-Pose-Estimator/master/max-human-pose-estimator.yaml
You can test or use this model
Once deployed, you can test the model from the command line. For example if running locally:
curl -F "image=@assets/p3.jpg" -XPOST http://localhost:5000/model/predict
You should see a JSON response like that below:
The information returned from the model can be used to construct and visualize pose lines for the humans detected in the image, such as shown in the example below. For more details see the GitHub README.
Complete the node-red-contrib-model-asset-exchange module setup instructions and import the human-pose-estimator getting started flow.
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Process image, video, audio, or text data using deep learning models from the Model Asset Exchange in Node-RED flows.
Artificial intelligenceDeep Learning+
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