This how-to-guide shows you how to easily detect people in an image with Clarifai's Weapon Detector. The Weapon Detector automatically identifies and classifies weapons in images and videos. AI-powered weapon detection promises to be an important public-safety technology.

Getting Started

First things first. You will need to set up a Clarifai account and create an application. This how-to article shows you how to detect weapons with Clarifai API and through Portal. If you would like to detect weapons via API, you will also need to generate an API key.

Detecting people with Clarifai Portal

You can do almost anything that Clarifai can do with Clarifai Portal, and we work hard to make Portal the world's easiest interface for using AI. Detecting people with Portal is as simple as uploading your data, and setting up the right workflow. 

Create your application and choose your base workflow

Simply log in to Clarifai Portal and create a new application. Select "General Detection" as your base workflow. 

Navigate to Model Mode and create a new workflow

Next, we will want to create a new workflow that uses the celebrity model. Just navigate to Model Mode on the right hand sidebar and click "Create New Workflow" in the upper righthand corner of the screen. 

Add the weapon "visual-detector" to your workflow

Now we will add just one model to the work flow: the weapon "visual-detector". Be sure to select "clarifai" as the user in the lefthand dropdown menu. You can then filter your results by model type. Select "visual detector". Click "ADD" to add the model to your workflow, and then click "CREATE WORKFLOW"

Select your new "weapon" workflow as the app workflow

Now navigate to view your image in Explorer. In the righthand sidebar you can click the "APP WORKFLOW" tab, and click the gear icon. Finally select your new workflow, and view your predictions. 

First Create Your New Weapon Workflow

from clarifai_grpc.grpc.api import service_pb2, resources_pb2
from clarifai_grpc.grpc.api.status import status_code_pb2

# This is how you authenticate.
metadata = (('authorization', 'Key {{YOUR_CLARIFAI_API_KEY}}'),)

post_workflows_response = stub.PostWorkflows(
    service_pb2.PostWorkflowsRequest(
      workflows=[
        resources_pb2.Workflow(
          id="my-weapon-workflow",
          nodes=[
            resources_pb2.WorkflowNode(
              id="logo",
              model=resources_pb2.Model(
                id="6afba5f2e2787adfc0f71dcfca3eb364",
                model_version=resources_pb2.ModelVersion(
                  id="721a84fe09b64093b68193b23a29b5bf"
                )
              )
            ),
          ]
        )
      ]
    ),
    metadata=metadata
)

if post_workflows_response.status.code != status_code_pb2.SUCCESS:
    raise Exception("Post workflows failed, status: " + post_workflows_response.status.description)

Now Use Your Workflow to Make Predictions on Images

from clarifai_grpc.grpc.api import service_pb2, resources_pb2
from clarifai_grpc.grpc.api.status import status_code_pb2

# This is how you authenticate.
metadata = (('authorization', 'Key {{YOUR_CLARIFAI_API_KEY}}'),)

post_workflow_results_response = stub.PostWorkflowResults(
    service_pb2.PostWorkflowResultsRequest(
        workflow_id="my-weapon-workflow", 
        inputs=[
            resources_pb2.Input(
                data=resources_pb2.Data(
                    image=resources_pb2.Image(
                        url="https://samples.clarifai.com/metro-north.jpg"
                    )
                )
            )
        ]
    ),
    metadata=metadata
)
if post_workflow_results_response.status.code != status_code_pb2.SUCCESS:
    raise Exception("Post workflow results failed, status: " + post_workflow_results_response.status.description)

# We'll get one WorkflowResult for each input we used above. Because of one input, we have here
# one WorkflowResult.
results = post_workflow_results_response.results[0]

# Each model we have in the workflow will produce one output.
for output in results.outputs:
    model = output.model

    print("Predicted concepts for the model `%s`" % model.name)
    for concept in output.data.concepts:
        print("\t%s %.2f" % (concept.name, concept.value))