People and vehicles are often sharing the same spaces, especially in busy urban environments. The people and vehicle detector makes it easy to identify these two important object types with one model. 

For example, the following detected regions and probability scores might be suggested for this image. 

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 label food items with the Clarifai API and through Portal. If you would like to detect people and vehicles via API, you will also need to generate an API key.

Identify celebrities in 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. To detect people and vehicles in Portal, upload your images and create a new "people and vehicle" workflow. 

Create your application and choose your base workflow

To use the people and vehicle model, we will first create an app that uses "General Detection" as the base workflow. 

Navigate to Model Mode and create a new workflow

Next, we will want to create a new workflow that uses the people and vehicle 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 people and vehicle "visual detector" to your workflow

Now we will add just one model to the work flow: the people and vehicle "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 "people and vehicle" 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. 

Detect people and vehicles in a local image via API

Use the following Python snippet as an example of how to run a prediction on an image hosted on your local computer. For more details and information on working with predictions in our other client languages, please refer to our API documentation.

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}}'),)

with open("{YOUR_IMAGE_FILE_LOCATION}", "rb") as f:
    file_bytes = f.read()

request = service_pb2.PostModelOutputsRequest(
    model_id='240a8f047a6ef4328331b5c6fb3952ca',
    inputs=[
        resources_pb2.Input(
            data=resources_pb2.Data(
                image=resources_pb2.Image(
                    base64=file_bytes
                )
            )
        )      
    ])
response = stub.PostModelOutputs(request, metadata=metadata)

if response.status.code != status_code_pb2.SUCCESS:
    raise Exception("Request failed, status code: " + str(response.status.code))

for concept in response.outputs[0].data.concepts:
    print('%12s: %.2f' % (concept.name, concept.value))

Detect people and vehicles in images hosted on the web via API

Here is an example of how to run a prediction on an image that is hosted on a URL. This snippet is in Python, but we offer support for many other client languages. Please refer to our API documentation for additional information. 

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}}'),)

request = service_pb2.PostModelOutputsRequest(
    model_id='240a8f047a6ef4328331b5c6fb3952ca',
    inputs=[
      resources_pb2.Input(data=resources_pb2.Data(image=resources_pb2.Image(url='{{YOUR_IMAGE_URL}}')))
    ])
response = stub.PostModelOutputs(request, metadata=metadata)

if response.status.code != status_code_pb2.SUCCESS:
    raise Exception("Request failed, status code: " + str(response.status.code))

for concept in response.outputs[0].data.concepts:
    print('%12s: %.2f' % (concept.name, concept.value))