This how-to-guide shows you how to easily analyze colors in an image with Clarifai's Color Model. The Color Model is popular with designers and is useful in a wide range of image analysis scenarios. Color can be a helpful way to filter and sort a visual dataset.  

For example, the Color Model might return the following information for the image above:

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 analyze color with Clarifai API and through Portal. If you would like to analyze color via API, you will also need to generate an API key.

Analyze color 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. 

Create your application and choose "General" as your base workflow

To use the color model, we will first create an app that uses "General" 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 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 color model to your workflow

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

Select your new "color" 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. 

Analyze color in a local image with the 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='eeed0b6733a644cea07cf4c60f87ebb7',
    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))

Analyze color on a remote image with the 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='eeed0b6733a644cea07cf4c60f87ebb7',
    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))