Analyzing images using Visual Recognition built-in models
IBM Watson Visual Recognition comes with built-in models that you can use to analyze images for scenes, objects, and many other categories without any training.
Demonstration
Watch this video to see how to use the built-in visual recognition models.
Python notebook example
Here is an example of Python code that uses Visual Recognition built-in models to analyze a sample image. This sample code calls the Visual Recognition API and can be run in a notebook in IBM Watson Studio.
!pip install --upgrade "watson-developer-cloud>=1.0,<2.0"
from watson_developer_cloud import VisualRecognitionV3
visual_recognition = VisualRecognitionV3( '2016-05-20', api_key='<your-API-key>' )
image_url = 'https://watson-developer-cloud.github.io/doc-tutorial-downloads/visual-recognition/visual-recognition-food-fruit.png'
import json
parms = json.dumps( { 'url' : image_url, 'classifier_ids' : [ 'food' ] } )
results = visual_recognition.classify( parameters = parms )
print( json.dumps( results['images'][0]['classifiers'][0]['classes'], indent=2 ) )
For information about how to look up your API key, see: Building Visual Recognition apps.
Sample image
Sample output
[
{
"class": "lemon",
"score": 0.583,
"type_hierarchy": "/fruit/citrus/lemon"
},
{
"class": "citrus",
"score": 0.719
},
{
"class": "fruit",
"score": 0.901
},
{
"class": "apple",
"score": 0.526,
"type_hierarchy": "/fruit/accessory fruit/apple"
},
{
"class": "accessory fruit",
"score": 0.526
},
{
"class": "orange",
"score": 0.518,
"type_hierarchy": "/fruit/citrus/orange"
},
{
"class": "banana",
"score": 0.5,
"type_hierarchy": "/fruit/banana"
}
]