analyze_view.py

import json
import requests

class MyClass(GeneratedClass):

    # ...

    def onInput_picture(self, p):        
        '''
        Identify objects in an image by calling an external microservice
        Input: location of an image that was taken using the camera
        '''
        self.log('Image location is {}'.format(p))
        with open(p) as f:
         # formdata payload: the captured image 
         files = {'image' : f}
         print('Sending inference request to {} ...'.format(resnet_inference_url))
         # send a POST request to the service's /model/predict endpoint
         r = requests.post(resnet_inference_url, files = files)
        
         if r.status_code > 200:
           self.logger.error('Inference request returned HTTP code {} and message {}'.format(r.status_code, r.text))
         else:
           # HTTP 200 response returns JSON; parse and extract 'predictions' array
           objects = r.json()['predictions']
           if len(objects) == 0:
              # ... no objects could be identified
              # sad response, e.g. 
              self.tts.say('I saw something but I don\'t know what it is.')
           else:
              for object in objects:
                # label: identifies the object type
                # probability: quantifies the confidence [0...1], 1 = highest confidence
                self.log('Object: {} probability: {}'.format(object['label'], object['probability']))
                if object['probability'] > 0.05:
                  self.tts.say('I see a {}'.format(object['label']))
                else:
                  self.tts.say('I think there is a {}? '.format(object['label']))

    # ...