Metadata-Version: 1.0
Name: custom_vision_client
Version: 0.0.8
Summary: A client for the Microsoft Azure Custom Vision Service
Home-page: https://github.com/CatalystCode/py_custom_vision_client
Author: Clemens Wolff
Author-email: clewolff@microsoft.com
License: License :: OSI Approved :: MIT License
Description-Content-Type: UNKNOWN
Description: .. image:: https://travis-ci.org/CatalystCode/py_custom_vision_client.svg?branch=master
          :target: https://travis-ci.org/CatalystCode/py_custom_vision_client
        
        .. image:: https://img.shields.io/pypi/v/custom_vision_client.svg
          :target: https://pypi.python.org/pypi/custom_vision_client/
        
        py_custom_vision_client
        =======================
        
        This repository contains a simple Python client for the `Custom Vision Service <https://azure.microsoft.com/en-us/services/cognitive-services/custom-vision-service/>`_.
        
        Usage
        `````
        
        .. sourcecode :: py
        
          # first, train a model
        
          from custom_vision_client import TrainingClient, TrainingConfig
        
          azure_region = "southcentralus"
          training_key = "my-training-key"  # from settings pane on customvision.ai
        
          training_client = TrainingClient(TrainingConfig(azure_region, training_key))
          project_id = training_client.create_project("my-project-name").Id
        
          training_client.create_tag(project_id, "Cat")
          training_client.create_tag(project_id, "Dog")
        
          training_client.add_training_images(project_id, ["kitten.jpg"], "Cat")
          training_client.add_training_images(project_id, ["akita.png", "spitz.png"], "Dog")
          training_client.add_training_images(project_id, ["best-animal-pals.jpg"], "Cat", "Dog")
        
          model_id = training_client.trigger_training(project_id).Id
        
          # then, use the model to predict:
        
          from custom_vision_client import PredictionClient, PredictionConfig
        
          azure_region = "southcentralus"
          prediction_key = "my-prediction-key"  # from settings pane on customvision.ai
        
          prediction_client = PredictionClient(PredictionConfig(azure_region, project_id, prediction_key))
        
          predictions = prediction_client.classify_image("cat.jpg", model_id)  # could also be a url to a file
          best_prediction = max(predictions, key=lambda _: _.Probability)
          print(best_prediction.Tag)
        
        Command-line interface
        ``````````````````````
        
        You can also interact with the Custom Vision Service via a command-line interface:
        
        .. sourcecode :: sh
        
          # first, train a model
          python3 -m custom_vision_client.training \
            --key="my-training-key" \
            --projectname="my-project-name" \
            --imagesroot="/path/to/images"
        
          # then, use the model to predict:
          python3 -m custom_vision_client.prediction \
            --key="my-prediction-key" \
            --projectid="my-project-id-from-training" \
            --modelid="my-model-id-from-training" \
            --image="path-or-url-to-image"
        
        The command-line interface assumes that your training images are organized in folders
        such that every folder contains all the training images for that label:
        
        .. sourcecode :: sh
        
          /path/to/images
          ├── label_one
          │   ├── image_1.jpg
          │   ├── image_2.png
          │   └── image_3.png
          └── label_two
              ├── image_4.jpg
              └── image_5.jpg
        
Platform: UNKNOWN
