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        "author": "Clemens Wolff",
        "author_email": "clewolff@microsoft.com",
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        "description": ".. image:: https://travis-ci.org/CatalystCode/py_custom_vision_client.svg?branch=master\n  :target: https://travis-ci.org/CatalystCode/py_custom_vision_client\n\n.. image:: https://img.shields.io/pypi/v/custom_vision_client.svg\n  :target: https://pypi.python.org/pypi/custom_vision_client/\n\npy_custom_vision_client\n=======================\n\nThis repository contains a simple Python client for the `Custom Vision Service <https://azure.microsoft.com/en-us/services/cognitive-services/custom-vision-service/>`_.\n\nUsage\n`````\n\n.. sourcecode :: py\n\n  # first, train a model\n\n  from custom_vision_client import TrainingClient, TrainingConfig\n\n  azure_region = \"southcentralus\"\n  training_key = \"my-training-key\"  # from settings pane on customvision.ai\n\n  training_client = TrainingClient(TrainingConfig(azure_region, training_key))\n  project_id = training_client.create_project(\"my-project-name\").Id\n\n  training_client.create_tag(project_id, \"Cat\")\n  training_client.create_tag(project_id, \"Dog\")\n\n  training_client.add_training_images(project_id, [\"kitten.jpg\"], \"Cat\")\n  training_client.add_training_images(project_id, [\"akita.png\", \"spitz.png\"], \"Dog\")\n  training_client.add_training_images(project_id, [\"best-animal-pals.jpg\"], \"Cat\", \"Dog\")\n\n  model_id = training_client.trigger_training(project_id).Id\n\n  # then, use the model to predict:\n\n  from custom_vision_client import PredictionClient, PredictionConfig\n\n  azure_region = \"southcentralus\"\n  prediction_key = \"my-prediction-key\"  # from settings pane on customvision.ai\n\n  prediction_client = PredictionClient(PredictionConfig(azure_region, project_id, prediction_key))\n\n  predictions = prediction_client.classify_image(\"cat.jpg\", model_id)  # could also be a url to a file\n  best_prediction = max(predictions, key=lambda _: _.Probability)\n  print(best_prediction.Tag)\n\nCommand-line interface\n``````````````````````\n\nYou can also interact with the Custom Vision Service via a command-line interface:\n\n.. sourcecode :: sh\n\n  # first, train a model\n  python3 -m custom_vision_client.training \\\n    --key=\"my-training-key\" \\\n    --projectname=\"my-project-name\" \\\n    --imagesroot=\"/path/to/images\"\n\n  # then, use the model to predict:\n  python3 -m custom_vision_client.prediction \\\n    --key=\"my-prediction-key\" \\\n    --projectid=\"my-project-id-from-training\" \\\n    --modelid=\"my-model-id-from-training\" \\\n    --image=\"path-or-url-to-image\"\n\nThe command-line interface assumes that your training images are organized in folders\nsuch that every folder contains all the training images for that label:\n\n.. sourcecode :: sh\n\n  /path/to/images\n  \u251c\u2500\u2500 label_one\n  \u2502\u00a0\u00a0 \u251c\u2500\u2500 image_1.jpg\n  \u2502\u00a0\u00a0 \u251c\u2500\u2500 image_2.png\n  \u2502\u00a0\u00a0 \u2514\u2500\u2500 image_3.png\n  \u2514\u2500\u2500 label_two\n      \u251c\u2500\u2500 image_4.jpg\n      \u2514\u2500\u2500 image_5.jpg\n",
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        "license": "License :: OSI Approved :: MIT License",
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        "name": "custom_vision_client",
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