custom_vision_client 0.0.8

用法

# 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)

命令行界面

您还可以通过命令行界面与Custom Vision Service进行交互

# 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"

命令行界面假定您的训练图像按文件夹组织，每个文件夹都包含该标签的所有训练图像

/path/to/images
├── label_one
│   ├── image_1.jpg
│   ├── image_2.png
│   └── image_3.png
└── label_two
    ├── image_4.jpg
    └── image_5.jpg