keras-image-classification-wrapper 0.0.1

A thin wrapper around keras image classification applications.

keras-image-classification-wrapper

A thin wrapper around keras image classification applications.

Installation

pip install keras-image-classification-wrapper

Usage

• </code></pre>
  </li>
  </ul>
  <p>def classify(
  image: Union[str, bytes, pillow.Image.Image],
  results: int = 3,
  model: str = INCEPTIONV3,
  ) -> tuple:</p>
  <pre><code>
     Classify an image.
     
     `results` has to be less that 5, since keras applications don't give more than five results.
     
     `model` has to be one of: `XCEPTION`, `VGG16`, `VGG19`, `RESNET50`, `RESNET101`, `RESNET152`, `RESNET50V2`, `RESNET101V2`, `RESNET152V2`, `INCEPTIONV3`, `INCEPTIONRESNETV2`, `MOBILENET`, `MOBILENETV2`, `DENSENET121`, `DENSENET169`, `DENSENET201`, `NASNETMOBILE`, `NASNETLARGE`, `EFFICIENTNETB0`, `EFFICIENTNETB1`, `EFFICIENTNETB2`, `EFFICIENTNETB3`, `EFFICIENTNETB4`, `EFFICIENTNETB5`, `EFFICIENTNETB6`, `EFFICIENTNETB7`. Take a look at [model characteristics](https://keras.io/api/applications/#available-models), if you are not sure, which one to choose.
  
   - ``` python
  def load_model(model: str) -> None:
  

  Preload a model.

  Loading of desired model is done automatically at the first call of classify. But it can take significant time, if weights need to be downloaded. So you can preload a model.

  Usage examples

  With local files:

  import keras_image_classification as image_classification
  
  file_path = "path/to/image.png"
  
  labels = image_classification.classify(file_path, results = 3, model = image_classification.INCEPTIONV3)
  print(labels)
  

  With byte-like objects (here with requests):

  import requests
  import keras_image_classification as image_classification
  
  response = requests.get("https://http.cat/100")
  assert response.status_code == 200
  
  labels = image_classification.classify(response.content, results = 3, model = image_classification.INCEPTIONV3)
  print(labels)
  

  You can also pass pillow images directly:

  import PIL as pillow
  import keras_image_classification as image_classification
  
  file_path = "path/to/image.png"
  image = pillow.Image.open(file_path)
  
  labels = image_classification.classify(image, results = 3, model = image_classification.INCEPTIONV3)
  print(labels)
  

  Output:

  ({'label': 'Persian_cat', 'probability': 0.7992992997169495}, {'label': 'web_site', 'probability': 0.03164924681186676}, {'label': 'jigsaw_puzzle', 'probability': 0.0135102029889822})