Plugin to train and make computer vision predictions using machine learning models.
- numpy (
pip install numpy)
- opencv (
pip install cv2)
Also make sure that your OpenCV installation comes with the
dnnmodule. To test it:
>>> import cv2.dnn
Initialize self. See help(type(self)) for accurate signature.
predict(img, model_file, classes=None, resize=None, color_convert=None)¶
Make predictions for an input image using a model file. Supported model formats include all the types supported by cv2.dnn (currently supported: Caffe, TensorFlow, Torch, Darknet, DLDT).
- model_file – Path to the model file
- img – Path to the image
- classes – List of string labels associated with the output values (e.g. [‘negative’, ‘positive’]). If not set then the index of the output neuron with highest value will be returned.
- resize – Tuple or list with the resize factor to be applied to the image before being fed to the model (default: None)
- color_convert – Color conversion to be applied to the image before being fed to the model.
It points to a cv2 color conversion constant (e.g.
cv2.COLOR_BGR2GRAY) and it can be either the constant value itself or a string (e.g. ‘COLOR_BGR2GRAY’).
- numpy (