DCD: Deep Crossing detector¶
Given a collection of Blob objects (see
Blob), the crossing detector module allows to apply a pre-computed model of the area to each of the blobs and, if specified,
use a function approximator (in this case a convolutional neural network) in order to distinguish between Blob representing single individual and touching individuals.
detect_crossings(list_of_blobs, video, model_area, use_network=True, return_store_objects=False, plot_flag=True)¶
- list_of_blobs : <ListOfBlobs object>
Collection of the Blob objects extracted from the video
- video : <Video object>
Object containing all the parameters of the video.
- model_area : function
Model of the area of a single individual
- use_network : bool
If True the Deep Crossing Detector is used to distinguish between individuals and crossings images. Otherwise only the model area is applied
- return_store_objects : bool
If True the instantiations of the class
Store_Accuracy_and_Lossare returned by the function
- plot_flag : bool
If True a figure representing the values of the loss function, accuracy and accuracy per class for both the training and validation set.