DataVisualization

The class for managing the data of the main repositories

A collection of methods to simplify your code.

smltk.datavisualization.DataVisualization

Features management

smltk.datavisualization.DataVisualization.get_df	Create a DataFrame from the data of the main repositories
smltk.datavisualization.DataVisualization.get_inference_df	Create a DataFrame from the data of the main repositories

Images management

smltk.datavisualization.DataVisualization.get_inference_objects	Rescale boxes with probability greater than the threshold
smltk.datavisualization.DataVisualization.get_inference_objects_df	Create a DataFrame from the prediction of object detection
smltk.datavisualization.DataVisualization.plot_inference_objects	Plot image with boxes

Detailed list

class smltk.datavisualization.DataVisualization(*args, **kwargs)

bboxes_cxcywh_to_xyxy(bboxes)

Concatenate a sequence of tensors along a new dimension

Arguments:

bboxes (sequence of Tensors):

list of boxes Tensors

Returns:

sequence of Tensors

get_df(data)

Create a DataFrame from the data of the main repositories

Arguments:

data (mixed):

data loaded from one of the main repositories

Returns:

Pandas DataFrame

get_inference_df(data, x_test, y_test, y_pred)

Create a DataFrame from the data of the main repositories

Arguments:

x_test (Pandas DataFrame):

features used for the prediction

y_test (list of str):

list of the targets

y_pred (list of str):

list of the predictions

Returns:

Pandas DataFrame

get_inference_objects(image, prediction, threshold=0.7)

Rescale boxes with probability greater than the threshold

Arguments:

image (PIL Image):

object of type PIL Image

prediction (dict):

prediction of the model with pred_logits and pred_boxes

threshold (float):

probability value used like threshold, default 0.7

Returns:

tuple of sequences of Tensors about probabilities and boxes

get_inference_objects_df(probability, boxes)

Create a DataFrame from the prediction of object detection

Arguments:

probability (sequence of Tensors):

list of probabilities Tensors

boxes (sequence of Tensors):

list of boxes Tensors

Returns:

Pandas DataFrame

plot_inference_objects(image, probability, boxes)

Plot image with boxes

Arguments:

image (PIL Image):

object of type PIL Image

probability (sequence of Tensors):

list of probabilities Tensors

boxes (sequence of Tensors):

list of boxes Tensors

Returns:

plot

rescale_bboxes(bboxes, size)

Rescale boxes on image size

Arguments:

bboxes (sequence of Tensors):

list of boxes Tensors

size (tuple):

width and height of image

Returns:

sequence of Tensors