Visualize Module

Vizualization module.

landmarker.visualize.detection_report(true_landmarks, pred_landmarks, dim, dim_orig, pixel_spacing, padding, class_names=None, radius=[2, 2.5, 3, 4], digits=2, unit='mm', output_dict=False)

Calculate the detection report.

Parameters:

• true_landmarks (Tensor) – torch.Tensor The true landmarks, with shape (n, p, 2). n is the number of samples, p is the number of landmarks, and 2 is respectively the y and x coordinates of the landmarks.

• pred_landmarks (Tensor) – torch.Tensor The predicted landmarks, with shape (n, p, 2). n is the number of samples, p is the number of landmarks, and 2 is respectively the y and x coordinates of the landmarks.

• dim (tuple[int, ...] | Tensor) – tuple[int, …] | torch.Tensor The dimension of the image, with shape (2,). 2 is respectively the height and width of the image.

• dim_orig (Tensor) – torch.Tensor The original dimension of the image, with shape (2,). 2 is respectively the height and width of the image.

• pixel_spacing (Tensor) – torch.Tensor The pixel spacing of the image, with shape (2,). 2 is respectively the height and width of the image.

• padding (Tensor) – torch.Tensor The padding of the image, with shape (2,). 2 is respectively the height and width of the image.

• class_names (list[str] | None) – list The names of the landmarks. The default is None.

• radius (list[float]) – list[float], optional The radius of the success detection rate (SDR). The default is [2, 2.5, 3, 4].

• digits (int) – int, optional The number of digits to round the statistics. The default is 2.

• unit (str) – str, optional The unit of distance. The default is ‘mm’.

• output_dict (bool) – bool, optional Whether to output the detection report as a dictionary. The default is False.

Returns:

dict

The detection report. Only output when output_dict is True.

Return type:

report

landmarker.visualize.inspection_plot(ds, idx, heatmap_generator=None, save_path=None, fig_title='Landmark Dataset Inspection Plot')

Plots the transformed image, heatmap, and original image with landmarks for the given dataset indices.

Parameters:

• ds (LandmarkDataset) – Dataset to inspect.

• idx (int | Iterable[int]) – Indices of the dataset to inspect.

• heatmap_generator (HeatmapGenerator, optional) – Heatmap generator to use. Defaults to None.

• save_path (str, optional) – Path to save the plot to. If None, the plot is not saved. Defaults to None.

• fig_title (str, optional) – Title of the figure. Defaults to “Landmark Dataset Inspection Plot”.

landmarker.visualize.plot_cpe(true_landmarks, pred_landmarks, dim, dim_orig, pixel_spacing, padding, class_names=None, group=True, title=None, save_path=None, stat='proportion', unit='mm', kind='ecdf')

Calculate the cumulative point-to-point error (CPE) and plot the CPE curve.

Parameters:

• y_true – numpy.ndarray The true values of the target variable, with shape (n, p, 2). n is the number of samples, p is the number of landmarks, and 2 is respectively the y and x coordinates of the landmarks.

• y_pred – numpy.ndarray The predicted values of the target variable, with shape (n, p, 2). n is the number of samples, p is the number of landmarks, and 2 is respectively the y and x coordinates of the landmarks.

• class_names (list[str] | None) – list The names of the landmarks. The default is None.

• group (bool) – bool, optional Whether to group the CPE curves by landmarks. The default is True.

• title (str | None) – str, optional The title of the plot. The default is None.

• save_path (str | None) – str, optional The path to save the plot. The default is None.

• stat (str) – str, optional The type of statistic to plot. The default is ‘proportion’.

• unit (str) – str, optional The unit of distance. The default is ‘mm’.

• kind (str) – str, optional The type of plot. The default is ‘ecdf’. Possible values are ‘ecdf’, ‘kde’, and ‘hist’.

• true_landmarks (Tensor) –

• pred_landmarks (Tensor) –

• dim (tuple[int, ...] | Tensor) –

• dim_orig (Tensor) –

• pixel_spacing (Tensor) –

• padding (Tensor) –