data_visualization

Description

Helper functions for visualization and analysis of image datasets.

Functions

apply_mask(img, mask[, color, alpha])	Apply monochrome (possibly non-binary) mask to image of same size with alpha value.
apply_masks(img, masks[, colors, alphas])
compare_masks(*masks[, colors])	Merge several monochrome masks in different colors into the same image.
mean_proportion_pos_px(masks)	From the given samples, calculate the mean of the proportion of positive pixels per mask.
neg_pixel_prop(data[, max_num_samples, ...])	Collect the mean proportion of negative pixels in the binary segmentation mask data.
to_img(tens)	Transform a (cuda) tensor to a PIL.Image.Image.
to_monochrome_img(img_t)	torch.Tensor to monochrome PIL.Image.Image in 'L' (=8-bit) mode.
visualize_classification_data(dataset[, ...])	Visualize a dataset yielding tuples of the form (input, target_class_identifier).
visualize_mask_transforms(titled_datasets[, ...])	Visualize difference between original and the transformed masks of datasets.
visualize_segmentation_data(dataset[, ...])	Visualize a dataset yielding tuples of the form (input, target_mask).