#!/usr/bin/env python
# coding: utf-8

# # Parametric maps
# This notebook demonstrates how parametric maps can be made. In such parametric images, pixel intensity corresponds to  measurements of the objects, for example area.

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import pyclesperanto_prototype as cle

from skimage.io import imread, imsave, imshow
import matplotlib
import numpy as np


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# load data
image = imread('https://samples.fiji.sc/blobs.png')
blobs = cle.push(image)

cle.imshow(blobs)


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binary = cle.threshold_otsu(blobs)

cle.imshow(binary)


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labels = cle.connected_components_labeling_box(binary)

cle.imshow(labels, labels=True)


# # Quantitative maps a.k.a. parametric images
# ## Pixel count map

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pixel_count_map = cle.label_pixel_count_map(labels)

cle.imshow(pixel_count_map, color_map='jet')


# ## Extension ratio map
# The extension ratio is a shape descriptor derived from the maximum distance of pixels to their object's centroid divided by the average distance of pixels to the centroid.

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extension_ratio_map = cle.extension_ratio_map(labels)

cle.imshow(extension_ratio_map, color_map='jet')


# ## Mean / minimum / maximum / standard-deviation intensity map

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mean_intensity_map = cle.label_mean_intensity_map(blobs, labels)
cle.imshow(mean_intensity_map, color_map='jet')


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minimum_intensity_map = cle.minimum_intensity_map(blobs, labels)
cle.imshow(minimum_intensity_map, color_map='jet')


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maximum_intensity_map = cle.maximum_intensity_map(blobs, labels)
cle.imshow(maximum_intensity_map, color_map='jet')


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stddev_intensity_map = cle.standard_deviation_intensity_map(blobs, labels)
cle.imshow(stddev_intensity_map, color_map='jet')


# ## Neigbor count maps

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enlarged_labels = cle.extend_labeling_via_voronoi(labels)
cle.imshow(enlarged_labels, labels=True)


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touching_neighbor_count_map = cle.touching_neighbor_count_map(enlarged_labels)
cle.imshow(touching_neighbor_count_map, color_map='jet')


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proximal_neighbor_count_map = cle.proximal_neighbor_count_map(labels, max_distance=50)
cle.imshow(proximal_neighbor_count_map, color_map='jet')


# ## Distance to neighbor maps

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n_nearest_neighbor_distance_map = cle.average_distance_of_n_closest_neighbors_map(labels, n=3)
cle.imshow(n_nearest_neighbor_distance_map, color_map='jet')


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