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

# # Bead segmentation and measurements
# This notebook shows how to detect hot spots / local maxima / beads in an image and how to measure their FWHM. To make the algorithm work, the beads should be sufficiently sparse.

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import pyclesperanto_prototype as cle

cle.get_device()


# Let's start by making an image showing local maxima.

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import numpy as np
from skimage.io import imshow

random_image = np.random.random([512,512])
binary_image = random_image > 0.9995

# push to GPU
input_image = cle.push(binary_image * random_image)

# blur the image
sigma = 3
starting_point = cle.gaussian_blur(input_image, sigma_x=sigma, sigma_y=sigma)

# show input image
starting_point


# ## Local maxima detection

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maxima = cle.detect_maxima_box(starting_point)
maxima


# ## Local threshold determination

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# Label maxima
labeled_maxima = cle.label_spots(maxima)

# read out intensities at the maxima
max_intensities = cle.read_intensities_from_map(labeled_maxima, starting_point)
print(max_intensities)

# calculate thresholds
thresholds = cle.multiply_image_and_scalar(max_intensities, scalar=0.5)
print(thresholds)


# ## Make a local threshold image

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# Extend labeled maxima until they touch
voronoi_label_image = cle.extend_labeling_via_voronoi(labeled_maxima)
voronoi_label_image


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# Replace labels with thresholds
threshold_image = cle.replace_intensities(voronoi_label_image, thresholds)
threshold_image


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# Apply threshold
binary_segmented = cle.greater(starting_point, threshold_image)
binary_segmented


# ## Measure bounding boxes

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# Label objects
labels = cle.connected_components_labeling_box(binary_segmented)
labels


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# Derive statistics
stats = cle.statistics_of_labelled_pixels(label_image=labels)

print('Bounding box widths', stats['bbox_width'])
print('Bounding box heights', stats['bbox_height'])


# The bounding box width and height should be approximately twice as large as the sigma used for bluring the image at the very beginning.

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print("Sigma", sigma)


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