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

# # Label neighbor filters
# 
# In this notebook, we demonstrate how neighbor-based filters work in the contexts of measurements of cells in tissues. We also determine neighbor of neighbors and extend the radius of such filters.
# 
# See also
# * [Image Processing Filters for Grids of Cells Analogous to Filters Processing Grids of Pixels](https://www.frontiersin.org/articles/10.3389/fcomp.2021.774396/)

# In[1]:


import pyclesperanto_prototype as cle
import numpy as np
import matplotlib
from numpy.random import random

cle.select_device("RTX")


# In[2]:


# Generate artificial cells as test data
tissue = cle.artificial_tissue_2d()
touch_matrix = cle.generate_touch_matrix(tissue)

cle.imshow(tissue, labels=True)


# # Associate artificial measurements to the cells

# In[3]:


centroids = cle.label_centroids_to_pointlist(tissue)

coordinates = cle.pull_zyx(centroids)
values = random([coordinates.shape[1]])

for i, y in enumerate(coordinates[1]):
    if (y < 128):
        values[i] = values[i] * 10 + 45
    else:
        values[i] = values[i] * 10 + 90

measurements = cle.push_zyx(np.asarray([values]))

# visualize measurments in space
parametric_image = cle.replace_intensities(tissue, measurements)
cle.imshow(parametric_image, min_display_intensity=0, max_display_intensity=100, color_map='jet')


# # Local averaging smoothes edges
# By averaging measurments locally, we can reduce the noise, but we also introduce a stripe where the region touch

# In[4]:


local_mean_measurements = cle.mean_of_touching_neighbors(measurements, touch_matrix)

parametric_image = cle.replace_intensities(tissue, local_mean_measurements)
cle.imshow(parametric_image, min_display_intensity=0, max_display_intensity=100, color_map='jet')


# # Edge preserving filters: median
# By averaging using a median filter, we can also reduce noise while keeping the edge between the regions sharp

# In[5]:


local_median_measurements = cle.median_of_touching_neighbors(measurements, touch_matrix)

parametric_image = cle.replace_intensities(tissue, local_median_measurements)
cle.imshow(parametric_image, min_display_intensity=0, max_display_intensity=100, color_map='jet')


# # Increasing filter radius: neighbors of neighbors
# In order to increase the radius of the operation, we need to determin neighbors of touching neighbors

# In[6]:


neighbor_matrix = cle.neighbors_of_neighbors(touch_matrix)

local_median_measurements = cle.median_of_touching_neighbors(measurements, neighbor_matrix)

parametric_image = cle.replace_intensities(tissue, local_median_measurements)
cle.imshow(parametric_image, min_display_intensity=0, max_display_intensity=100, color_map='jet')


# ## Short-cuts for visualisation only
# If you're not so much interested in the vectors of measurements, there are shortcuts: For example for visualizing the mean value of neighboring pixels with different radii:

# In[7]:


# visualize measurments in space
measurement_image = cle.replace_intensities(tissue, measurements)
print('original')
cle.imshow(measurement_image, min_display_intensity=0, max_display_intensity=100, color_map='jet')

for radius in range(0, 5):
    print('Radius', radius)
    # note: this function takes a parametric image the label map instead of a vector and the touch_matrix used above
    parametric_image = cle.mean_of_touching_neighbors_map(measurement_image, tissue, radius=radius)
    cle.imshow(parametric_image, min_display_intensity=0, max_display_intensity=100, color_map='jet')


# In[ ]: