*This notebook is part of the collection accompanying the talk "Analyzing Satellite Images With Python Scientific Stack" by Milos Miljkovic given at PyData NYC 2014. The content is BSD licensed.*

Oftentimes, it is necessary to select a subset of pixels from an image. The reductive procedure in which a number of pixels are marked as a representative feature of the image is called sampling. Type of sampling depends on intended use of the subset - image size rescaling, signal processing, or machine learning have different requirements. Strategies and algorithms for sampling can be divided into two broad categories:

- Applying a mask made of geometrically regular patterns.
- Applying a mask made of algorithmically random selected points.

In the first case, repeating, regular patterns may have negative effect on subsequent image analyses and processing. The second way of selecting sample pixels necessitates use of algorithms depending on randomness. This introduced randomness should be "evenly" distributed across the image with no big gaps or tightly spaced pixels.

In this notebook we will explore two types of sampling:

- Uniform random sampling.
- Poisson-disc sampling.

This algorithm uses random numbers from uniform distribution to select (x,y) coordinates of sampled pixels. Unfortunately, the result of the uniform random sampling is poor. Pixels tend to be selected in such way that heavy under- and over-sampling occurs. Points have a tendency to either clustering together or are spread wide apart.

Unlike random sampling, this algorithm avoids scattering selection points across the image. Selection set is incrementally built from existing points by applying a set of rules imposing distance and distribution constrains.

The implementation of the Poisson-disc sampling presented in this notebook is based on the Bridson paper describing an efficient *O(n)* algorithm, and Java code by Jason Davies, one of D3 developers.

In [1]:

```
# To install watermark extension execute:
# %install_ext https://raw.githubusercontent.com/HyperionAnalytics/watermark/master/watermark.py
%load_ext watermark
```

In [2]:

```
%watermark -v -m -p numpy,scipy,matplotlib
```

In [3]:

```
import math
import random
import numpy as np
from random import random
from matplotlib import pyplot as plt
from matplotlib.collections import PolyCollection
from scipy.spatial import Voronoi, voronoi_plot_2d
from IPython.display import HTML
```

In [4]:

```
HTML('<iframe src=http://bl.ocks.org/mbostock/raw/dbb02448b0f93e4c82c3 '
'style="background-color: white;" width=970 height=520></iframe>')
```

Out[4]:

Red dots represent “active” samples. At each iteration, one is selected randomly from the set of all active samples. Then, up to k candidate samples (shown as hollow black dots), are randomly generated within an annulus surrounding the selected sample.

The annulus extends from radius r to 2r, where r is the minimum allowable distance between any two samples. Candidate samples within radius r from an existing sample are rejected; this “exclusion zone” is shown in gray, along with a black line connecting the rejected candidates with the existing sample that is too close. If the candidate is acceptable, it is added as a new active sample.

A background grid of size r/√2 is used to accelerate the distance check for each candidate. Because each cell can only contain at most one sample, only a fixed number of neighboring cells need to be inspected.

If none of the k candidates are acceptable, the selected active sample is marked as inactive and will no longer be used to generate candidates. Inactive samples are shown in black.

When no samples remain active, the algorithm ends. When no samples remain active, the algorithm ends.

Mike Bostock

In [5]:

```
%matplotlib inline
```

In [6]:

```
plt.rcParams.update({'figure.facecolor': 'w',
'font.size': 14,
'figure.figsize': [12.0, 9.0]})
```

In [7]:

```
def random_select(img_width, img_hight, num_pts):
"""
Select random points in image
Input:
img_width - integer, 1 to n
img_hight - integer, 1 to n
num_pts - integer, 1 to img_width*img_hight
Output:
sample_pts_array - floats array, shape[img_width*img_hight, 2]
"""
lst = []
for n in range(num_pts):
lst.append([random()*img_width, random()*img_hight])
sample_pts_array = np.asarray(lst)
return sample_pts_array
```

In [8]:

```
def poisson_disc_select(img_width, img_hight, r, n_try):
"""
Select points from Poisson disc
Input:
img_width - integer, 1 to n
img_hight - integer, 1 to n
r - minimum didtance between two points, float
n_try - number of randomly sampled points per try, integer, 1 - n
Output:
sample_pts_array - floats array, shape[img_width*img_hight, 2]
"""
r_square = r**2
A = 3*r_square
cell_size = r/math.sqrt(2)
grid_width = int(math.ceil(img_width/cell_size))
grid_hight = int(math.ceil(img_hight/cell_size))
grid = [None]*grid_width*grid_hight
queue = list()
queue_size = 0
sample_size = 0
def distance(x, y):
x_idx = int(x/cell_size)
y_idx = int(y/cell_size)
x0 = max(x_idx-2, 0)
y0 = max(y_idx-2, 0)
x1 = min(x_idx+3, grid_width)
y1 = min(y_idx+3, grid_hight)
for w in range(y0, y1):
p = w*grid_width
for h in range(x0, x1):
if grid[p+h]:
s = grid[p+h]
dx = s[0]-x
dy = s[1]-y
if dx**2 + dy**2 < r_square:
return False
return True
def set_point(x, y):
nonlocal queue, grid, queue_size, sample_size
s = [x, y]
queue.append(s)
grid[grid_width*int(y/cell_size) + int(x/cell_size)] = s;
queue_size += 1
sample_size += 1
return s
# Set first data point
if sample_size == 0:
x = random()*img_width
y = random()*img_hight
set_point(x, y)
while queue_size:
x_idx = int(random()*queue_size)
s = queue[x_idx]
# Generate random point in annulus [r, 2r]
for y_idx in range(0, n_try):
a = 2*math.pi*random()
b = math.sqrt(A*random() + r_square)
x = s[0] + b*math.cos(a)
y = s[1] + b*math.sin(a)
# Set point if farther than r from any other point
if 0 <= x and x < img_width and 0 <= y and y < img_hight and distance(x, y):
set_point(x, y)
del queue[x_idx]
queue_size -= 1
sample_pts = list(filter(None, grid))
sample_pts_array = np.asfarray(sample_pts)
return sample_pts_array
```

In [9]:

```
def plot_vor(vor, x, y, offset, title):
"""
Plot Voronoi diagram
Input:
vor - Voronoi object
x - x axis limit max, float
y - y axis limit max, float
offset - x,y limits offset, float
title - title, string
"""
voronoi_plot_2d(vor)
plt.xlim(0-offset, x+offset)
plt.ylim(0-offset, y+offset)
plt.title(title)
plt.show()
```

In [10]:

```
def plot_scatter(pts, x, y, title):
"""
Scatter plot
Input:
pts - floats array
title - string
"""
p = plt.scatter(pts[:, 0], pts[:, 1], marker='o', edgecolors='k', linewidths=1.0)
p.set_facecolor('k')
plt.xlim(0, x)
plt.ylim(0, y)
plt.title(title)
plt.show()
```

In [11]:

```
def plot_hist(a, bins, color, title):
"""
Histogram plot
Input:
a - list of floats
bins - integer
title - string
"""
plt.subplots()
plt.hist(a, bins, color=color)
plt.title(title)
plt.show()
```

In [12]:

```
def vor_patches(vor, x, y, fc, title_prefix):
"""
Plot Voronoi patches
Input:
vor - Voronoi object
x - integer
y - integer
fc - patch face color, string
title - string
Output:
nv - Finite Voroni patches with vertices within image boundaries, list of floats arrays
"""
nv = []
for region in vor.regions[1::]:
if all(v >= 0 for v in region):
b = vor.vertices[region, :]
if np.all(0 <= b[:, 0]) and np.all(x >= b[:, 0]) and \
np.all(0 <= b[:, 1]) and np.all(y >= b[:, 1]):
nv.append(b)
fig, ax = plt.subplots()
coll = PolyCollection(nv, edgecolors='w', facecolors=fc)
ax.add_collection(coll)
plt.xlim(0, x)
plt.ylim(0, y)
plt.title(title_prefix + ' - Finite patches with vertices within image boundaries\n'
'Number of patches: ' + str(len(nv)))
plt.show()
return nv
```

In [13]:

```
def sort_patch_vert(patches):
"""
Sort Voronoi patch verteces counter clockwise
Input:
patches - list of floats array
Output:
patches_sorted - list of floats array
"""
patches_sorted = []
for p in patches:
if p.shape[0] != 0:
c = p.mean(axis=0)
angles = np.arctan2(p[:,1] - c[1], p[:,0] - c[0])
p_sorted = p[np.argsort(angles)]
patches_sorted.append(p_sorted)
return patches_sorted
```

In [14]:

```
def polygon_area(vertices):
"""
Calculate polygon area (shoelace algorithm)
Input:
vertices - floats array
Output:
area - float
"""
n = len(vertices)
area = 0.0
for i in range(n):
j = (i + 1) % n
area += vertices[i][0] * vertices[j][1]
area -= vertices[j][0] * vertices[i][1]
area = abs(area) / 2.0
return area
```

In [15]:

```
def patches_area(patches):
"""
Calculate Voronoi patches areas
Input:
patches - list of floats arrays
Output:
areas - list of floats
"""
areas = []
for p in patches:
a = polygon_area(p)
areas.append(a)
return areas
```

In [16]:

```
x = 640
y = 480
num_pts = 1000
r = 14
n_try = 30
```

In [17]:

```
pts_random = random_select(x, y, num_pts)
pts_poisson = poisson_disc_select(x, y, r, n_try)
```

In the case of uniform random selection, points have a tendency to either clustering together or are spread wide apart, while Poisson-disc selection produces points evenly distributed across the image.

In [18]:

```
plot_scatter(pts_random, x, y, 'Randomly sampled points')
plot_scatter(pts_poisson, x, y, 'Poisson-disc sampled points')
```

To visualize how many pixels a single point is representative of we can use areas of Voronoi regions belonging to each selected (x,y) coordinate. The difference in area sizes is even more obvious when plotting only finite regions whose all vertices are within image boundaries.

In [19]:

```
vor_random = Voronoi(pts_random)
vor_poisson = Voronoi(pts_poisson)
```

In [20]:

```
plot_vor(vor_random, x, y, 0, 'Voronoi plot of randomly sampled points')
plot_vor(vor_poisson, x, y, 0, 'Voronoi plot of Poisson-disc sampled points')
```

In [21]:

```
nv_random = vor_patches(vor_random, x, y, 'g', 'Random selection')
nv_poisson = vor_patches(vor_poisson, x, y, 'b', 'Poisson-disc selection')
```