# RGB Image Analysis with Python

02.27.2015

## Description

This is a quick python program I threw together to resolve one of the world’s most ancient and unresolved mysteries: `blue/black` or `white/gold`?

## Dependencies

``````# We need these tools first to install the pip packages below:
sudo apt-get install build-essential python-dev python-pip python-imaging

pip install numpy
pip install matplotlib
``````

## Source

``````import numpy as np
import mpl_toolkits.mplot3d.axes3d as p3
import matplotlib.pyplot as plt
import colorsys
from PIL import Image

# (1) Import the file to be analyzed!
img_file = Image.open("thedress.jpg")

# (2) Get image width & height in pixels
[xs, ys] = img_file.size
max_intensity = 100
hues = {}

# (3) Examine each pixel in the image file
for x in xrange(0, xs):
for y in xrange(0, ys):
# (4)  Get the RGB color of the pixel
[r, g, b] = img[x, y]

# (5)  Normalize pixel color values
r /= 255.0
g /= 255.0
b /= 255.0

# (6)  Convert RGB color to HSV
[h, s, v] = colorsys.rgb_to_hsv(r, g, b)

# (7)  Marginalize s; count how many pixels have matching (h, v)
if h not in hues:
hues[h] = {}
if v not in hues[h]:
hues[h][v] = 1
else:
if hues[h][v] < max_intensity:
hues[h][v] += 1

# (8)   Decompose the hues object into a set of one dimensional arrays we can use with matplotlib
h_ = []
v_ = []
i = []
colours = []

for h in hues:
for v in hues[h]:
h_.append(h)
v_.append(v)
i.append(hues[h][v])
[r, g, b] = colorsys.hsv_to_rgb(h, 1, v)
colours.append([r, g, b])

# (9)   Plot the graph!
fig = plt.figure()
ax = p3.Axes3D(fig)
ax.scatter(h_, v_, i, s=5, c=colours, lw=0)

ax.set_xlabel('Hue')
ax.set_ylabel('Value')
ax.set_zlabel('Intensity')
plt.show()
``````

## Breakdown

Basically, we are going to scan a given image file, pixel by pixel. For each pixel, we will determine the color in `(r, g, b)`.

``````# (1) Import the file to be analyzed!
img_file = Image.open("thedress.jpg")

# (2) Get image width & height in pixels
[xs, ys] = img_file.size
max_intensity = 100
hues = {}

# (3) Examine each pixel in the image file
for x in xrange(0, xs):
for y in xrange(0, ys):
# (4)  Get the RGB color of the pixel
[r, g, b] = img[x, y]

# (5)  Normalize pixel color values
r /= 255.0
g /= 255.0
b /= 255.0
``````

### Change Colorspaces

Then we map from `(r, g, b)` to `(h, s, v)` (hue, saturation and value). We are doing this because the HSV model give us a nice “rainbow” in the H dimension, essentially sorting the colors from lowest wavelength to highest.

``````    # (6)  Convert RGB color to HSV
[h, s, v] = colorsys.rgb_to_hsv(r, g, b)
``````

### Integrate Saturation

We have a 3-dimensional color space, and for some subset of points in this space, we have assigned a value corresponding to the number of pixels in the image that share that color. Those are 4 dimensions we need to somehow plot!

To simplify, we are going to marginalize over the `saturation` parameter. We’re essentially going to integrate over `s`, from 0 to 1, for every pair of `(h, v)` that appears in our image.

For every `(h, v)` pair in our color space that represents a non-zero number of pixels, we take the sum of all pixels that share those `(h, v)` values, regardless of `saturation`. Now we have something that can be represented using 3 dimensions:

``````hues(h, v) = i
``````

The idea of this `hues` structure is that for any given `(h, v)`, `hues[h][v]` represents the number of pixels appearing in the image with those hue and value parameters. In this application we have set a maximum value for any `i` because outliers will distort the Z-axis of the graph. Therefore, any colours that appear in more pixels than `max_intensity` will appear as clusters on the roof of our chart.

``````    # (7)  Marginalize s; count how many pixels have matching (h, v)
if h not in hues:
hues[h] = {}
if v not in hues[h]:
hues[h][v] = 1
else:
if hues[h][v] < max_intensity:
hues[h][v] += 1
``````

### Linearize Data

Having arrived at this `hues` object, we need to now construct three separate arrays, for `h`, `v`, and `i`. We also keep a fourth array, called `colours`. This allows us to tag each point in the chart with the color it represents (assume `saturation=1.0`). The idea is that picking an index `k`, the value of `(h[k], s[k], v[k])` is the color of data point `k`; in addition, its RGB equivalent is located in `colours[k]`.

``````# (8)   Decompose the hues object into a set of one dimensional arrays we can use with matplotlib
h_ = []
v_ = []
i = []
colours = []

for h in hues:
for v in hues[h]:
h_.append(h)
v_.append(v)
i.append(hues[h][v])
[r, g, b] = colorsys.hsv_to_rgb(h, 1, v)
colours.append([r, g, b])
``````

This step is necessary because that’s how the `Axes3D.scatter()` method’s arguments are setup.

### Render

``````# (9)   Plot the graph!
fig = plt.figure()
ax = p3.Axes3D(fig)
ax.scatter(h_, v_, i, s=5, c=colours, lw=0)

ax.set_xlabel('Hue')
ax.set_ylabel('Value')
ax.set_zlabel('Intensity')