Cerealization II

The Crunch Redux

but first!

a message from our sponsors:

CEREAL KILLLER CAFE

"Claims in the talk are suspect.

all cereals are healthy

- just add more fruit"

CAN YOU TELL

WHAT'S INSIDE

JUST LOOKING

ON THE OUTSIDE

CODE & DATA

EMPOWER

YOU TO GO

A → B

some cereals, no good

what data to get?

 and features?

preparing the data

Image Feature Vector

import cv2

cv2.imread('cerealbox.png')
img = cv2.imread('../img/img_042_gmbooberry.jpg', 0)

ret,thresh1 = cv2.threshold(img,33,255,cv2.THRESH_BINARY)
ret,thresh2 = cv2.threshold(img,33,255,cv2.THRESH_BINARY_INV)
ret,thresh3 = cv2.threshold(img,33,255,cv2.THRESH_TRUNC)
ret,thresh4 = cv2.threshold(img,33,255,cv2.THRESH_TOZERO)
ret,thresh5 = cv2.threshold(img,33,255,cv2.THRESH_TOZERO_INV)
 
thresh = ['img','thresh1','thresh2','thresh3','thresh4','thresh5']
 

for i in xrange(6):
    
    plt.subplot(2,3,i+1),plt.imshow(eval(thresh[i]),'gray')
    
    plt.title(thresh[i])
 
plt.show()

THRESHOLD @ 33

THRESHOLD @ 128

THRESHOLD @ 233

img = cv2.imread('../img/img_042_gmbooberry.jpg')

imgray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 180,255,cv2.THRESH_BINARY_INV)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

cv2.drawContours(img, contours, -1, (0,0,255), 2)

plt.figure(figsize=(20,10))
plt.axis("off")
plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB));

reading values

>>> img.shape
(500,700)

>>> img.size
350000

Image Dimensions

(Interstellar can't touch this)

array([ 181.12238527,  199.1831504 ,  
        206.51429651,   80.67819854,
         65.41130384,   77.77899992])

Colour Mean,

Colour Standard Deviation

climbing up the ladder

defining your palette

Newborns are nearly color-blind. For the first few months they can see only the brightest colors. Everything else is just shades of gray. By age three their vision is pretty normal, but by then they may have been spoiled on brightly-colored things because those things are often designed specifically for children. Almost as if young children inhabit a separate universe, color-coded so they know what's for them.

colour, for humans

cv2.cvtColor(img, BGR2HSV)

 - Saturation / Intensity +

# Simplified Code

img_hsv = cv2.cvtColor(img, BGR2HSV)

# Extract the Hue values
s = img_hsv[:,:,1]
v = img_hsv[:,:,2]

# Intensity metric
i = s * v

Colour Blends

# Simplified Code

img_hsv = cv2.cvtColor(img, BGR2HSV)

# Extract the Hue values
h = img_hsv[:,:,0]

# Distance from Primary colours

primary = h % 40
secondary = abs(primary - 40)
blend = max(primary,secondary)

Most Frequent Colour vs Dominant Colour Cluster

def closest_colour(requested_colour):
    pri_color = ['Red', 'Orange', 'Yellow', 'Green', 'Blue',
                 'Violet', 'Brown', 'Black', 'Grey', 'White']
    pri_color = [x.lower() for x in pri_color]
    min_colours = {}
    #for key, name in webcolors.css3_hex_to_names.items():
    for key, name in [x for x in itertools.ifilter(lambda x: x[1] in pri_color,
                                                    webcolors.css3_hex_to_names.items())]:
        r_c, g_c, b_c = webcolors.hex_to_rgb(key)
        rd = (r_c - requested_colour[0]) ** 2
        gd = (g_c - requested_colour[1]) ** 2
        bd = (b_c - requested_colour[2]) ** 2
        min_colours[(rd + gd + bd)] = name

    return min_colours[min(min_colours.keys())]
closest_colour([123, 134, 255])

>>> 'violet'

 White for Lightness

machine learning

spokes-characters

Haar Features

Haar Cascades

Haar Cascades

even a 24x24 window results

over  160 000 features

Object Detection

Sorry, Leprechauns, with those marshmallows, 

it probably isn’t good for you 

gaze recognition 

Our Features

mean colour, std color

histograms

downsampled image

dominant colours

degree of colour relationship

has white background

has spokes-character

has downward gaze

VS

cereals.cf_score.hist(bins=25)
sns.barplot(x="has_character", y="cf_score",
            hue='cf_target_market', data=cereals)
sns.stripplot(x="cf_target_market", y="cf_score", data=cereals)
sns.barplot(x="has_white_bg", y="cf_score", data=cereals)
sns.barplot(x="has_character", y="cf_score", data=cereals)
sns.barplot(x="has_downard_gaze", y="cf_score", data=cereals)
sns.barplot(x="most_dominant_colour", y="cf_score", data=cereals);

PARAGON

AWARD

cf_score dominant_colour has_character
82 grey no

DUKE NUKEM

AWARD

cf_score dominant_colour has_downward_gaze
46 yellow yes

CROSSED WIRES 

AWARD

cf_score dominant_colour has_downward_gaze
64 grey yes

honestly good

cf_score dominant_colour has_character
36 white no

secretly terrible

We're Hiring!

apply@droste.hk

m@droste.hk

gh : @tijptjik

mart van de ven

Cerealization II : The Crunch Redux

By Droste

Cerealization II : The Crunch Redux

PyCon Hong Kong '15 talk on predicting nutritional values based on box cover designs.

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