Generate Color Histogram around a contoured object

Question

Hey OpenCV/Emgu gurus,

I have an image that I am generating contour for, see below. I am trying to generate a color histogram based pruning of search space of images

Answer 1

Perhaps something like this? Done using the Python bindings, but easy to translate the methods to other bindings...

#!/usr/local/bin/python

import cv 
import colorsys

# get orginal image
orig = cv.LoadImage('car.jpg')

# show orginal 
cv.ShowImage("orig", orig)

# get mask image
maskimg = cv.LoadImage('carcontour.jpg')

# split original image into hue and value
hsv = cv.CreateImage(cv.GetSize(orig),8,3)
hue = cv.CreateImage(cv.GetSize(orig),8,1)
val = cv.CreateImage(cv.GetSize(orig),8,1)
cv.CvtColor(maskimg,hsv,cv.CV_BGR2HSV)
cv.Split(hsv, hue, None, val, None)

# build mask from val image, select values NOT black
mask = cv.CreateImage(cv.GetSize(orig),8,1)
cv.Threshold(val,mask,0,255,cv.CV_THRESH_BINARY)

# show the mask
cv.ShowImage("mask", mask)

# calculate colour (hue) histgram of only masked area
hue_bins = 180 
hue_range = [0,180]
hist = cv.CreateHist([hue_bins], cv.CV_HIST_ARRAY, [hue_range], 1) 
cv.CalcHist([hue],hist,0,mask)

# create the colour histogram 
(_, max_value, _, _) = cv.GetMinMaxHistValue(hist)
histimg = cv.CreateImage((hue_bins*2, 200), 8, 3) 
for h in range(hue_bins):
  bin_val = cv.QueryHistValue_1D(hist,h)
  norm_val = cv.Round((bin_val/max_value)*200)
  rgb_val = colorsys.hsv_to_rgb(float(h)/180.0,1.0,1.0) 
  cv.Rectangle(histimg,(h*2,0),
                ((h+1)*2-1, norm_val),
                cv.RGB(rgb_val[0]*255,rgb_val[1]*255,rgb_val[2]*255),
                cv.CV_FILLED)
cv.ShowImage("hist",histimg)

# wait for key press
cv.WaitKey(-1)

This is a little bit clunky finding the mask - I wonder perhaps due to JPEG compression artefacts in the image... If you had the original contour it is easy enough to "render" this to a mask instead.

The example histogram rendering function is also a wee bit basic - but I think it shows the idea (and how the car is predominately red!). Note how OpenCV's interpretation of Hue ranges only from [0-180] degrees.

EDIT: if you want to use the mask to count colours in the original image - edit as so from line 15 downwards:

# split original image into hue
hsv = cv.CreateImage(cv.GetSize(orig),8,3)
hue = cv.CreateImage(cv.GetSize(orig),8,1)
cv.CvtColor(orig,hsv,cv.CV_BGR2HSV)
cv.Split(hsv, hue, None, None, None)

# split mask image into val
val = cv.CreateImage(cv.GetSize(orig),8,1)
cv.CvtColor(maskimg,hsv,cv.CV_BGR2HSV)
cv.Split(hsv, None, None, val, None)

(I think this is more what was intended, as the mask is then derived separately and applied to a completely different image. The histogram is roughly the same in both cases...)