from pylab import *
import tables
from collections import Counter

# simple visualization of projected RGB space
cspace = zeros((256,256,3))
for i in range(256):
    for j in range(256):
        x = (i/255.0)
        y = (j/255.0)
        if x+y>1: continue
        z = 1-x-y
        rgb = array([x,y,z])
        cspace[i,j,:] = rgb
imshow(cspace)

# color histogram in projected RGB space
def chist(image,r=15):
    if image.dtype==dtype('B'): image = image/255.0
    result = zeros((r+1,r+1))
    assert image.ndim==3
    rgb = image.reshape(-1,3)
    intensity = sum(rgb,axis=1)
    rgb = rgb[(intensity>0.02)*(intensity<0.98)]
    rgb = r*rgb*1.0/sum(rgb,axis=1)[:,newaxis]
    xy = array(rgb,'i')[:,:2]
    index = xy[:,0]*r+xy[:,1]
    counts = Counter(index)
    for k,v in counts.items(): result[k//r,k%r] = v
    return result

# computing a color histogram of an image
image = imread("raw_images/DSCN0314.JPG")
subplot(121); imshow(image)
c = chist(image); c = c*1.0/amax(c)
cs = (c[:,:,newaxis]**.5)*cspace[::16,::16,:]; cs = cs*1.0/amax(cs)
subplot(122); imshow(cs,interpolation='nearest',cmap=cm.gray)

# conversion to Lab color space
from skimage import io,color
lab = color.rgb2lab(image)
for i in range(3):
    subplot(1,3,i+1); imshow(lab[:,:,i],cmap=cm.gray)
    print amin(lab[:,:,i]),amax(lab[:,:,i])


def ifloor(a): return array(a,'i')

def chist(image,iq=3,cq=7):
    lab = color.rgb2lab(image).reshape(-1,3)
    lab = lab[lab[:,0]>1.0]
    lab[:,0] = lab[:,0]*iq/100.0
    lab[:,1] = (lab[:,1]+128.0)*cq/255.0
    lab[:,2] = (lab[:,2]+128.0)*cq/255.0
    index = ifloor(lab[:,0])*cq*cq+ifloor(lab[:,1])*cq+ifloor(lab[:,2])
    counts = Counter(index)
    result = zeros((iq+1)*(cq+1)*(cq+1))
    for k,v in counts.items(): result[k] = v
    return result

image = imread("raw_images/DSCN0243.JPG")
subplot(121); imshow(image)
subplot(122); imshow(chist(image).reshape(8,-1)**.5,interpolation='nearest')

hdf = tables.openFile("1k.h5","r")

hists = [chist(image) for image in hdf.root.icons]

subplot(121); imshow(hdf.root.icons[20])
subplot(122); imshow(hists[20].reshape(8,-1),interpolation='nearest')

# search by color histogram similarity
def search_example():
    figsize(12,8)
    from scipy.spatial import distance
    for ff,t in enumerate([0,20,40,60,80]):
        dists = distance.cdist(hists[t].reshape(1,-1),hists,metric='cosine')[0]
        nearest = argsort(dists)
        for f,i in enumerate(nearest[:5]):
            subplot(5,5,ff*5+f+1); axis("off"); imshow(hdf.root.icons[i]); title("%d %.2f"%(i,dists[i]))

search_example()