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CDH.py
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CDH.py
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#!/usr/bin/python
# -*- coding: utf-8 -*-
## Content Based Image Retrieval using Color Difference Histogram
# In[8]:
# importing libraries
import math
import cv2
import numpy as np
import matplotlib.pyplot as plt
import pymongo
from pymongo import MongoClient
client = MongoClient('mongodb://localhost:27017')
# In[9]:
#print("This")
Database = np.zeros(10000*108).reshape(10000,108)
for entry in range(10000):
imagename = str(entry+1)+'.jpg'
print(entry+1)
img = cv2.imread(imagename)
width, height, channels = img.shape
# In[10]:
Lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
# In[11]:
lnum = 10
anum = 3
bnum = 3
cnum = lnum * anum * bnum
onum = 18
hist = np.zeros(cnum + onum)
QuantizedImage = np.zeros(width * height).reshape(width, height)
L = a = b = 0
for i in range(width):
for j in range(height):
L = int(Lab[i][j][0] * lnum / 100.0)
if L >= lnum - 1:
L = lnum - 1
elif L < 0:
L = 0
a = int(Lab[i][j][1] * anum / 254.0)
if a >= anum - 1:
a = anum - 1
elif a < 0:
a = 0
b = int(Lab[i][j][2] * bnum / 254.0)
if b >= anum - 1:
b = anum - 1
elif b < 0:
b = 0
QuantizedImage[i][j] = bnum * anum * L + bnum * a + b
# In[12]:
def coOrdinateTransform(arr, width, height):
Lab = np.zeros(3 * width * height).reshape(width, height, 3)
for i in range(width):
for j in range(height):
Lab[i][j][0] = arr[i][j][0]
Lab[i][j][1] = arr[i][j][1]
Lab[i][j][2] = arr[i][j][2]
Lab[i][j][1] = arr[i][j][1] + 127.0
if Lab[i][j][1] >= 254.0:
Lab[i][j][1] = 254.0 - 1.0
if Lab[i][j][1] < 0:
Lab[i][j][1] = 0
Lab[i][j][2] = arr[i][j][2] + 127.0
if Lab[i][j][2] >= 254.0:
Lab[i][j][2] = 254.0 - 1.0
if Lab[i][j][2] < 0:
Lab[i][j][2] = 0
return Lab
# In[13]:
lab = coOrdinateTransform(Lab, width, height)
# In[14]:
def maxgrad_and_mingrad_Lab(arr,num,wid,hei):
gxx = gyy = gxy = 0.0
rh = gh = bh = 0.0
rv = gv = bv = 0.0
theta = 0.0
ori = np.zeros(wid * hei).reshape(wid, hei)
for i in range(1, wid - 1):
for j in range(1, hei - 1):
rh=arr[i-1,j+1,0] + 2*arr[i,j + 1,0] + arr[i+1, j+1,0] - (arr[i-1, j - 1, 0] + 2 * arr[i,j-1, 0] + arr[i + 1, j - 1, 0])
gh=arr[i-1,j+1,1] + 2*arr[i,j + 1,1] + arr[i+ 1,j+1,1] - (arr[i-1, j - 1, 1] + 2 * arr[i,j-1, 1] + arr[i + 1, j - 1, 1])
bh=arr[i-1,j+1,2] + 2*arr[i,j + 1,2] + arr[i+ 1,j+1,2] - (arr[i-1, j - 1, 2] + 2 * arr[i,j-1, 2] + arr[i + 1, j - 1, 2])
rv=arr[i+1,j-1,0] + 2*arr[i+1, j, 0] + arr[i+ 1,j+1,0] - (arr[i-1, j - 1, 0] + 2 * arr[i-1,j, 0] + arr[i - 1, j + 1, 0])
gv=arr[i+1,j-1,1] + 2*arr[i+1, j, 1] + arr[i+ 1,j+1,1] - (arr[i-1, j - 1, 1] + 2 * arr[i-1,j, 1] + arr[i - 1, j + 1, 1])
bv=arr[i+1,j-1,2] + 2*arr[i+1, j, 2] + arr[i+ 1,j+1,2] - (arr[i-1, j - 1, 2] + 2 * arr[i-1,j, 2] + arr[i - 1, j + 1, 2])
gxx = rh * rh + gh * gh + bh * bh
gyy = rv * rv + gv * gv + bv * bv
gxy = rh * rv + gh * gv + bh * bv
theta = round(math.atan(2.0 * gxy / (gxx - gyy + 0.00001))/ 2.0, 4)
G1 = G2 = 0.0
G1 = math.sqrt(abs(0.5 * ((gxx + gyy) + (gxx - gyy) * math.cos(2.0 * theta) + 2.0 * gxy * math.sin(2.0 * theta))))
G2=math.sqrt(abs(0.5*((gxx+gyy)+(gxx-gyy)*math.cos(2.0*(theta +(math.pi/2.0)))+ 2.0 * gxy * math.sin(2.0*(theta+ (math.pi / 2.0))))))
dir = 0
if max(G1, G2) == G1:
dir = 90.0 + theta * 180.0 / math.pi
ori[i, j] = int(dir * num / 360.0)
else:
dir = 180.0 + (theta + math.pi / 2.0) * 180.0 / math.pi
ori[i, j] = int(dir * num / 360.0)
if ori[i, j] >= num - 1:
ori[i, j] = num - 1
return ori
# In[15]:
ori = maxgrad_and_mingrad_Lab(lab, onum, width, height)
# In[16]:
def compute(ColorX,ori,Lab,wid,hei,CSA,CSB,D):
Arr = np.zeros(3 * wid * hei).reshape(wid, hei, 3)
Arr = coOrdinateTransform(Lab, wid, hei)
Matrix = np.zeros(CSA + CSB).reshape(CSA + CSB)
hist = np.zeros(CSA + CSB).reshape(CSA + CSB)
# -------------------calculate the color difference of different directions------------
# ----------direction=0--------------------
for i in range(wid):
for j in range(hei - D):
value = 0.0
if ori[i, j + D] == ori[i, j]:
value = math.sqrt(math.pow(Arr[i, j + D, 0] - Arr[i, j,0], 2) + math.pow(Arr[i, j + D, 1]- Arr[i, j, 1], 2) + math.pow(Arr[i,j + D, 2] - Arr[i, j, 2], 2))
Matrix[int(ColorX[i, j])] += value
if ColorX[i, j + D] == ColorX[i, j]:
value = math.sqrt(math.pow(Arr[i, j + D, 0] - Arr[i, j,0], 2) + math.pow(Arr[i, j + D, 1]- Arr[i, j, 1], 2) + math.pow(Arr[i,j + D, 2] - Arr[i, j, 2], 2))
Matrix[int(ori[i, j] + CSA)] += value
# -----------direction=90---------------------
for i in range(wid - D):
for j in range(hei):
value = 0.0
if ori[i + D, j] == ori[i, j]:
value = math.sqrt(math.pow(Arr[i + D, j, 0] - Arr[i, j,0], 2) + math.pow(Arr[i + D, j, 1]- Arr[i, j, 1], 2) + math.pow(Arr[i+ D, j, 2] - Arr[i, j, 2], 2))
Matrix[int(ColorX[i, j])] += value
if ColorX[i + D, j] == ColorX[i, j]:
value = math.sqrt(math.pow(Arr[i + D, j, 0] - Arr[i, j,0], 2) + math.pow(Arr[i + D, j, 1]- Arr[i, j, 1], 2) + math.pow(Arr[i+ D, j, 2] - Arr[i, j, 2], 2))
Matrix[int(ori[i, j] + CSA)] += value
# -----------direction=135---------------------
for i in range(wid - D):
for j in range(hei - D):
value = 0.0
if ori[i + D, j + D] == ori[i, j]:
value = math.sqrt(math.pow(Arr[i + D, j + D, 0]- Arr[i, j, 0], 2) + math.pow(Arr[i+ D, j + D, 1] - Arr[i, j, 1], 2)+ math.pow(Arr[i + D, j + D, 2]- Arr[i, j, 2], 2))
Matrix[int(ColorX[i, j])] += value
if ColorX[i + D, j + D] == ColorX[i, j]:
value = math.sqrt(math.pow(Arr[i + D, j + D, 0]- Arr[i, j, 0], 2) + math.pow(Arr[i+ D, j + D, 1] - Arr[i, j, 1], 2)+ math.pow(Arr[i + D, j + D, 2]- Arr[i, j, 2], 2))
Matrix[int(ori[i, j] + CSA)] += value
# -----------direction=45---------------------
for i in range(D, wid):
for j in range(hei - D):
value = 0.0
if ori[i - D, j + D] == ori[i, j]:
value = math.sqrt(math.pow(Arr[i - D, j + D, 0]- Arr[i, j, 0], 2) + math.pow(Arr[i- D, j + D, 1] - Arr[i, j, 1], 2)+ math.pow(Arr[i - D, j + D, 2]- Arr[i, j, 2], 2))
Matrix[int(ColorX[i, j])] += value
if ColorX[i - D, j + D] == ColorX[i, j]:
value = math.sqrt(math.pow(Arr[i - D, j + D, 0]- Arr[i, j, 0], 2) + math.pow(Arr[i- D, j + D, 1] - Arr[i, j, 1], 2)+ math.pow(Arr[i - D, j + D, 2]- Arr[i, j, 2], 2))
Matrix[int(ori[i, j] + CSA)] += value
for i in range(CSA + CSB):
hist[i] = (Matrix[i]) / 4.0
return hist
# In[19]:
D = 1
hist = compute(QuantizedImage,ori,Lab,width,height,cnum,onum,D)
# In[20]:
Database[entry] = hist
print("Entered for !"+imagename)
collection = client.test_database.coraltest
collection.insert({"distances":Database.tolist(),
"name":'Coral Dataset'})
print (Database[0])