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MTH.py
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# coding: utf-8
# # Image Retrieval Based on Multi-Texton Histogram
# In[5]:
# importing libraries
import math
import cv2
import numpy as np
import matplotlib.pyplot as plt
# In[7]:
from pymongo import MongoClient
client = MongoClient('mongodb://localhost:27017')
#path = "dataset_path"
Database = []
for entry in range(1000):
imagename = path + str(entry)+'.jpg'
print(imagename)
img = cv2.imread(imagename)
width, height, channels = img.shape
# # Texture Orientation Detection
# In[8]:
CSA = 64
CSB = 18
arr = np.zeros(3*width*height).reshape(width,height,3)
ori = np.zeros(width * height).reshape(width, height)
# In[9]:
gxx = gyy = gxy = 0.0
rh = gh = bh = 0.0
rv = gv = bv = 0.0
theta = np.zeros(width*height).reshape(width,height)
for i in range(1, width-2):
for j in range(1, height-2):
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 = math.sqrt(rh * rh + gh * gh + bh * bh)
gyy = math.sqrt(rv * rv + gv * gv + bv * bv)
gxy = rh * rv + gh * gv + bh * bv
theta[i,j] = (math.acos(gxy / (gxx * gyy + 0.0001))*180 / math.pi)
ImageX = np.zeros(width * height).reshape(width, height)
# # Color Quantization in RGB Color Space
# In[10]:
R = G = B = 0
VI = SI = HI = 0
for i in range(0, width):
for j in range(0, height):
R = img[i,j][0]
G = img[i,j][1]
B = img[i,j][2]
if (R >=0 and R <= 64):
VI = 0;
if (R >= 65 and R <= 128):
VI = 1;
if (R >= 129 and R <= 192):
VI = 2;
if (R >= 193 and R <= 255):
VI = 3;
if (G>= 0 and G <= 64):
SI = 0;
if (G >= 65 and G <= 128):
SI = 1;
if (G >= 129 and G <= 192):
SI = 2;
if (G >= 193 and G <= 255):
SI = 3;
if (B >= 0 and B <= 64):
HI = 0;
if (B >= 65 and B <= 128):
HI = 1;
if (B >= 129 and B <= 192):
HI = 2;
if (B >= 193 and B <= 255):
HI = 3;
ImageX[i, j] = 16 * VI + 4 * SI + HI
# In[11]:
for i in range(0, width):
for j in range(0, height):
ori[i,j] = round(theta[i,j]*CSB/180)
if(ori[i,j]>=CSB-1):
ori[i,j]=CSB-1
# # Texton Detection
# In[12]:
Texton = np.zeros(width * height).reshape(width, height)
for i in range(0,(int)(width/2)):
for j in range(0,(int)(height/2)):
if(ImageX[2*i,2*j] == ImageX[2*i+1,2*j+1]):
Texton[2 * i, 2 * j] = ImageX[2 * i, 2 * j];
Texton[2 * i + 1, 2 * j] = ImageX[2 * i + 1, 2 * j];
Texton[2 * i, 2 * j + 1] = ImageX[2 * i, 2 * j + 1];
Texton[2 * i + 1, 2 * j + 1] = ImageX[2 * i + 1, 2 * j + 1];
if (ImageX[2*i,2*j+1] == ImageX[2*i+1,2*j]):
Texton[2 * i, 2 * j] = ImageX[2 * i, 2 * j];
Texton[2 * i + 1, 2 * j] = ImageX[2 * i + 1, 2 * j];
Texton[2 * i, 2 * j + 1] = ImageX[2 * i, 2 * j + 1];
Texton[2 * i + 1, 2 * j + 1] = ImageX[2 * i + 1, 2 * j + 1];
if (ImageX[2*i,2*j] == ImageX[2*i+1,2*j]):
Texton[2 * i, 2 * j] = ImageX[2 * i, 2 * j];
Texton[2 * i + 1, 2 * j] = ImageX[2 * i + 1, 2 * j];
Texton[2 * i, 2 * j + 1] = ImageX[2 * i, 2 * j + 1];
Texton[2 * i + 1, 2 * j + 1] = ImageX[2 * i + 1, 2 * j + 1];
if (ImageX[2*i,2*j] == ImageX[2*i,2*j+1]):
Texton[2 * i, 2 * j] = ImageX[2 * i, 2 * j];
Texton[2 * i + 1, 2 * j] = ImageX[2 * i + 1, 2 * j];
Texton[2 * i, 2 * j + 1] = ImageX[2 * i, 2 * j + 1];
Texton[2 * i + 1, 2 * j + 1] = ImageX[2 * i + 1, 2 * j + 1];
# # Multi-Texton Histogram
# In[13]:
MatrixH = np.zeros(CSA + CSB).reshape(CSA + CSB)
MatrixV = np.zeros(CSA + CSB).reshape(CSA + CSB)
MatrixRD = np.zeros(CSA + CSB).reshape(CSA + CSB)
MatrixLD = np.zeros(CSA + CSB).reshape(CSA + CSB)
D = 1 #distance parameter
for i in range(0, width):
for j in range(0, height-D):
if(ori[i, j+D] == ori[i, j]):
MatrixH[(int)(Texton[i,j])] += 1;
if(Texton[i, j + D] == Texton[i, j]):
MatrixH[(int)(CSA + ori[i, j])] += 1;
for i in range(0, width-D):
for j in range(0, height):
if(ori[i + D, j] == ori[i, j]):
MatrixV[(int)(Texton[i,j])] += 1;
if(Texton[i + D, j] == Texton[i, j]):
MatrixV[(int)(CSA + ori[i, j])] += 1;
for i in range(0, width-D):
for j in range(0, height-D):
if(ori[i + D, j + D] == ori[i, j]):
MatrixRD[(int)(Texton[i,j])] += 1;
if(Texton[i + D, j + D] == Texton[i, j]):
MatrixRD[(int)(CSA + ori[i, j])] += 1;
for i in range(D, width):
for j in range(0, height-D):
if(ori[i - D, j + D] == ori[i, j]):
MatrixLD[(int)(Texton[i,j])] += 1;
if(Texton[i - D, j + D] == Texton[i, j]):
MatrixLD[(int)(CSA + ori[i, j])] += 1;
# # Feature Vectors
# In[14]:
MTH = np.zeros(CSA + CSB).reshape(CSA + CSB)
for i in range(0, CSA + CSB):
MTH[i] = ( MatrixH[i] + MatrixV[i] + MatrixRD[i] + MatrixLD[i])/4.0
# In[16]:
#print(MTH)
Database.append(MTH)
#print(Database[entry])
entry+=1
print("Entered for "+imagename)
Database = np.array(Database)
collection = client.MTH.coralTest
collection.insert({"distances":Database.tolist(),"name":'Coral Dataset'})
print (Database[0])