image-forgery-detection

import sys import cv2 import numpy as np from utils import pointsInsideCircle, compare, zigzag from math import pi as PI W = 8 #block size for comparision Dsim = 0.1 #threshold for symmetry Nd = 25 #nearest block quadrants_points = pointsInsideCircle(W/4) #(i,j) position of blocks which are partially/completely inside circle of radius W/2 zigzag_points = zigzag(W/2) test_image = cv2.imread(sys.argv[1],0) height,width = test_image.shape[:2] #print (height,width) vectors_list = [] for j in range(0,height-W+1): for i in range(0,width-W+1): block = test_image[j:j+W,i:i+W] dct_block = cv2.dct(np.float32(block)) feature_block = [[],[],[],[]] for index,coeff_list in enumerate(zigzag_points): for coeff in coeff_list: feature_block[index].append(dct_block[coeff[0],coeff[1]]) feature_block_np = np.array(feature_block) feature_vector = [] for quadrant,points in quadrants_points.iteritems(): summ = 0 for point in points: summ = summ + feature_block_np[point[0],point[1]] feature_vector.append(summ/PI) vectors_list.append(np.array(feature_vector)) vectors_list2 = cv2.sort(np.array(vectors_list),cv2.SORT_EVERY_ROW) print "vectors calculated" import json with open('data.json', 'w') as outfile: json.dump(vectors_list2.tolist(), outfile) i=0 blocks = [] for i in range(0,len(vectors_list)): if i%width == 0: print i/width posA = [i/width,i%width] j = i+1 for j in range(i+1,len(vectors_list)): posB = [j/width,j%width] if compare(vectors_list[i],vectors_list[j],posA,posB,Dsim,Nd): print (posA,posB) blocks.append([posA,posB]) output_image = cv2.imread(sys.argv[1],1) for block in blocks: x1 = block[0][0] x1_8 = block[0][0]+W y1 = block[0][1] y1_8 = block[0][1]+W output_image[x1:x1_8,y1:y1_8] = [0,0,255] x2 = block[1][0] x2_8 = block[1][0]+W y2 = block[1][1] y2_8 = block[1][1]+W output_image[x2:x2_8,y2:y2_8]=[0,255,0] cv2.imwrite("output.jpg",output_image) print "feature vectors extracted"