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knnsingle.py
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knnsingle.py
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import cv2
import numpy as np
import sys
# Load the images
if sys.argv > 2:
img =cv2.imread(sys.argv[1])
# Convert them to grayscale
imgg =cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# imgg = cv2.fastNlMeansDenoising(imgg,None,10,10,20)
# SURF extraction
surf = cv2.SURF(128)
kp = surf.detect(imgg)
kp, descritors = surf.compute(imgg,kp)
# surf = cv2.SURF(1024)
# Setting up samples and responses for kNN
samples = np.array(descritors)
responses = np.arange(len(kp),dtype = np.float32)
# kNN training
knn = cv2.KNearest()
knn.train(samples,responses)
modelImages = sys.argv[2:]
for modelImage in modelImages:
template = cv2.imread(modelImage) #loading a template image and searching for similar keypoints
templateg= cv2.cvtColor(template,cv2.COLOR_BGR2GRAY)
# templateg = cv2.fastNlMeansDenoising(templateg,10,10,10,20)
keys = surf.detect(templateg)
keys,desc = surf.compute(templateg, keys) # get surf keypoints from template image
count = 0
lent = len(desc)
for h,des in enumerate(desc):
# print h#,des
# print des.shape
des = des.astype(np.float32).reshape((-1,128))
retval, results, neigh_resp, dists = knn.find_nearest(des,1)
res,dist = int(results[0][0]),dists[0][0]
if dist<0.1: # draw matched keypoints in red color
color = (0,0,255)
count += 1
else: # draw unmatched in blue color
#print dist
color = (255,0,0)
x,y = kp[res].pt # draw matched key points on original image
center = (int(x),int(y))
cv2.circle(img,center,2,color,-1)
x,y = keys[h].pt # draw matched key points on template image
center = (int(x),int(y))
cv2.circle(template,center,2,color,-1)
# print ""
cv2.imshow('img similarity = %s'%(count*100/lent),img) #similarity = TP/P = count/lent
cv2.imshow('tm',template)
cv2.waitKey(0)
cv2.destroyAllWindows()