detector = cv2.FeatureDetector_create("SIFT")

descriptor = cv2.DescriptorExtractor_create("SIFT")

skp = detector.detect(img)

skp, sd = descriptor.compute(img, skp)

tkp = detector.detect(template)

tkp, td = descriptor.compute(template, tkp)

flann_params = dict(algorithm=1, trees=4)

flann = cv2.flann_Index(sd, flann_params)

idx, dist = flann.knnSearch(td, 1, params={})

del flann

dist = dist[:,0]/2500.0

dist = dist.reshape(-1,).tolist()

idx = idx.reshape(-1).tolist()

indices = range(len(dist))

indices.sort(key=lambda i: dist[i])

dist = [dist[i] for i in indices]

idx = [idx[i] for i in indices]

skp_final = []

for i, dis in itertools.izip(idx, dist):

if dis < distance:

skp_final.append(skp[i])

flann = cv2.flann_Index(td, flann_params)

idx, dist = flann.knnSearch(sd, 1, params={})

del flann

dist = dist[:,0]/2500.0

dist = dist.reshape(-1,).tolist()

idx = idx.reshape(-1).tolist()

indices = range(len(dist))

indices.sort(key=lambda i: dist[i])

dist = [dist[i] for i in indices]

idx = [idx[i] for i in indices]

tkp_final = []

for i, dis in itertools.izip(idx, dist):

if dis < distance:

tkp_final.append(tkp[i])

h1, w1 = img.shape[:2]

h2, w2 = template.shape[:2]

nWidth = w1+w2

nHeight = max(h1, h2)

hdif = (h1-h2)/2

newimg = np.zeros((nHeight, nWidth, 3), np.uint8)

newimg[hdif:hdif+h2, :w2] = template

newimg[:h1, w2:w1+w2] = img

maxlen = min(len(skp), len(tkp))

if num < 0 or num > maxlen:

num = maxlen

for i in range(num):

pt_a = (int(tkp[i].pt[0]), int(tkp[i].pt[1]+hdif))

pt_b = (int(skp[i].pt[0]+w2), int(skp[i].pt[1]))

cv2.line(newimg, pt_a, pt_b, (255, 0, 0))

# img = cv2.imread(sys.argv[1])

# temp = cv2.imread(sys.argv[2])

img = cv2.imread("mug.jpg")

#img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

#img = misc.imresize(img,(500,500,3))

temp = cv2.imread("mug4.JPG")

#temp = cv2.cvtColor(temp,cv2.COLOR_BGR2GRAY)

#print temp.shape

temp = misc.imresize(temp,(512,512))

try:

dist = int(sys.argv[3])

except IndexError:

dist = 200

try:

num = int(sys.argv[4])

except IndexError:

num = -1

skp, tkp = findKeyPoints(img, temp, dist)

newimg = drawKeyPoints(img, temp, skp, tkp, num)

cv2.imshow("image", newimg)

img = cv2.imread('mug4.JPG')

gray= cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

sift = cv2.SIFT()

kp = sift.detect(gray,None)

img=cv2.drawKeypoints(gray,kp,flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)

cv2.imshow('sift_keypoints.jpg',img)

img = cv2.imread(filename)

gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

gray = np.float32(gray)

if gray.shape!=(512,512):

gray = cv2.resize(gray, (512,512), interpolation=cv2.INTER_LINEAR)

dst = cv2.cornerHarris(gray,2,3,0.04)

#result is dilated for marking the corners, not important

dst = cv2.dilate(dst,None)

cords = []

nrow, ncol = dst.shape

#print nrow,ncol

gap = min(nrow,ncol)*0.1

lasti=lastj=-2

for i in range(nrow):

for j in range(ncol):

#print i,j

if dst[i,j]>thres*dst.max():

if max(abs(i-lasti),abs(j-lastj))>gap:

cords.append((i,j))

lasti=i

lastj=j

#print i,j

return cords

# Threshold for an optimal value, it may vary depending on the image.

#img[dst>thres*dst.max()]=[0,0,255]

# kms =KMeans()

# kms.fit(cords)

# centers = kms.cluster_centers_

# return centers

# for a in centers:

# i = int(a[0])

# j = int(a[1])

# print i,j

# img[i,j]=[0,0,255]

#cv2.imshow('img',img)