def SIFT(filename, r_mag):
"""
Returns the interest points found
"""
s = 3
k = 2 ** (1.0 / s)
I = cv2.imread(filename)
I_bw = cv2.imread(filename, 0)
I1 = misc.imresize(I_bw, 50, 'bilinear').astype(int)
I2 = misc.imresize(I1, 50, 'bilinear').astype(int)
I3 = misc.imresize(I2, 50, 'bilinear').astype(int)
dim = I_bw.shape
# Sigma value we defined, yields more responses than the one below;
#sigma1 = [math.sqrt(0.5), math.sqrt(1), math.sqrt(2), math.sqrt(4),
#math.sqrt(8), math.sqrt(16), math.sqrt(32), math.sqrt(64),
#math.sqrt(128), math.sqrt(256), math.sqrt(512)]
# Sigma values for FIRST SCALE ONLY proposed by Lowe, yields less responses:
sigma1 = numpy.array([1.3, 1.6, 1.6 * k, 1.6 * (k ** 2), 1.6 * (k ** 3), 1.6 * (k ** 4)])
print "creating gaussian pyramids.."
o1sc = numpy.zeros((I_bw.shape[0],I_bw.shape[1],5))
o2sc = numpy.zeros((I1.shape[0],I1.shape[1],5))
o3sc = numpy.zeros((I2.shape[0],I2.shape[1],5))
o4sc = numpy.zeros((I3.shape[0],I3.shape[1],5))
for i in range(0,5):
o1sc[:,:,i] = scipy.ndimage.filters.gaussian_filter(I_bw,sigma = sigma1[i])
o2sc[:,:,i] = scipy.ndimage.filters.gaussian_filter(I1,sigma = sigma1[i]) #+2
o3sc[:,:,i] = scipy.ndimage.filters.gaussian_filter(I2,sigma = sigma1[i]) #+4
o4sc[:,:,i] = scipy.ndimage.filters.gaussian_filter(I3,sigma = sigma1[i]) # +6
# Calculate difference of gaussian images.
print "creating difference of gaussian pyramids.."
DoG_pictures_scale_1 = numpy.zeros((I_bw.shape[0],I_bw.shape[1],4))
DoG_pictures_scale_2 = numpy.zeros((I1.shape[0],I1.shape[1],4))
DoG_pictures_scale_3 = numpy.zeros((I2.shape[0],I2.shape[1],4))
DoG_pictures_scale_4 = numpy.zeros((I3.shape[0],I3.shape[1],4))
for i in range(0,4):
# CT: TRY WITH HELPERFUNCTION MINUS
DoG_pictures_scale_1[:,:,i] = o1sc[:,:,i+1] - o1sc[:,:,i]
#DoG_pictures_scale_1[:,:,i] = h.matrix_substraction(o1sc[:,:,i+1],o1sc[:,:,i]) # ct
DoG_pictures_scale_2[:,:,i] = o2sc[:,:,i+1] - o2sc[:,:,i]
DoG_pictures_scale_3[:,:,i] = o3sc[:,:,i+1] - o3sc[:,:,i]
DoG_pictures_scale_4[:,:,i] = o4sc[:,:,i+1] - o4sc[:,:,i]
#print DoG_pictures_scale_1[:,:,1]
#cv2.imshow('image',DoG_pictures_scale_1[:,:,0])
#cv2.waitKey(0)
# Initialize arrays for keypoints
DoG_extrema_points_1_1 = []
DoG_extrema_points_1_2 = []
DoG_extrema_points_2 = []
DoG_extrema_points_3 = []
DoG_extrema_points_4 = []
print("Finding points for scale 1")
for y in range(3, I_bw.shape[0] - 3):
for x in range(3, I_bw.shape[1] - 3):
if (find_max_new(DoG_pictures_scale_1,1,y,x, r_mag) == 1):
DoG_extrema_points_1_1.append([y,x,0])
if (find_max_new(DoG_pictures_scale_1,2,y,x, r_mag) == 1):
DoG_extrema_points_1_2.append([y,x,1])
dogn1 = numpy.array(DoG_extrema_points_1_1)
dogn2 = numpy.array(DoG_extrema_points_1_2)
if (len(dogn1) > 1) and (len(dogn2)>1):
result = numpy.vstack([dogn1, dogn2])
#print result
print "Number of points in first octave: %d" % len(result)
#h.points_to_txt(result, "interest_points_sc1.txt", "\n")
h.points_to_txt_3_points(result, "interest_points_sc1.txt", "\n")
h.color_pic(I, result, filename[:-4] + "-sift_sc1-"+ "r-" + str(r_mag) + ".jpg")
# TODO add scales
"""
print "Finding points for scale 2"
for y in range(3, I1.shape[0] - 3):
for x in range(3, I1.shape[1] - 3):
if (find_max_new(DoG_pictures_scale_2,y,x, r_mag) == 1):
DoG_extrema_points_2.append([y,x,0])
print len(DoG_extrema_points_2)
dogn2 = numpy.array(DoG_extrema_points_2)
if (len(dogn2) > 1):
result1 = numpy.vstack([dogn2])
print "Number of points in second octave: %d" % len(result1)
h.points_to_txt(result1, "interest_points_sc2.txt", "\n")
h.color_pic(I1, result1, filename[:-4] + "-sift_sc2-"+ "r-" + str(r_mag) + ".jpg")
print "Finding points for scale 3"
for y in range(3, I2.shape[0] - 3):
for x in range(3, I2.shape[1] - 3):
if (find_max_new(DoG_pictures_scale_3,y,x, r_mag) == 1):
DoG_extrema_points_3.append([y,x,0])
dogn3 = numpy.array(DoG_extrema_points_3)
if (len(dogn3) > 1):
result2 = numpy.vstack([dogn3])
print "LENGTH", len(result2)
print "Number of points in third octave: %d" % len(result2)
h.points_to_txt(result2, "interest_points_sc3.txt", "\n")
h.color_pic(I2, result2, filename[:-4] + "-sift_sc3-"+ "r-" + str(r_mag) + ".jpg")
print "Finding points for scale 4"
for y in range(3, I3.shape[0] - 3):
for x in range(3, I3.shape[1] - 3):
if (find_max_new(DoG_pictures_scale_4,y,x, r_mag) == 1):
DoG_extrema_points_4.append([y,x,0])
dogn4 = numpy.array(DoG_extrema_points_4)
if (len(dogn4) > 1):
result3 = numpy.vstack([dogn4])
print "Number of points in fourth octave: %d" % len(result3)
h.points_to_txt(result3, "interest_points_sc4.txt", "\n")
h.color_pic(I3, result3, filename[:-4] + "-sift_sc4-"+ "r-" + str(r_mag) + ".jpg")
"""
return(result)
def findSurfPoints(filename):
clear = " " * 50
I_bw = cv2.imread(filename, 0).astype(float)
I = cv2.imread(filename)
sigma = 1.2
gausspictures = numpy.zeros((I_bw.shape[0],I_bw.shape[1],8))
for i in range (0,7):
gausspictures[:,:,i] = cv2.filter2D(I_bw,-1, getGauss(1.2,i))
deriv9 = numpy.zeros((I_bw.shape[0],I_bw.shape[1],5))
deriv15 = numpy.zeros((I_bw.shape[0],I_bw.shape[1],5))
deriv21 = numpy.zeros((I_bw.shape[0],I_bw.shape[1],5))
deriv27 = numpy.zeros((I_bw.shape[0],I_bw.shape[1],5))
for y in range (10, I_bw.shape[0]-10):
for x in range (10, I_bw.shape[1]-10):
deriv9[y,x,0] = gausspictures[y,x+1,0] + gausspictures[y,x-1,0] - 2 * gausspictures[y,x,0] # DXX
deriv9[y,x,1] = gausspictures[y+1,x,0] + gausspictures[y-1,x,0] - 2 * gausspictures[y,x,0] # DYY
deriv9[y,x,2] = gausspictures[y+1,x+1,0] - gausspictures[y+1,x-1,0] - gausspictures[y-1,x+1,0] + gausspictures[y-1,x-1,0] # DXY
deriv9[y,x,3] = gausspictures[y,x+1,0] - gausspictures[y,x-1,0] # DX
deriv9[y,x,4] = gausspictures[y+1,x,0] - gausspictures[y+1,x,0] # DY
# TODO Also calculate ds
deriv15[y,x,0] = gausspictures[y,x+1,1] + gausspictures[y,x-1,1] - 2 * gausspictures[y,x,1] # DXX
deriv15[y,x,1] = gausspictures[y+1,x,1] + gausspictures[y-1,x,1] - 2 * gausspictures[y,x,1] # DYY
deriv15[y,x,2] = gausspictures[y+1,x+1,1] - gausspictures[y+1,x-1,1] - gausspictures[y-1,x+1,1] + gausspictures[y-1,x-1,1] # DXY
deriv15[y,x,3] = gausspictures[y,x+1,1] - gausspictures[y,x-1,1]
deriv15[y,x,4] = gausspictures[y+1,x,1] - gausspictures[y+1,x,1]
deriv21[y,x,0] = gausspictures[y,x+1,2] + gausspictures[y,x-1,2] - 2 * gausspictures[y,x,2] # DXX
deriv21[y,x,1] = gausspictures[y+1,x,2] + gausspictures[y-1,x,2] - 2 * gausspictures[y,x,2] # DYY
deriv21[y,x,2] = gausspictures[y+1,x+1,2] - gausspictures[y+1,x-1,2] - gausspictures[y-1,x+1,2] + gausspictures[y-1,x-1,2] # DXY
deriv21[y,x,3] = gausspictures[y,x+1,2] - gausspictures[y,x-1,2]
deriv21[y,x,4] = gausspictures[y+1,x,2] - gausspictures[y+1,x,2]
deriv27[y,x,0] = gausspictures[y,x+1,3] + gausspictures[y,x-1,3] - 2 * gausspictures[y,x,3] # DXX
deriv27[y,x,1] = gausspictures[y+1,x,3] + gausspictures[y-1,x,3] - 2 * gausspictures[y,x,3] # DYY
deriv27[y,x,2] = gausspictures[y+1,x+1,3] - gausspictures[y+1,x-1,3] - gausspictures[y-1,x+1,3] + gausspictures[y-1,x-1,3] # DXY
deriv27[y,x,3] = gausspictures[y,x+1,3] - gausspictures[y,x-1,3]
deriv27[y,x,4] = gausspictures[y+1,x,3] - gausspictures[y+1,x,3]
hessian9 = numpy.zeros((I_bw.shape[0],I_bw.shape[1]))
hessian15 = numpy.zeros((I_bw.shape[0],I_bw.shape[1]))
hessian21 = numpy.zeros((I_bw.shape[0],I_bw.shape[1]))
hessian27 = numpy.zeros((I_bw.shape[0],I_bw.shape[1]))
for y in range (10, I_bw.shape[0]-10):
for x in range (10, I_bw.shape[1]-10):
hessian9[y,x] = (deriv9[y,x,0] * deriv9[y,x,1]) - (0.9*deriv9[y,x,2])**2
hessian15[y,x] = (deriv15[y,x,0] * deriv15[y,x,1]) - (0.9*deriv15[y,x,2])**2
hessian21[y,x] = (deriv21[y,x,0] * deriv21[y,x,1]) - (0.9*deriv21[y,x,2])**2
hessian27[y,x] = (deriv27[y,x,0] * deriv27[y,x,1]) - (0.9*deriv27[y,x,2])**2
scale1hessian = numpy.zeros((I_bw.shape[0],I_bw.shape[1],4))
scale1hessian[:,:,0] = hessian9
scale1hessian[:,:,1] = hessian15
scale1hessian[:,:,2] = hessian21
scale1hessian[:,:,3] = hessian27
extrema_points_1_1 = []
extrema_points_1_2 = []
for y in range(0,I_bw.shape[0]):
for x in range(0,I_bw.shape[1]):
Flag = False
if find_max_new(scale1hessian,1,y,x) == 1 and (accurate_keypoint(deriv15[y,x,:]) == 1):
extrema_points_1_1.append([y,x,(9/9*1.2)])
Flag = True
if Flag == False and find_max_new(scale1hessian,2,y,x) == 1 and (accurate_keypoint(deriv21[y,x,:]) == 1):
extrema_points_1_2.append([y,x,(15/9*1.2)])
dogn1 = numpy.array(extrema_points_1_1)
dogn2 = numpy.array(extrema_points_1_2)
if (len(dogn1) > 1) and (len(dogn2)>1):
result = numpy.vstack([dogn1, dogn2])
print ("Number of points in first octave: %d" % len(result))
h.points_to_txt_3_points(result, "SURF_interest_points_o1.txt", "\n")
h.color_pic(I, result, filename[:-4] + "Surfo1" + ".jpg")
