i tried to compare two images and use Hu moment to compare contour extracted from these images: https://docs.google.com/file/d/0ByS6Z5WRz-h2WHEzNnJucDlRR2s/edit and https://docs.google.com/file/d/0ByS6Z5WRz-h2VnZyVWRRWEFva0k/edit The second image is equal to the first only it's rotated and i expected as result same Humoments. They are a little bit different.
Humoments sign on the right (first image):
[[ 6.82589151e-01]
[ 2.06816713e-01]
[ 1.09088295e-01]
[ 5.30020870e-03]
[ -5.85888607e-05]
[ -6.85171823e-04]
[ -1.13181280e-04]]
Humoments sign on the right (second image):
[[ 6.71793060e-01]
[ 1.97521128e-01]
[ 9.15619847e-02]
[ 9.60179567e-03]
[ -2.44655863e-04]
[ -2.68791106e-03]
[ -1.45592441e-04]]
In this video: http://www.youtube.com/watch?v=O-hCEXi3ymU at 4th minut i watched he obtained exactly the same. Where i wrong?
Here's my code:
nomeimg = "Sassatelli 1984 ruotato.jpg"
#nomeimg = "Sassatelli 1984 n. 165 mod1.jpg"
img = cv2.imread(nomeimg)
gray = cv2.imread(nomeimg,0)
ret,thresh = cv2.threshold(gray,127,255,cv2.THRESH_BINARY_INV)
element = cv2.getStructuringElement(cv2.MORPH_CROSS,(4,4))
imgbnbin = thresh
imgbnbin = cv2.dilate(imgbnbin, element)
#find contour
contours,hierarchy=cv2.findContours(imgbnbin,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
#Elimination small contours
Areacontours = list()
area = cv2.contourArea(contours[i])
if (area > 90 ):
Areacontours.append(contours[i])
contours = Areacontours
print('found objects')
print(len(contours))
#contorus[3] for sing in first image
#contours[0] for sign in second image
print("humoments")
mom = cv2.moments(contours[0])
Humoments = cv2.HuMoments(mom)
print(Humoments)
I think your numbers are probably ok, the differences between them are moderately small. As the guy says in the video you link to (around 3min):
To get some meaningful answers we take a log transform
so if we do -np.sign(a)*np.log10(np.abs(a))
on the data you post above, we get:
First image:
[[ 0.16584062]
[ 0.68441437]
[ 0.96222185]
[ 2.27570703]
[-4.23218495]
[-3.16420051]
[-3.9462254 ]]
Second image:
[[ 0.17276449]
[ 0.70438644]
[ 1.0382848 ]
[ 2.01764754]
[-3.61144437]
[-2.57058511]
[-3.83686117]]
The fact they are not identical is to be expected. You are starting out with rasterized images which you then process quite a lot to get some of the contours which you pass in.
From the opencv docs:
In case of raster images, the computed Hu invariants for the original and transformed images are a bit different.