46 lines
1.7 KiB
Python
46 lines
1.7 KiB
Python
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def harris(image, sigma=10, kappa=0.04):
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derivees = gradient(image)
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d_x = derivees[0]
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d_y = derivees[1]
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noyau = noyau_gaussien3(sigma)
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#TODO: convol DoG
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d_xx_lissee = ndimage.convolve(d_x * d_x,noyau , mode = 'nearest')
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d_yx_lissee = ndimage.convolve(d_x * d_y,noyau , mode = 'nearest')
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d_yy_lissee = ndimage.convolve(d_y * d_y,noyau , mode = 'nearest')
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determinant = d_xx_lissee * d_yy_lissee -d_yx_lissee * d_yx_lissee
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trace = d_xx_lissee + d_yy_lissee
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image_harris = determinant - kappa * trace * trace
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return image_harris
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def maxlocal(image_harris, seuil,size_patch=10):
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""" Array*float -> Array """
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output = np.zeros(image_harris.shape)
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voisinage = np.zeros((3,3))
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for ligne in range(size_patch,image_harris.shape[0]-size_patch):
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for colonne in range(size_patch,image_harris.shape[1]-size_patch):
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current_val = image_harris[ligne, colonne]
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voisinage = image_harris[ligne - 1: ligne + 2, colonne - 1:colonne + 2].copy()
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voisinage[(1,1)] = 0
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output[ligne, colonne] = np.logical_and(current_val > seuil,
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current_val > np.amax(voisinage))
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print(output)
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return(output)
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def coord_maxlocal(image,seuil,size_patch=3):
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#seuil=np.mean(image_extrema)
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""" Array -> Array """
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x,y = np.nonzero(maxlocal(image,seuil))
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return np.array((y,x)).T
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def get_corners(pict):
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image_harris = harris(pict,sigma=3,kappa=0.06)
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corners = coord_maxlocal(image_harris,np.mean(image_harris))
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return corners
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pict1 = np.array(Image.open("set1-1.png").convert('L'),dtype='float')
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pict2 = np.array(Image.open("set1-2.png").convert('L'),dtype='float')
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corners1=get_corners(pict1)
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corners2=get_corners(pict2)
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