def get_stain_matrix(I, beta=0.15, alpha=1):
"""
Get stain matrix (2x3)
:param I:
:param beta:
:param alpha:
:return:
"""
OD = ut.RGB_to_OD(I).reshape((-1, 3))
OD = (OD[(OD > beta).any(axis=1), :])
_, V = np.linalg.eigh(np.cov(OD, rowvar=False))
V = V[:, [2, 1]]
if V[0, 0] < 0: V[:, 0] *= -1
if V[0, 1] < 0: V[:, 1] *= -1
That = np.dot(OD, V)
phi = np.arctan2(That[:, 1], That[:, 0])
minPhi = np.percentile(phi, alpha)
maxPhi = np.percentile(phi, 100 - alpha)
v1 = np.dot(V, np.array([np.cos(minPhi), np.sin(minPhi)]))
v2 = np.dot(V, np.array([np.cos(maxPhi), np.sin(maxPhi)]))
if v1[0] > v2[0]:
HE = np.array([v1, v2])
else:
HE = np.array([v2, v1])
return ut.normalize_rows(HE)
def get_stain_matrix(I, threshold=0.8, lamda=0.1):
"""
Get 2x3 stain matrix. First row H and second row E
:param I:
:param threshold:
:param lamda:
:return:
"""
mask = ut.notwhite_mask(I, thresh=threshold).reshape((-1,))
OD = ut.RGB_to_OD(I).reshape((-1, 3))
OD = OD[mask]
dictionary = spams.trainDL(OD.T, K=2, lambda1=lamda, mode=2, modeD=0, posAlpha=True, posD=True, verbose=False).T
if dictionary[0, 0] < dictionary[1, 0]:
dictionary = dictionary[[1, 0], :]
dictionary = ut.normalize_rows(dictionary)
return dictionary