preds = np.zeros(shape=(len(B1), NC))
for i in range(num_batches):
idx = range(i * batch, (i + 1) * batch)
K = A[idx]
M, N, O = TD.Patch_gen(K, 29, C)
preds[idx] = f_eval(M, N, O)
if num_batches * batch < A.shape[0]:
tot = num_batches * batch
K = A[tot:]
M, N, O = TD.Patch_gen(K, 29, C)
preds[tot:A.shape[0]] = f_eval(M, N, O)
P = np.argmax(preds, axis=-1)
MM = np.ravel_multi_index(A.T, np.asarray(B.shape))
print MM.shape
print P.shape
Final_pred = np.zeros(B1.shape)
Final_pred[MM] = P
Lab = B1.reshape(Sha)
Segs = Final_pred.reshape(Sha)
Dice = np.append(Dice, [E.Dice_score(Segs, Lab, 1)])
print Dice
io.savemat("/home/xvt131/Biomediq/Results/valitest/%s" % (img[-32:-4]),
mdict={
"Seg": Segs,
"Lab": Lab
})
Sha = B.shape
preds = np.zeros(shape=(len(B1), 2))
for i in range(num_batches):
idx = range(i * batch, (i + 1) * batch)
K = A[idx]
M = TD.Patch_gen_three(K, PS, C)
M = M.reshape(batch, 1, PS, PS, PS)
preds[idx] = f_eval(M)
if num_batches * batch < A.shape[0]:
tot = num_batches * batch
K = A[tot:]
M = TD.Patch_gen_three(K, PS, C)
M = M.reshape(len(K), 1, PS, PS, PS)
preds[tot:A.shape[0]] = f_eval(M)
Final_pred = np.argmax(preds, axis=-1)
Lab = B1.reshape(Sha)
Final_pred = Final_pred.reshape(Sha)
Dice = np.append(Dice, [E.Dice_score(Final_pred, Lab, 1)])
print Dice
io.savemat("/home/xvt131/Functions/Adhish_copy/3D_strain/%s" % (img[44:]),
mdict={
"Seg": Final_pred,
"Lab": Lab
})
import scipy.io as io
import Evaluation as E
with np.load("/home/xvt131/Network_adapt/triplanar_Params_WI.npz") as f:
param_values = [f['arr_%d' % i] for i in range(len(f.files))]
lasagne.layers.set_all_param_values(output, param_values)
for img in DP.get_paths("/home/xvt131/Functions/Adhish_copy/Validating-Rand"):
A, B, C = TD.Tri_Image_Load(img)
B1 = B.reshape(np.prod(B.shape))
batch = 100
num_batches = A.shape[0] / batch
Sha = B.shape
print Sha
TibiaD = []
FemoralD = []
preds = np.zeros(shape = ( len(B1), 2 ))
for i in range(num_batches):
idx = range(i*batch, (i+1)*batch)
K = A[idx]
M, N, O, P= TD.Patch_gen(K, 29, C)
preds[idx] = f_eval(M,N,O, P)
Final_pred = np.argmax(preds, axis = -1)
print Final_pred.shape
Lab = B1.reshape(Sha)
Final_pred = Final_pred.reshape(Sha)
TibiaD += [E.Dice_score(Final_pred, Lab, 1)]
print TibiaD
io.savemat("/home/xvt131/Network_adapt/EvalTri/Seg_%s" %(img[-36:-30]), mdict= {"Seg2_%s" %(img[-36:-30]):Final_pred,"Lab_%s" %(img[-36:-30]):Lab} )
A, B, C = TD.Tri_Image_Load(img)
B1 = B.reshape(np.prod(B.shape))
batch = 100
num_batches = A.shape[0] / batch
Sha = B.shape
TibiaD = []
FemoralD = []
preds = np.zeros(shape=(len(B1), 2))
for i in range(num_batches):
idx = range(i * batch, (i + 1) * batch)
K = A[idx]
M = TD.Patch_gen_three(K, 29, C)
M = M.reshape(batch, 1, PS, PS, PS)
preds[idx] = f_eval(M)
Final_pred = np.argmax(preds, axis=-1)
Lab = B1.reshape(Sha)
Final_pred = Final_pred.reshape(Sha)
TibiaD += [E.Dice_score(Final_pred, Lab, 1)]
FemoralD += [E.Dice_score(Final_pred, Lab, 2)]
print TibiaD
io.savemat("/home/xvt131/Functions/Adhish_copy/EvalThree/Seg_%s" %
(img[-36:-30]),
mdict={
"Seg_%s" % (img[-36:-30]): Final_pred,
"Label_%s" % (img[-36:-30]): Lab
})
