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predict.py
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predict.py
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import glob
import nibabel as nib
import numpy as np
from skimage.transform import resize
from keras.models import load_model
import tensorflow as tf
from keras import backend as K
img_path = glob.glob("/storage/research/Intern19_v2/KidneyTumorSegmentationChallenge/scratch/data/case_ (*)/imaging.nii")
mask_path = glob.glob("/storage/research/Intern19_v2/KidneyTumorSegmentationChallenge/scratch/data/case_ (*)/segmentation.nii")
images=[]
for i in range(2,3):
a=[]
a=nib.load(img_path[i])
a=a.get_data()
print("image (%d) loaded"%(i))
a=resize(a,(a.shape[0],512,512))
a=a[:,:,:]
for j in range(a.shape[0]):
images.append((a[j,:,:]))
valid_X=np.asarray(images)
'''
masks=[]
for i in range(200,210):
b=[]
b=nib.load(mask_path[i])
b=b.get_data()
print("mask (%d) loaded"%(i))
b=resize(b,(b.shape[0],512,512))
b=b[30:80,:,:]
for j in range(b.shape[0]):
masks.append((b[j,:,:]))
masks=np.asarray(masks)
'''
def dice_loss(y_true, y_pred):
numerator = 2 * tf.reduce_sum(y_true * y_pred)
# some implementations don't square y_pred
denominator = tf.reduce_sum(y_true + tf.square(y_pred))
return numerator / (denominator + tf.keras.backend.epsilon())
def dice_coef(y_true, y_pred):
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = K.sum(y_true_f * y_pred_f)
return (2. * intersection + K.epsilon()) / (K.sum(y_true_f) + K.sum(y_pred_f) + K.epsilon())
def iou_loss_core(y_true, y_pred, smooth=1):
intersection = K.sum(K.abs(y_true * y_pred), axis=-1)
union = K.sum(y_true,-1) + K.sum(y_pred,-1) - intersection
iou = (intersection + smooth) / ( union + smooth)
return iou
def tversky(y_true, y_pred,smooth=1):
y_true_pos = K.flatten(y_true)
y_pred_pos = K.flatten(y_pred)
true_pos = K.sum(y_true_pos * y_pred_pos)
false_neg = K.sum(y_true_pos * (1-y_pred_pos))
false_pos = K.sum((1-y_true_pos)*y_pred_pos)
alpha = 0.7
return (true_pos + smooth)/(true_pos + alpha*false_neg + (1-alpha)*false_pos + smooth)
def tversky_loss(y_true, y_pred):
return 1 - tversky(y_true,y_pred)
def depth_softmax(matrix):
sigmoid = lambda x: 1 / (1 + K.exp(-x))
sigmoided_matrix = sigmoid(matrix)
softmax_matrix = sigmoided_matrix / K.sum(sigmoided_matrix, axis=0)
return softmax_matrix
valid_X = valid_X.reshape(-1,512,512,1)
#valid_ground=masks.reshape(-1,512,512,1)
#1-50
model1=load_model('unetrahul.h5', custom_objects={'dice_coef': dice_coef,'depth_softmax':depth_softmax })
#scores = model1.evaluate(valid_X, valid_ground, verbose=1)
pred=model1.predict(valid_X)
np.save("pred1.npy",pred)
print(pred)
#50-100
'''
model2=load_model('weightsbest50-100.hdf5')
scores = model2.evaluate(valid_X, valid_ground, verbose=1)
#100-150
model3=load_model('weightsbest100-150.hdf5')
scores = model3.evaluate(valid_X, valid_ground, verbose=1)
#150-200
model4=load_model('weightsbest150-200.hdf5')
scores = model4.evaluate(valid_X, valid_ground, verbose=1)
print("model 1::::::: %s: %.2f%%" % (model1.metrics_names[1], scores[1]*100))
print("model 2::::::: %s: %.2f%%" % (model2.metrics_names[1], scores[1]*100))
print("model 3::::::: %s: %.2f%%" % (model3.metrics_names[1], scores[1]*100))
print("model 4::::::: %s: %.2f%%" % (model4.metrics_names[1], scores[1]*100))
'''