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Update model.py #247

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Update model.py #247

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75d62a8
Update model.py
Tan342 May 18, 2023
20c5aa4
Update main.py
Tan342 May 18, 2023
f184784
Update data.py
Tan342 May 18, 2023
bbfc979
Update main.py
Tan342 May 18, 2023
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3 changes: 2 additions & 1 deletion data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,7 @@
import glob
import skimage.io as io
import skimage.transform as trans
from skimage import img_as_uint

Sky = [128,128,128]
Building = [128,0,0]
Expand Down Expand Up @@ -121,4 +122,4 @@ def labelVisualize(num_class,color_dict,img):
def saveResult(save_path,npyfile,flag_multi_class = False,num_class = 2):
for i,item in enumerate(npyfile):
img = labelVisualize(num_class,COLOR_DICT,item) if flag_multi_class else item[:,:,0]
io.imsave(os.path.join(save_path,"%d_predict.png"%i),img)
io.imsave(os.path.join(save_path,"%d_predict.png"%i),img_as_uint(img))
6 changes: 3 additions & 3 deletions main.py
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Expand Up @@ -15,8 +15,8 @@

model = unet()
model_checkpoint = ModelCheckpoint('unet_membrane.hdf5', monitor='loss',verbose=1, save_best_only=True)
model.fit_generator(myGene,steps_per_epoch=300,epochs=1,callbacks=[model_checkpoint])
model.fit_generator(myGene,steps_per_epoch=300,epochs=10,callbacks=[model_checkpoint])

testGene = testGenerator("data/membrane/test")
results = model.predict_generator(testGene,30,verbose=1)
saveResult("data/membrane/test",results)
results = model.predict(testGene,30,verbose=1)
saveResult("data/membrane/test",results)
2 changes: 1 addition & 1 deletion model.py
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Expand Up @@ -52,7 +52,7 @@ def unet(pretrained_weights = None,input_size = (256,256,1)):
conv9 = Conv2D(2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv10 = Conv2D(1, 1, activation = 'sigmoid')(conv9)

model = Model(input = inputs, output = conv10)
model = Model(inputs, conv10)

model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy'])

Expand Down