def main():
with open('/home/rbuddhad/NIH-XRAY/test_sml.txt') as f1:
lines1 = f1.readlines()
test_datagen = ImageDataGenerator()
test_batches = test_datagen.flow_from_directory(TEST_DATASET_PATH,
target_size=(1024, 1024),
shuffle=True,
class_mode=None,
batch_size=BATCH_SIZE)
test_crops_orig = crop_generator(test_batches, CROP_LENGTH, lines1) # 224
model = Unet(backbone_name='resnet18', encoder_weights=None)
model.load_weights('best_model1.h5')
model.compile(optimizer='Adam',
loss='mean_squared_error',
metrics=['mae', 'mean_squared_error'])
model.summary()
# callbacks = [EarlyStopping(monitor='val_loss', patience=10),
# ModelCheckpoint(filepath='best_model1.h5', monitor='val_loss', save_best_only=True),
# TensorBoard(log_dir='./logs', histogram_freq=0, write_graph=True, write_images=True)]
# model.fit_generator(generator=test_crops_orig,
# steps_per_epoch=100,
# validation_data=valid_crops_orig,
# callbacks=callbacks,
# validation_steps=200,
# epochs=1000,
# shuffle=True)
# model.predict(generator=test_crops_orig,
# steps=2,
# verbose=1)
# model.save('unet2.h5')
predict = model.predict_generator(generator=test_crops_orig,
steps=1,
verbose=1)
# predict = model.predict()
print(predict.shape, 'predict_batch_size')
for i in range(50):
plt.imshow(predict[i, :, :, 0], cmap='gray', vmin=0, vmax=1)
plt.show()
separator=',',
append=False)
callbacks_list = [checkpoint, earlystopper, csv_logger, reduce_lr]
history = model.fit_generator(train_gen, steps_per_epoch=train_steps, epochs=5,
validation_data=val_gen, validation_steps=val_steps,
verbose=1,
callbacks=callbacks_list)
#initialize the test generator
test_gen = test_generator(batch_size=1)
model.load_weights('model.h5')
predictions = model.predict_generator(test_gen,
steps=len(df_test),
verbose=1)
!ls
preds_test_thresh = (predictions >= 0.7).astype(np.uint8)
preds_test_thresh.shape
print(preds_test_thresh.min())
print(preds_test_thresh.max())
mask = preds_test_thresh[3,:,:,0]
plt.imshow(mask, cmap='Reds', alpha=0.3)
# This is a true mask
label='trn_dice_coef')
plt.title('Training Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Dice_coef')
plt.legend()
plt.show()
# PREDICT FROM VALIDATION SET (ONLY IMAGES WITH DEFECTS)
val_set = train2.iloc[idx:]
defects = list(val_set[val_set['e1'] != ''].sample(6).index)
defects += list(val_set[val_set['e2'] != ''].sample(6).index)
defects += list(val_set[val_set['e3'] != ''].sample(14).index)
defects += list(val_set[val_set['e4'] != ''].sample(6).index)
valid_batches = DataGenerator(val_set[val_set.index.isin(defects)],
preprocess=preprocess)
preds = model.predict_generator(valid_batches, verbose=1)
# PLOT PREDICTIONS
valid_batches = DataGenerator(val_set[val_set.index.isin(defects)])
print('Plotting predictions...')
print('KEY: yellow=defect1, green=defect2, blue=defect3, magenta=defect4')
for i, batch in enumerate(valid_batches):
plt.figure(figsize=(20, 36))
for k in range(16):
plt.subplot(16, 2, 2 * k + 1)
img = batch[0][k, ]
img = Image.fromarray(img.astype('uint8'))
img = np.array(img)
dft = 0
extra = ' has defect '
for j in range(4):
#for i,batch in enumerate(myGene):
# if(i >= num_batch):
# break
## original unet without pretrained
#model = unet(input_size=(512,512,3), lr = 0.0001)
## resnet34 or other unet with pretrained
model = Unet('resnet34', encoder_weights='imagenet')
model.compile(optimizer=Adam(lr=0.0001),
loss='binary_crossentropy',
metrics=['accuracy'])
model_checkpoint = ModelCheckpoint('unet_MoNuSeg.hdf5',
monitor='loss',
verbose=1,
save_best_only=True)
reduce_lr = ReduceLROnPlateau(monitor='loss',
factor=0.1,
patience=5,
mode='auto',
verbose=1)
early_stopping = EarlyStopping(monitor='loss', patience=10, verbose=1)
model.fit_generator(myGene,
steps_per_epoch=200,
epochs=1000,
callbacks=[model_checkpoint, reduce_lr, early_stopping])
testGene = testGenerator("data/MoNuSeg/test", as_gray=False)
results = model.predict_generator(testGene, 7, verbose=1)
saveResult("data/MoNuSeg/test", results)
valid_batches = DataGenerator(train2.iloc[idx:], preprocess=preprocess)
history = model.fit_generator(train_batches,
validation_data=valid_batches,
epochs=30,
verbose=2)
# PREDICT FROM VALIDATION SET (ONLY IMAGES WITH DEFECTS)
val_set = train2.iloc[idx:]
defects = list(val_set[val_set['e1'] != ''].sample(6).index)
defects += list(val_set[val_set['e2'] != ''].sample(6).index)
defects += list(val_set[val_set['e3'] != ''].sample(14).index)
defects += list(val_set[val_set['e4'] != ''].sample(6).index)
valid_batches = DataGenerator(val_set[val_set.index.isin(defects)],
preprocess=preprocess)
preds = model.predict_generator(valid_batches, verbose=1)
valid_batches = DataGenerator(val_set[val_set.index.isin(defects)])
print('Plotting predictions...')
print('KEY: yellow=defect1, green=defect2, blue=defect3, magenta=defect4')
for i, batch in enumerate(valid_batches):
plt.figure(figsize=(20, 36))
for k in range(1):
plt.subplot(16, 2, 2 * k + 1)
img = batch[0][k, ]
img = Image.fromarray(img.astype('uint8'))
img = np.array(img)
dft = 0
extra = ' has defect '
for j in range(1):
# In[118]:
Test_IoU = get_jaccard(test_image_folder,
test_mask_folder,
test_pred_generator,
batch_size=26)
# ### Predict Mask With Image
# In[127]:
# # Predict val or test images
image_names = os.listdir(val_image_folder)
image_names.sort()
predictions = model.predict_generator(val_pred_generator, steps=1)
ypred_new = predictions > 0.5
# In[128]:
#predictions.shape
# In[129]:
#print(predictions)
# In[130]:
#ypred_new = predictions > 0.5
# In[131]:
history=model.fit_generator(train_batches,validation_data=valid_batches,epochs=30,callbacks=callbacks)
plt.figure(figsize=(15,5))
plt.plot(range(history.epoch[-1]+1),history.history['val_dice_coef'],label='val_dice_coef')
plt.plot(range(history.epoch[-1]+1),history.history['dice_coef'],label='train_dice_coef')
plt.title('Training Accuracy')
plt.xlabel('Epochs')
plt.ylabel('Dice_coefs')
plt.legend()
plt.show()
val_set=train2.iloc[idx:]
defects=list(val_set[val_set['e1']!=' '].sample(6).index)
defects+=list(val_set[val_set['e2']!=' '].sample(6).index)
defects+=list(val_set[val_set['e3']!=' '].sample(14).index)
defects+=list(val_set[val_set['e4']!=' '].sample(6).index)
valid_batches = DataGenerator(val_set[val_set.index.isin(defects)])
preds = model.predict_generator(valid_batches)
for i,batch in enumerate(valid_batches):
plt.figure(figsize=(20,36))
for a in range(16):
plt.subplot(16,2,2*a+1)
img = batch[0][a,]
img = Image.fromarray(img.astype('uint8'))
img = np.array(img)
dft = 0
extra = ' has defect '
for j in range(4):
msk = batch[1][a,:,:,j]
if np.sum(msk)!=0:
dft=j+1
extra += ' '+str(j+1)
msk = mask2pad(msk,pad=2)
