In [17]:
from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D
from keras.layers import Input, concatenate, Conv2D, MaxPooling2D, Conv2DTranspose
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint
from keras import backend as K
#KERAS_BACKEND=tensorflow
#set "KERAS_BACKEND=tensorflow"
from __future__ import print_function
import os
import numpy as np
from skimage.io import imsave, imread
from keras.models import Model
from skimage.transform import resize
from skimage.io import imsave
import cv2
from skimage.transform import resize
import matplotlib.pyplot as plt
data_path = 'raw/'
image_rows = 420
image_cols = 580
In [40]:
def create_train_data():
train_data_path = os.path.join(data_path, 'train')
images = os.listdir(train_data_path)
total = len(images) // 2
imgs = np.ndarray((total, image_rows, image_cols), dtype=np.uint8)
imgs_mask = np.ndarray((total, image_rows, image_cols), dtype=np.uint8)
i = 0
print('-'*30)
print('Creating training images...')
print('-'*30)
for image_name in images:
if 'mask' in image_name:
continue
image_mask_name = image_name.split('.')[0] + '_mask.tif'
img = imread(os.path.join(train_data_path, image_name), as_grey=True)
img_mask = imread(os.path.join(train_data_path, image_mask_name), as_grey=True)
img = np.array([img])
img_mask = np.array([img_mask])
imgs[i] = img
imgs_mask[i] = img_mask
#if i % 100 == 0:
# print('Done: {0}/{1} images'.format(i, total))
i += 1
print('Loading done.............................')
np.save('imgs_train.npy', imgs)
np.save('imgs_mask_train.npy', imgs_mask)
print('Saving to .npy files done.')
def load_train_data():
imgs_train = np.load('imgs_train.npy')
imgs_mask_train = np.load('imgs_mask_train.npy')
return imgs_train, imgs_mask_train
def create_test_data():
train_data_path = os.path.join(data_path, 'test2')
images = os.listdir(train_data_path)
total = len(images)
imgs = np.ndarray((total, image_rows, image_cols), dtype=np.uint8)
imgs_id = np.ndarray((total, ), dtype=np.int32)
i = 0
print('-'*30)
print('Creating test images...')
print('-'*30)
for image_name in images:
img_id = int(image_name.split('.')[0])
img = imread(os.path.join(train_data_path, image_name), as_grey=True)
img = np.array([img])
imgs[i] = img
imgs_id[i] = img_id
i += 1
print('Loading done..............')
np.save('imgs_test2.npy', imgs)
np.save('imgs_id_test2.npy', imgs_id)
print('Saving to .npy files done.')
def load_test_data():
imgs_test = np.load('imgs_test2.npy')
imgs_id = np.load('imgs_id_test2.npy')
return imgs_test, imgs_id
create_train_data()
create_test_data()
In [4]:
#from keras.models import Model
K.set_image_data_format('channels_last') # TF dimension ordering in this code
img_rows = 96
img_cols = 96
smooth = 1.
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 + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)
def dice_coef_loss(y_true, y_pred):
return -dice_coef(y_true, y_pred)
inputs = Input((img_rows, img_cols, 1))
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(inputs)
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool1)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool2)
conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool3)
conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)
conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool4)
conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv5)
up6 = concatenate([Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(conv5), conv4], axis=3)
conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(up6)
conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv6)
up7 = concatenate([Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(conv6), conv3], axis=3)
conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(up7)
conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv7)
up8 = concatenate([Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(conv7), conv2], axis=3)
conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(up8)
conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv8)
up9 = concatenate([Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(conv8), conv1], axis=3)
conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(up9)
conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv9)
conv10 = Conv2D(1, (1, 1), activation='sigmoid')(conv9)
model = Model(inputs=[inputs], outputs=[conv10])
model.compile(optimizer=Adam(lr=1e-5), loss=dice_coef_loss, metrics=[dice_coef])
model_checkpoint = ModelCheckpoint('weights.h5', monitor='val_loss', save_best_only=True)
model.fit(imgs_train, imgs_mask_train, batch_size=32, nb_epoch=20, verbose=1, shuffle=True,
validation_split=0.2,
callbacks=[model_checkpoint])
model.summary()
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 96, 96, 1) 0
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 96, 96, 32) 320 input_1[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 96, 96, 32) 9248 conv2d_1[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 48, 48, 32) 0 conv2d_2[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, 48, 48, 64) 18496 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 48, 48, 64) 36928 conv2d_3[0][0]
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D) (None, 24, 24, 64) 0 conv2d_4[0][0]
__________________________________________________________________________________________________
conv2d_5 (Conv2D) (None, 24, 24, 128) 73856 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
conv2d_6 (Conv2D) (None, 24, 24, 128) 147584 conv2d_5[0][0]
__________________________________________________________________________________________________
max_pooling2d_3 (MaxPooling2D) (None, 12, 12, 128) 0 conv2d_6[0][0]
__________________________________________________________________________________________________
conv2d_7 (Conv2D) (None, 12, 12, 256) 295168 max_pooling2d_3[0][0]
__________________________________________________________________________________________________
conv2d_8 (Conv2D) (None, 12, 12, 256) 590080 conv2d_7[0][0]
__________________________________________________________________________________________________
max_pooling2d_4 (MaxPooling2D) (None, 6, 6, 256) 0 conv2d_8[0][0]
__________________________________________________________________________________________________
conv2d_9 (Conv2D) (None, 6, 6, 512) 1180160 max_pooling2d_4[0][0]
__________________________________________________________________________________________________
conv2d_10 (Conv2D) (None, 6, 6, 512) 2359808 conv2d_9[0][0]
__________________________________________________________________________________________________
conv2d_transpose_1 (Conv2DTrans (None, 12, 12, 256) 524544 conv2d_10[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 12, 12, 512) 0 conv2d_transpose_1[0][0]
conv2d_8[0][0]
__________________________________________________________________________________________________
conv2d_11 (Conv2D) (None, 12, 12, 256) 1179904 concatenate_1[0][0]
__________________________________________________________________________________________________
conv2d_12 (Conv2D) (None, 12, 12, 256) 590080 conv2d_11[0][0]
__________________________________________________________________________________________________
conv2d_transpose_2 (Conv2DTrans (None, 24, 24, 128) 131200 conv2d_12[0][0]
__________________________________________________________________________________________________
concatenate_2 (Concatenate) (None, 24, 24, 256) 0 conv2d_transpose_2[0][0]
conv2d_6[0][0]
__________________________________________________________________________________________________
conv2d_13 (Conv2D) (None, 24, 24, 128) 295040 concatenate_2[0][0]
__________________________________________________________________________________________________
conv2d_14 (Conv2D) (None, 24, 24, 128) 147584 conv2d_13[0][0]
__________________________________________________________________________________________________
conv2d_transpose_3 (Conv2DTrans (None, 48, 48, 64) 32832 conv2d_14[0][0]
__________________________________________________________________________________________________
concatenate_3 (Concatenate) (None, 48, 48, 128) 0 conv2d_transpose_3[0][0]
conv2d_4[0][0]
__________________________________________________________________________________________________
conv2d_15 (Conv2D) (None, 48, 48, 64) 73792 concatenate_3[0][0]
__________________________________________________________________________________________________
conv2d_16 (Conv2D) (None, 48, 48, 64) 36928 conv2d_15[0][0]
__________________________________________________________________________________________________
conv2d_transpose_4 (Conv2DTrans (None, 96, 96, 32) 8224 conv2d_16[0][0]
__________________________________________________________________________________________________
concatenate_4 (Concatenate) (None, 96, 96, 64) 0 conv2d_transpose_4[0][0]
conv2d_2[0][0]
__________________________________________________________________________________________________
conv2d_17 (Conv2D) (None, 96, 96, 32) 18464 concatenate_4[0][0]
__________________________________________________________________________________________________
conv2d_18 (Conv2D) (None, 96, 96, 32) 9248 conv2d_17[0][0]
__________________________________________________________________________________________________
conv2d_19 (Conv2D) (None, 96, 96, 1) 33 conv2d_18[0][0]
==================================================================================================
Total params: 7,759,521
Trainable params: 7,759,521
Non-trainable params: 0
__________________________________________________________________________________________________
In [42]:
def preprocess(imgs):
imgs_p = np.ndarray((imgs.shape[0], img_rows, img_cols), dtype=np.uint8)
for i in range(imgs.shape[0]):
imgs_p[i] = resize(imgs[i], (img_cols, img_rows), preserve_range=True)
imgs_p = imgs_p[..., np.newaxis]
return imgs_p
def train_and_predict():
print('-'*30)
print('Loading and preprocessing train data...')
print('-'*30)
imgs_train, imgs_mask_train = load_train_data()
imgs_train = preprocess(imgs_train)
imgs_mask_train = preprocess(imgs_mask_train)
imgs_train = imgs_train.astype('float32')
mean = np.mean(imgs_train) # mean for data centering
std = np.std(imgs_train) # std for data normalization
imgs_train -= mean
imgs_train /= std
imgs_mask_train = imgs_mask_train.astype('float32')
imgs_mask_train /= 255. # scale masks to [0, 1]
print('-'*30)
print('Creating and compiling model...')
print('-'*30)
model_checkpoint = ModelCheckpoint('weights.h5', monitor='val_loss', save_best_only=True)
print('-'*30)
print('Fitting model...')
print('-'*30)
model.fit(imgs_train, imgs_mask_train, batch_size=32, nb_epoch=20, verbose=1, shuffle=True,
validation_split=0.2,
callbacks=[model_checkpoint])
print('-'*30)
print('Loading and preprocessing test data...')
print('-'*30)
imgs_test, imgs_id_test = load_test_data()
imgs_test = preprocess(imgs_test)
imgs_test = imgs_test.astype('float32')
imgs_test -= mean
imgs_test /= std
# loading the trained weights for loading in the predict function
print('-'*30)
print('Loading saved weights...')
print('-'*30)
model.load_weights('weights.h5')
# creating the masks and saving the image masks as numpy
print('-'*30)
print('Predicting masks on test data...')
print('-'*30)
imgs_mask_test = model.predict(imgs_test, verbose=1)
np.save('imgs_mask_test.npy', imgs_mask_test)
print('-' * 30)
print('Saving predicted masks to files...')
print('-' * 30)
def predict():
create_test_data()
imgs_train, imgs_mask_train = load_train_data()
imgs_train = preprocess(imgs_train)
imgs_mask_train = preprocess(imgs_mask_train)
imgs_train = imgs_train.astype('float32')
mean = np.mean(imgs_train) # mean for data centering
std = np.std(imgs_train) # std for data normalization
imgs_train -= mean
imgs_train /= std
imgs_mask_train = imgs_mask_train.astype('float32')
imgs_mask_train /= 255. # scale masks to [0, 1]
imgs_test, imgs_id_test = load_test_data()
imgs_test = preprocess(imgs_test)
imgs_test = imgs_test.astype('float32')
imgs_test -= mean
imgs_test /= std
# loading the trained weights for loading in the predict function
print('-'*30)
print('Loading saved weights...')
print('-'*30)
model.load_weights('weights.h5')
# creating the masks and saving the image masks as numpy
print('-'*30)
print('Predicting masks on test data...')
print('-'*30)
imgs_mask_test = model.predict(imgs_test, verbose=1)
np.save('imgs_mask_test2.npy', imgs_mask_test)
print('-' * 30)
print('Saving predicted masks to files...')
print('-' * 30)
In [43]:
#confirming the backend for keras
K.backend()
predict()
------------------------------ Creating test images... ------------------------------ Loading done.............. Saving to .npy files done.
C:\Users\akuppal\AppData\Local\Continuum\anaconda3\lib\site-packages\skimage\transform\_warps.py:84: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.
warn("The default mode, 'constant', will be changed to 'reflect' in "
------------------------------ Loading saved weights... ------------------------------ ------------------------------ Predicting masks on test data... ------------------------------ 5/5 [==============================] - 0s 74ms/step ------------------------------ Saving predicted masks to files... ------------------------------
In [11]:
#training the model and predicting
#train_and_predict()
------------------------------ Loading and preprocessing train data... ------------------------------
C:\Users\akuppal\AppData\Local\Continuum\anaconda3\lib\site-packages\skimage\transform\_warps.py:84: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.
warn("The default mode, 'constant', will be changed to 'reflect' in "
------------------------------ Creating and compiling model... ------------------------------ ------------------------------ Fitting model... ------------------------------
C:\Users\akuppal\AppData\Local\Continuum\anaconda3\lib\site-packages\ipykernel_launcher.py:40: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.
Train on 4508 samples, validate on 1127 samples Epoch 1/20 4508/4508 [==============================] - 807s 179ms/step - loss: -0.0254 - dice_coef: 0.0254 - val_loss: -0.0204 - val_dice_coef: 0.0204 Epoch 2/20 4508/4508 [==============================] - 836s 186ms/step - loss: -0.0316 - dice_coef: 0.0316 - val_loss: -0.0373 - val_dice_coef: 0.0373 Epoch 3/20 4508/4508 [==============================] - 802s 178ms/step - loss: -0.1837 - dice_coef: 0.1837 - val_loss: -0.1925 - val_dice_coef: 0.1925 Epoch 4/20 4508/4508 [==============================] - 805s 179ms/step - loss: -0.3027 - dice_coef: 0.3027 - val_loss: -0.1510 - val_dice_coef: 0.1510 Epoch 5/20 4508/4508 [==============================] - 776s 172ms/step - loss: -0.3649 - dice_coef: 0.3649 - val_loss: -0.2540 - val_dice_coef: 0.2540 Epoch 6/20 4508/4508 [==============================] - 794s 176ms/step - loss: -0.4051 - dice_coef: 0.4051 - val_loss: -0.2782 - val_dice_coef: 0.2782 Epoch 7/20 4508/4508 [==============================] - 859s 191ms/step - loss: -0.4313 - dice_coef: 0.4313 - val_loss: -0.3146 - val_dice_coef: 0.3146 Epoch 8/20 4508/4508 [==============================] - 764s 169ms/step - loss: -0.4491 - dice_coef: 0.4491 - val_loss: -0.2968 - val_dice_coef: 0.2968 Epoch 9/20 4508/4508 [==============================] - 772s 171ms/step - loss: -0.4568 - dice_coef: 0.4568 - val_loss: -0.3381 - val_dice_coef: 0.3381 Epoch 10/20 4508/4508 [==============================] - 775s 172ms/step - loss: -0.4745 - dice_coef: 0.4745 - val_loss: -0.3454 - val_dice_coef: 0.3454 Epoch 11/20 4508/4508 [==============================] - 805s 179ms/step - loss: -0.4825 - dice_coef: 0.4825 - val_loss: -0.3590 - val_dice_coef: 0.3590 Epoch 12/20 4508/4508 [==============================] - 824s 183ms/step - loss: -0.5043 - dice_coef: 0.5043 - val_loss: -0.3540 - val_dice_coef: 0.3540 Epoch 13/20 4508/4508 [==============================] - 793s 176ms/step - loss: -0.5091 - dice_coef: 0.5091 - val_loss: -0.3483 - val_dice_coef: 0.3483 Epoch 14/20 4508/4508 [==============================] - 892s 198ms/step - loss: -0.5265 - dice_coef: 0.5265 - val_loss: -0.3760 - val_dice_coef: 0.3760 Epoch 15/20 4508/4508 [==============================] - 778s 173ms/step - loss: -0.5342 - dice_coef: 0.5342 - val_loss: -0.3876 - val_dice_coef: 0.3876 Epoch 16/20 4508/4508 [==============================] - 762s 169ms/step - loss: -0.5437 - dice_coef: 0.5437 - val_loss: -0.3499 - val_dice_coef: 0.3499 Epoch 17/20 4508/4508 [==============================] - 749s 166ms/step - loss: -0.5530 - dice_coef: 0.5530 - val_loss: -0.3603 - val_dice_coef: 0.3603 Epoch 18/20 4508/4508 [==============================] - 814s 181ms/step - loss: -0.5611 - dice_coef: 0.5611 - val_loss: -0.3732 - val_dice_coef: 0.3732 Epoch 19/20 4508/4508 [==============================] - 781s 173ms/step - loss: -0.5713 - dice_coef: 0.5713 - val_loss: -0.3648 - val_dice_coef: 0.3648 Epoch 20/20 4508/4508 [==============================] - 3604s 799ms/step - loss: -0.5745 - dice_coef: 0.5745 - val_loss: -0.3913 - val_dice_coef: 0.3913 ------------------------------ Loading and preprocessing test data... ------------------------------ ------------------------------ Loading saved weights... ------------------------------ ------------------------------ Predicting masks on test data... ------------------------------ 5508/5508 [==============================] - 315s 57ms/step ------------------------------ Saving predicted masks to files... ------------------------------
In [ ]:
In [48]:
import numpy as np
import cv2
test_array = np.load('imgs_test2.npy')
mask_array = np.load('imgs_mask_test2.npy')
#test_array = np.load('imgs_test.npy')
#mask_array = np.load('imgs_mask_test.npy')
test_id = np.load('imgs_id_test2.npy')
#test_id = np.load('imgs_id_test.npy')
img_idx = 2
test = test_array[img_idx].astype('float32')
mask = mask_array[img_idx].astype('float32')
mask = np.array(mask * 255, dtype = np.uint8)
resized_mask = cv2.resize(mask, (test.shape[1],test.shape[0]))
# contour detection in resized_mask
ret,thresh = cv2.threshold(resized_mask,127,255,0)
im2, contours, hierarchy = cv2.findContours(thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_NONE)
# apply the overlay
output = test.copy()
output = cv2.cvtColor(output,cv2.COLOR_GRAY2BGR)
# draw the contour
cv2.drawContours(output, contours, -1, (0,0,255), 3)
plt.imshow(test,cmap='gray')
plt.show()
plt.imshow(thresh,cmap='gray')
plt.show()
plt.imshow(output,cmap='gray')
plt.show()
In [ ]: