import sklearn
from lasagne.layers import InputLayer, Conv2DLayer, DenseLayer, MaxPool2DLayer, InverseLayer
### DATASET
dataSet = myTools.loadImages('../../images', 1024, 1024, 4)
dataSet = myTools.oneDimension(dataSet)
dataSet = dataSet.astype(numpy.uint8)
dataSet = myTools.cropCenter(dataSet, 80)
dataSet = myTools.augmentData(dataSet,
numOfTiles=4,
overlap=False,
imageWidth=819,
imageHeight=819)
dataSet = dataSet.astype(numpy.float32)
#plt.show(plt.imshow(dataSet[0][0], cmap=cm.binary))
### MASKS
masks = myTools.loadImages('../../masks', 819, 819, 1)
for x in numpy.nditer(masks, op_flags=['readwrite']):
if x > 0:
x[...] = 1
masks = masks.astype(numpy.float32)
try:
myTifs.seek(i)
images[i][0]=myTifs
except EOFError:
# Not enough frames in img
break
#plt.show(plt.imshow(images[0][0]))\
for j in range(30):
image=images[j, :, :, :]
image=image.reshape(1, 1, images.shape[2], images.shape[3])
image=image.astype(numpy.uint8)
image=myTools.augmentData(image, numOfTiles=1, overlap=False, imageWidth=512, imageHeight=512)
image=image.astype(numpy.float32)
imageMeans=numpy.mean(image, axis=(1,2,3), keepdims=True)
image=image-imageMeans
#setting parameters for the network
data_size=(None,1,images[0][0].shape[0],images[0][0].shape[1])
#load the pretrained network
myNet=myTools.createPretrainedNN2(data_size, modelFile=modelFilename, filters=numberOfFilters)
#make predictions for the image
res=myNet(image)
#results[j, :, :]=res[0][0]
scipy.misc.imsave('../../ISBIdata/areYouKiddingMe/res'+str(j)+'.png', res[0][0])
dataSet = np.array(np.zeros(shape=(30, 1, 512, 512)))
for i in range(30):
try:
myTifs.seek(i)
dataSet[i][0] = myTifs
except EOFError:
# Not enough frames in img
break
dataSet = dataSet.astype(numpy.uint8)
dataSet = myTools.augmentData(dataSet,
numOfTiles=1,
overlap=False,
imageWidth=512,
imageHeight=512) #830 850
dataSet = dataSet.astype(numpy.float32)
imageMeans = numpy.mean(dataSet, axis=(1, 2, 3), keepdims=True)
dataSet = dataSet - imageMeans
### MASKS
myTifsMasks = Image.open('../../ISBIdata/train-labels.tif')
masks = np.array(np.zeros(shape=(30, 1, 512, 512)))
for i in range(30):
#get the image path imagePath=sys.argv[1] #set output filename outName=sys.argv[2] #open the image image=Image.open(imagePath) #image as numpy array image=numpy.asarray(image) #keep only one dimension image=image[:,:,0] #most needed type casting image=image.astype(numpy.uint8) #crop the center image=myTools.cropCenter1(image, 83.1) #split image to 4 splits=myTools.augmentData(image.reshape(1,1,image.shape[0],image.shape[1]), numOfTiles=4, overlap=False, imageWidth=image.shape[0], imageHeight=image.shape[1]) #another vital type casting splits=splits.astype(numpy.float32) #keep only the 4 original tiles splits=splits[0:4,:,:,:] #setting parameters for the network data_size=(None,1,splits[0][0].shape[0],splits[0][0].shape[1]) #load the pretrained network myNet=myTools.createPretrainedNN(data_size) #make predictions for the 4 tiles print(splits.dtype) res=myNet(splits) #crop the center of the predictions res=myTools.cropCenter(res, 93) #concatenate on the x axis top=np.concatenate((res[0][0],res[2][0]),axis=1)
outName = sys.argv[2]
#open the image
image = Image.open(imagePath)
#image as numpy array
image = numpy.asarray(image)
#keep only one dimension
image = image[:, :, 0]
#most needed type casting
image = image.astype(numpy.uint8)
#crop the center
#image=myTools.cropCenter1(image, 100)
#this step is mysteriously needed
image = myTools.augmentData(image.reshape(1, 1, image.shape[0],
image.shape[1]),
numOfTiles=1,
overlap=False,
imageWidth=image.shape[0],
imageHeight=image.shape[1])
#another vital type casting
image = image.astype(numpy.float32)
#setting parameters for the network
data_size = (None, 1, image[0][0].shape[0], image[0][0].shape[1])
imageMean = numpy.mean(image, keepdims=True)
image = image - imageMean
#load the pretrained network
myNet1 = myTools.createPretrainedNN2(data_size,
modelFile='myModel102Plus90.npz',
filters=32)
#make predictions for the image
myTifs.seek(i)
images[i][0] = myTifs
except EOFError:
# Not enough frames in img
break
#plt.show(plt.imshow(images[0][0]))\
for j in range(30):
image = images[j, :, :, :]
image = image.reshape(1, 1, images.shape[2], images.shape[3])
image = image.astype(numpy.uint8)
image = myTools.augmentData(image,
numOfTiles=1,
overlap=False,
imageWidth=512,
imageHeight=512)
image = image.astype(numpy.float32)
imageMeans = numpy.mean(image, axis=(1, 2, 3), keepdims=True)
image = image - imageMeans
#setting parameters for the network
data_size = (None, 1, images[0][0].shape[0], images[0][0].shape[1])
#load the pretrained network
myNet = myTools.createPretrainedNN2(data_size,
modelFile=modelFilename,
filters=numberOfFilters)
#make predictions for the image
res = myNet(image)
#results[j, :, :]=res[0][0]
from sklearn.metrics import confusion_matrix
import sklearn
from lasagne.layers import InputLayer, Conv2DLayer, DenseLayer, MaxPool2DLayer, InverseLayer
### DATASET
dataSet=myTools.loadImages('../../images', 1024, 1024, 4)
dataSet=myTools.oneDimension(dataSet)
dataSet=dataSet.astype(numpy.uint8)
dataSet=myTools.cropCenter(dataSet, 80)
dataSet=myTools.augmentData(dataSet, numOfTiles=4, overlap=False, imageWidth=819, imageHeight=819)
dataSet=dataSet.astype(numpy.float32)
#plt.show(plt.imshow(dataSet[0][0], cmap=cm.binary))
### MASKS
masks=myTools.loadImages('../../masks', 819, 819, 1)
for x in numpy.nditer(masks, op_flags=['readwrite']):
if x>0:
x[...]=1
masks=masks.astype(numpy.float32)
masks=myTools.augmentData(masks, numOfTiles=4, overlap=False, imageWidth=819, imageHeight=819)
print('Learning rate: %f' % (argLR))
print('Weight decay: %f' % (argWD))
print('Number of epochs: %d' % (argEpochs))
random.seed(123)
### DATASET
dataSet=myTools.loadImages('../../images', 1024, 1024, 4)
dataSet=myTools.oneDimension(dataSet)
dataSet=dataSet.astype(numpy.uint8)
dataSet=myTools.cropCenter(dataSet, 83.1)#81.2 83.1
dataSet=myTools.augmentData(dataSet, numOfTiles=1, overlap=False, imageWidth=850, imageHeight=850)#830 850
dataSet=dataSet.astype(numpy.float32)
imageMeans=numpy.mean(dataSet, axis=(1,2,3), keepdims=True)
dataSet=dataSet-imageMeans
### MASKS
masks=myTools.loadImages('../../masksExpertBig', 850, 850, 1)
masks[masks<=50]=0