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) bot=np.concatenate((res[1][0],res[3][0]),axis=1) #concatenate the two halves to get the full image res=np.concatenate((top,bot),axis=0) plt.show(plt.imshow(res, cmap=cm.binary)) scipy.misc.imsave(outName, res)
import matplotlib.pyplot as plt import numpy as np import matplotlib.cm as cm from PIL import Image import numpy import numpy as np import sys import myTools import scipy myTools.createPretrainedNN((None,1,100,100))
#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.augmentMasks(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])
#load the pretrained network
myNet = myTools.createPretrainedNN(data_size)
#make predictions for the image
res = myNet(image)
#crop the center of the mask
res = myTools.cropCenter(res, 80)
plt.show(plt.imshow(res[0][0], cmap=cm.binary))
scipy.misc.imsave(outName, res[0][0])
import matplotlib.pyplot as plt import numpy as np import matplotlib.cm as cm from PIL import Image import numpy import numpy as np import sys import myTools import scipy myTools.createPretrainedNN((None, 1, 100, 100))