import scipy #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)
from os import listdir
from os.path import isfile, join
from sklearn.metrics import mean_squared_error
from math import sqrt
#get the produced masks' path
imagePath = sys.argv[1]
#get all images' names
filenames = [f for f in listdir(imagePath) if isfile(join(imagePath, f))]
rmseList = list()
for f in filenames:
#open the image
myMask = Image.open(imagePath + '/' + f, 'r')
#image as numpy array
myMask = numpy.asarray(myMask)
myMask = myTools.cropCenter1(myMask, 100)
#open the expert mask
theMask = Image.open('../../masksExpertTest/' + '/' + f, 'r')
#image as numpy array
theMask = numpy.asarray(theMask)
thisRMSE = sqrt(mean_squared_error(theMask, myMask))
rmseList.append(thisRMSE)
print(numpy.mean(rmseList))
import scipy
#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, 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)
from sklearn.metrics import mean_squared_error
from math import sqrt
#get the produced masks' path
imagePath=sys.argv[1]
#get all images' names
filenames = [f for f in listdir(imagePath) if isfile(join(imagePath, f))]
rmseList=list()
for f in filenames:
#open the image
myMask=Image.open(imagePath + '/' + f, 'r')
#image as numpy array
myMask=numpy.asarray(myMask)
myMask=myTools.cropCenter1(myMask, 100)
#open the expert mask
theMask=Image.open('../../masksExpertTest/' + '/' + f, 'r')
#image as numpy array
theMask=numpy.asarray(theMask)
thisRMSE = sqrt(mean_squared_error(theMask, myMask))
rmseList.append(thisRMSE)
print(numpy.mean(rmseList))
#get the image path imagePath=sys.argv[1] #get the threshold level thresh=float(sys.argv[2]) #get the erosion shape er=int(sys.argv[3]) #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, 80) #split image to 4 splits=myTools.augmentMasks(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 res=myNet(splits) #concatenate on the x axis top=np.concatenate((res[0][0],res[1][0]),axis=1) bot=np.concatenate((res[2][0],res[3][0]),axis=1)
import scipy #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, 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))