the main class for running Gary Cottrell's The Model
@author Davis Liang
@version 1.0
@date August 6, 2015
"""
def __init__(self):
print('Initializing The Model')
if __name__ == '__main__':
dataPath = "/Users/Davis/Desktop/theModel/data/dummy" #setting up dataPath to dataset directory
imp1 = importer.importerClass(
) #initializing a new importer class to import images
x = imp1.load(dataPath) #import all images from the directory dataPath
imData = x[
0] #holds all the images in the dataset. imData[i] corresponds to image i.
imName = x[
1] #holds the name of the image. imName[i] corresponds the name of image i. See how the names are formatted to extract label data.
imLabel = x[
2] #holds all the labeling data to be used in the network. imLabel[i] corresponds to labeling for image i.
pp = preprocessor.preprocessorClass() #initialize a preprocessor class
gaborDict = pp.createConvFilterBank(5, 8) #build your gabor dictionaries
filtData = [None] * len(
imData) #initialize an array to hold the filtered Data
#filter your entire dataset
@author Davis Liang
@version 1.0
@date August 6, 2015
"""
def __init__(self):
print('Initializing The Model')
if __name__ == '__main__':
dataPath = "/Users/Davis/Desktop/theModel/data/dummy" #setting up dataPath to dataset directory
imp1 = importer.importerClass() #initializing a new importer class to import images
x = imp1.load(dataPath) #import all images from the directory dataPath
imData = x[0] #holds all the images in the dataset. imData[i] corresponds to image i.
imName = x[1] #holds the name of the image. imName[i] corresponds the name of image i. See how the names are formatted to extract label data.
imLabel = x[2] #holds all the labeling data to be used in the network. imLabel[i] corresponds to labeling for image i.
pp = preprocessor.preprocessorClass() #initialize a preprocessor class
gaborDict = pp.createConvFilterBank(5,8) #build your gabor dictionaries
filtData = [None]*len(imData) #initialize an array to hold the filtered Data
#filter your entire dataset
for i in range(len(imData)):
print(' filtering image {}').format(i+1)
filtData[i] = pp.filterData(imData[i], gaborDict)
return gabor #return the gabor dictionary. #def PCA(self, trainSet): if __name__ == '__main__': pp = preprocessorClass() gaborDict = pp.createConvFilterBank(5,8) #import an image #then, filter it and see what the dimensionality is, if it makes sense, and how we would split that up dataPath = "/Users/Davis/Desktop/theModel/data/dummy" imp1 = importer.importerClass() x = imp1.load(dataPath) imData = x[0] imName = x[1] imLabel = x[2] filtIm = pp.filterData(imData[0], gaborDict) #imgplot = plt.imshow(imData[0]) #plt.show() #imgplot = plt.imshow((gaborDict[:,:,0,3]).real) #plt.show() #imgplot = plt.imshow(filtIm[:,:,3,0].real)
# your gabors are now built...
print(' gabors have been successfully built')
return gabor #return the gabor dictionary.
#def PCA(self, trainSet):
if __name__ == '__main__':
pp = preprocessorClass()
gaborDict = pp.createConvFilterBank(5, 8)
#import an image
#then, filter it and see what the dimensionality is, if it makes sense, and how we would split that up
dataPath = "/Users/Davis/Desktop/theModel/data/dummy"
imp1 = importer.importerClass()
x = imp1.load(dataPath)
imData = x[0]
imName = x[1]
imLabel = x[2]
filtIm = pp.filterData(imData[0], gaborDict)
#imgplot = plt.imshow(imData[0])
#plt.show()
#imgplot = plt.imshow((gaborDict[:,:,0,3]).real)
#plt.show()
#imgplot = plt.imshow(filtIm[:,:,3,0].real)
