def _preprocess(self, images):
"""
Extract causal neighborhoods from images.
@type images: C{ndarray}/C{list}
@param images: array or list of images to process
@rtype: C{tuple}
@return: one array storing inputs (neighborhoods) and one array storing outputs (pixels)
"""
def process(image):
inputs, outputs = generate_data_from_image(image, self.input_mask, self.output_mask)
inputs = asarray(
inputs.T.reshape(
image.shape[0] - self.input_mask.shape[0] + 1, image.shape[1] - self.input_mask.shape[1] + 1, -1
),
dtype="float32",
)
outputs = asarray(
outputs.T.reshape(
image.shape[0] - self.input_mask.shape[0] + 1, image.shape[1] - self.input_mask.shape[1] + 1, -1
),
dtype="float32",
)
return inputs, outputs
inputs, outputs = zip(*mapp(process, images))
return asarray(inputs), asarray(outputs)
def _preprocess(self, images):
"""
Extract causal neighborhoods from images.
@type images: C{ndarray}/C{list}
@param images: array or list of images to process
@rtype: C{tuple}
@return: one array storing inputs (neighborhoods) and one array storing outputs (pixels)
"""
def process(image):
inputs, outputs = generate_data_from_image(image, self.input_mask,
self.output_mask)
inputs = asarray(inputs.T.reshape(
image.shape[0] - self.input_mask.shape[0] + 1,
image.shape[1] - self.input_mask.shape[1] + 1, -1),
dtype='float32')
outputs = asarray(outputs.T.reshape(
image.shape[0] - self.input_mask.shape[0] + 1,
image.shape[1] - self.input_mask.shape[1] + 1, -1),
dtype='float32')
return inputs, outputs
inputs, outputs = zip(*mapp(process, images))
return asarray(inputs), asarray(outputs)
def _preprocess(self, images):
"""
Extract causal neighborhoods from images.
"""
def process(image):
inputs, outputs = generate_data_from_image(image, self.input_mask,
self.output_mask)
inputs = asarray(inputs.T.reshape(
image.shape[0] - self.input_mask.shape[0] + 1,
image.shape[1] - self.input_mask.shape[1] + 1, -1),
dtype='float32')
outputs = asarray(outputs.T.reshape(
image.shape[0] - self.input_mask.shape[0] + 1,
image.shape[1] - self.input_mask.shape[1] + 1, -1),
dtype='float32')
return inputs, outputs
inputs, outputs = zip(*mapp(process, images))
return asarray(inputs), asarray(outputs)
def _preprocess(self, images): """ Extract causal neighborhoods from images. """ def process(image): inputs, outputs = generate_data_from_image( image, self.input_mask, self.output_mask) inputs = asarray( inputs.T.reshape( image.shape[0] - self.input_mask.shape[0] + 1, image.shape[1] - self.input_mask.shape[1] + 1, -1), dtype='float32') outputs = asarray( outputs.T.reshape( image.shape[0] - self.input_mask.shape[0] + 1, image.shape[1] - self.input_mask.shape[1] + 1, -1), dtype='float32') return inputs, outputs inputs, outputs = zip(*mapp(process, images)) return asarray(inputs), asarray(outputs)
from mapp import mapp
from player import player
x=1
y=9
my_mapp=mapp()
PJ=player()
for ii in range(10):
d=int(raw_input("direction?"))
x,y=PJ.move(d,x,y)
my_mapp.location(x,y)
my_mapp.printmapp()
