def load_data(self,data_opt={'did':0}):
did = data_opt['did']
if did==0:
"""We will create a dummy imagenet data of one single image."""
data = np.random.rand(1, 220, 220, 3).astype(np.float32)
label = np.zeros((1,1000)).astype(np.float32)
label[0][np.random.randint(1000, size=1)] = 1
#label = np.random.randint(1000, size=1)
elif did==1:
fns = data_opt['fns']
pid = data_opt['pid']
lls = data_opt['lls']
data = np.empty((0, INPUT_DIM, INPUT_DIM, 3)).astype(np.float32)
label = np.zeros((0, OUTPUT_DIM)).astype(np.float32)
for fid,filename in enumerate(fns):
image = skimage.io.imread(filename).astype(np.uint8)
image = transform.scale_and_extract(transform.as_rgb(image), 256).astype(np.float32) * 255. - self.jf_data_mean
if _JEFFNET_FLIP:
image = image[::-1, :].copy()
data = np.vstack((data,self.subpatch(image,pid)))
tmp_ll = np.zeros((data.shape[0]-label.shape[0],1000)).astype(np.float32)
tmp_ll[:,lls] = 1
label = np.vstack((label,tmp_ll))
#print data.shape,label.shape
self.dataset = core_layers.NdarrayDataLayer(name='input', sources=[data, label])
def load_and_preprocess(net, imagepath, center_only=False,
scale=True, center_size=256):
image = mpimg.imread(imagepath)
# first, extract the center_sizexcenter_size center.
image = transform.scale_and_extract(transform.as_rgb(image), center_size)
# convert to [0,255] float32
if scale:
image = image.astype(np.float32) * 255.
# Flip the image
image = image[::-1, :].copy()
# subtract the mean
image -= net._data_mean
# oversample the images
images = net.oversample(image, center_only)
return images
def classifiyWholeImage(self,image):
# first, extract the 227x227 center.
image = transform.scale_and_extract(transform.as_rgb(image), 256)
# convert to [0,255] float32
image = image.astype(np.float32) * 255.
if _JEFFNET_FLIP:
# Flip the image if necessary, maintaining the c_contiguous order
image = image[::-1, :].copy()
# subtract the mean
image -= self._data_mean
# oversample the images
image = scipy.ndimage.interpolation.zoom(image,(227./256.,227./256.,1))
images = image[None,:,:,:]
predictions = self.classify_direct(images)
return predictions.mean(0)
def prepare_image(self, image):
"""Returns image of shape `(256, 256, 3)`, as expected by
`transform` when `classify_direct = True`.
"""
from decaf.util import transform # soft dep
_JEFFNET_FLIP = True
# first, extract the 256x256 center.
image = transform.scale_and_extract(transform.as_rgb(image), 256)
# convert to [0,255] float32
image = image.astype(np.float32) * 255.
if _JEFFNET_FLIP:
# Flip the image if necessary, maintaining the c_contiguous order
image = image[::-1, :].copy()
# subtract the mean
image -= self.net_._data_mean
return image
def classify(self, image, center_only=False):
"""Classifies an input image.
Input:
image: an image of 3 channels and has data type uint8. Only the
center region will be used for classification.
Output:
scores: a numpy vector of size 1000 containing the
predicted scores for the 1000 classes.
"""
# first, extract the 256x256 center.
image = transform.scale_and_extract(transform.as_rgb(image), 256)
# convert to [0,255] float32
image = image.astype(np.float32) * 255.
if _JEFFNET_FLIP:
# Flip the image if necessary, maintaining the c_contiguous order
image = image[::-1, :].copy()
# subtract the mean
image -= self._data_mean
# oversample the images
images = DecafNet.oversample(image, center_only)
predictions = self.classify_direct(images)
return predictions.mean(0)
def classify(self, image, center_only=False):
"""Classifies an input image.
Input:
image: an image of 3 channels and has data type uint8. Only the
center region will be used for classification.
Output:
scores: a numpy vector of size 1000 containing the
predicted scores for the 1000 classes.
"""
# first, extract the 256x256 center.
image = transform.scale_and_extract(transform.as_rgb(image), 256)
# convert to [0,255] float32
image = image.astype(np.float32) * 255.
if _JEFFNET_FLIP:
# Flip the image if necessary, maintaining the c_contiguous order
image = image[::-1, :].copy()
# subtract the mean
image -= self._data_mean
# oversample the images
images = DecafNet.oversample(image, center_only)
predictions = self.classify_direct(images)
return predictions.mean(0)
