def extract_features(file_name):
global X
image = imread(file_name)
image.resize((image.shape[0], image.shape[1], 1))
# overfeat expects rgb, so replicate the grayscale values twice
image = np.repeat(image, 3, 2)
image = imresize(image, (231, 231)).astype(np.float32)
# numpy loads image with colors as last dimension, so transpose tensor
h = image.shape[0]
w = image.shape[1]
c = image.shape[2]
image = image.reshape(w * h, c)
image = image.transpose()
image = image.reshape(c, h, w)
b = overfeat.fprop(image)
b = b.flatten()
top = [(b[i], i) for i in xrange(len(b))]
top.sort()
klasses = [
overfeat.get_class_name(top[-(i + 1)][1]) for i in xrange(len(top))
]
X.append(klasses)
def getTopClassesNames(self, numTopClasses): self.outputAtLastLayer = self.outputAtLastLayer.flatten() top = [(self.outputAtLastLayer[i], i) for i in xrange(len(self.outputAtLastLayer))] #pdb.set_trace() top.sort() #topNew = [] print "\nTop classes :" for i in xrange(numTopClasses): #topNew.append(top[i]) print(overfeat.get_class_name(top[-(i+1)][1])) + '/t' + str(top[-(i+1)][1])
def top_n_predictions(likelihoods, n=1):
'''
returns top n predictions given an overfeat likelihoods vector whose
index corresponds with a category
'''
assert len(likelihoods) == 1000
assert n >= 1 and n <= 1000
Prediction = namedtuple('Prediction', ['name_index','likelihood'])
predictions = (Prediction(i,v) for i,v in enumerate(likelihoods))
# sort prediction by descending likelihood
predictions = sorted(predictions, key=lambda x: -x.likelihood)
return [overfeat.get_class_name(pred.name_index) for pred in predictions[0:n]]
def extract_overfeat(image_path):
# if overfeat_initialized == None or not overfeat_initialized:
# overfeat_initialized = True
print "Overfeat: ", image_path
image = imread(image_path)
print "Image shape: ", image.shape
if len(image.shape) == 2 or image.shape[2] == 2:
image = skimage.color.gray2rgb(image)
elif image.shape[2] == 4:
image_rgb = numpy.zeros((image.shape[0], image.shape[1], 3),
numpy.uint8)
image_rgb[:, :, 0] = image[:, :, 0]
image_rgb[:, :, 1] = image[:, :, 1]
image_rgb[:, :, 2] = image[:, :, 2]
image = image_rgb
h0 = image.shape[0]
w0 = image.shape[1]
d0 = float(min(h0, w0))
image = image[int(round((h0 - d0) / 2.)):int(round((h0 - d0) / 2.) + d0),
int(round((w0 - d0) / 2.)):int(round((w0 - d0) / 2.) +
d0), :]
image = imresize(image, (231, 231)).astype(numpy.float32)
#image = cv2.resize(image, (231, 231)).astype(numpy.float32)
# numpy loads image with colors as last dimension, transpose tensor
h = image.shape[0]
w = image.shape[1]
c = image.shape[2]
image = image.reshape(w * h, c)
image = image.transpose()
image = image.reshape(c, h, w)
print "Image size :", image.shape
out_categories = overfeat.fprop(image)
#layer 21,22,23
layer_output = overfeat.get_output(20)
print "Layer size: ", layer_output.shape
layer_output = layer_output.flatten()
descriptors = []
descriptors.append(layer_output)
out_categories = out_categories.flatten()
top = [(out_categories[i], i) for i in xrange(len(out_categories))]
top.sort()
print "\nTop classes :"
for i in xrange(5):
print(overfeat.get_class_name(top[-(i + 1)][1]))
return descriptors
def overfeat_predictions(likelihoods, n=1):
'''
returns top n predictions given an overfeat likelihoods
vector whose index corresponds with a category
'''
assert len(likelihoods) == 1000
assert n >= 1 and n <= 1000
Prediction = namedtuple('Prediction', ['name_index', 'likelihood'])
predictions = (Prediction(i, v) for i, v in enumerate(likelihoods))
# sort prediction by descending likelihood
predictions = sorted(predictions, key=lambda x: -x.likelihood)
return [
overfeat.get_class_name(pred.name_index) for pred in predictions[0:n]
]
def extract_overfeat(image_path):
# if overfeat_initialized == None or not overfeat_initialized:
# overfeat_initialized = True
print "Overfeat: ", image_path
image = imread(image_path)
print "Image shape: ", image.shape
if len(image.shape) == 2 or image.shape[2] == 2:
image = skimage.color.gray2rgb(image)
elif image.shape[2] == 4:
image_rgb = numpy.zeros((image.shape[0],image.shape[1], 3), numpy.uint8)
image_rgb[:,:,0] = image[:,:,0]
image_rgb[:,:,1] = image[:,:,1]
image_rgb[:,:,2] = image[:,:,2]
image = image_rgb
h0 = image.shape[0]
w0 = image.shape[1]
d0 = float(min(h0, w0))
image = image[int(round((h0-d0)/2.)):int(round((h0-d0)/2.)+d0),
int(round((w0-d0)/2.)):int(round((w0-d0)/2.)+d0), :]
image = imresize(image, (231, 231)).astype(numpy.float32)
#image = cv2.resize(image, (231, 231)).astype(numpy.float32)
# numpy loads image with colors as last dimension, transpose tensor
h = image.shape[0]
w = image.shape[1]
c = image.shape[2]
image = image.reshape(w*h, c)
image = image.transpose()
image = image.reshape(c, h, w)
print "Image size :", image.shape
out_categories = overfeat.fprop(image)
#layer 21,22,23
layer_output = overfeat.get_output(20)
print "Layer size: ", layer_output.shape
layer_output = layer_output.flatten()
descriptors = []
descriptors.append(layer_output)
out_categories = out_categories.flatten()
top = [(out_categories[i], i) for i in xrange(len(out_categories))]
top.sort()
print "\nTop classes :"
for i in xrange(5):
print(overfeat.get_class_name(top[-(i+1)][1]))
return descriptors
# resize and crop into a 231x231 image
h0 = image.shape[0]
w0 = image.shape[1]
d0 = float(min(h0, w0))
image = image[int(round((h0-d0)/2.)):int(round((h0-d0)/2.)+d0),
int(round((w0-d0)/2.)):int(round((w0-d0)/2.)+d0), :]
image = imresize(image, (231, 231)).astype(numpy.float32)
# numpy loads image with colors as last dimension, transpose tensor
h = image.shape[0]
w = image.shape[1]
c = image.shape[2]
image = image.reshape(w*h, c)
image = image.transpose()
image = image.reshape(c, h, w)
print "Image size :", image.shape
# initialize overfeat. Note that this takes time, so do it only once if possible
overfeat.init('../../data/default/net_weight_0', 0)
# run overfeat on the image
b = overfeat.fprop(image)
# display top 5 classes
b = b.flatten()
top = [(b[i], i) for i in xrange(len(b))]
top.sort()
print "\nTop classes :"
for i in xrange(5):
print(overfeat.get_class_name(top[-(i+1)][1]))
w0 = image.shape[1]
d0 = float(min(h0, w0))
h1 = int(round(231*h0/d0))
w1 = int(round(231*w0/d0))
image = imresize(image, (h1, w1)).astype(numpy.float32)
image = image[int(round((h0-d0)/2.)):int(round((h0-d0)/2.)+231),
int(round((w0-d0)/2.)):int(round((w0-d0)/2.)+231), :]
# numpy loads image with colors as last dimension, transpose tensor
h = image.shape[0]
w = image.shape[1]
c = image.shape[2]
image = image.reshape(w*h, c)
image = image.transpose()
image = image.reshape(c, h, w)
print "Image size :", image.shape
# initialize overfeat. Note that this takes time, so do it only once if possible
overfeat.init('../../data/default/net_weight_0', 0)
# run overfeat on the image
b = overfeat.fprop(image)
# display top 5 classes
b = b.flatten()
top = [(b[i], i) for i in xrange(len(b))]
top.sort()
print "\nTop classes :"
for i in xrange(5):
print(overfeat.get_class_name(top[-(i+1)][1]))