def save_mean(mean, filename):
"""
Saves mean to file
Arguments:
mean -- the mean as an np.ndarray
filename -- the location to save the image
"""
if filename.endswith('.binaryproto'):
blob = caffe_pb2.BlobProto()
blob.num = 1
blob.channels = mean.shape[0]
blob.height = mean.shape[1]
blob.width = mean.shape[2]
blob.data.extend(mean.astype(float).flat)
with open(filename, 'wb') as outfile:
outfile.write(blob.SerializeToString())
elif filename.endswith(('.jpg', '.jpeg', '.png')):
save_image(mean, filename)
else:
raise ValueError('unrecognized file extension')
python类BlobProto()的实例源码
def load_mean_file(mean_file):
if mean_file.endswith('.npy'):
return np.load(mean_file)
with open(mean_file, 'rb') as infile:
blob = caffe_pb2.BlobProto()
blob.MergeFromString(infile.read())
if blob.HasField('shape'):
blob_dims = blob.shape
assert len(blob_dims) == 4, 'Shape should have 4 dimensions - shape is "%s"' % blob.shape
elif blob.HasField('num') and blob.HasField('channels') and \
blob.HasField('height') and blob.HasField('width'):
blob_dims = (blob.num, blob.channels, blob.height, blob.width)
else:
raise ValueError('blob does not provide shape or 4d dimensions')
#pixel = np.reshape(blob.data, blob_dims[1:]).mean(1).mean(1)
#print pixel.shape
#t.set_mean('data', pixel)
mean=np.reshape(blob.data, blob_dims[1:])
#mean=mean[:,(256-224)//2:(256-224)//2+224,(256-224)//2:(256-224)//2+224]
return mean
def _load_binaryproto(file):
blob = caffe_pb2.BlobProto()
data = open(file, 'rb').read()
blob.ParseFromString(data)
arr = np.array(caffe.io.blobproto_to_array(blob))
return arr[0]
def load_mean_bgr():
""" bgr mean pixel value image, [0, 255]. [height, width, 3] """
with open("data/ResNet_mean.binaryproto", mode='rb') as f:
data = f.read()
blob = caffe_pb2.BlobProto()
blob.ParseFromString(data)
mean_bgr = caffe.io.blobproto_to_array(blob)[0]
assert mean_bgr.shape == (3, 224, 224)
return mean_bgr.transpose((1, 2, 0))
classify-samples.py 文件源码
项目：have-fun-with-machine-learning
作者: humphd
项目源码
文件源码
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def setup(self):
mean_file = os.path.join(self.model_dir, 'mean.binaryproto')
labels_file = os.path.join(self.model_dir, 'labels.txt')
self.transformer = caffe.io.Transformer({'data': self.net.blobs['data'].data.shape})
self.transformer.set_transpose('data', (2, 0, 1))
self.transformer.set_raw_scale('data', 255)
self.transformer.set_channel_swap('data', (2, 1, 0))
# set mean pixel
with open(mean_file, 'rb') as infile:
blob = caffe_pb2.BlobProto()
blob.MergeFromString(infile.read())
if blob.HasField('shape'):
blob_dims = blob.shape
assert len(blob_dims) == 4, 'Shape should have 4 dimensions - shape is %s' % blob.shape
elif blob.HasField('num') and blob.HasField('channels') and \
blob.HasField('height') and blob.HasField('width'):
blob_dims = (blob.num, blob.channels, blob.height, blob.width)
else:
raise ValueError('blob does not provide shape or 4d dimensions')
pixel = np.reshape(blob.data, blob_dims[1:]).mean(1).mean(1)
self.transformer.set_mean('data', pixel)
# This is overkill here, since we only have 2 labels, but here's how we might read them.
# Later we'd grab the label we want based on position (e.g., 0=dolphin, 1=seahorse)
self.labels = np.loadtxt(labels_file, str, delimiter='\n')
def get_transformer(deploy_file, mean_file=None):
"""
Returns an instance of caffe.io.Transformer
Arguments:
deploy_file -- path to a .prototxt file
Keyword arguments:
mean_file -- path to a .binaryproto file (optional)
"""
network = caffe_pb2.NetParameter()
with open(deploy_file) as infile:
text_format.Merge(infile.read(), network)
if network.input_shape:
dims = network.input_shape[0].dim
else:
dims = network.input_dim[:4]
#dims = network.input_dim
t = caffe.io.Transformer(
inputs = {'data': dims}
)
t.set_transpose('data', (2,0,1)) # transpose to (channels, height, width)
# color images
if dims[1] == 3:
# channel swap
t.set_channel_swap('data', (2,1,0))
if mean_file:
# set mean pixel
with open(mean_file) as infile:
blob = caffe_pb2.BlobProto()
blob.MergeFromString(infile.read())
if blob.HasField('shape'):
blob_dims = blob.shape
assert len(blob_dims) == 4, 'Shape should have 4 dimensions - shape is "%s"' % blob.shape
elif blob.HasField('num') and blob.HasField('channels') and \
blob.HasField('height') and blob.HasField('width'):
blob_dims = (blob.num, blob.channels, blob.height, blob.width)
else:
raise ValueError('blob does not provide shape or 4d dimensions')
pixel = np.reshape(blob.data, blob_dims[1:]).mean(1).mean(1)
t.set_mean('data', pixel)
return t
# Load image to caffe
def get_transformer(deploy_file, mean_file=None):
"""
Returns an instance of caffe.io.Transformer
Arguments:
deploy_file -- path to a .prototxt file
Keyword arguments:
mean_file -- path to a .binaryproto file (optional)
"""
network = caffe_pb2.NetParameter()
with open(deploy_file) as infile:
text_format.Merge(infile.read(), network)
if network.input_shape:
dims = network.input_shape[0].dim
else:
dims = network.input_dim[:4]
t = caffe.io.Transformer(
inputs = {'data': dims}
)
t.set_transpose('data', (2,0,1)) # transpose to (channels, height, width)
# color images
if dims[1] == 3:
# channel swap
t.set_channel_swap('data', (2,1,0))
if mean_file:
# set mean pixel
with open(mean_file,'rb') as infile:
blob = caffe_pb2.BlobProto()
blob.MergeFromString(infile.read())
if blob.HasField('shape'):
blob_dims = blob.shape
assert len(blob_dims) == 4, 'Shape should have 4 dimensions - shape is "%s"' % blob.shape
elif blob.HasField('num') and blob.HasField('channels') and \
blob.HasField('height') and blob.HasField('width'):
blob_dims = (blob.num, blob.channels, blob.height, blob.width)
else:
raise ValueError('blob does not provide shape or 4d dimensions')
pixel = np.reshape(blob.data, blob_dims[1:]).mean(1).mean(1)
t.set_mean('data', pixel)
return t
classifier.py 文件源码
项目：Barebones-Flask-and-Caffe-Classifier
作者: alex-paterson
项目源码
文件源码
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def get_transformer(deploy_file, mean_file=None):
"""
Returns an instance of caffe.io.Transformer
Arguments:
deploy_file -- path to a .prototxt file
Keyword arguments:
mean_file -- path to a .binaryproto file (optional)
"""
network = caffe_pb2.NetParameter()
with open(deploy_file) as infile:
text_format.Merge(infile.read(), network)
if network.input_shape:
dims = network.input_shape[0].dim
else:
dims = network.input_dim[:4]
t = caffe.io.Transformer(
inputs = {'data': dims}
)
t.set_transpose('data', (2,0,1)) # transpose to (channels, height, width)
# color images
if dims[1] == 3:
# channel swap
t.set_channel_swap('data', (2,1,0))
if mean_file:
# set mean pixel
with open(mean_file,'rb') as infile:
blob = caffe_pb2.BlobProto()
blob.MergeFromString(infile.read())
if blob.HasField('shape'):
blob_dims = blob.shape
assert len(blob_dims) == 4, 'Shape should have 4 dimensions - shape is "%s"' % blob.shape
elif blob.HasField('num') and blob.HasField('channels') and \
blob.HasField('height') and blob.HasField('width'):
blob_dims = (blob.num, blob.channels, blob.height, blob.width)
else:
raise ValueError('blob does not provide shape or 4d dimensions')
pixel = np.reshape(blob.data, blob_dims[1:]).mean(1).mean(1)
t.set_mean('data', pixel)
return t
def get_transformer(deploy_file, mean_file=None):
"""
Returns an instance of caffe.io.Transformer
Arguments:
deploy_file -- path to a .prototxt file
Keyword arguments:
mean_file -- path to a .binaryproto file (optional)
"""
network = caffe_pb2.NetParameter()
with open(deploy_file) as infile:
text_format.Merge(infile.read(), network)
if network.input_shape:
dims = network.input_shape[0].dim
else:
dims = network.input_dim[:4]
t = caffe.io.Transformer(
inputs = {'data': dims}
)
t.set_transpose('data', (2,0,1)) # transpose to (channels, height, width)
# color images
if dims[1] == 3:
# channel swap
t.set_channel_swap('data', (2,1,0))
if mean_file:
# set mean pixel
with open(mean_file,'rb') as infile:
blob = caffe_pb2.BlobProto()
blob.MergeFromString(infile.read())
if blob.HasField('shape'):
blob_dims = blob.shape
assert len(blob_dims) == 4, 'Shape should have 4 dimensions - shape is "%s"' % blob.shape
elif blob.HasField('num') and blob.HasField('channels') and \
blob.HasField('height') and blob.HasField('width'):
blob_dims = (blob.num, blob.channels, blob.height, blob.width)
else:
raise ValueError('blob does not provide shape or 4d dimensions')
pixel = np.reshape(blob.data, blob_dims[1:]).mean(1).mean(1)
t.set_mean('data', pixel)
return t
def get_transformer(deploy_file, mean_file=None):
"""
Returns an instance of caffe.io.Transformer
Arguments:
deploy_file -- path to a .prototxt file
Keyword arguments:
mean_file -- path to a .binaryproto file (optional)
"""
network = caffe_pb2.NetParameter()
with open(deploy_file) as infile:
text_format.Merge(infile.read(), network)
if network.input_shape:
dims = network.input_shape[0].dim
else:
dims = network.input_dim[:4]
t = caffe.io.Transformer(
inputs = {'data': dims}
)
t.set_transpose('data', (2,0,1)) # transpose to (channels, height, width)
# color images
if dims[1] == 3:
# channel swap
t.set_channel_swap('data', (2,1,0))
if mean_file:
# set mean pixel
with open(mean_file,'rb') as infile:
blob = caffe_pb2.BlobProto()
blob.MergeFromString(infile.read())
if blob.HasField('shape'):
blob_dims = blob.shape
assert len(blob_dims) == 4, 'Shape should have 4 dimensions - shape is "%s"' % blob.shape
elif blob.HasField('num') and blob.HasField('channels') and \
blob.HasField('height') and blob.HasField('width'):
blob_dims = (blob.num, blob.channels, blob.height, blob.width)
else:
raise ValueError('blob does not provide shape or 4d dimensions')
pixel = np.reshape(blob.data, blob_dims[1:]).mean(1).mean(1)
t.set_mean('data', pixel)
return t
