pycaffe学习之（一）：io.py源码解读
主要是定义了一个transformer，preprocess和deprocess操作，完成caffe和python之间数据的格式转换等，注意caffe： BGR,0-255，C*H*W而python是RGB, H*W*C,0-1的数据格式。
import numpy as np
import skimage.io #导入scikit-image 图像处理库
from scipy.ndimage import zoom #scipy.ndimage子模块致力于图像处理，zoom 放缩
from skimage.transform import resize # 图像的形变与缩放，使用的是skimage的transform模块try: # 异常处理
# Python3 will most likely not be able to load protobuf
from caffe.proto import caffe_pb2
except:
import sys
if sys.version_info >= (3, 0):
print("Failed to include caffe_pb2, things might go wrong!")
else:
raise ## proto / datum / ndarray conversion
def blobproto_to_array(blob, return_diff=False): # blob转为array
"""
Convert a blob proto to an array. In default, we will just return the data,
unless return_diff is True, in which case we will return the diff.
"""
# Read the data into an array
if return_diff:
data = np.array(blob.diff)
else:
data = np.array(blob.data)
# Reshape the array
if blob.HasField('num') or blob.HasField('channels') or blob.HasField('height') or blob.HasField('width'):
# Use legacy 4D shape
return data.reshape(blob.num, blob.channels, blob.height, blob.width)
else:
return data.reshape(blob.shape.dim) # 定义从array转为blob的函数
def array_to_blobproto(arr, diff=None):
"""Converts a N-dimensional array to blob proto. If diff is given, also
convert the diff. You need to make sure that arr and diff have the same
shape, and this function does not do sanity check.
"""
blob = caffe_pb2.BlobProto()
blob.shape.dim.extend(arr.shape)
blob.data.extend(arr.astype(float).flat)
if diff is not None:
blob.diff.extend(diff.astype(float).flat)
return blob#将一组array转为一个序列blob组
def arraylist_to_blobprotovector_str(arraylist):
"""Converts a list of arrays to a serialized blobprotovec, which could be
then passed to a network for processing.
"""
vec = caffe_pb2.BlobProtoVector()
vec.blobs.extend([array_to_blobproto(arr) for arr in arraylist])
return vec.SerializeToString()#将一个序列blob组转为一组array（循环调用前面的单个的转换函数）
def blobprotovector_str_to_arraylist(str):
"""Converts a serialized blobprotovec to a list of arrays.
"""
vec = caffe_pb2.BlobProtoVector()
vec.ParseFromString(str)
return [blobproto_to_array(blob) for blob in vec.blobs]#将三维的数组转为caffe的datum,若数组是uint8数据类型，转为str ,否则转换为float
def array_to_datum(arr, label=None):
"""Converts a 3-dimensional array to datum. If the array has dtype uint8,
the output data will be encoded as a string. Otherwise, the output data
will be stored in float format.
"""
if arr.ndim != 3:
raise ValueError('Incorrect array shape.')
datum = caffe_pb2.Datum()
datum.channels, datum.height, datum.width = arr.shape
if arr.dtype == np.uint8:
datum.data = arr.tostring()
else:
datum.float_data.extend(arr.flat)
if label is not None:
datum.label = label
return datum
#将datum转为array,只转数据，label就不转了，因为可以通过datum.label轻松访问到。
def datum_to_array(datum):
"""Converts a datum to an array. Note that the label is not returned,
as one can easily get it by calling datum.label.
"""
if len(datum.data):
return np.fromstring(datum.data, dtype=np.uint8).reshape(
datum.channels, datum.height, datum.width)
else:
return np.array(datum.float_data).astype(float).reshape(
datum.channels, datum.height, datum.width)
# 下面是预处理preprocess
#定义一个类 Transformer
class Transformer:
"""
Transform input for feeding into a Net.
Note: this is mostly for illustrative purposes and it is likely better
to define your own input preprocessing routine for your needs.
Parameters
----------
net : a Net for which the input should be prepared
"""
def __init__(self, inputs): #初始化，其中self是对自身的引用
self.inputs = inputs
self.transpose = {}
self.channel_swap = {}
self.raw_scale = {}
self.mean = {}
self.input_scale = {}
def __check_input(self, in_):
if in_ not in self.inputs:
raise Exception('{} is not one of the net inputs: {}'.format(
in_, self.inputs)) #定义预处理，完成的主要是将图片转为caffe能接受的格式
# 将H' x W' x K转为K x H x W；float型；转为BGR，尺度变为0-255，减去均值再乘以尺度因子
def preprocess(self, in_, data):
"""
Format input for Caffe:
- convert to single
- resize to input dimensions (preserving number of channels)
- transpose dimensions to K x H x W
- reorder channels (for instance color to BGR)
- scale raw input (e.g. from [0, 1] to [0, 255] for ImageNet models)
- subtract mean
- scale feature
Parameters
----------
in_ : name of input blob to preprocess for
data : (H' x W' x K) ndarray （输入的数据格式是rows * cols *channels）
Returns
-------
caffe_in : (K x H x W) ndarray for input to a Net （返回的数据格式是适合caffe的channels *rows * cols）
"""
self.__check_input(in_)
caffe_in = data.astype(np.float32, copy=False)
transpose = self.transpose.get(in_)
channel_swap = self.channel_swap.get(in_)
raw_scale = self.raw_scale.get(in_)
mean = self.mean.get(in_)
input_scale = self.input_scale.get(in_)
in_dims = self.inputs[in_][2:]
if caffe_in.shape[:2] != in_dims:
caffe_in = resize_image(caffe_in, in_dims)
if transpose is not None:
caffe_in = caffe_in.transpose(transpose)
if channel_swap is not None:
caffe_in = caffe_in[channel_swap, :, :]
if raw_scale is not None:
caffe_in *= raw_scale
if mean is not None:
caffe_in -= mean
if input_scale is not None:
caffe_in *= input_scale
return caffe_in#将caffe的格式转换回图片的格式
def deprocess(self, in_, data):
"""
Invert Caffe formatting; see preprocess().
"""
self.__check_input(in_)
decaf_in = data.copy().squeeze()
transpose = self.transpose.get(in_)
channel_swap = self.channel_swap.get(in_)
raw_scale = self.raw_scale.get(in_)
mean = self.mean.get(in_)
input_scale = self.input_scale.get(in_)
if input_scale is not None:
decaf_in /= input_scale
if mean is not None:
decaf_in += mean
if raw_scale is not None:
decaf_in /= raw_scale
if channel_swap is not None:
decaf_in = decaf_in[np.argsort(channel_swap), :, :]
if transpose is not None:
decaf_in = decaf_in.transpose(np.argsort(transpose))
return decaf_indef set_transpose(self, in_, order):
"""
Set the input channel order for e.g. RGB to BGR conversion
as needed for the reference ImageNet model.
Parameters
----------
in_ : which input to assign this channel order
order : the order to transpose the dimensions
"""
self.__check_input(in_)
if len(order) != len(self.inputs[in_]) - 1:
raise Exception('Transpose order needs to have the same number of '
'dimensions as the input.')
self.transpose[in_] = order#RGB通道转换，注意！要这样做的前提是颜色通道已经先转换到了第一维，也就是先执行transpose
def set_channel_swap(self, in_, order):
"""
Set the input channel order for e.g. RGB to BGR conversion
as needed for the reference ImageNet model.
N.B. this assumes the channels are the first dimension AFTER transpose.
Parameters
----------
in_ : which input to assign this channel order
order : the order to take the channels.
(2,1,0) maps RGB to BGR for example.
"""
self.__check_input(in_)
if len(order) != self.inputs[in_][1]:
raise Exception('Channel swap needs to have the same number of '
'dimensions as the input channels.')
self.channel_swap[in_] = order#将输入乘以一个尺度因子，因为python里面的数据是0-1的，而caffe很多模型都是0-255的
def set_raw_scale(self, in_, scale):
"""
Set the scale of raw features s.t. the input blob = input * scale.
While Python represents images in [0, 1], certain Caffe models
like CaffeNet and AlexNet represent images in [0, 255] so the raw_scale
of these models must be 255.
Parameters
----------
in_ : which input to assign this scale factor
scale : scale coefficient
"""
self.__check_input(in_)
self.raw_scale[in_] = scaledef set_mean(self, in_, mean):
"""
Set the mean to subtract for centering the data.
Parameters
----------
in_ : which input to assign this mean.
mean : mean ndarray (input dimensional or broadcastable)
"""
self.__check_input(in_)
ms = mean.shape
if mean.ndim == 1:
# broadcast channels
if ms[0] != self.inputs[in_][1]:
raise ValueError('Mean channels incompatible with input.')
mean = mean[:, np.newaxis, np.newaxis]
else:
# elementwise mean
if len(ms) == 2:
ms = (1,) + ms
if len(ms) != 3:
raise ValueError('Mean shape invalid')
if ms != self.inputs[in_][1:]:
raise ValueError('Mean shape incompatible with input shape.')
self.mean[in_] = mean#注意这里的尺度变换是子啊减去均值之后的，而前面的raw_scale则是在减均值之前
def set_input_scale(self, in_, scale):
"""
Set the scale of preprocessed inputs s.t. the blob = blob * scale.
N.B. input_scale is done AFTER mean subtraction and other preprocessing
while raw_scale is done BEFORE.
Parameters
----------
in_ : which input to assign this scale factor
scale : scale coefficient
"""
self.__check_input(in_)
self.input_scale[in_] = scale下面是图片的io操作
## Image IO
#得到的图片格式是适合python的哦！
def load_image(filename, color=True):
"""
Load an image converting from grayscale or alpha as needed.
Parameters
----------
filename : string
color : boolean
flag for color format. True (default) loads as RGB while False
loads as intensity (if image is already grayscale).
Returns
-------
image : an image with type np.float32 in range [0, 1]
of size (H x W x 3) in RGB or
of size (H x W x 1) in grayscale.
"""
img = skimage.img_as_float(skimage.io.imread(filename, as_grey=not color)).astype(np.float32)
if img.ndim == 2:
img = img[:, :, np.newaxis]
if color:
img = np.tile(img, (1, 1, 3))
elif img.shape[2] == 4:
img = img[:, :, :3]
return img#尺度resize，一般默认是线性插值
def resize_image(im, new_dims, interp_order=1):
"""
Resize an image array with interpolation.
Parameters
----------
im : (H x W x K) ndarray
new_dims : (height, width) tuple of new dimensions.
interp_order : interpolation order, default is linear.
Returns
-------
im : resized ndarray with shape (new_dims[0], new_dims[1], K)
"""
if im.shape[-1] == 1 or im.shape[-1] == 3:
im_min, im_max = im.min(), im.max()
if im_max > im_min:
# skimage is fast but only understands {1,3} channel images
# in [0, 1].
im_std = (im - im_min) / (im_max - im_min)
resized_std = resize(im_std, new_dims, order=interp_order)
resized_im = resized_std * (im_max - im_min) + im_min
else:
# the image is a constant -- avoid divide by 0
ret = np.empty((new_dims[0], new_dims[1], im.shape[-1]),
dtype=np.float32)
ret.fill(im_min)
return ret
else:
# ndimage interpolates anything but more slowly.
scale = tuple(np.array(new_dims, dtype=float) / np.array(im.shape[:2]))
resized_im = zoom(im, scale + (1,), order=interp_order)
return resized_im.astype(np.float32)#进行裁剪镜像等操作，一个输入可以得到10个输出（4个角+center）*(mirror=2)=10
def oversample(images, crop_dims):
"""
Crop images into the four corners, center, and their mirrored versions.
Parameters
----------
image : iterable of (H x W x K) ndarrays
crop_dims : (height, width) tuple for the crops.
Returns
-------
crops : (10*N x H x W x K) ndarray of crops for number of inputs N.
"""
# Dimensions and center.
im_shape = np.array(images[0].shape)
crop_dims = np.array(crop_dims)
im_center = im_shape[:2] / 2.0
# Make crop coordinates
h_indices = (0, im_shape[0] - crop_dims[0])
w_indices = (0, im_shape[1] - crop_dims[1])
crops_ix = np.empty((5, 4), dtype=int)
curr = 0
for i in h_indices:
for j in w_indices:
crops_ix[curr] = (i, j, i + crop_dims[0], j + crop_dims[1])
curr += 1
crops_ix[4] = np.tile(im_center, (1, 2)) + np.concatenate([
-crop_dims / 2.0,
crop_dims / 2.0
])
crops_ix = np.tile(crops_ix, (2, 1))
# Extract crops
crops = np.empty((10 * len(images), crop_dims[0], crop_dims[1],
im_shape[-1]), dtype=np.float32)
ix = 0
for im in images:
for crop in crops_ix:
crops[ix] = im[crop[0]:crop[2], crop[1]:crop[3], :]
ix += 1
crops[ix-5:ix] = crops[ix-5:ix, :, ::-1, :] # flip for mirrors
return crops
参考资料：https://github.com/BVLC/caffe/blob/master/python/caffe/io.py