insightface人脸识别代码记录(二)（数据处理）

一、前言

这部分主要围绕insightface目录下~/src/image_iter.py进行记录。其实，src目录下好多和前面目录重复的文件，好像是作者最开始是基于此目录进行训练的吧。
目录地址：insightface人脸识别代码记录(总)(基于MXNet)

二、主要内容

结合此脚本下的FaceImageIter类来进行记录MXNet中关于数据处理的一般形式，主要记录此类下面的__init__,next(),next_sample()，其他方法捎带简单解释。

可以看到此类继承的是mxnet.io.DataIter类，而此类正是MXNet中的构造数据迭代器的基础类。主要重写的是此类下的next方法。因为在MXNet中调用mxnet.io.DataIter接口，需要传送入数据(self.getdata())、标签(self.getlabel())、pad方式(self.getpad())和index信息(self.getindex())，而一般next方法就是将这几个数据封装到一起。

1.__init__:首先，读取.rec文件的路径，然后通过recordio.MXIndexedRecordIO接口来进行读取，然后读取idx为0的数据，这个idx对应.idx文件下的id为0的元素（这里见下面关于InsightFace Record格式），然后调用recordio脚本里的unpack方法，返回一个IRHeader，这个是关于图像记录的头文件，具体介绍见附录Fig1。然后根据header的flag的不同，进行不同的处理，获得图片的唯一标识idx，然后存放于self.imgidx。然后提下这个self.seq和shuffle，(因为后续会用到这个数据的含义，所以解释下)这个是区别MXNet数据读取方式的标志，详细见附录Fig2。剩下的就是一些数据增强的操作了。

2.next_sample():根据self.seq，来获得不同数据处理情况下的label和img。

3.next():获取next_sample()得到的label和img，并对其进行一系列数据增强操作，然后利用mxnet.io.DataBatch进行封装处理。

最后提下，imdecode，read_image,postprocess_data。
imdecode是因为recordio脚本下unpack获得的是图像编码形式，需要用此函数下的mx.image.imdecode将其转化为ndarray格式，便于后续处理；
read_image就是处理只有.lst文件情况下的图像读取方式；
postprocess_data是对所获的图像做一个维度变化操作，原因是imdecode转化后的ndarray格式是(h,w,c)格式的，和cv2.imread()所获的格式一样，需要转化为(c,h,w)格式。

image_iter.py：

from __future__ import absolute_import
from __future__ import division
from __future__ import print_functionimport os
import random
import logging
...
...class FaceImageIter(io.DataIter):def __init__(self, batch_size, data_shape,path_imgrec = None,shuffle=False, aug_list=None, mean = None,rand_mirror = False, cutoff = 0, color_jittering = 0,images_filter = 0,data_name='data', label_name='softmax_label', **kwargs):super(FaceImageIter, self).__init__()assert path_imgrecif path_imgrec:logging.info('loading recordio %s...',path_imgrec)path_imgidx = path_imgrec[0:-4]+".idx"self.imgrec = recordio.MXIndexedRecordIO(path_imgidx, path_imgrec, 'r')  # pylint: disable=redefined-variable-types = self.imgrec.read_idx(0)header, _ = recordio.unpack(s)if header.flag>0:print('header0 label', header.label)self.header0 = (int(header.label[0]), int(header.label[1]))#assert(header.flag==1)#self.imgidx = range(1, int(header.label[0]))self.imgidx = []self.id2range = {
    }self.seq_identity = range(int(header.label[0]), int(header.label[1]))for identity in self.seq_identity:s = self.imgrec.read_idx(identity)header, _ = recordio.unpack(s)a,b = int(header.label[0]), int(header.label[1])count = b-aif count<images_filter:continueself.id2range[identity] = (a,b)self.imgidx += range(a, b)print('id2range', len(self.id2range))else:self.imgidx = list(self.imgrec.keys)if shuffle:self.seq = self.imgidxself.oseq = self.imgidxprint(len(self.seq))else:self.seq = Noneself.mean = meanself.nd_mean = Noneif self.mean:self.mean = np.array(self.mean, dtype=np.float32).reshape(1,1,3)self.nd_mean = mx.nd.array(self.mean).reshape((1,1,3))self.check_data_shape(data_shape)self.provide_data = [(data_name, (batch_size,) + data_shape)]self.batch_size = batch_sizeself.data_shape = data_shapeself.shuffle = shuffleself.image_size = '%d,%d'%(data_shape[1],data_shape[2])self.rand_mirror = rand_mirrorprint('rand_mirror', rand_mirror)self.cutoff = cutoffself.color_jittering = color_jitteringself.CJA = mx.image.ColorJitterAug(0.125, 0.125, 0.125)self.provide_label = [(label_name, (batch_size,))]#print(self.provide_label[0][1])self.cur = 0self.nbatch = 0self.is_init = Falsedef reset(self):...def num_samples(self):return len(self.seq)def next_sample(self):"""Helper function for reading in next sample."""#set total batch size, for example, 1800, and maximum size for each people, for example 45if self.seq is not None:while True:if self.cur >= len(self.seq):raise StopIterationidx = self.seq[self.cur]self.cur += 1#有.rec文件和.idx文件的情况if self.imgrec is not None:s = self.imgrec.read_idx(idx)header, img = recordio.unpack(s)label = header.labelif not isinstance(label, numbers.Number):label = label[0]return label, img, None, None#只有.ist文件的情况else:label, fname, bbox, landmark = self.imglist[idx]return label, self.read_image(fname), bbox, landmark#只有.rec文件的情况else:s = self.imgrec.read()if s is None:raise StopIterationheader, img = recordio.unpack(s)return header.label, img, None, Nonedef brightness_aug(self, src, x):...def contrast_aug(self, src, x):...def saturation_aug(self, src, x):...def color_aug(self, img, x):...def mirror_aug(self, img):...def compress_aug(self, img):...def next(self):if not self.is_init:self.reset()self.is_init = True"""Returns the next batch of data."""#print('in next', self.cur, self.labelcur)self.nbatch+=1batch_size = self.batch_sizec, h, w = self.data_shapebatch_data = nd.empty((batch_size, c, h, w))if self.provide_label is not None:batch_label = nd.empty(self.provide_label[0][1])i = 0try:while i < batch_size:label, s, bbox, landmark = self.next_sample()_data = self.imdecode(s)if _data.shape[0]!=self.data_shape[1]:...                  if self.rand_mirror:...                 if self.color_jittering>0:...               if self.nd_mean is not None:... if self.cutoff>0:...data = [_data]try:self.check_valid_image(data)except RuntimeError as e:logging.debug('Invalid image, skipping: %s', str(e))continuefor datum in data:assert i < batch_size, 'Batch size must be multiples of augmenter output length'#print(datum.shape)batch_data[i][:] = self.postprocess_data(datum)batch_label[i][:] = labeli += 1except StopIteration:if i<batch_size:raise StopIterationreturn io.DataBatch([batch_data], [batch_label], batch_size - i)def check_data_shape(self, data_shape):...def check_valid_image(self, data):...def imdecode(self, s):"""Decodes a string or byte string to an NDArray.See mx.img.imdecode for more details."""img = mx.image.imdecode(s) #mx.ndarrayreturn imgdef read_image(self, fname):"""Reads an input image `fname` and returns the decoded raw bytes.Example usage:---------->>> dataIter.read_image('Face.jpg') # returns decoded raw bytes."""with open(os.path.join(self.path_root, fname), 'rb') as fin:img = fin.read()return imgdef augmentation_transform(self, data):"""Transforms input data with specified augmentation."""for aug in self.auglist:data = [ret for src in data for ret in aug(src)]return datadef postprocess_data(self, datum):"""Final postprocessing step before image is loaded into the batch."""return nd.transpose(datum, axes=(2, 0, 1))class FaceImageIterList(io.DataIter):...

附录：

Fig1:
recordio脚本地址:https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/recordio.py
以下截图均来自于recordio脚本。

从上图我们可以看到IRHeader有4个属性：flag,label,id,id2。
flag:分两种情况，flag为0和不为0。为0，代表只有单个数据，这时label为一个数字，即创建lst时所创建的数字，代表数据所属类别，此时id2也就为0；若flag不为0，则flag代表label的size,此时，label不再是一个数字，而是一个array，存储这此数据的长度（推测，如果有对这个含义清楚的，希望指正），此时id2不再为0，而是起到前面id标识的作用，因为此时数据大于1个，id自然无法唯一标识。
而flag大于1的情况下，label的值好像来自于对应header的所获图像编码。推测依据如下：


Fig2:
mxnet.image.ImageIter接口的记录。
mxnet.image.ImageIter接口继承自MXNet框架下的基础数据迭代器构造类mxnet.io.DataIter，该接口是python代码实现的图像数据迭代器，既可读取.rec文件，也可以以图像+.lst方式来读取数据。
mxnet.image.ImageIter类地址：https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/image/image.py

class ImageIter(io.DataIter):def __init__(...):...#处理.rec文件格式if path_imgrec:logging.info('%s: loading recordio %s...',class_name, path_imgrec)#存在.idx文件和.rec文件的情况if path_imgidx:self.imgrec = recordio.MXIndexedRecordIO(path_imgidx, path_imgrec, 'r')self.imgidx = list(self.imgrec.keys)#只有.rec文件的情况else:self.imgrec = recordio.MXRecordIO(path_imgrec, 'r')self.imgidx = Noneelse:self.imgrec = Nonearray_fn = _mx_np.array if is_np_array() else nd.array#.lst文件+原图像的情况if path_imglist:logging.info('%s: loading image list %s...', class_name, path_imglist)with open(path_imglist) as fin:imglist = {
    }imgkeys = []for line in iter(fin.readline, ''):line = line.strip().split('\t')label = array_fn(line[1:-1], dtype=dtype)key = int(line[0])imglist[key] = (label, line[-1])imgkeys.append(key)self.imglist = imglistelif isinstance(imglist, list):logging.info('%s: loading image list...', class_name)result = {
    }imgkeys = []index = 1for img in imglist:key = str(index)index += 1if len(img) > 2:label = array_fn(img[:-1], dtype=dtype)elif isinstance(img[0], numeric_types):label = array_fn([img[0]], dtype=dtype)else:label = array_fn(img[0], dtype=dtype)result[key] = (label, img[-1])imgkeys.append(str(key))self.imglist = resultelse:self.imglist = None......
#根据imgkeys和self.imgidx可推测如下情况。#.lst文件+原图像的情况if self.imgrec is None:self.seq = imgkeys#存在.idx文件和.rec文件的情况elif shuffle or num_parts > 1 or path_imgidx:assert self.imgidx is not Noneself.seq = self.imgidx#只有.rec文件的情况else:self.seq = None

结尾

差不多到这里就结束了。在这里记录下insightface数据处理的方法，同时也学习了MXNet框架。
另外，记一下另外两个经常用到的数据处理的接口：
图像分类：mxnet.io.ImageRecordIter()
目标检测：mxnet.io.ImageDetRecordIter()

InsightFace Record格式:
key = 0 ， value_header => [identities_key_start, identities_key_end]

key∈[1, identities_key_start), value_header => [identity_label],value_content => [face_image]

key∈[identities_key_start, identities_key_end), value_header => [identity_key_start, identity_key_end]

参考

MXNet源码解读：数据读取高级类（2）— mxnet.image.ImageIter
MXNet源码解读：数据读取基础类—mxnet.io.DataIter
InsightFace - 使用篇, 如何一键刷分LFW 99.80%, MegaFace 98%.