二、数据准备
1)下载图片
图片来源于ImageNet中的鲤鱼分类,下载地址:https://pan.baidu.com/s/1Ry0ywIXVInGxeHi3uu608g 提取码: wib3
在桌面新建文件夹目标检测,把下载好的压缩文件n01440764.tar放到其中,并解压
2)选择图片
在此数据集中,大部分图片都较为清晰,但是有极少数图片像素点少,不清晰。像素点少的图片不利于模型训练或模型测试,选出部分图片文件,在目标检测路径下输入jupyter notebook,新建一个get_some_qualified_images的文件:
代码运行完成后,在桌面的目标检测文件夹中,会有一个selected_images文件夹,如下图所示:
import os import random from PIL import Image import shutil #获取1000张图片中随机选出数量为sample_number*2的一部分图片的路径 def get_some_imagePath(dirPath, sample_number): fileName_list = os.listdir(dirPath) all_filePath_list = [ os.path.join(dirPath, fileName) for fileName in fileName_list ] all_imagePath_list = [ filePath for filePath in all_filePath_list if '.jpg' in filePath ] some_filePath_list = random.sample( all_filePath_list, k=sample_number * 2) return some_filePath_list #获取一部分像素足够,即长,宽都大于300的图片 def get_some_qualified_images(dirPath, sample_number, new_dirPath): some_imagePath_list = get_some_imagePath(dirPath, sample_number) if not os.path.isdir(new_dirPath): os.mkdir(new_dirPath) i = 0 for imagePath in some_imagePath_list: image = Image.open(imagePath) width, height = image.size if width > 300 and height > 300: i += 1 new_imagePath = 'selected_images/%03d.jpg' % i #在处理图像的时候常常需要将一个图像复制到另一个文件夹中,Python可以很方便的利用shutil.copy(src,dst)函数实现这个操作 #返回复制图像的文件路径 shutil.copy( imagePath, new_imagePath) if i == sample_number: break #获取数量为100的合格样本存放到selected_images文件夹中 get_some_qualified_images('n01440764', 100, 'selected_images')
3)缩小图片
前面我们选出了100张像素足够的图片存放在selected_images文件夹中,即淘汰了像素过小的图片。接着我们实现将像素过大的图片做缩小
import os from PIL import Image def get_small_images(dirPath, new_dirPath): fileName_list = os.listdir(dirPath) filePath_list = [os.path.join(dirPath, fileName) for fileName in fileName_list] imagePath_list = [filePath for filePath in filePath_list if '.jpg' in filePath] if not os.path.isdir(new_dirPath): os.mkdir(new_dirPath) for imagePath in imagePath_list: image = Image.open( imagePath ) width, height = image.size imageName = imagePath.split('\\')[-1] save_path = os.path.join(new_dirPath, imageName) if width >= 600 and height >= 600: minification = min(width, height) // 300 #缩小倍数 new_width = width // minification new_height = height // minification resized_image = image.resize( (new_width, new_height),Image.ANTIALIAS ) print('图片%s由原来的宽%d,高%d,缩小为宽%d,高%d' % (imageName, width, height, new_width, new_height)) resized_image.save(save_path) else: image.save(save_path) get_small_images('selected_images', 'smaller_images')
4)给图片打标签
使用打标签工具LabelImg,下载页面链接:https://tzutalin.github.io/labelImg/
下载后解压,打开:
在输入法为英文输入的情况下,按键盘上的w键则可以开始绘制方框,方框会框住图片中的物体。完成绘制方框后,还需要为方框标上类别,如下图所示。
注意:每完成一张图的打标签,一定要记得保存!!!,初次使用可以在edit选项中设置正方形和矩形框:
在本文演示中,需要给图片中的鲤鱼和人脸2个类别打标签。鲤鱼的标签名叫做fish,人脸的标签名叫human_face,打标签的结果如上图所示
注意:用方框框住物体时,尽量框住物体的所有部位,例如本文中的鱼,鱼鳍是一个重要特征。保证框住物体所有部位的情况下,也不要使方框四周留出过多空白。用LabelImg软件打标签会给每张图片产生对应的xml文件
还有:打标签很耗时间!!!
每次打完标签,会生成对应的xml数据,感兴趣的可以查看一下某个xml文件,其中记录了标签及bounding box坐标:
5)xml转csv
xml转csv的意思是,将xml文件中的信息整合到csv文件中,其中利用的是xml模块
import os import pandas as pd import xml.etree.ElementTree as ET from sklearn.model_selection import train_test_split def xmlPath_list_to_df(xmlPath_list): xmlContent_list = [] for xmlPath in xmlPath_list: print(xmlPath) tree = ET.parse(xmlPath) root = tree.getroot() for member in root.findall('object'): value = ( root.find('filename').text,#文件名 int( root.find('size')[0].text),#width int( root.find('size')[1].text),#height member[0].text,#标签 int( member[4][0].text),#xmin int( member[4][1].text),#ymin int( member[4][2].text),#xmax int( member[4][3].text)#ymax ) xmlContent_list.append(value) column_name = ['filename', 'width', 'height', 'class', 'xmin', 'ymin', 'xmax', 'ymax'] xmlContent_df = pd.DataFrame( xmlContent_list, columns = column_name ) return xmlContent_df def dirPath_to_csv(dirPath): fileName_list = os.listdir(dirPath) all_xmlPath_list = [os.path.join(dirPath, fileName) for fileName in fileName_list if '.xml' in fileName] train_xmlPath_list, test_xmlPath_list = train_test_split(all_xmlPath_list, test_size=0.1, random_state=1) train_df = xmlPath_list_to_df( train_xmlPath_list) train_df.to_csv('train.csv') print('成功产生文件train.csv,训练集共有%d张图片' % len(train_xmlPath_list) ) test_df = xmlPath_list_to_df(test_xmlPath_list) test_df.to_csv('test.csv') print('成功产生文件test.csv,测试集共有%d张图片' % len(test_xmlPath_list) ) dirPath_to_csv('smaller_images')
将函数train_test_split的参数random_state的值设为1,这样每次划分的训练集和测试集总是相同。如果不设置此参数,则每次划分的训练集和测试集不同。上面一段代码的运行结果如下:
我们以train.csv文件来看看xml转换为csv后的信息:
6)csv转tfrecord
由于下面的代码我们需要模块
from object_detection.utils import dataset_util
该模块是我们在Tensorflow object detection API 搭建物体识别模型(一)中下载的,要想使用该模块,我们需要添加环境变量PATHPATH。方法如下:右键计算机->属性
其中变量值包含下载的objec_detection路径及slim路径,如E:\ML\models-master\research;E:\ML\models-master\research\slim
#csv转tfrecords import os import pandas as pd import tensorflow as tf from object_detection.utils import dataset_util import shutil def csv2tfrecord( csv_path, imageDir_path, tfrecord_path): objectInfo_df = pd.read_csv(csv_path) tfrecord_writer = tf.python_io.TFRecordWriter(tfrecord_path) for filename, group in objectInfo_df.groupby('filename'): height = group.iloc[0]['height'] width = group.iloc[0]['width'] filename_bytes = filename.encode('utf-8') image_path = os.path.join( imageDir_path, filename) with open(image_path, 'rb') as file: encoded_jpg = file.read() image_format = b'jpg' xmin_list = list(group['xmin'] / width ) xmax_list = list(group['xmax'] / width ) ymin_list = list(group['ymin'] / height ) ymax_list = list(group['ymax'] / height ) classText_list = [ classText.encode('utf-8') for classText in group['class']] classLabel_list = [ classText_to_classLabel(classText) for classText in group['class']] tf_example = tf.train.Example( features=tf.train.Features( feature = { 'image/height' : dataset_util.int64_feature(height), 'image/width' : dataset_util.int64_feature(width), 'image/filename' : dataset_util.bytes_feature(filename_bytes), 'image/source_id' : dataset_util.bytes_feature(filename_bytes), 'image/encoded' : dataset_util.bytes_feature(encoded_jpg), 'image/format' : dataset_util.bytes_feature(image_format), 'image/object/bbox/xmin' : dataset_util.float_list_feature(xmin_list), 'image/object/bbox/xmax' : dataset_util.float_list_feature(xmax_list), 'image/object/bbox/ymin' : dataset_util.float_list_feature(ymin_list), 'image/object/bbox/ymax' : dataset_util.float_list_feature(ymax_list), 'image/object/class/text' : dataset_util.bytes_list_feature(classText_list), 'image/object/class/label' : dataset_util.int64_list_feature(classLabel_list), })) tfrecord_writer.write(tf_example.SerializeToString()) tfrecord_writer.close() print('成功产生tfrecord文件,保存在路径:%s' % tfrecord_path) #如果训练自己的模型,目标检测类别不同,需要修改此处 def classText_to_classLabel(row_label): if row_label == 'fish': return 1 elif row_label == 'human_face': return 2 else: return None dir_name = 'training' if not os.path.isdir(dir_name): os.mkdir(dir_name) csv2tfrecord('train.csv', 'smaller_images', 'training/train.tfrecord') csv2tfrecord('test.csv', 'smaller_images', 'training/test.tfrecord')
运行上面的代码,目标检测文件夹中会产生一个文件夹training,如下图所示:
7)编写pbtxt文件
在目标检测的文件夹training中,创建文本文件my_label_map.pbtxt。复制下面一段内容到文本文件my_label_map.pbtxt中:
item { name : "fish" id : 1 } item { name : "human_face" id : 2 }
8)编写配置文件
可以在object_detection文件夹中的samples/config路径下,找到原生配置文件ssdlite_mobilenet_v2_coco.config,先复制1份到桌面文件目标检测的文件夹training中,并做如下修改:
2
5
,读者根据自己的电脑配置,可以调高或者调低"training/train.tfrecord"
"training/my_label_map.pbtxt"
"training/test.tfrecord"
"training/my_label_map.pbtxt"
修改配置文件ssdlite_mobilenet_v2_coco.config并保存后,此时文件夹training中有4个文件,如下图所示:
9)模型训练
接着请读者参考:Tensorflow object detection API 搭建物体识别模型(三)
本文链接:http://task.lmcjl.com/news/12680.html