安装Anacond：https://blog.csdn.net/ITLearnHall/article/details/81708148
安装Pycharm:https://blog.csdn.net/qq_15698613/article/details/86502371
一:MyQR 生成动态二维码100个
import random from MyQR import myqr import os for i in range(1,101): z=''.join(str(random.choice(range(10))) for _ in range(10))#生成长度为10的随机数 name=z+".png"#设置图片名字 myqr.run( version=1, # 设置容错率 level='L', brightness=1.0, words=z, save_name=name )
二：下载图片，将图片和二维码大小进行修改
需要安装opencv参考
https://blog.csdn.net/iracer/article/details/80498732
import os import cv2 #读取文件夹下文件 def read_directory(directory_name,save_img): i=0 for fileName in os.listdir(directory_name): #print(fileName) obsPath=directory_name+"/"+fileName obsSave_path=save_img+"/"+fileName resize_img(obsPath,obsSave_path) i+=1 if(i==100): break def resize_img(path_img,save_img): im1 = cv2.imread(path_img) im2 = cv2.resize(im1,(716,1000),) # 为图片重新指定尺寸 cv2.imwrite(save_img,im2) read_directory("D:/pythonLearnDemo/AnacondaJqueryDemo/neg/neg","D:/pythonLearnDemo/AnacondaJqueryDemo/neg/resize_neg")
三：将照片和二维码进行随机合并
import cv2 import numpy as np import matplotlib.pyplot as plt import random #读取照片 directory_name="D:/pythonLearnDemo/AnacondaJqueryDemo/neg/resize_neg" myqrPaths=os.listdir("D:/pythonLearnDemo/AnacondaJqueryDemo/neg/myqrCode") lens=len(os.listdir(directory_name)) for fileName in os.listdir(directory_name): obsPathName=directory_name+"/"+fileName yNum=0 xNum=0 for i in range(1,3): dst = cv2.imread(obsPathName)#背景图 ranNum=random.randint(0,lens) print("ranNum:",ranNum,"NAME:",myqrPaths[ranNum]) srcPath="D:/pythonLearnDemo/AnacondaJqueryDemo/neg/myqrCode"+"/"+myqrPaths[ranNum] x_loc=0 y_loc=0 if(i==1): y_loc=random.randint(141,350) yNum=y_loc x_loc=random.randint(141,260) xNum=x_loc else: y_loc=random.randint(yNum+170,641) x_loc=random.randint(xNum+141,500) center = (x_loc,y_loc) #dst = cv2.imread("D:/pythonLearnDemo/AnacondaJqueryDemo/neg/opencv-seamless-cloning-example.jpg.jpg") print(srcPath) src = cv2.imread(srcPath) src_mask = np.zeros(src.shape, src.dtype) poly = np.array([[0, 2], [258, 2], [258, 260], [0, 260]], np.int32) cv2.fillPoly(src_mask, [poly], (255, 255, 255)) output = cv2.seamlessClone(src, dst, src_mask, center, cv2.NORMAL_CLONE) mrgPath="D:/pythonLearnDemo/AnacondaJqueryDemo/neg/images"+"/"+fileName obsPathName=mrgPath cv2.imwrite(mrgPath, output);
下载安装Tensorflow object detection API ：https://github.com/tensorflow/models下载到本地，解压放到需要使用的路径下
四：Protobuf 安装与配置
Protobuf(Google Protocol Buffers)是GG开发的的一套用于数据存储，网络通信时用于协议编解码的工具库。和XML，Json数据差不多，把数据按某种形式保存起来。相对与XML和Json的不同之处，它是一种二进制的数据格式，具有更高的传输，打包和解包效率。
在https://github.com/google/protobuf/releases 网站中选择windows 版本，解压后将bin文件夹中的【protoc.exe】放到models中的research目录下，执行下面的代码，将object_detection/protos下的.proto文件转换成.py文件
''' 需要到protoc所在的目录下执行protoc ''' import os os.chdir( 'models-master/research' )#可以写 for each in os.listdir( 'object_detection/protos' ):#可以写models-master（Tensorflow object detection API 解压后文件）可对路径 if each.endswith('proto'): os.system('protoc object_detection/protos/%s --python_out=.' % each)
运行jupyter notebook，找到../models-master/research/object_detection下的
object_detection_tutorial.ipynb进行运行
demo结果
如果代码成功运行的话，运行结果如下图所示：
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：
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：
<annotation> <folder>images</folder> <filename>1.jpg</filename> <path>D:\pythonLearnDemo\AnacondaJqueryDemo\neg\images\1.jpg</path> <source> <database>Unknown</database> </source> <size> <width>716</width> <height>1000</height> <depth>3</depth> </size> <segmented>0</segmented> <object> <name>qrCode</name> <pose>Unspecified</pose> <truncated>0</truncated> <difficult>0</difficult> <bndbox> <xmin>141</xmin> <ymin>222</ymin> <xmax>355</xmax> <ymax>436</ymax> </bndbox> </object> <object> <name>qrCode</name> <pose>Unspecified</pose> <truncated>0</truncated> <difficult>0</difficult> <bndbox> <xmin>369</xmin> <ymin>422</ymin> <xmax>561</xmax> <ymax>605</ymax> </bndbox> </object> </annotation>
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
# -*- coding: utf-8 -*- """ @author: Zbb 将文件夹内所有XML文件的信息记录到CSV文件中 """ import os import pandas as pd import xml.etree.ElementTree as ET from sklearn.model_selection import train_test_split path = 'D:/pythonLearnDemo/saveLabels' def xml_to_csv(path): xml_list = [] for xml_file in glob.glob(path + '/*.xml'): tree = ET.parse(xml_file) root = tree.getroot() for member in root.findall('object'): value = (root.find('filename').text, int(root.find('size')[0].text), int(root.find('size')[1].text), member[0].text, int(member[4][0].text), int(member[4][1].text), int(member[4][2].text), int(member[4][3].text) ) xml_list.append(value) column_name = ['filename', 'width', 'height', 'class', 'xmin', 'ymin', 'xmax', 'ymax'] xml_df = pd.DataFrame(xml_list, columns=column_name) return xml_df
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 main(): image_path = path xml_df = xml_to_csv(image_path) xml_df.to_csv('tv_vehicle_labels.csv', index=None) print('Successfully converted xml to csv.')
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)) #main() dirPath_to_csv(path)
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
# -*- coding: utf-8 -*- """ @author: Zbb 将文件夹内所有XML文件的信息记录到CSV文件中 """ import os import io import pandas as pd import tensorflow as tf from PIL import Image from object_detection.utils import dataset_util from collections import namedtuple, OrderedDict 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('D:/pythonLearnDemo/train.csv', 'D:/pythonLearnDemo/AnacondaJqueryDemo/neg/images', 'D:/pythonLearnDemo/training/train.tfrecord') csv2tfrecord('D:/pythonLearnDemo/test.csv', 'D:/pythonLearnDemo/AnacondaJqueryDemo/neg/images', 'D:/pythonLearnDemo/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：
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 : "qrCode" id : 1 }
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：
第9行的num_classes，对于本文来说，此数设置为1
第141行的batch_size，对于本文来说，此数设置为3，读者根据自己的电脑配置，可以调高或者调低
第177行input_path设置成"D:/pythonLearnDemo/training/train.tfrecord"（我设置的为绝对路径）
第179行label_map_path设置成"D:/pythonLearnDemo/training/my_label_map.pbtxt"
第191行input_path设置成"D:/pythonLearnDemo/training/test.tfrecord"
第193行label_map_path设置成"D:/pythonLearnDemo/training/my_label_map.pbtxt"
第158、159这2行需要删除
第二处为eval_config中的num_examples，它表示在验证阶段需要执行的图片数量，本次验证数量为10(可以在create_pascal_tf_record.py中，输出对应的examples_list长度，就可以知道这个大小)。
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：
# SSD with Mobilenet v1 configuration for MSCOCO Dataset. # Users should configure the fine_tune_checkpoint field in the train config as # well as the label_map_path and input_path fields in the train_input_reader and # eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that # should be configured. model { ssd { num_classes: 1 box_coder { faster_rcnn_box_coder { y_scale: 10.0 x_scale: 10.0 height_scale: 5.0 width_scale: 5.0 } } matcher { argmax_matcher { matched_threshold: 0.5 unmatched_threshold: 0.5 ignore_thresholds: false negatives_lower_than_unmatched: true force_match_for_each_row: true } } similarity_calculator { iou_similarity { } } anchor_generator { ssd_anchor_generator { num_layers: 6 min_scale: 0.2 max_scale: 0.95 aspect_ratios: 1.0 aspect_ratios: 2.0 aspect_ratios: 0.5 aspect_ratios: 3.0 aspect_ratios: 0.3333 } } image_resizer { fixed_shape_resizer { height: 300 width: 300 } } box_predictor { convolutional_box_predictor { min_depth: 0 max_depth: 0 num_layers_before_predictor: 0 use_dropout: false dropout_keep_probability: 0.8 kernel_size: 1 box_code_size: 4 apply_sigmoid_to_scores: false conv_hyperparams { activation: RELU_6, regularizer { l2_regularizer { weight: 0.00004 } } initializer { truncated_normal_initializer { stddev: 0.03 mean: 0.0 } } batch_norm { train: true, scale: true, center: true, decay: 0.9997, epsilon: 0.001, } } } } feature_extractor { type: 'ssd_mobilenet_v1' min_depth: 16 depth_multiplier: 1.0 conv_hyperparams { activation: RELU_6, regularizer { l2_regularizer { weight: 0.00004 } } initializer { truncated_normal_initializer { stddev: 0.03 mean: 0.0 } } batch_norm { train: true, scale: true, center: true, decay: 0.9997, epsilon: 0.001, } } } loss { classification_loss { weighted_sigmoid { } } localization_loss { weighted_smooth_l1 { } } hard_example_miner { num_hard_examples: 3000 iou_threshold: 0.99 loss_type: CLASSIFICATION max_negatives_per_positive: 3 min_negatives_per_image: 0 } classification_weight: 1.0 localization_weight: 1.0 } normalize_loss_by_num_matches: true post_processing { batch_non_max_suppression { score_threshold: 1e-8 iou_threshold: 0.6 max_detections_per_class: 100 max_total_detections: 100 } score_converter: SIGMOID } } } train_config: { batch_size: 3 optimizer { rms_prop_optimizer: { learning_rate: { exponential_decay_learning_rate { initial_learning_rate: 0.004 decay_steps: 800720 decay_factor: 0.95 } } momentum_optimizer_value: 0.9 decay: 0.9 epsilon: 1.0 } } #fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt" #from_detection_checkpoint: true # Note: The below line limits the training process to 200K steps, which we # empirically found to be sufficient enough to train the pets dataset. This # effectively bypasses the learning rate schedule (the learning rate will # never decay). Remove the below line to train indefinitely. num_steps: 200000 data_augmentation_options { random_horizontal_flip { } } data_augmentation_options { ssd_random_crop { } } } train_input_reader: { tf_record_input_reader { input_path: "D:/pythonLearnDemo/training/train.tfrecord" } label_map_path: "D:/pythonLearnDemo/training/my_label_map.pbtxt" } eval_config: { num_examples: 10 # Note: The below line limits the evaluation process to 10 evaluations. # Remove the below line to evaluate indefinitely. max_evals: 10 } eval_input_reader: { tf_record_input_reader { input_path: "D:/pythonLearnDemo/training/test.tfrecord" } label_map_path: "D:/pythonLearnDemo/training/my_label_map.pbtxt" shuffle: false num_readers: 1 }
三、模型训练
1）错误一：
在桌面的目标检测文件夹中打开cmd，即在路径中输入cmd后按Enter键运行。在cmd中运行命令：
1. python D:/tensorflowObject/models-master/research/object_detection/model_main.py --pipeline_config_path=D:/pythonLearnDemo/training/ssd_mobilenet_v1_coco.config --model_dir=D:/pythonLearnDemo/training/model_training --alsologtostderr 2. python３ object_detection/model_main.py --pipeline_config_path=D:/pythonLearnDemo/training/ssd_mobilenet_v1_coco.config --model_dir=D:/pythonLearnDemo/training/model_training --num_train_steps=500000 #设置训练步长 --num_eval_steps=2000 --alsologtostderr
运行结果如下图所示：
可以看出缺少pycocotools库，在linux系统中安装pycocotools库只需要运行命令：pip install pycocotools，但是在Windows上安装则复杂得多：
首先下载Microsoft C++ build 14.0，链接：https://go.microsoft.com/fwlink/?LinkId=691126，初始化安装程序->自定义->选择功能只勾选Windows 10 SDK 10.0.10240->安装，过程比较漫长，一般安装有VS完整版的，不会缺失
然后下载安装pycocotools，链接：https://github.com/philferriere/cocoapi，解压到当前文件夹，进入文件夹cocoapi-master中的文件夹PythonAPI，在此文件夹下打开cmd，输入：python setup.py build_ext install
然后重新训练，模型训练稳定地进行一段时间后，会做一次模型评估，如下图所示。所以如果读者看到下图中的情况，则说明模型训练很顺利：
将训练模型导出
python D:/tensorflowObject/models-master/research/object_detection/export_inference_graph.py --input_type=image_tensor --pipeline_config_path=D:/pythonLearnDemo/training/ssd_mobilenet_v1_coco.config --trained_checkpoint_prefix=D:/pythonLearnDemo/training/model_training/model.ckpt-20000 #之前默认设置20000次 --output_directory=fish_inference_graph
3）查看模型训练状况
模型训练稳定地进行后，在桌面的目标检测文件夹中重新打开cmd。在cmd中运行命令：
D:/pythonLearnDemo/training/model_training（路径为之前保存训练模型的地方）--trained_checkpoint_prefix=D:/pythonLearnDemo/training/model_training/ tensorboard --logdir=D:/pythonLearnDemo/training/model_training --host=127.0.0.1
可查看数据
测试：
测试代码下载地址 链接：https://pan.baidu.com/s/1kqFlP6vdsMgwyYRPFPmxnA 提取码：68rc
在测试过程中，
出现不了展示图片大的效果，然后修改成了保存
