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WIN10 +pytorch版yolov3训练自己数据集

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WIN10 +pytorch版yolov3训练自己数据集

pytorch版yolov3训练自己数据集

目录

  • 1. 环境搭建
  • 2. 数据集构建
  • 3. 训练模型
  • 4. 测试模型
  • 5. 评估模型
  • 6. 可视化
  • 7. 高级进阶-网络结构更改

1. 环境搭建

  1. 将github库download下来。
git clone .git
  1. 建议在linux环境下使用anaconda进行搭建
conda create -n yolov3 python=3.7
  1. 安装需要的软件
pip install -r requirements.txt

环境要求:

  • python >= 3.7
  • pytorch >= 1.1
  • numpy
  • tqdm
  • opencv-python

其中只需要注意pytorch的安装:

到,python版本,cuda版本等选择命令即可。

关于深度学习环境搭建请参看:.html

anaconda常用用法:.html

2. 数据集构建

1. xml文件生成需要Labelimg软件

在Windows下使用:
wget .5.1/labelImg.exe
  • 使用快捷键:
Ctrl + u  加载目录中的所有图像,鼠标点击Open dir同功能
Ctrl + r  更改默认注释目标目录(xml文件保存的地址) 
Ctrl + s  保存
Ctrl + d  复制当前标签和矩形框
space     将当前图像标记为已验证
w         创建一个矩形框
d         下一张图片
a         上一张图片
del       删除选定的矩形框
Ctrl++    放大
Ctrl--    缩小
↑→↓←        键盘箭头移动选定的矩形框

2. VOC2007 数据集格式

-data- VOCdevkit2007- VOC2007- Annotations (标签XML文件,用对应的图片处理工具人工生成的)- ImageSets (生成的方法是用sh或者MATLAB语言生成)- Main- test.txt- train.txt- trainval.txt- val.txt- JPEGImages(原始文件)- labels (xml文件对应的txt文件)

通过以上软件主要构造好JPEGImages和Annotations文件夹中内容,Main文件夹中的txt文件可以通过python脚本生成:

import os  
import random  trainval_percent = 0.8
train_percent = 0.8  
xmlfilepath = 'Annotations'  
txtsavepath = 'ImageSets\Main'  
total_xml = os.listdir(xmlfilepath)  num=len(total_xml)  
list=range(num)  
tv=int(num*trainval_percent)  
tr=int(tv*train_percent)  
trainval= random.sample(list,tv)  
train=random.sample(trainval,tr)  ftrainval = open('ImageSets/Main/trainval.txt', 'w')  
ftest = open('ImageSets/Main/test.txt', 'w')  
ftrain = open('ImageSets/Main/train.txt', 'w')  
fval = open('ImageSets/Main/val.txt', 'w')  for i  in list:  name=total_xml[i][:-4]+'\n'  if i in trainval:  ftrainval.write(name)  if i in train:  ftrain.write(name)  else:  fval.write(name)  else:  ftest.write(name)  ftrainval.close()  
ftrain.close()  
fval.close()  
ftest.close()

生成labels文件,voc_label.py文件具体内容如下:

# -*- coding: utf-8 -*-
"""
Created on Tue Oct  2 11:42:13 2018
将本文件放到VOC2007目录下,然后就可以直接运行
需要修改的地方:
1. sets中替换为自己的数据集
2. classes中替换为自己的类别
3. 将本文件放到VOC2007目录下
4. 直接开始运行
"""import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
sets=[('2007', 'train'), ('2007', 'val'), ('2007', 'test')]  #替换为自己的数据集
classes = ["head", "eye", "nose"]     #修改为自己的类别
#classes = ["eye", "nose"]def convert(size, box):dw = 1./(size[0])dh = 1./(size[1])x = (box[0] + box[1])/2.0 - 1y = (box[2] + box[3])/2.0 - 1w = box[1] - box[0]h = box[3] - box[2]x = x*dww = w*dwy = y*dhh = h*dhreturn (x,y,w,h)
def convert_annotation(year, image_id):in_file = open('VOC%s/Annotations/%s.xml'%(year, image_id))  #将数据集放于当前目录下out_file = open('VOC%s/labels/%s.txt'%(year, image_id), 'w')tree=ET.parse(in_file)root = tree.getroot()size = root.find('size')w = int(size.find('width').text)h = int(size.find('height').text)for obj in root.iter('object'):difficult = obj.find('difficult').textcls = obj.find('name').textif cls not in classes or int(difficult)==1:continuecls_id = classes.index(cls)xmlbox = obj.find('bndbox')b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))bb = convert((w,h), b)out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
wd = getcwd()
for year, image_set in sets:if not os.path.exists('VOC%s/labels/'%(year)):os.makedirs('VOC%s/labels/'%(year))image_ids = open('VOC%s/ImageSets/Main/%s.txt'%(year, image_set)).read().strip().split()list_file = open('%s_%s.txt'%(year, image_set), 'w')for image_id in image_ids:list_file.write('VOC%s/JPEGImages/%s.jpg\n'%(year, image_id))convert_annotation(year, image_id)list_file.close()   
#os.system("cat 2007_train.txt 2007_val.txt > train.txt")     #修改为自己的数据集用作训练

到底为止,VOC格式数据集构造完毕,但是还需要继续构造符合darknet格式的数据集(coco)。

需要说明的是:如果打算使用coco评价标准,需要构造coco中json格式,如果要求不高,只需要VOC格式即可,使用作者写的mAP计算程序即可。

voc的xml转coco的json文件脚本:xml2json.py

# -*- coding: utf-8 -*-
"""
Created on Tue Aug 28 15:01:03 2018
需要改动xml_path and json_path
"""
#!/usr/bin/python
# -*- coding:utf-8 -*-
# @Description: xml转换到coco数据集json格式import os, sys, json,xmltodictfrom xml.etree.ElementTree import ElementTree, Element
from collections import OrderedDictXML_PATH = "/home/learner/datasets/VOCdevkit2007/VOC2007/Annotations/test"
JSON_PATH = "./test.json"
json_obj = {}
images = []
annotations = []
categories = []
categories_list = []
annotation_id = 1def read_xml(in_path):'''读取并解析xml文件'''tree = ElementTree()tree.parse(in_path)return treedef if_match(node, kv_map):'''判断某个节点是否包含所有传入参数属性node: 节点kv_map: 属性及属性值组成的map'''for key in kv_map:if node.get(key) != kv_map.get(key):return Falsereturn Truedef get_node_by_keyvalue(nodelist, kv_map):'''根据属性及属性值定位符合的节点,返回节点nodelist: 节点列表kv_map: 匹配属性及属性值map'''result_nodes = []for node in nodelist:if if_match(node, kv_map):result_nodes.append(node)return result_nodesdef find_nodes(tree, path):'''查找某个路径匹配的所有节点tree: xml树path: 节点路径'''return tree.findall(path)print ("-----------------Start------------------")
xml_names = []
for xml in os.listdir(XML_PATH):#os.path.splitext(xml)#xml=xml.replace('Cow_','')xml_names.append(xml)'''xml_path_list=os.listdir(XML_PATH)
os.path.split
xml_path_list.sort(key=len)'''
xml_names.sort(key=lambda x:int(x[:-4]))
new_xml_names = []
for i in xml_names:j = 'Cow_' + inew_xml_names.append(j)#print xml_names
#print new_xml_names
for xml in new_xml_names:tree = read_xml(XML_PATH + "/" + xml)object_nodes = get_node_by_keyvalue(find_nodes(tree, "object"), {})if len(object_nodes) == 0:print (xml, "no object")continueelse:image = OrderedDict()file_name = os.path.splitext(xml)[0];  # 文件名#print os.path.splitext(xml)para1 = file_name + ".jpg"height_nodes = get_node_by_keyvalue(find_nodes(tree, "size/height"), {})para2 = int(height_nodes[0].text)width_nodes = get_node_by_keyvalue(find_nodes(tree, "size/width"), {})para3 = int(width_nodes[0].text)fname=file_name[4:]para4 = int(fname)for f,i in [("file_name",para1),("height",para2),("width",para3),("id",para4)]:image.setdefault(f,i)#print(image)images.append(image)    #构建imagesname_nodes = get_node_by_keyvalue(find_nodes(tree, "object/name"), {})xmin_nodes = get_node_by_keyvalue(find_nodes(tree, "object/bndbox/xmin"), {})ymin_nodes = get_node_by_keyvalue(find_nodes(tree, "object/bndbox/ymin"), {})xmax_nodes = get_node_by_keyvalue(find_nodes(tree, "object/bndbox/xmax"), {})ymax_nodes = get_node_by_keyvalue(find_nodes(tree, "object/bndbox/ymax"), {})# print ymax_nodesfor index, node in enumerate(object_nodes):annotation = {}segmentation = []bbox = []seg_coordinate = []     #坐标seg_coordinate.append(int(xmin_nodes[index].text))seg_coordinate.append(int(ymin_nodes[index].text))seg_coordinate.append(int(xmin_nodes[index].text))seg_coordinate.append(int(ymax_nodes[index].text))seg_coordinate.append(int(xmax_nodes[index].text))seg_coordinate.append(int(ymax_nodes[index].text))seg_coordinate.append(int(xmax_nodes[index].text))seg_coordinate.append(int(ymin_nodes[index].text))segmentation.append(seg_coordinate)width = int(xmax_nodes[index].text) - int(xmin_nodes[index].text)height = int(ymax_nodes[index].text) - int(ymin_nodes[index].text)area = width * heightbbox.append(int(xmin_nodes[index].text))bbox.append(int(ymin_nodes[index].text))bbox.append(width)bbox.append(height)annotation["segmentation"] = segmentationannotation["area"] = areaannotation["iscrowd"] = 0fname=file_name[4:]annotation["image_id"] = int(fname)annotation["bbox"] = bboxcate=name_nodes[index].textif cate=='head':category_id=1elif cate=='eye':category_id=2elif cate=='nose':category_id=3annotation["category_id"] = category_idannotation["id"] = annotation_idannotation_id += 1annotation["ignore"] = 0annotations.append(annotation)if category_id in categories_list:passelse:categories_list.append(category_id)categorie = {}categorie["supercategory"] = "none"categorie["id"] = category_idcategorie["name"] = name_nodes[index].textcategories.append(categorie)json_obj["images"] = images
json_obj["type"] = "instances"
json_obj["annotations"] = annotations
json_obj["categories"] = categoriesf = open(JSON_PATH, "w")
#json.dump(json_obj, f)
json_str = json.dumps(json_obj)
f.write(json_str)
print ("------------------End-------------------")

(运行bash yolov3/data/get_coco_dataset.sh,仿照格式将数据放到其中)

但是这个库还需要其他模型:

3. 创建*.names file,

其中保存的是你的所有的类别,每行一个类别,如data/coco.names:

head
eye
nose

4. 更新data/coco.data,其中保存的是很多配置信息

classes = 3 # 改成你的数据集的类别个数
train = ./data/2007_train.txt # 通过voc_label.py文件生成的txt文件
valid = ./data/2007_test.txt # 通过voc_label.py文件生成的txt文件
names = data/coco.names # 记录类别
backup = backup/ # 记录checkpoint存放位置
eval = coco # 选择map计算方式

5. 更新cfg文件,修改类别相关信息

打开cfg文件夹下的yolov3.cfg文件,大体而言,cfg文件记录的是整个网络的结构,是核心部分,具体内容讲解请见:.html

只需要更改每个[yolo]层前边卷积层的filter个数即可:

每一个[region/yolo]层前的最后一个卷积层中的 filters=num(yolo层个数)*(classes+5) ,5的意义是5个坐标,论文中的tx,ty,tw,th,po

举个例子:我有三个类,n = 3, 那么filter = 3 * (3+5) = 21

[convolutional]
size=1
stride=1
pad=1
filters=255 # 改为 21
activation=linear[yolo]
mask = 6,7,8
anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
classes=80 # 改为 3
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1

6. 数据集格式说明

- yolov3- data- 2007_train.txt- 2007_test.txt- coco.names- coco.data- annotations(json files)- images(将2007_train.txt中的图片放到train2014文件夹中,test同理)- train2014- 0001.jpg- 0002.jpg- val2014- 0003.jpg- 0004.jpg- labels(voc_labels.py生成的内容需要重新组织一下)- train2014- 0001.txt- 0002.txt- val2014- 0003.txt- 0004.txt- samples(存放待测试图片)

2007_train.txt内容示例:

/home/dpj/yolov3-master/data/images/val2014/Cow_1192.jpg
/home/dpj/yolov3-master/data/images/val2014/Cow_1196.jpg
.....

注意images和labels文件架构一致性,因为txt是通过简单的替换得到的:

images -> labels
.jpg -> .txt

3. 训练模型

预训练模型:

  • Darknet *.weights format: .weights
  • PyTorch *.pt format: 

开始训练:

python train.py --data data/coco.data --cfg cfg/yolov3.cfg

如果日志正常输出那证明可以运行了

如果中断了,可以恢复训练

python train.py --data data/coco.data --cfg cfg/yolov3.cfg --resume

4. 测试模型

将待测试图片放到data/samples中,然后运行

python detect.py --weights weights/best.pt

5. 评估模型

python test.py --weights weights/latest.pt

如果使用cocoAPI使用以下命令:

git clone  && cd cocoapi/PythonAPI && make && cd ../.. && cp -r cocoapi/PythonAPI/pycocotools yolov3
cd yolov3python3 test.py --save-json --img-size 416
Namespace(batch_size=32, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data_cfg='data/coco.data', img_size=416, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3-spp.weights')
Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB)Class    Images   Targets         P         R       mAP        F1
Calculating mAP: 100%|█████████████████████████████████████████| 157/157 [05:59<00:00,  1.71s/it]all     5e+03  3.58e+04     0.109     0.773      0.57     0.186Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.335Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.565Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.349Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.151Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.360Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.493Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.280Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.432Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.458Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.255Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.494Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.620python3 test.py --save-json --img-size 608 --batch-size 16
Namespace(batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data_cfg='data/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3-spp.weights')
Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB)Class    Images   Targets         P         R       mAP        F1
Computing mAP: 100%|█████████████████████████████████████████| 313/313 [06:11<00:00,  1.01it/s]all     5e+03  3.58e+04      0.12      0.81     0.611     0.203Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.366Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.607Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.386Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.207Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.391Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.485Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.296Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.464Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.494Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.331Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.517Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.618

6. 可视化

可以使用python -c from utils import utils;utils.plot_results()

创建drawLog.py

def plot_results():# Plot YOLO training results file 'results.txt'import globimport numpy as npimport matplotlib.pyplot as plt#import os; os.system('rm -rf results.txt && wget .txt')plt.figure(figsize=(16, 8))s = ['X', 'Y', 'Width', 'Height', 'Objectness', 'Classification', 'Total Loss', 'Precision', 'Recall', 'mAP']files = sorted(glob.glob('results.txt'))for f in files:results = np.loadtxt(f, usecols=[2, 3, 4, 5, 6, 7, 8, 17, 18, 16]).T  # column 16 is mAPn = results.shape[1]for i in range(10):plt.subplot(2, 5, i + 1)plt.plot(range(1, n), results[i, 1:], marker='.', label=f)plt.title(s[i])if i == 0:plt.legend()plt.savefig('./plot.png')
if __name__ == "__main__":plot_results()

7. 高级进阶-网络结构更改

详细cfg文件讲解:.html

参考资料以及网络更改经验:.html

欢迎在评论区进行讨论,也便于我继续完善该教程。

代码改变世界

WIN10 +pytorch版yolov3训练自己数据集

pytorch版yolov3训练自己数据集

目录

  • 1. 环境搭建
  • 2. 数据集构建
  • 3. 训练模型
  • 4. 测试模型
  • 5. 评估模型
  • 6. 可视化
  • 7. 高级进阶-网络结构更改

1. 环境搭建

  1. 将github库download下来。
git clone .git
  1. 建议在linux环境下使用anaconda进行搭建
conda create -n yolov3 python=3.7
  1. 安装需要的软件
pip install -r requirements.txt

环境要求:

  • python >= 3.7
  • pytorch >= 1.1
  • numpy
  • tqdm
  • opencv-python

其中只需要注意pytorch的安装:

到,python版本,cuda版本等选择命令即可。

关于深度学习环境搭建请参看:.html

anaconda常用用法:.html

2. 数据集构建

1. xml文件生成需要Labelimg软件

在Windows下使用:
wget .5.1/labelImg.exe
  • 使用快捷键:
Ctrl + u  加载目录中的所有图像,鼠标点击Open dir同功能
Ctrl + r  更改默认注释目标目录(xml文件保存的地址) 
Ctrl + s  保存
Ctrl + d  复制当前标签和矩形框
space     将当前图像标记为已验证
w         创建一个矩形框
d         下一张图片
a         上一张图片
del       删除选定的矩形框
Ctrl++    放大
Ctrl--    缩小
↑→↓←        键盘箭头移动选定的矩形框

2. VOC2007 数据集格式

-data- VOCdevkit2007- VOC2007- Annotations (标签XML文件,用对应的图片处理工具人工生成的)- ImageSets (生成的方法是用sh或者MATLAB语言生成)- Main- test.txt- train.txt- trainval.txt- val.txt- JPEGImages(原始文件)- labels (xml文件对应的txt文件)

通过以上软件主要构造好JPEGImages和Annotations文件夹中内容,Main文件夹中的txt文件可以通过python脚本生成:

import os  
import random  trainval_percent = 0.8
train_percent = 0.8  
xmlfilepath = 'Annotations'  
txtsavepath = 'ImageSets\Main'  
total_xml = os.listdir(xmlfilepath)  num=len(total_xml)  
list=range(num)  
tv=int(num*trainval_percent)  
tr=int(tv*train_percent)  
trainval= random.sample(list,tv)  
train=random.sample(trainval,tr)  ftrainval = open('ImageSets/Main/trainval.txt', 'w')  
ftest = open('ImageSets/Main/test.txt', 'w')  
ftrain = open('ImageSets/Main/train.txt', 'w')  
fval = open('ImageSets/Main/val.txt', 'w')  for i  in list:  name=total_xml[i][:-4]+'\n'  if i in trainval:  ftrainval.write(name)  if i in train:  ftrain.write(name)  else:  fval.write(name)  else:  ftest.write(name)  ftrainval.close()  
ftrain.close()  
fval.close()  
ftest.close()

生成labels文件,voc_label.py文件具体内容如下:

# -*- coding: utf-8 -*-
"""
Created on Tue Oct  2 11:42:13 2018
将本文件放到VOC2007目录下,然后就可以直接运行
需要修改的地方:
1. sets中替换为自己的数据集
2. classes中替换为自己的类别
3. 将本文件放到VOC2007目录下
4. 直接开始运行
"""import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
sets=[('2007', 'train'), ('2007', 'val'), ('2007', 'test')]  #替换为自己的数据集
classes = ["head", "eye", "nose"]     #修改为自己的类别
#classes = ["eye", "nose"]def convert(size, box):dw = 1./(size[0])dh = 1./(size[1])x = (box[0] + box[1])/2.0 - 1y = (box[2] + box[3])/2.0 - 1w = box[1] - box[0]h = box[3] - box[2]x = x*dww = w*dwy = y*dhh = h*dhreturn (x,y,w,h)
def convert_annotation(year, image_id):in_file = open('VOC%s/Annotations/%s.xml'%(year, image_id))  #将数据集放于当前目录下out_file = open('VOC%s/labels/%s.txt'%(year, image_id), 'w')tree=ET.parse(in_file)root = tree.getroot()size = root.find('size')w = int(size.find('width').text)h = int(size.find('height').text)for obj in root.iter('object'):difficult = obj.find('difficult').textcls = obj.find('name').textif cls not in classes or int(difficult)==1:continuecls_id = classes.index(cls)xmlbox = obj.find('bndbox')b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))bb = convert((w,h), b)out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
wd = getcwd()
for year, image_set in sets:if not os.path.exists('VOC%s/labels/'%(year)):os.makedirs('VOC%s/labels/'%(year))image_ids = open('VOC%s/ImageSets/Main/%s.txt'%(year, image_set)).read().strip().split()list_file = open('%s_%s.txt'%(year, image_set), 'w')for image_id in image_ids:list_file.write('VOC%s/JPEGImages/%s.jpg\n'%(year, image_id))convert_annotation(year, image_id)list_file.close()   
#os.system("cat 2007_train.txt 2007_val.txt > train.txt")     #修改为自己的数据集用作训练

到底为止,VOC格式数据集构造完毕,但是还需要继续构造符合darknet格式的数据集(coco)。

需要说明的是:如果打算使用coco评价标准,需要构造coco中json格式,如果要求不高,只需要VOC格式即可,使用作者写的mAP计算程序即可。

voc的xml转coco的json文件脚本:xml2json.py

# -*- coding: utf-8 -*-
"""
Created on Tue Aug 28 15:01:03 2018
需要改动xml_path and json_path
"""
#!/usr/bin/python
# -*- coding:utf-8 -*-
# @Description: xml转换到coco数据集json格式import os, sys, json,xmltodictfrom xml.etree.ElementTree import ElementTree, Element
from collections import OrderedDictXML_PATH = "/home/learner/datasets/VOCdevkit2007/VOC2007/Annotations/test"
JSON_PATH = "./test.json"
json_obj = {}
images = []
annotations = []
categories = []
categories_list = []
annotation_id = 1def read_xml(in_path):'''读取并解析xml文件'''tree = ElementTree()tree.parse(in_path)return treedef if_match(node, kv_map):'''判断某个节点是否包含所有传入参数属性node: 节点kv_map: 属性及属性值组成的map'''for key in kv_map:if node.get(key) != kv_map.get(key):return Falsereturn Truedef get_node_by_keyvalue(nodelist, kv_map):'''根据属性及属性值定位符合的节点,返回节点nodelist: 节点列表kv_map: 匹配属性及属性值map'''result_nodes = []for node in nodelist:if if_match(node, kv_map):result_nodes.append(node)return result_nodesdef find_nodes(tree, path):'''查找某个路径匹配的所有节点tree: xml树path: 节点路径'''return tree.findall(path)print ("-----------------Start------------------")
xml_names = []
for xml in os.listdir(XML_PATH):#os.path.splitext(xml)#xml=xml.replace('Cow_','')xml_names.append(xml)'''xml_path_list=os.listdir(XML_PATH)
os.path.split
xml_path_list.sort(key=len)'''
xml_names.sort(key=lambda x:int(x[:-4]))
new_xml_names = []
for i in xml_names:j = 'Cow_' + inew_xml_names.append(j)#print xml_names
#print new_xml_names
for xml in new_xml_names:tree = read_xml(XML_PATH + "/" + xml)object_nodes = get_node_by_keyvalue(find_nodes(tree, "object"), {})if len(object_nodes) == 0:print (xml, "no object")continueelse:image = OrderedDict()file_name = os.path.splitext(xml)[0];  # 文件名#print os.path.splitext(xml)para1 = file_name + ".jpg"height_nodes = get_node_by_keyvalue(find_nodes(tree, "size/height"), {})para2 = int(height_nodes[0].text)width_nodes = get_node_by_keyvalue(find_nodes(tree, "size/width"), {})para3 = int(width_nodes[0].text)fname=file_name[4:]para4 = int(fname)for f,i in [("file_name",para1),("height",para2),("width",para3),("id",para4)]:image.setdefault(f,i)#print(image)images.append(image)    #构建imagesname_nodes = get_node_by_keyvalue(find_nodes(tree, "object/name"), {})xmin_nodes = get_node_by_keyvalue(find_nodes(tree, "object/bndbox/xmin"), {})ymin_nodes = get_node_by_keyvalue(find_nodes(tree, "object/bndbox/ymin"), {})xmax_nodes = get_node_by_keyvalue(find_nodes(tree, "object/bndbox/xmax"), {})ymax_nodes = get_node_by_keyvalue(find_nodes(tree, "object/bndbox/ymax"), {})# print ymax_nodesfor index, node in enumerate(object_nodes):annotation = {}segmentation = []bbox = []seg_coordinate = []     #坐标seg_coordinate.append(int(xmin_nodes[index].text))seg_coordinate.append(int(ymin_nodes[index].text))seg_coordinate.append(int(xmin_nodes[index].text))seg_coordinate.append(int(ymax_nodes[index].text))seg_coordinate.append(int(xmax_nodes[index].text))seg_coordinate.append(int(ymax_nodes[index].text))seg_coordinate.append(int(xmax_nodes[index].text))seg_coordinate.append(int(ymin_nodes[index].text))segmentation.append(seg_coordinate)width = int(xmax_nodes[index].text) - int(xmin_nodes[index].text)height = int(ymax_nodes[index].text) - int(ymin_nodes[index].text)area = width * heightbbox.append(int(xmin_nodes[index].text))bbox.append(int(ymin_nodes[index].text))bbox.append(width)bbox.append(height)annotation["segmentation"] = segmentationannotation["area"] = areaannotation["iscrowd"] = 0fname=file_name[4:]annotation["image_id"] = int(fname)annotation["bbox"] = bboxcate=name_nodes[index].textif cate=='head':category_id=1elif cate=='eye':category_id=2elif cate=='nose':category_id=3annotation["category_id"] = category_idannotation["id"] = annotation_idannotation_id += 1annotation["ignore"] = 0annotations.append(annotation)if category_id in categories_list:passelse:categories_list.append(category_id)categorie = {}categorie["supercategory"] = "none"categorie["id"] = category_idcategorie["name"] = name_nodes[index].textcategories.append(categorie)json_obj["images"] = images
json_obj["type"] = "instances"
json_obj["annotations"] = annotations
json_obj["categories"] = categoriesf = open(JSON_PATH, "w")
#json.dump(json_obj, f)
json_str = json.dumps(json_obj)
f.write(json_str)
print ("------------------End-------------------")

(运行bash yolov3/data/get_coco_dataset.sh,仿照格式将数据放到其中)

但是这个库还需要其他模型:

3. 创建*.names file,

其中保存的是你的所有的类别,每行一个类别,如data/coco.names:

head
eye
nose

4. 更新data/coco.data,其中保存的是很多配置信息

classes = 3 # 改成你的数据集的类别个数
train = ./data/2007_train.txt # 通过voc_label.py文件生成的txt文件
valid = ./data/2007_test.txt # 通过voc_label.py文件生成的txt文件
names = data/coco.names # 记录类别
backup = backup/ # 记录checkpoint存放位置
eval = coco # 选择map计算方式

5. 更新cfg文件,修改类别相关信息

打开cfg文件夹下的yolov3.cfg文件,大体而言,cfg文件记录的是整个网络的结构,是核心部分,具体内容讲解请见:.html

只需要更改每个[yolo]层前边卷积层的filter个数即可:

每一个[region/yolo]层前的最后一个卷积层中的 filters=num(yolo层个数)*(classes+5) ,5的意义是5个坐标,论文中的tx,ty,tw,th,po

举个例子:我有三个类,n = 3, 那么filter = 3 * (3+5) = 21

[convolutional]
size=1
stride=1
pad=1
filters=255 # 改为 21
activation=linear[yolo]
mask = 6,7,8
anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
classes=80 # 改为 3
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1

6. 数据集格式说明

- yolov3- data- 2007_train.txt- 2007_test.txt- coco.names- coco.data- annotations(json files)- images(将2007_train.txt中的图片放到train2014文件夹中,test同理)- train2014- 0001.jpg- 0002.jpg- val2014- 0003.jpg- 0004.jpg- labels(voc_labels.py生成的内容需要重新组织一下)- train2014- 0001.txt- 0002.txt- val2014- 0003.txt- 0004.txt- samples(存放待测试图片)

2007_train.txt内容示例:

/home/dpj/yolov3-master/data/images/val2014/Cow_1192.jpg
/home/dpj/yolov3-master/data/images/val2014/Cow_1196.jpg
.....

注意images和labels文件架构一致性,因为txt是通过简单的替换得到的:

images -> labels
.jpg -> .txt

3. 训练模型

预训练模型:

  • Darknet *.weights format: .weights
  • PyTorch *.pt format: 

开始训练:

python train.py --data data/coco.data --cfg cfg/yolov3.cfg

如果日志正常输出那证明可以运行了

如果中断了,可以恢复训练

python train.py --data data/coco.data --cfg cfg/yolov3.cfg --resume

4. 测试模型

将待测试图片放到data/samples中,然后运行

python detect.py --weights weights/best.pt

5. 评估模型

python test.py --weights weights/latest.pt

如果使用cocoAPI使用以下命令:

git clone  && cd cocoapi/PythonAPI && make && cd ../.. && cp -r cocoapi/PythonAPI/pycocotools yolov3
cd yolov3python3 test.py --save-json --img-size 416
Namespace(batch_size=32, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data_cfg='data/coco.data', img_size=416, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3-spp.weights')
Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB)Class    Images   Targets         P         R       mAP        F1
Calculating mAP: 100%|█████████████████████████████████████████| 157/157 [05:59<00:00,  1.71s/it]all     5e+03  3.58e+04     0.109     0.773      0.57     0.186Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.335Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.565Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.349Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.151Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.360Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.493Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.280Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.432Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.458Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.255Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.494Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.620python3 test.py --save-json --img-size 608 --batch-size 16
Namespace(batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data_cfg='data/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3-spp.weights')
Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB)Class    Images   Targets         P         R       mAP        F1
Computing mAP: 100%|█████████████████████████████████████████| 313/313 [06:11<00:00,  1.01it/s]all     5e+03  3.58e+04      0.12      0.81     0.611     0.203Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.366Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.607Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.386Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.207Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.391Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.485Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.296Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.464Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.494Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.331Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.517Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.618

6. 可视化

可以使用python -c from utils import utils;utils.plot_results()

创建drawLog.py

def plot_results():# Plot YOLO training results file 'results.txt'import globimport numpy as npimport matplotlib.pyplot as plt#import os; os.system('rm -rf results.txt && wget .txt')plt.figure(figsize=(16, 8))s = ['X', 'Y', 'Width', 'Height', 'Objectness', 'Classification', 'Total Loss', 'Precision', 'Recall', 'mAP']files = sorted(glob.glob('results.txt'))for f in files:results = np.loadtxt(f, usecols=[2, 3, 4, 5, 6, 7, 8, 17, 18, 16]).T  # column 16 is mAPn = results.shape[1]for i in range(10):plt.subplot(2, 5, i + 1)plt.plot(range(1, n), results[i, 1:], marker='.', label=f)plt.title(s[i])if i == 0:plt.legend()plt.savefig('./plot.png')
if __name__ == "__main__":plot_results()

7. 高级进阶-网络结构更改

详细cfg文件讲解:.html

参考资料以及网络更改经验:.html

欢迎在评论区进行讨论,也便于我继续完善该教程。

代码改变世界

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