一、之前工作回顾

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• 在【python-opencv】 sobel算子 图像边缘检测 图像二值化处理 并进行框标定 这一篇文章中，讨论了如何使用边缘检测算法将图片二值化处理。
• 其中th, im_th = cv2.threshold(im_in, 40, 255, cv2.THRESH_BINARY)这一行代码中，40这个值可以根据不同的图片改变，以达到二值化的最佳状态。
• 在这一步，为之后实现图像的框标定，提取出图片中物体的x, y, w, h的值奠定了基础。

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• 在【python-opencv】对图片二值化处理后，对图像进行框标定 这篇文章中，讨论了如何对二值化的图片进行框标定，并返回图片中物体的x, y, w, h的值。
• 其中MIN_BOX = 5000这个参数应当随着物体大小的改变而改变，对于小的蛾子，参数应当调小。
• 在这一步，为之后将返回的图片中物体的x, y, w, h的值以voc格式写入xml文件提供了基础。

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二、图片处理过程

（1）图片的裁剪

• 裁剪掉周围没有物体的地方，裁剪后的图片大小固定
• 图片裁剪.py

# -*- coding: utf-8 -*-
import cv2
import numpy as npdef cut_pic(img):# 裁剪图片imgs = cv2.resize(img, (0, 0), fx=0.2, fy=0.2, interpolation=cv2.INTER_NEAREST)[25:25 + 700, 210:210 + 760]# plt.imshow(img)return imgs

（2）生成xml文件的相关代码

• 在将图片裁剪完成之后，运行 框标定.py 即可以生成相关的voc数据集格式文件。

• 生成的xml文件格式 详见 数据标注范例

• 生成图片的xml文件.py

# -*- coding:utf-8 -*-
# https://zhuanlan.zhihu.com/p/54269963# from xml.etree import ElementTree as  etree
from xml.etree.ElementTree import Element
from xml.etree.ElementTree import SubElement
from xml.etree.ElementTree import ElementTree
from xml.dom import minidom
import osdef generate_head(pic_filename, pic_path):# generate root nodeannotation = Element('annotation')# generate first child-node folderfolder = SubElement(annotation, 'folder')folder.text = "VOC2007"# generate second child-node filenamefilename = SubElement(annotation, 'filename')filename.text = pic_filename# generate child-node pathpath = SubElement(annotation, 'path')path.text = pic_path# generate third child-node sourcesource = SubElement(annotation, 'source')# child-node of source nodedatabase = SubElement(source, 'database')database.text = "ezi"# generate fourth child-node sourcesize = SubElement(annotation, 'size')# child-node of sizewidth = SubElement(size, 'width')width.text = "760"# child-node of sizeheight = SubElement(size, 'height')height.text = "700"# child-node of sizedepth = SubElement(size, 'depth')depth.text = "3"# generate fifth child-node foldersegmented = SubElement(annotation, 'segmented')segmented.text = "0"return annotationdef generate_object(annotation, pic_label, x, y, w, h):# generate child-node object of annotationobject = SubElement(annotation, 'object')# child-node of objectname = SubElement(object, 'name')name.text = pic_label# child-node of objectpose = SubElement(object, 'pose')pose.text = "Unspecified"# child-node of objecttruncated = SubElement(object, 'truncated')truncated.text = "0"# child-node of objectdifficult = SubElement(object, 'difficult')difficult.text = "0"# child-node of objectbndbox = SubElement(object, 'bndbox')# child-node of bndboxxmin = SubElement(bndbox, 'xmin')xmin.text = str(x)# child-node of bndboxymin = SubElement(bndbox, 'ymin')ymin.text = str(y)# child-node of bndboxxmax = SubElement(bndbox, 'xmax')xmax.text = str(x + w)# child-node of bndboxymax = SubElement(bndbox, 'ymax')ymax.text = str(y + h)return annotationdef write_xml(annotation, xml_path):dir_name = os.path.dirname(xml_path)if not os.path.exists(dir_name):os.makedirs(dir_name)# os.path.split(xml_path)[-1] 得到文件名# open(xml_path, "w").close()# 打包为tree，以便写入tree = ElementTree(annotation)# write out xml datatree.write(xml_path, encoding='utf-8')

• sobel算子边缘检测.py

# -*- coding: utf-8 -*-
import cv2
import numpy as np
from 图片裁剪 import cut_picdef fillHole(im_in):# Threshold.# Set values equal to or above 40 to 255.# Set values below 40 to 0.# https://blog.csdn.net/a19990412/article/details/81172426 thresh的用法th, im_th = cv2.threshold(im_in, 55, 255, cv2.THRESH_BINARY)# Copy the thresholded image.im_floodfill = im_th.copy()# print(im_th)# Mask used to flood filling.# Notice the size needs to be 2 pixels than the image.h, w = im_th.shape[:2]mask = np.zeros((h + 2, w + 2), np.uint8)# Floodfill from point (0, 0)cv2.floodFill(im_floodfill, mask, (0, 0), 255)# Invert floodfilled imageim_floodfill_inv = cv2.bitwise_not(im_floodfill)# Combine the two images to get the foreground.im_out = im_th | im_floodfill_invreturn im_outdef baweraopen(image, size):'''@image:单通道二值图，数据类型uint8@size:欲去除区域大小(黑底上的白区域)'''output = image.copy()nlabels, labels, stats, centroids = cv2.connectedComponentsWithStats(image)for i in range(1, nlabels - 1):regions_size = stats[i, 4]if regions_size < size:x0 = stats[i, 0]y0 = stats[i, 1]x1 = stats[i, 0] + stats[i, 2]y1 = stats[i, 1] + stats[i, 3]for row in range(y0, y1):for col in range(x0, x1):if labels[row, col] == i:output[row, col] = 0return outputdef sobel_cal(imgs):if imgs.size != 1596000:# 裁剪图片imgs = cut_pic(imgs)# plt.imshow(img)# 高斯模糊去噪img = cv2.medianBlur(imgs, 7)# 高斯模糊去噪# img = cv2.medianBlur(img, 7)hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)H, S, V = cv2.split(hsv)lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)lab_l = lab[..., 0]lab_a = lab[..., 1]lab_b = lab[..., 2]# cv2.imshow("image1", lab_b)color_space = S# Sobel算子x = cv2.Sobel(color_space, cv2.CV_16S, 1, 0)  # 对x求一阶导y = cv2.Sobel(color_space, cv2.CV_16S, 0, 1)  # 对y求一阶导absX = cv2.convertScaleAbs(x)absY = cv2.convertScaleAbs(y)sobel = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)# b.设置卷积核5*5kernel = np.ones((2, 2), np.uint8)# 腐蚀的作用说白了就是让暗的区域变大，而膨胀的作用就是让亮的区域变大# 图像的膨胀dst = cv2.dilate(sobel, kernel)# 空洞填充out = fillHole(dst)# c.图像的腐蚀，默认迭代次数erosion = cv2.erode(out, kernel)# 图像的膨胀dst = cv2.dilate(erosion, kernel)# 去除小的斑点out = baweraopen(dst, 300)return imgs, outif __name__ == '__main__':# 读取图像# 此处注意cv2.imread读出来的是bgr格式要转为rgbimg = cv2.imread('img/13.jpg')img, out = sobel_cal(img)# 显示图形# cv2.imwrite('10.jpg', img)cv2.imshow('out', out)cv2.waitKey()

• 框标定.py

import cv2
import numpy as npfrom sobel算子边缘检测 import sobel_cal
from 生成图片的xml文件 import generate_object, generate_head, write_xml
import os# 设置最小框的面积
MIN_BOX = 2500def draw_min_rect_circle(img, cnts, pic_filename, pic_label, pic_path, xml_path):  # conts = contoursimg = np.copy(img)annotation = generate_head(pic_filename, pic_path)for cnt in cnts:x, y, w, h = cv2.boundingRect(cnt)if w * h < MIN_BOX:continueannotation = generate_object(annotation, pic_label, x, y, w, h)# print(x, y, w, h)cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)  # bluewrite_xml(annotation, xml_path)return imgdef run(pic_filename, pic_label, pic_path, xml_path):imgs = cv2.imread(pic_path)  # a black objects on white image is better# print(pic_path)# print(imgs)# img为裁剪过后的图片,image为二值化之后的黑白图片。imgs, image = sobel_cal(imgs)# cv2.imshow("contour", image)# cv2.waitKey()thresh = cv2.GaussianBlur(image, (5, 5), 0)thresh = cv2.Canny(thresh, 0, 104)contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)# print(hierarchy, ":hierarchy")"""[[[-1 -1 -1 -1]]] :hierarchy  # cv2.Canny()[[[ 1 -1 -1 -1][ 2  0 -1 -1][ 3  1 -1 -1][-1  2 -1 -1]]] :hierarchy  # cv2.threshold()"""# 传入的是没有高斯去噪的图片imgs = draw_min_rect_circle(imgs, contours, pic_filename, pic_label, pic_path, xml_path)# cv2.imshow("contours", imgs)    # cv2.waitKey()if __name__ == '__main__':pic_root = "./dataset"xml_root = "./Annotations"for i in os.listdir(pic_root):pic_label = ifor j in os.listdir(os.path.join(pic_root, pic_label)):pic_filename = jpic_path = os.path.join(pic_root, pic_label, j)# 生成到对应的文件夹下，但弄出来很麻烦，我直接生成在annotation里面了# xml_path = os.path.join(xml_root, pic_label, str(j[:-3] + "xml"))xml_path = os.path.join(xml_root, str(j[:-3] + "xml"))print(pic_filename, pic_label, pic_path, xml_path)run(pic_filename, pic_label, pic_path, xml_path)

三、使用yolov4模型，训练预测蛾子数据集

• 使用生成的xml文件构建蛾子的voc数据集。具体步骤参考 项目步骤记录。

• 项目刚刚开始，数据集还不充分，这里写下这些记录一下过程。

• 结果展示
  

四、项目步骤记录

1、使用 image_preprocessing/generate_xml_dir/图片裁剪.py 文件将原来的图片裁剪成统一格式的文件。
2、image_preprocessing/generate_xml_dir/生成图片的xml文件.py 用于存放生成框标定的文件
3、使用genenrate_xml_dir 文件夹下的 框标定.py 来生产xml文件
4、将对应目录下的xml和jpg文件剪切到yolov4 voc数据集 相关目录
5、使用 yolov4-pytorch-master/VOCdevkit 文件夹下的 voc2yolo4.py 来生成yolov4-pytorch-master/VOCdevkit/VOC2007/ImageSets/Main 文件夹下的 train.txt等文件。
6、运行 yolov4-pytorch-master/voc_annotation.py 文件，记得更改classes为自己的classes。
7、在使用 yolov4-pytorch-master/predict.py 预测的时候，记得更改 yolov4-pytorch-master/yolo.py 下的"anchors_path"和"classes_path"两个文件路径为自己的路径，及log下的pth和model_data下的classes.txt
8、使用predict.py进行预测，发现结果棉铃虫效果较好，其他的稍微差一些。注意事项：
1、opencv不能读取含中文路径的图片，涉及图片的文件夹路径一律使用英文
2、image_preprocessing/generate_xml_dir/sobel算子边缘检测.py 里面对于不同的蛾子，cv2.threshold的参数应该不一样，这一次没有管它们的差别，如果想要更加精细化训练，可以考虑加上

五、数据标注范例

<annotation><folder>VOC2007</folder><filename>DSC00142.JPG</filename><source><database>ezi</database></source><size><width>760</width><height>700</height><depth>3</depth></size><segmented>0</segmented><object><name>mlc</name><pose>Unspecified</pose><truncated>0</truncated><difficult>0</difficult><bndbox><xmin>483</xmin><ymin>446</ymin><xmax>582</xmax><ymax>549</ymax></bndbox></object><object><name>mlc</name><pose>Unspecified</pose><truncated>0</truncated><difficult>0</difficult><bndbox><xmin>131</xmin><ymin>442</ymin><xmax>221</xmax><ymax>532</ymax></bndbox></object>
</annotation>

六、关于xml的创建、读写问题

（1）xml的创建

# -*- coding:utf-8 -*-
# https://zhuanlan.zhihu.com/p/54269963# from xml.etree import ElementTree as  etree
from xml.etree.ElementTree import Element
from xml.etree.ElementTree import SubElement
from xml.etree.ElementTree import ElementTree
from xml.dom import minidom# generate root node
root = Element('root')# generate first child-node head
head = SubElement(root, 'head')# child-node of head node
title = SubElement(head, 'title')
title.text = "Well Dola!"# generate second child-node body
body = SubElement(root, 'body')
body.text = "I Love Dola!"tree = ElementTree(root)# write out xml data
tree.write('result.xml', encoding='utf-8')

• 结果 ：result.xml

<root><head><title>Well Dola!</title></head><body>I Love Dola!</body>
</root>

（2）在xml有根元素时写入

• customer.xml

<customer ID="C003"><name>kavin</name><phone>32467</phone><comments><![CDATA[A small but healthy company.]]></comments>
</customer>

• 在 customer.xml 中写入的范例代码

from xml.dom.minidom import parse
import xml.dom.minidom
import osdef writeXML():domTree = parse("./customer.xml")# 文档根元素rootNode = domTree.documentElement# 新建一个customer节点customer_node = domTree.createElement("customer")customer_node.setAttribute("ID", "C003")# 创建name节点,并设置textValuename_node = domTree.createElement("name")name_text_value = domTree.createTextNode("kavin")name_node.appendChild(name_text_value)  # 把文本节点挂到name_node节点customer_node.appendChild(name_node)# 创建phone节点,并设置textValuephone_node = domTree.createElement("phone")phone_text_value = domTree.createTextNode("32467")phone_node.appendChild(phone_text_value)  # 把文本节点挂到name_node节点customer_node.appendChild(phone_node)# 创建comments节点,这里是CDATAcomments_node = domTree.createElement("comments")cdata_text_value = domTree.createCDATASection("A small but healthy company.")comments_node.appendChild(cdata_text_value)customer_node.appendChild(comments_node)rootNode.appendChild(customer_node)with open('added_customer.xml', 'w') as f:# 缩进 - 换行 - 编码domTree.writexml(f, addindent='  ', encoding='utf-8')if __name__ == '__main__':writeXML()

• 结果

<?xml version="1.0" encoding="utf-8"?>
<customer ID="C003"><name>kavin</name><phone>32467</phone><comments><![CDATA[A small but healthy company.]]></comments><customer ID="C003"><name>kavin</name><phone>32467</phone><comments><![CDATA[A small but healthy company.]]>    </comments></customer>
</customer>

（3）xml的读取

• movie.xml

<collection shelf="New Arrivals"><movie title="Enemy Behind"><type>War, Thriller</type><format>DVD</format><year>2003</year><rating>PG</rating><stars>10</stars><description>Talk about a US-Japan war</description></movie><movie title="Transformers"><type>Anime, Science Fiction</type><format>DVD</format><year>1989</year><rating>R</rating><stars>8</stars><description>A schientific fiction</description></movie><movie title="Trigun"><type>Anime, Action</type><format>DVD</format><episodes>4</episodes><rating>PG</rating><stars>10</stars><description>Vash the Stampede!</description></movie><movie title="Ishtar"><type>Comedy</type><format>VHS</format><rating>PG</rating><stars>2</stars><description>Viewable boredom</description></movie>
</collection>

• 读取代码

# https://www.cnblogs.com/smart-zihan/p/12015192.htmlfrom xml.dom.minidom import parse
import xml.dom.minidom
import osdef is_xml_exist(xml_path):xml_exist = os.path.exists(xml_path)if not xml_exist:return Falsereturn Truedef read_movie_xml():path = "movie.xml"if not is_xml_exist(path):print("%s is not exist" % path)else:# 使用minidom解析器打开XML文档open_xml = parse(path)root_node = open_xml.documentElementshelf_attrib = "shelf"if root_node.hasAttribute(shelf_attrib):print("Lable: %s\tAttrib: %s\t\tValue: %s" % (root_node.nodeName, shelf_attrib, root_node.getAttribute(shelf_attrib)))print("")# 在集合中获取所有电影movie_node = "movie"movies = root_node.getElementsByTagName(movie_node)# 打印每部电影的详细信息for movie in movies:print("**** Movie ****")if movie.hasAttribute("title"):print("Title: %s" % movie.getAttribute("title"))type_movie = movie.getElementsByTagName('type')[0]print("Type: %s" % type_movie.childNodes[0].data)format_movie = movie.getElementsByTagName('format')[0]print("Format: %s" % format_movie.childNodes[0].data)rating_movie = movie.getElementsByTagName('rating')[0]print("Rating: %s" % rating_movie.childNodes[0].data)descrip_movie = movie.getElementsByTagName('description')[0]print("Rating: %s" % descrip_movie.childNodes[0].data)print("")if __name__ == "__main__":read_movie_xml()

参考

• 我的关联文章
  【python-opencv】 sobel算子 图像边缘检测 图像二值化处理 并进行框标定
  【python-opencv】对图片二值化处理后，对图像进行框标定
• 参考文章或博客
  YOLOv4: Optimal Speed and Accuracy of Object Detection
  Python创建、修改、保存XML文件——xml.etree.ElementTree模块