常用scripts整理--2020.05.13

平時(shí)在進(jìn)行數(shù)據(jù)處理時(shí)，經(jīng)常會(huì)用到一些腳本文件，可以提高效率，我把自己常用到的進(jìn)行整理總結(jié)，希望可以有所幫助

1.批量修改圖片名字

#coding=utf-8 ''' code by zzg 2020-04-07 ''' import os import re import sys path = r"***自己的圖片文件夾" fileList = os.listdir(path) # 待修改文件夾 print("修改前：" + str(fileList)) # 輸出文件夾中包含的文件 os.chdir(path) # 將當(dāng)前工作目錄修改為待修改文件夾的位置 num = 0 # 名稱(chēng)變量 for fileName in fileList: # 遍歷文件夾中所有文件pat = ".+.(jpg|jpeg|JPG)" # 匹配文件名正則表達(dá)式pattern = re.findall(pat, fileName) # 進(jìn)行匹配# print(pattern)print('num：', num, 'filename:', fileName)os.rename(fileName, ('1' + '_' + str(num) + '.'+ pattern[0])) # 文件重新命名num = num + 1 # 改變編號(hào)，繼續(xù)下一項(xiàng) print("---------------------------------------------------") sys.stdin.flush() # 刷新 print("修改后：" + str(os.listdir(path))) # 輸出修改后文件夾中包含的文件

2.批量修改圖片大小

#coding=utf-8 ''' code by zzg 2020-04-07 ''' #提取目錄下所有圖片,更改尺寸后保存到另一目錄 from PIL import Image import os.path import globdef convertjpg(jpgfile,outdir,width=128,height=128):img=Image.open(jpgfile)try:new_img=img.resize((width,height),Image.BILINEAR) new_img.save(os.path.join(outdir,os.path.basename(jpgfile)))except Exception as e:print(e) for jpgfile in glob.glob("train/*.jpg"):convertjpg(jpgfile,"train01/")

3.批量打空標(biāo)簽

# coding:utf-8 ''' code by zzg 2020-04-04''' ##針對(duì)負(fù)樣本的圖片批量生成空白標(biāo)簽import os,sys import glob from PIL import Image import pdb# the direction/path of Image,Label src_img_dir = "images/" src_xml_dir = "xml/"img_Lists = glob.glob(src_img_dir + '/*.jpg') # print(img_Lists)img_basenames = [] for item in img_Lists:img_basenames.append(os.path.basename(item)) # print(img_basenames)img_name = [] for item in img_basenames:temp1, temp2 = os.path.splitext(item)img_name.append(temp1) # print(img_name)#pdb.set_trace() for img in img_name:im = Image.open((src_img_dir + '/' + img + '.jpg'))width, height = im.size#print(width)xml_file = open((src_xml_dir + '/' + img + '.xml'), 'w')xml_file.write('<annotation>n')xml_file.write('<folder>VOC2007</folder>n')xml_file.write('<filename>' +str(img) + '.jpg' + '</filename>n')xml_file.write('<source>n')xml_file.write('<database>' + 'Unknown' + '</database>n')xml_file.write('</source>n')xml_file.write('<size>n')xml_file.write('<width>' + str(width) + '</width>n')xml_file.write('<height>' + str(height) + '</height>n')xml_file.write('<depth>3</depth>n')xml_file.write('</size>n')#print(len(gt))xml_file.write('</annotation>')print("finshed convert!!")

4.批量xml轉(zhuǎn)json（一個(gè)文件夾下所有xml轉(zhuǎn)到對(duì)應(yīng)的json）

''' code by zzg 2020-05-13 ''' #批量修改文件夾下的xml為json并存儲(chǔ)到另一個(gè)文件夾import glob import xmltodict import jsonpath = 'xml/' path2 = 'json/'def pythonXmlToJson(path):xml_dir = glob.glob(path + '*.xml')# print(len(xml_dir))for x in xml_dir:with open(x) as fd:convertedDict = xmltodict.parse(fd.read())jsonStr = json.dumps(convertedDict, indent=1)print("jsonStr=",jsonStr)print(x.split('.')[0])json_file = x.split('.')[0].split('/')[-1] +'.json'with open(path2 + '/' + json_file, 'w') as json_file:json_file.write(jsonStr)print("xml_json finished!")print(len(xml_dir)) pythonXmlToJson(path)

5.批量修改圖片后綴（jpg-->JPG）

先在txt中編輯 ren *.JPG *.jpg 后改.txt為.bat

6.批量移動(dòng)文件夾下的指定文件（比如xml和jpg混合，移出jpg）

code by zzg 2020-05-12 ''' #復(fù)制或者移動(dòng)一個(gè)文件夾下的所有圖片或者其他指定文件到另一個(gè)文件夾 import os import shutil path = 'train/' new_path = 'image/' new_path1 = 'xml/' count = 0 for root, dirs, files in os.walk(path):for i in range(len(files)):#if (files[i][-3:] == 'jpg' or files[i][-3:] == 'JPG'):if (files[i][-3:] == 'xml'):count += 1file_path = root + '/' + files[i]new_file_path = new_path1 + '/' + files[i]shutil.copy(file_path, new_file_path)#shutil.move(file_path, new_file_path))print(count) print("move finished!!")

7.為圖片添加m*n的矩形格網(wǎng)

""" code by zzg --2020-05-27 """ ##為圖片添加m*n的矩形格網(wǎng) from PIL import Image from PIL import ImageDraw import osinput_img = r'resize1.jpg'# 生成格網(wǎng) (filepath,filename) = os.path.split(input_img) img = Image.open(input_img) img_d = ImageDraw.Draw(img) x_len, y_len = img.size x_step = x_len/13 y_step = y_len/13 print(x_len) print(y_len) x_len = int(x_len) x_step = int(x_step) y_len = int(y_len) y_step = int(y_step)for x in range(0, x_len, x_step):img_d.line(((x, 0), (x, y_len)), (0, 0, 0)) for y in range(0, y_len, y_step):j = y_len - y - 1img_d.line(((0, j), (x_len, j)), (0, 0, 0)) img.save(os.path.join(filepath,"grid1_13_"+filename) )# 為格網(wǎng)編碼 左下角開(kāi)始 cnt = 1 for i in range(0,x_len,x_step):img_d.text((i,y_len-y_step), str(cnt).encode("utf8"),fill=(255,0,0))cnt+=1 cnt = 1 for j in range(y_step,y_len,y_step):z = y_len - jimg_d.text((0,z), str(cnt).encode("utf8"),fill=(255,0,0) )cnt+=1 img.save(os.path.join(filepath, "grid_geocoding_"+filename))

示例：

8.批量截取圖片指定內(nèi)容并保存以及重寫(xiě)對(duì)應(yīng)的xml（或者用作批量修改圖片名字以及對(duì)應(yīng)的xml標(biāo)簽）

''' code by zzg 2020-05-30 ''' #!/usr/bin/python # -*- coding: UTF-8 -*- # get annotation object bndbox locationtry:import xml.etree.cElementTree as ET except ImportError:import xml.etree.ElementTree as ETimport os,sys import glob import cv2 from PIL import Image import matplotlib.pyplot as plt import pdb#the direction/path of Image,Label src_img_dir = "image" src_xml_dir = "xml" dst_img_dir = "image-crop" dst_xml_dir = "xml-crop"img_Lists = glob.glob(src_img_dir + '/*.jpeg') #print(img_Lists)img_basenames = [] for item in img_Lists:img_basenames.append(os.path.basename(item))#print(img_basenames)img_name = [] for item in img_basenames:temp1, temp2 = os.path.splitext(item)img_name.append(temp1)# print(img_name)cnt = 0 for img in img_name:# im0 = Image.open((src_img_dir + '/' + img + '.jpeg'))# print(type(im0))# width, height = im0.size# print(im0.size)#print(width)## read the scr_imageim = cv2.imread(src_img_dir + '/' + img + '.jpeg')# print(type(im))# print(im.shape)# width, height = im.shape[:2][::-1] ##get w and h# print(width, height)##read the scr_xmlAnotPath = src_xml_dir + '/' + img + '.xml'# print(AnotPath)tree = ET.ElementTree(file=AnotPath) # print(tree)root = tree.getroot()# print(root)ObjectSet = root.findall('object')ObjBndBoxSet = []ObjBndBoxSet1 = {} for Object in ObjectSet:ObjName = Object.find('name').textBndBox = Object.find('bndbox')x1 = int(BndBox.find('xmin').text)#-1 y1 = int(BndBox.find('ymin').text)#-1x2 = int(BndBox.find('xmax').text)#-1y2 = int(BndBox.find('ymax').text)#-1BndBoxLoc = [ObjName,x1,y1,x2,y2]# print(x1,y1,x2,y2)ObjBndBoxSet.append(BndBoxLoc) # if ObjName in ObjBndBoxSet:# ObjBndBoxSet1[ObjName].append(BndBoxLoc)#如果字典結(jié)構(gòu)中含有這個(gè)類(lèi)別了，那么這個(gè)目標(biāo)框要追加到其值的末尾# else:# ObjBndBoxSet1[ObjName] = [BndBoxLoc]#如果字典結(jié)構(gòu)中沒(méi)有這個(gè)類(lèi)別，那么這個(gè)目標(biāo)框就直接賦值給其值吧 print(ObjBndBoxSet)#get the face [name,x01,y01,x02,y02] = ObjBndBoxSet[0]# print(len(ObjBndBoxSet))width = x02 - x01height = y02 - y01img1 = cv2.rectangle(im,(x01,y01),(x02,y02),(255,0,0),2)img2 = im[y01:y02, x01:x02]# plt.imshow(img2)# plt.show()# save the crop-image in dst_cropcv2.imwrite(dst_img_dir + '/' + img + '.jpeg',img2)# rewrite xml to dst_xmlxml_file = open((dst_xml_dir + '/' + img + '.xml'), 'w')xml_file.write('<annotation>n')xml_file.write('<folder>VOC2007</folder>n')xml_file.write('<filename>' +str(img) + '.jpeg' + '</filename>n')xml_file.write('<size>n')xml_file.write('<width>' + str(width) + '</width>n')xml_file.write('<height>' + str(height) + '</height>n')xml_file.write('<depth>3</depth>n')xml_file.write('</size>n')print("===========start rewrite bndbox==============")for x in ObjBndBoxSet[1:]:# print(x)[classname,x1,y1,x2,y2] = x x1 = x1 - x01y1 = y1 - y01x2 = x2 - x01y2 = y2 - y01 xml_file.write('<object>n')xml_file.write('<name>' + classname + '</name>n')xml_file.write('<pose>Unspecified</pose>n')xml_file.write('<truncated>0</truncated>n')xml_file.write('<difficult>0</difficult>n')xml_file.write('<bndbox>n')xml_file.write('<xmin>' + str(x1) + '</xmin>n')xml_file.write('<ymin>' + str(y1) + '</ymin>n') xml_file.write('<xmax>' + str(x2) + '</xmax>n')xml_file.write('<ymax>' + str(y2) + '</ymax>n')xml_file.write('</bndbox>n')xml_file.write('</object>n') xml_file.write('</annotation>')cnt += 1print(cnt)print("=======================finished!===================")

9.轉(zhuǎn)csv為voc_xml格式，用于目標(biāo)檢測(cè)

CSV格式''' code by zzg-2020-06-02 ''' import os import numpy as np import codecs import pandas as pd import json from glob import glob import cv2 import shutil from sklearn.model_selection import train_test_split from IPython import embed#1.標(biāo)簽路徑 csv_file = "train.csv" saved_path = "VOC2007/" #保存路徑 image_save_path = "./JPEGImages/" image_raw_parh = "train/"#2.創(chuàng)建要求文件夾 if not os.path.exists(saved_path + "Annotations"):os.makedirs(saved_path + "Annotations") if not os.path.exists(saved_path + "JPEGImages/"):os.makedirs(saved_path + "JPEGImages/") if not os.path.exists(saved_path + "ImageSets/Main/"):os.makedirs(saved_path + "ImageSets/Main/")#3.獲取待處理文件 data = pd.read_csv(csv_file,header=None,index_col=False,names=['image_id','width','height','bbox','source'])##合并相同名字的行 data_lite = data[['image_id','bbox']] # print(data_lite) data_lite['bbox'] = data_lite['bbox'].apply(lambda x: ','+ x) data1 = data_lite.groupby(by='image_id').sum() # data1 = data_lite.groupby(by='image_id')['bbox'].sum() data1['bbox'] = data1['bbox'].apply(lambda x : x[1:]) data1 = data1[0:3373] ##去除最后一行標(biāo)簽 # print(data1)total_csv_annotations = {} for row in data1.itertuples():# print(row[0],row[1])total_csv_annotations[row[0]] = row[1]##適用于沒(méi)用行中沒(méi)有相同名字的csv # total_csv_annotations = {} # annotations = pd.read_csv(csv_file,header=None).values # print(annotations )# key = annotation[0].split(os.sep)[-1] # value = np.array(annotation[3:]) # value = value[0] # # print(key) # # print(type(value)) # # print(value) # # print(total_csv_annotations.keys())# # total_csv_annotations[key] = value # total = total_csv_annotations # print(total)#4.讀取標(biāo)注信息并寫(xiě)入 xml # print(total_csv_annotations.items())count = 0 for filename,label in total_csv_annotations.items():#embed()# print(filename)count += 1print(count)height, width, channels = cv2.imread(image_raw_parh + filename + '.jpg').shape#embed()with codecs.open(saved_path + "Annotations/"+filename+'.xml',"w","utf-8") as xml:xml.write('<annotation>n')xml.write('t<folder>' + 'VOC2007' + '</folder>n')xml.write('t<filename>' + filename + '.jpg' + '</filename>n')xml.write('t<source>n')xml.write('tt<database>Unknown</database>n')xml.write('t</source>n')xml.write('t<size>n')xml.write('tt<width>'+ str(width) + '</width>n')xml.write('tt<height>'+ str(height) + '</height>n')xml.write('tt<depth>' + str(channels) + '</depth>n')xml.write('t</size>n')xml.write('tt<segmented>0</segmented>n')if isinstance(label,float):## 空白xml.write('</annotation>')continue# print(label)label = label.replace('[','').replace(']','').replace(' ', '').split(',')# print(label)box_cnt = len(label) // 4# print(label[3])for i in range(box_cnt):xmin = int(float(label[i*4]))ymin = int(float(label[i*4+1]))width = int(float(label[i*4+2]))height= int(float(label[i*4+3]))xmax = xmin + widthymax = ymin + height# classname = 'wheat'if xmax <= xmin:passelif ymax <= ymin:passelse:xml.write('t<object>n')xml.write('tt<name>'+'wheat'+'</name>n')xml.write('tt<pose>Unspecified</pose>n')xml.write('tt<truncated>1</truncated>n')xml.write('tt<difficult>0</difficult>n')xml.write('tt<bndbox>n')xml.write('ttt<xmin>' + str(xmin) + '</xmin>n')xml.write('ttt<ymin>' + str(ymin) + '</ymin>n')xml.write('ttt<xmax>' + str(xmax) + '</xmax>n')xml.write('ttt<ymax>' + str(ymax) + '</ymax>n')xml.write('tt</bndbox>n')xml.write('t</object>n')print(filename,xmin,ymin,xmax,ymax)xml.write('</annotation>')#5.split files for txt txtsavepath = saved_path + "ImageSets/Main/" ftrainval = open(txtsavepath+'/trainval.txt', 'w') # ftest = open(txtsavepath+'/test.txt', 'w') ftrain = open(txtsavepath+'/train.txt', 'w') fval = open(txtsavepath+'/val.txt', 'w') total_files = glob(saved_path+"./Annotations/*.xml") total_files = [i.split("/")[-1].split(".xml")[0] for i in total_files] #test_filepath = "" for file in total_files:ftrainval.write(file + "n")#6.move images to voc JPEGImages folder for image in glob(image_raw_parh+"/*.jpg"):shutil.copy(image,saved_path+image_save_path)train_files,val_files = train_test_split(total_files,test_size=0.15,random_state=42)for file in train_files:ftrain.write(file + "n") #val for file in val_files:fval.write(file + "n")ftrainval.close() ftrain.close() fval.close()

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