复杂数据集，召回、精度等突破方法记录【以电科院过检识别模型为参考】
目录
一、数据分析与数据集构建
二、所有相关的脚本
三、模型效果
一、数据分析与数据集构建
由于电科院数据集有17w-18w张，标签错误的非常多，且漏标非常多，但是所有有效时间只有半个月左右，显卡是M60，训练速度特别慢，所以需要尽量留足训练时间，至少是1周左右，而且为了保证训练的轮数尽量多，还需要使得数据集尽量有效，减少冗余
数据复杂情况如下：
由于只训练缺陷类，效果难以达到较好的情况，所以这里考虑加入正常数据，作为辅助，做法流程是：
只筛选缺陷看看带出来多少正常——在里面剔除不需要的类（这里是6和19）——然后由于正常类不能和异常交叉存在，所以剔除和异常交叉的正常类的标，IOU阈值取0.5
得到数据情况如下：
由于“绝缘子正常”太多，这里考虑删除一部分，使得绝缘子正常的数量也能在1000-2000，做法是先统计“5_class27_0518_接着剔除和异常交叉的正常_0.5_抽取绝缘子正常”，然后统计每个类和绝缘子共存的情况，看看哪些较多，能否剔除该类中共存的绝缘子达到目的，数据统计如下：
可见绝缘子并不是很大一部分分布在某一个或者几个类里面的，所以这里无法剔除，只是对“绝缘子破损”进行增强来弥补该类的数据不足
最终训练使用的数据是“6_2_class27_0518_接着剔除和异常交叉的正常_0.5_split”，然后将数据20-25%作为val，其余进行train，进行训练，寻找最佳的方法
数据集每类平衡的规则是：不足2000的增强到2000幅，补充的对照样本（绝缘子正常等）不足1000的增强到1000，尽量均衡的前提下正样本不能多
寻找到最佳方法后，所有是train，不留val，使得尽量多的数据参与训练，以得到最佳模型
二、所有相关的脚本
1_abcd当指定类和它相关类iou过大时剔除该指定类
import osdef calculate_iou(box1, box2):# 提取边界框的坐标和尺寸x1, y1, w1, h1 = box1[1:]x2, y2, w2, h2 = box2[1:]# 计算边界框的右下角坐标x1_right, y1_bottom = x1 + w1, y1 + h1x2_right, y2_bottom = x2 + w2, y2 + h2# 计算相交区域的坐标x_intersect = max(x1, x2)y_intersect = max(y1, y2)x_intersect_right = min(x1_right, x2_right)y_intersect_bottom = min(y1_bottom, y2_bottom)# 计算相交区域的宽度和高度intersect_width = max(0, x_intersect_right - x_intersect)intersect_height = max(0, y_intersect_bottom - y_intersect)# 计算相交区域的面积intersect_area = intersect_width * intersect_heightif intersect_area<0.000001:return 1# 计算两个边界框的面积box1_area = w1 * h1box2_area = w2 * h2# 计算最小并集whole_area = float(box1_area + box2_area - intersect_area)min_area = float(min(box1_area,min(box2_area,whole_area)))# 计算IOUiou = intersect_area /min_areareturn ioudef filter_annotations(queding_id,id_list,filename):list1 = []list2 = []filtered_annotations = []with open(filename, 'r') as file:lines = file.readlines()print('all:\n',lines)for line in lines:class_label, x, y, width, height = line.split(' ')x, y, width, height = float(x), float(y), float(width), float(height)class_id = int(class_label)if int(class_id) == queding_id:list1.append([class_id, x, y, width, height])elif int(class_id) in id_list:list2.append([class_id, x, y, width, height])else:filtered_annotations.append(line)for annotation1 in list1:iou_greater_than_0_2 = Falsefor annotation2 in list2:iou = calculate_iou(annotation1, annotation2)if iou > 0.2:print('iou,',iou)iou_greater_than_0_2 = Truebreakif not iou_greater_than_0_2:line_dst1 = str(annotation1[0])+" "+str(annotation1[1])+" "+str(annotation1[2])+" "+str(annotation1[3])+" "+str(annotation1[4])+"\n"filtered_annotations.append(line_dst1)for annotation2 in list2:line_dst2 = str(annotation2[0])+" "+str(annotation2[1])+" "+str(annotation2[2])+" "+str(annotation2[3])+" "+str(annotation2[4])+"\n"filtered_annotations.append(line_dst2)with open(filename,"w",encoding="utf-8") as f:for line in filtered_annotations:f.write(line)return filtered_annotationsif __name__=='__main__':"""queding_id = 0id_list = [1,2,3,4]--------------------------queding_id = 5id_list = [6,7]--------------------------queding_id = 10id_list = [11,12,13]"""queding_id = 10id_list = [11,12,13]folder_path='./1_class27'for root,_,files in os.walk(folder_path):if len(files)>0:for file in files:if file.endswith('.txt'):print('---------------')print(file)file_path=os.path.join(root,file)res = filter_annotations(queding_id,id_list,file_path)for l in res:print(l)2splitImgAndLabelByLabelid
# -*- encoding:utf-8 -*-
import os
import cv2
import sys
import shutil
from pathlib import Pathsuffixs = [".png"]if len(sys.argv) != 2:print("input as:\n python 1splitImgAndLabelByLabelid.py imgFolder")sys.exit()path = sys.argv[1]if not os.path.exists(path):print("sorry, you input empty floder ! ")sys.exit()file_type_list = ['txt']for name in os.listdir(path):print("-"*20)print("name,",name)file_path=os.path.join(path,name)file_type=file_path.split('.')[-1]for suffix in suffixs:file_name=file_path[0:file_path.rfind('.', 1)]+suffixif os.path.exists(file_name):image=cv2.imread(file_name)if image is None:continueelse:breakif(file_type in file_type_list):bef=open(file_path)ids=[]for line in bef.readlines():linenew = line.strip().split(" ")if len(linenew) == 5:ids.append(int(linenew[0]))ids_len=len(ids)if ids_len == 0:save_path = "empty"if not os.path.exists(save_path):os.mkdir(save_path)shutil.move(file_path,save_path)shutil.move(file_name,save_path)elif ids_len == 1:save_path = str(ids[0])if not os.path.exists(save_path):os.mkdir(save_path)shutil.move(file_path,save_path)shutil.move(file_name,save_path)else:ids.sort()if ids[0] == ids[-1]:save_path = str(ids[0])if not os.path.exists(save_path):os.mkdir(save_path)shutil.move(file_path,save_path)shutil.move(file_name,save_path)else:save_path = "various"if not os.path.exists(save_path):os.mkdir(save_path)shutil.move(file_path,save_path)shutil.move(file_name,save_path)print(ids)3_copyfilesbyclassid
# encoding:utf-8import os
import cv2
import shutilsuffixs = [".JPG",".PNG",".bmp",".jpeg",".jpg",".png"]def backup_txt_files(src_dir, dst_dir):for root,_,files in os.walk(src_dir):for file in files:if file.endswith('.txt'):# select labelsrc_path = os.path.join(root, file)rel_path = os.path.relpath(src_path,src_dir)dst_path = os.path.join(dst_dir, rel_path)new_label_data = []with open(src_path, "r", encoding="utf-8") as f:for line in f:line_tmp = line.strip().split(" ")if len(line_tmp) == 5:if int(line_tmp[0]) == 6 :continueline_dst = line_tmp[0]+" "+line_tmp[1]+" "+line_tmp[2]+" "+line_tmp[3]+" "+line_tmp[4]+"\n"new_label_data.append(line_dst)if len(new_label_data)>0:# process labeldst_folder=os.path.dirname(dst_path)os.makedirs(dst_folder, exist_ok=True)with open(dst_path,"w",encoding="utf-8") as f:for line in new_label_data:f.write(line)# process imagefor suffix in suffixs:file_name=src_path[0:src_path.rfind('.', 1)]+suffixif os.path.exists(file_name):image=cv2.imread(file_name)if image is not None:shutil.copy(file_name, dst_folder)break# 指定源路径和备份路径（最好使用绝对路径）
src_dir = 'various'
dst_dir = 'various_6'# 执行备份操作
backup_txt_files(src_dir, dst_dir)4_ccccc补充various到单类中
# encoding:utf-8import os
import shutil
from termios import PARODD
import cv2
import randomdef backup_txt_files(src_dir, sample_dir,class_id,num_thresh):src_num_files = len([f for f in os.listdir(src_dir) if os.path.isfile(os.path.join(src_dir, f))])//2if src_num_files > num_thresh:exit()# search_res=[]for root,_,files in os.walk(sample_dir):for file in files:if file.endswith('.txt'):flag = Falselabel_path = os.path.join(root, file)with open(label_path, "r", encoding="utf-8") as f:for line in f:line_tmp = line.strip().split(" ")if len(line_tmp) == 5:if int(line_tmp[0]) == class_id :flag = Trueif flag == False:continuefile_name=label_path[0:label_path.rfind('.', 1)]+".jpg"if os.path.exists(file_name):image=cv2.imread(file_name)if image is not None:search_res.append((file_name,label_path))# shufrandom.shuffle(search_res)sample_num_files = len(search_res)//2# save_path=src_dir+"_various"os.makedirs(save_path,exist_ok=True)# add_num = num_thresh - src_num_filesprint(src_dir,' ',src_num_files,' ',add_num)if sample_num_files < add_num:for file,label in search_res:shutil.move(file,save_path)shutil.move(label,save_path)else:for i in range(add_num):shutil.move(search_res[i][0],save_path)shutil.move(search_res[i][1],save_path)# 指定源路径和备份路径（最好使用绝对路径）
src_dir = 'single'
sample_dir = 'various'
num_thresh = 3000# 执行备份操作
for folder in os.listdir(src_dir):print('-'*40)backup_txt_files(os.path.join(src_dir,folder),sample_dir,int(folder),num_thresh)5_dedadada当指定类标过多时删去标抹去标签区域
import os
import random
import cv2def process(label_path,class_id):if label_path.endswith('.txt'):# select label# print('-'*40)# print('label_path,',label_path)new_label_data = []with open(label_path, "r", encoding="utf-8") as f:for line in f:line_tmp = line.strip().split(" ")if len(line_tmp) == 5:if int(line_tmp[0]) == class_id :# print(class_id)# process imagefile_name=label_path[0:label_path.rfind('.', 1)]+'.jpg'if os.path.exists(file_name):# print('draw&ignore ',class_id,' ',file_name)image=cv2.imread(file_name)if image is not None:# class_label = line_tmp[0]x, y, width, height = map(float, line_tmp[1:])x_min = int((x - width/2) * image.shape[1])y_min = int((y - height/2) * image.shape[0])x_max = int((x + width/2) * image.shape[1])y_max = int((y + height/2) * image.shape[0])cv2.rectangle(image, (x_min, y_min), (x_max, y_max), (125, 125, 125), -1)cv2.imwrite(file_name,image)# ignore labelcontinueline_dst = line_tmp[0]+" "+line_tmp[1]+" "+line_tmp[2]+" "+line_tmp[3]+" "+line_tmp[4]+"\n"# print('~~~~liuxia,',int(line_tmp[0]),class_id,line_dst)new_label_data.append(line_dst)# print('new_label_data,',new_label_data)with open(label_path,"w",encoding="utf-8") as f:for line in new_label_data:f.write(line)def getfilelistbyclassid(path,class_id,ignoreid):file_list=[]for folder in os.listdir(path):if ignoreid==1:if str(class_id) in folder:continueelif ignoreid==2:if str(class_id)+"_various" != folder:continuefolder_path=os.path.join(path,folder)for file in os.listdir(folder_path):if file.endswith('.txt'):label_path=os.path.join(folder_path,file)with open(label_path, "r", encoding="utf-8") as f:for line in f:line_tmp = line.strip().split(" ")if len(line_tmp) == 5:if int(line_tmp[0]) == class_id :file_list.append(label_path)breakreturn file_listif __name__=='__main__':id_list = [0,1]path='./images'for class_id in id_list:# print('-'*40)# print('dddd,',class_id)id_path=os.path.join(path,str(class_id))file_num=len([f for f in os.listdir(id_path) if os.path.isfile(os.path.join(id_path, f))])//2if file_num > 1000:# 当前超出限制，把当前之外的抹去（注意当前的还未处理，需要加，2023年05月20日11:44:58）for folder in os.listdir(path):if folder == str(class_id):continuefor file in os.listdir(os.path.join(path,folder)):if file.endswith('.txt'):label_path = os.path.join(os.path.join(path,folder), file)process(label_path,class_id)else:various_id_path=os.path.join(path,str(class_id)+"_various")various_file_num=len([f for f in os.listdir(id_path) if os.path.isfile(os.path.join(id_path, f))])//2file_various_num=various_file_num+file_numif file_various_num < 1000:# 另外的超出的标抹去file_list=getfilelistbyclassid(path,class_id,ignoreid=1)if len(file_list)+file_various_num>1000:random.shuffle(file_list)for i in range(len(file_list)+file_various_num-1000):process(file_list[i],class_id)else:# various超出的标抹去various_file_list=getfilelistbyclassid(path,class_id,ignoreid=2)random.shuffle(various_file_list)for i in range(len(various_file_list)+file_num-1000):process(various_file_list[i],class_id) # 另外的需要全部抹去other_file_list=getfilelistbyclassid(path,class_id,ignoreid=1)random.shuffle(other_file_list)for i in range(len(other_file_list)):process(other_file_list[i],class_id) # 抹去数据过多类的标：# 0,5,10,12,15,20,23# 1、如单类大于1000# 则删除various及其他中的标，同时抹去图像上的区域# 2、如单类小于1000，但是结合various大于1000# 则删除其他中的标，同时抹去图像上的区域# 3、单类+various还是小于1000# 则在其他中找到满足1000，则删除剩余的标，同时抹去图像上的区域
三、模型效果
待补充