index (tsinghua.edu.cn)

模板转换jinjia2包链接：https://pan.baidu.com/s/1ycb_zf8oS88HF0FvpXrYFg?pwd=ym9n
提取码：ym9n

import os
from jinja2 import Environment, PackageLoaderclass xml_fill:def __init__(self, path, width, height, depth=3, database='Unknown', segmented=0):environment = Environment(loader=PackageLoader('source', 'XML_template'), keep_trailing_newline=True)self.annotation_template = environment.get_template('voc_template.xml')abspath = os.path.abspath(path)self.template_parameters = {'path': abspath,'filename': os.path.basename(abspath),'folder': os.path.basename(os.path.dirname(abspath)),'width': width,'height': height,'depth': depth,'database': database,'segmented': segmented,'objects': []}def add_obj_box(self, name, xmin, ymin, xmax, ymax, pose='Unspecified', truncated=0, difficult=0):self.template_parameters['objects'].append({'name': name,'xmin': xmin,'ymin': ymin,'xmax': xmax,'ymax': ymax,'pose': pose,'truncated': truncated,'difficult': difficult,})def save_xml(self, annotation_path):with open(annotation_path, 'w') as file:content = self.annotation_template.render(**self.template_parameters)file.write(content)

import json
import os
from PIL import Image
from voc_xml_generator import xml_fill
tt100k_parent_dir = "G:\\"def find_image_size(filename):with Image.open(filename) as img:img_weight = img.size[0]img_height = img.size[1]img_depth = 3return img_weight, img_height, img_depthdef load_mask(annos, datadir, imgid, filler):img = annos["imgs"][imgid]path = img['path']for obj in img['objects']:name = obj['category']box = obj['bbox']xmin = int(box['xmin'])ymin = int(box['ymin'])xmax = int(box['xmax'])ymax = int(box['ymax'])filler.add_obj_box(name, xmin, ymin, xmax, ymax)work_sapce_dir = os.path.join(tt100k_parent_dir, "TT100K\\VOCdevkit\\")
if not os.path.isdir(work_sapce_dir):os.mkdir(work_sapce_dir)
work_sapce_dir = os.path.join(work_sapce_dir, "VOC20230102\\")
if not os.path.isdir(work_sapce_dir):os.mkdir(work_sapce_dir)
jpeg_images_path = os.path.join(work_sapce_dir, 'JPEGImages')
annotations_path = os.path.join(work_sapce_dir, 'Annotations')
if not os.path.isdir(jpeg_images_path):os.mkdir(jpeg_images_path)
if not os.path.isdir(annotations_path):os.mkdir(annotations_path)datadir = tt100k_parent_dir + "TT100K\\data"filedir = datadir + "\\annotations.json"
ids = open(datadir + "\\train\\ids.txt").read().splitlines()
annos = json.loads(open(filedir).read())for i, value in enumerate(ids):imgid = valuefilename = datadir + "\\train\\" + imgid + ".jpg"width,height,depth = find_image_size(filename)filler = xml_fill(filename, width, height, depth)load_mask(annos, datadir, imgid, filler)filler.save_xml(annotations_path + '\\' + imgid + '.xml')print("%s.xml saved\n"%imgid)

import xml.etree.ElementTree as ET
import os
import random
from shutil import movetype45="i2,i4,i5,il100,il60,il80,io,ip,p10,p11,p12,p19,p23,p26,p27,p3,p5,p6,pg,ph4,ph4.5,ph5,pl100,pl120,pl20,pl30,pl40,pl5,pl50,pl60,pl70,pl80,pm20,pm30,pm55,pn,pne,po,pr40,w13,w32,w55,w57,w59,wo"
type45 = type45.split(',')
classes = type45TRAIN_RATIO = 80def clear_hidden_files(path):dir_list = os.listdir(path)for i in dir_list:abspath = os.path.join(os.path.abspath(path), i)if os.path.isfile(abspath):if i.startswith("._"):os.remove(abspath)else:clear_hidden_files(abspath)def convert(size, box):dw = 1./size[0]dh = 1./size[1]x = (box[0] + box[1])/2.0y = (box[2] + box[3])/2.0w = 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(image_id):in_file = open('VOC/2022/ANNOTATIONS/%s.xml' %image_id)out_file = open('VOC/2022/YOLOLabels/%s.txt' %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')in_file.close()out_file.close()wd = os.getcwd()
wd = os.getcwd()
data_base_dir = os.path.join(wd, "VOC/")
if not os.path.isdir(data_base_dir):os.mkdir(data_base_dir)
work_sapce_dir = os.path.join(data_base_dir, "2022/")
if not os.path.isdir(work_sapce_dir):os.mkdir(work_sapce_dir)
annotation_dir = os.path.join(work_sapce_dir, "ANNOTATIONS/")
if not os.path.isdir(annotation_dir):os.mkdir(annotation_dir)
clear_hidden_files(annotation_dir)
image_dir = os.path.join(work_sapce_dir, "IMAGE/")
if not os.path.isdir(image_dir):os.mkdir(image_dir)
clear_hidden_files(image_dir)
yolo_labels_dir = os.path.join(work_sapce_dir, "YOLOLabels/")
if not os.path.isdir(yolo_labels_dir):os.mkdir(yolo_labels_dir)
clear_hidden_files(yolo_labels_dir)
yolov5_images_dir = os.path.join(data_base_dir, "images/")
if not os.path.isdir(yolov5_images_dir):os.mkdir(yolov5_images_dir)
clear_hidden_files(yolov5_images_dir)
yolov5_labels_dir = os.path.join(data_base_dir, "labels/")
if not os.path.isdir(yolov5_labels_dir):os.mkdir(yolov5_labels_dir)
clear_hidden_files(yolov5_labels_dir)
yolov5_images_train_dir = os.path.join(yolov5_images_dir, "train/")
if not os.path.isdir(yolov5_images_train_dir):os.mkdir(yolov5_images_train_dir)
clear_hidden_files(yolov5_images_train_dir)
yolov5_images_test_dir = os.path.join(yolov5_images_dir, "val/")
if not os.path.isdir(yolov5_images_test_dir):os.mkdir(yolov5_images_test_dir)
clear_hidden_files(yolov5_images_test_dir)
yolov5_labels_train_dir = os.path.join(yolov5_labels_dir, "train/")
if not os.path.isdir(yolov5_labels_train_dir):os.mkdir(yolov5_labels_train_dir)
clear_hidden_files(yolov5_labels_train_dir)
yolov5_labels_test_dir = os.path.join(yolov5_labels_dir, "val/")
if not os.path.isdir(yolov5_labels_test_dir):os.mkdir(yolov5_labels_test_dir)
clear_hidden_files(yolov5_labels_test_dir)train_file = open(os.path.join(wd, "yolov5_train.txt"), 'w')
test_file = open(os.path.join(wd, "yolov5_val.txt"), 'w')
train_file.close()
test_file.close()
train_file = open(os.path.join(wd, "yolov5_train.txt"), 'a')
test_file = open(os.path.join(wd, "yolov5_val.txt"), 'a')
list_imgs = os.listdir(image_dir) # list image files
prob = random.randint(1, 100)
for i in range(0,len(list_imgs)):path = os.path.join(image_dir,list_imgs[i])if os.path.isfile(path):image_path = image_dir + list_imgs[i]voc_path = list_imgs[i](nameWithoutExtention, extention) = os.path.splitext(os.path.basename(image_path))(voc_nameWithoutExtention, voc_extention) = os.path.splitext(os.path.basename(voc_path))annotation_name = nameWithoutExtention + '.xml'annotation_path = os.path.join(annotation_dir, annotation_name)label_name = nameWithoutExtention + '.txt'label_path = os.path.join(yolo_labels_dir, label_name)prob = random.randint(1, 100)if(prob < TRAIN_RATIO): # train datasetif os.path.exists(annotation_path):train_file.write(image_path + '\n')convert_annotation(nameWithoutExtention) # convert labelmove(image_path, yolov5_images_train_dir + voc_path)move(label_path, yolov5_labels_train_dir + label_name)else: # test datasetif os.path.exists(annotation_path):test_file.write(image_path + '\n')convert_annotation(nameWithoutExtention) # convert labelmove(image_path, yolov5_images_test_dir + voc_path)move(label_path, yolov5_labels_test_dir + label_name)
train_file.close()
test_file.close()

class LoadImgLabels(Dataset):# root = "YOLO/VOC"def __init__(self,root,mode):super(LoadImgLabels, self).__init__()self.root = rootself.mode = modeimg_path = get_path(os.path.join(root,'images',self.mode))lab_path = get_path(os.path.join(root,'labels',self.mode))self.img_files = get_file(img_path)self.label_files = img2label_paths(self.img_files)def __len__(self):return len()def __getitem__(self, item):return

# 获得（不同操作系统）标准路径

def get_path(path):p = str(Path(path))return p

# 得到路径下的每个文件

def get_file(path):file = []if os.path.isdir(path):file += glob.iglob(path + os.sep + '*.*')return file

# 由图片的文件得到对应标签的文件

def img2label_paths(img_paths):sa, sb = os.sep + 'images' + os.sep, os.sep + 'labels' + os.sepreturn [x.replace(sa, sb, 1).replace(os.path.splitext(x)[-1], '.txt') for x in img_paths]

# 缓存标签

def cache_labels(img_files, label_files, path='labels.cache'):x = {}pbar = tqdm(zip(img_files, label_files), desc='Scanning images', total=len(img_files))for (img, label) in pbar:print(img,label)try:l=[]im = Image.open(img)im.verify()shape = im.sizeif os.path.isfile(label):with open(label,'r') as f:l = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32)if len(l) == 0:l = np.zeros((0, 5), dtype=np.float32)x[img] = [l,shape]except:passtorch.save(x, path)return x

class LoadImgLabels(Dataset):# root = "../VOC"def __init__(self,root,mode,img_size):super(LoadImgLabels, self).__init__()self.root = rootself.mode = modeself.img_size = img_size  # 输入图片分辨率大小img_path = get_path(os.path.join(root,'images',self.mode))if os.path.isfile('labels.cache'):print("读取缓存标签文件'labels.cache'")cache = torch.load('labels.cache')else:print("生成缓存标签文件'labels.cache'")self.img_files = get_file(img_path)self.label_files = img2label_paths(self.img_files)cache = cache_labels(self.img_files, self.label_files)labels, shapes = zip(*cache.values())self.labels = list(labels)self.shapes = np.array(shapes, dtype=np.float64)self.img_files = list(cache.keys())self.label_files = img2label_paths(cache.keys())def __len__(self):return len(self.img_files)def __getitem__(self, index):return 0

# 加载图片

并根据设定的输入大小与图片原大小的比例ratio进行resize;

if img_size = 640:(1080, 1920)———>(360, 640)

def load_image(img_files, img_size , index):    #   img_size = 640path = img_files[index]img = cv2.imread(path)h0 ,w0 = img.shape[:2]r = img_size / max(h0,w0)if r != 1:interp = cv2.INTER_AREA if r < 1 else cv2.INTER_LINEARimg = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=interp)return img, (h0, w0), img.shape[:2] #   (1080, 1920)———>(360, 640)

# 图像缩放: 保持图片的宽高比例，剩下的部分采用灰色填充。

def Make_squqre(img, new_shape=(640, 640), color=(114, 114, 114)):# Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232shape = img.shape[:2]  # 当前图片大小if isinstance(new_shape, int):new_shape = (new_shape, new_shape)# ----------------计算填充大小-----------------------------------------r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])# r = 1.0ratio = r, r  # ratio = (1.0,1.0)new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # 填充宽度,高度# 计算上下左右填充大小dw /= 2dh /= 2top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))left, right = int(round(dw - 0.1)), int(round(dw + 0.1))# ------------------进行填充-------------------------------------------img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)return img, ratio, (dw, dh)

对标签的处理

# 1根据pad调整框的标签坐标

# 2调整框的标签，xyxy->xywh

# 3归一化标签0 - 1

        labels = []x = self.labels[index]if x.size > 0:# 根据pad调整框的标签坐标:注意label是真实位置,没有归一化的labels = x.copy()labels[:, 1] = ratio[0] * w * (x[:, 1] - x[:, 3] / 2) + pad[0]labels[:, 2] = ratio[1] * h * (x[:, 2] - x[:, 4] / 2) + pad[1]labels[:, 3] = ratio[0] * w * (x[:, 1] + x[:, 3] / 2) + pad[0]labels[:, 4] = ratio[1] * h * (x[:, 2] + x[:, 4] / 2) + pad[1]nL = len(labels)if nL:  labels[:, 1:5] = xyxy2xywh(labels[:, 1:5])# 重新归一化标签0 - 1labels[:, [2, 4]] /= img.shape[0]  # normalized height 0~1labels[:, [1, 3]] /= img.shape[1]  # normalized width 0~1labels_out = torch.zeros((nL, 6))if nL:labels_out[:, 1:] = torch.from_numpy(labels)

# 左上角右下角坐标格式转换成中心点+宽高坐标格式

def xyxy2xywh(x):# Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-righty = torch.zeros_like(x) if isinstance(x, torch.Tensor) else np.zeros_like(x)y[:, 0] = (x[:, 0] + x[:, 2]) / 2  # x centery[:, 1] = (x[:, 1] + x[:, 3]) / 2  # y centery[:, 2] = x[:, 2] - x[:, 0]  # widthy[:, 3] = x[:, 3] - x[:, 1]  # heightreturn y

dataloader

import torch
from contextlib import contextmanager
from tqdm import tqdmfrom YOLO.dataset.dataset import LoadImgLabels# 定义生成器 _RepeatSampler
class _RepeatSampler(object):def __init__(self, sampler):self.sampler = samplerdef __iter__(self):while True:yield from iter(self.sampler)
# 定义DataLoader（一个python生成器）
class InfiniteDataLoader(torch.utils.data.dataloader.DataLoader):def __init__(self, *args, **kwargs):super().__init__(*args, **kwargs)object.__setattr__(self, 'batch_sampler', _RepeatSampler(self.batch_sampler))self.iterator = super().__iter__()def __len__(self):return len(self.batch_sampler.sampler)def __iter__(self): # 实现了__iter__方法的对象是可迭代的for i in range(len(self)):yield next(self.iterator)
@contextmanager
def torch_distributed_zero_first(local_rank: int):""if local_rank not in [-1, 0]:torch.distributed.barrier() # Synchronizes all processesyieldif local_rank == 0:torch.distributed.barrier()# 利用自定义的数据集(LoadImagesAndLabels)创建dataloader
def create_dataloader(path, mode , imgsz, batch_size,rank=-1):with torch_distributed_zero_first(rank):dataset = LoadImgLabels(path, mode, imgsz)batch_size = min(batch_size, len(dataset))dataloader = InfiniteDataLoader(dataset,#  torch.utils.data.DataLoaderbatch_size=batch_size,shuffle=True,collate_fn=LoadImgLabels.collate_fn,pin_memory=True)return dataloader, datasetdataloader, dataset = create_dataloader("G:\VOC", 'train',640, 2)pbar = enumerate(dataloader)
nb = len(dataloader)
pbar = tqdm(pbar, total=nb)for i, (imgs, targets, path) in pbar:ni = i + nb * 1imgs = imgs / 255.0print(imgs.size(),targets.size())

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YOLO交通标志识别数据集准备

# 加载图片

对标签的处理

# 1根据pad调整框的标签坐标

# 2调整框的标签，xyxy->xywh

# 3归一化标签0 - 1

dataloader

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