@记录使用的过程，前提是安装好所有的必要资源后，
第一部分，准备图片，格式为.jpg 第二部分，标注图片，方法参考：方法参考链接 第三部分，将标注好的图片，使用代码转换成YOLOV5需要的TXT文件；cy_xml_txt2.py
import os.path
import xml.etree.ElementTree as ET
class_names = ['pig']#设置标注名称
xmlpath = 'D:\VOC007\Annotations\' # xml文件的位置
txtpath = 'D:\VOC007\labels\' # 导出txt的位置
files=[]
for root, dirs, files in os.walk(xmlpath):
None
number = len(files)
i = 0
while i < number:
name = files[i][0:-4]
xml_name = name + ".xml"
txt_name = name + ".txt"
xml_file_name = xmlpath + xml_name
txt_file_name = txtpath + txt_name
xml_file = open(xml_file_name)
tree = ET.parse(xml_file)
root = tree.getroot()
filename = root.find('filename').text
image_name = root.find('filename').text
w = int(root.find('size').find('width').text)
h = int(root.find('size').find('height').text)
f_txt = open(txt_file_name, 'w+')
content = ""
first = True
for obj in root.iter('object'):
name = obj.find('name').text
class_num = class_names.index(name)
xmlbox = obj.find('bndbox')
x1 = int(xmlbox.find('xmin').text)
x2 = int(xmlbox.find('xmax').text)
y1 = int(xmlbox.find('ymin').text)
y2 = int(xmlbox.find('ymax').text)
if first:
content += str(class_num) + " " +
str((x1+x2)/2/w) + " " + str((y1+y2)/2/h) + " " +
str((x2-x1)/w) + " " + str((y2-y1)/h)
first=False
else:
content += "n" +
str(class_num) + " " +
str((x1 + x2) / 2 / w) + " " + str((y1 + y2) / 2 / h) + " " +
str((x2 - x1) / w) + " " + str((y2 - y1) / h)
print(str(i/(number - 1) * 100) + "%n")
f_txt.write(content)
f_txt.close()
xml_file.close()
i += 1
print("done!")
第四部分，再次转换为yaml文件需要的路径；
cy_voc2yolov5.py
import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
import random
from shutil import copyfile
# 根据自己的数据标签修改
classes=["pig"]
def 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])/2.0
y = (box[2] + box[3])/2.0
w = box[1] - box[0]
h = box[3] - box[2]
x = x*dw
w = w*dw
y = y*dh
h = h*dh
return (x,y,w,h)
def convert_annotation(image_id):
in_file = open('D:VOCdevkitVOC007Annotations%s.xml' %image_id)
out_file = open('D:VOCdevkitVOC007labels%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').text
cls = obj.find('name').text
if cls not in classes or int(difficult) == 1:
continue
cls_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, "D:\VOCdevkit\")
if not os.path.isdir(data_base_dir):
os.mkdir(data_base_dir)
work_sapce_dir = os.path.join(data_base_dir, "VOC007\")
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, "JPEGImages\")
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, "labels\")
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
probo = random.randint(1, 100)
print("Probobility: %d" % probo)
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)
probo = random.randint(1, 100)
print("Probobility: %d" % probo)
if(probo < 80): # train dataset
if os.path.exists(annotation_path):
train_file.write(image_path + 'n')
convert_annotation(nameWithoutExtention) # convert label
copyfile(image_path, yolov5_images_train_dir + voc_path)
copyfile(label_path, yolov5_labels_train_dir + label_name)
else: # test dataset
if os.path.exists(annotation_path):
test_file.write(image_path + 'n')
convert_annotation(nameWithoutExtention) # convert label
copyfile(image_path, yolov5_images_test_dir + voc_path)
copyfile(label_path, yolov5_labels_test_dir + label_name)
train_file.close()
test_file.close()
第五部分，创建一个或者修改一个yaml文件；
circle.yaml
train: D:VOCdevkitimages #改为自己的训练文件的路径 val: D:VOCdevkitimages #改为自己的测试文件的路径 # number of classes nc: 1 # class names names: ['pig']第六部分，修改train.py文件；
parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='initial weights path')
parser.add_argument('--cfg', type=str, default='', help='model.yaml path')
parser.add_argument('--data', type=str, default=ROOT / 'data/circle.yaml', help='dataset.yaml path')
parser.add_argument('--batch-size', type=int, default=8, help='total batch size for all GPUs')
第七部分，修改datasets.py
utilsloggersdatasets.py
dataloader = loader(dataset,
batch_size=batch_size,
# num_workers=nw,
num_workers=0,#这里设置为0，否则提示文件太小
sampler=sampler,
pin_memory=True,
collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn)
return dataloader, dataset
第八部分，安装mandb到Anaconda prompt ,别安装到项目里；如果已经安装了，就卸载；否则报错；
第九部分，训练好了，将权重换成训练好的best.pt
