关于自己学习yolov5中的一些总结

关于自己学习yolov5中的一些总结

关于自己训练yolov5的小总结

–下载所需要的包–
打开Anaconda Prompt (anaconda3)
激活所需环境 activate 环境名
下载所缺失的包 （四个镜像下载源，仅更改最后simple/后的内容即可，simple/后有空格不能省略）

pip install -i / 包名版本号
pip install -i / cudatoolkit11.2
pip install -i / cudatoolkit11.2
pip install -i / cudatoolkit11.2

pip install -i / pyqt5-tools
pip install -i pyqt5-tools
pip install -i / pyqt5-tools
pip install -i / tensorflow

–查看可视化结果： –
进入paddle环境后进入d盘 输入d:
进入指定文件夹 输入cd PycharmPycharmProjectsyolov5-5.0
输入 tensorboard --logdir=runs/train 后复制链接localhost:6006/ 查看可视化结果
(如果代码已经训练完成 输入tensorboard --logdir=runs后复制链接localhost:6006/ 查看可视化结果)

—学习如何训练自己的模型教程—

—竹片_图 文件夹—
都是未标记的原始文件。
1,2,3分别是三种不同颜色的无缺陷图片，4是缺陷图片

–标记文件并训练—
打开VOCdevkit，删除下面的所有文件
运行zhuan1.py，VOCdevkit下会生成三个文件夹
将未标记文件放在VOC2007下的JPEGImages里
双击运行lambleImg.py文件
点击打开目录，是你所需要标记的文件目录，即VOC2007下的JPEGImages
点击改变存放目录，将标记结果保存到VOC2007下的Annotations
开始标记，最好直到标记结束再退出
再次运行zhuan1.py, VOCdevkit下的各个文件夹下自动分出训练集和测试集

–关于train.py—
第462行改变训练轮数

关于detect.py–
每次train.py训练完成后需要修改detect.py中第151行，runs/train/exp/weights/best.pt ，将exp改成最新运行的exp(数字)，然后将所需要测试的图片放进data下的image文件夹中，运行detect.py 得到训练结果，可以在runs/detect/exp(最新)下查看运行结果

我的zhuan1.py文件

ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
import random
from shutil import copyfileclasses = ['wu','lasi','lie','kong','mei','huang','hei','sanjiao']
# classes=["ball"]TRAIN_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('VOCdevkit/VOC2007/Annotations/%s.xml' % image_id)out_file = open('VOCdevkit/VOC2007/YOLOLabels/%s.txt' % image_id, 'w')tree = ET.parse(in_file)root = t()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, "VOCdevkit/")
if not os.path.isdir(data_base_dir):os.mkdir(data_base_dir)
work_sapce_dir = os.path.join(data_base_dir, "D:/Pycharm/PycharmProjects/yolov5-5.0/VOCdevkit/VOC2007/")
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, "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, ""), 'w')
test_file = open(os.path.join(wd, ""), 'w')
train_file.close()
test_file.close()
train_file = open(os.path.join(wd, ""), 'a')
test_file = open(os.path.join(wd, ""), 'a')
list_imgs = os.listdir(image_dir)  # list image files
prob = random.randint(1, 100)
print("Probability: %d" % prob)
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)print("Probability: %d" % prob)if (prob < TRAIN_RATIO):  # train datasetif ists(annotation_path):train_file.write(image_path + 'n')convert_annotation(nameWithoutExtention)  # convert labelcopyfile(image_path, yolov5_images_train_dir + voc_path)copyfile(label_path, yolov5_labels_train_dir + label_name)else:  # test datasetif ists(annotation_path):test_file.write(image_path + 'n')convert_annotation(nameWithoutExtention)  # convert labelcopyfile(image_path, yolov5_images_test_dir + voc_path)copyfile(label_path, yolov5_labels_test_dir + label_name)
train_file.close()
test_file.close()