原文：YOLOV8血细胞检测 - 知乎 (zhihu.com)
一、数据集准备
数据集下载参考如下文章
YOLOX算法实现血细胞检测-CSDN博客
voc格式的数据集需要转换成yolo格式
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 = ["RBC", "WBC", "Platelets"]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])/2.0y = (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)in_file = open('Annotations/%s.xml' %image_id)# out_file = open('VOCdevkit/VOC2007/YOLOLabels/%s.txt' %image_id, 'w')out_file = open('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, "VOCdevkit/")
# if not os.path.isdir(data_base_dir):
# os.mkdir(data_base_dir)
# work_sapce_dir = os.path.join(data_base_dir, "VOC2007/")
# if not os.path.isdir(work_sapce_dir):
# os.mkdir(work_sapce_dir)
work_sapce_dir = '.'
data_base_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)
yolov8_images_dir = os.path.join(data_base_dir, "images/")
if not os.path.isdir(yolov8_images_dir):os.mkdir(yolov8_images_dir)
clear_hidden_files(yolov8_images_dir)
yolov8_labels_dir = os.path.join(data_base_dir, "labels/")
if not os.path.isdir(yolov8_labels_dir):os.mkdir(yolov8_labels_dir)
clear_hidden_files(yolov8_labels_dir)
yolov8_images_train_dir = os.path.join(yolov8_images_dir, "train/")
if not os.path.isdir(yolov8_images_train_dir):os.mkdir(yolov8_images_train_dir)
clear_hidden_files(yolov8_images_train_dir)
yolov8_images_test_dir = os.path.join(yolov8_images_dir, "val/")
if not os.path.isdir(yolov8_images_test_dir):os.mkdir(yolov8_images_test_dir)
clear_hidden_files(yolov8_images_test_dir)
yolov8_labels_train_dir = os.path.join(yolov8_labels_dir, "train/")
if not os.path.isdir(yolov8_labels_train_dir):os.mkdir(yolov8_labels_train_dir)
clear_hidden_files(yolov8_labels_train_dir)
yolov8_labels_test_dir = os.path.join(yolov8_labels_dir, "val/")
if not os.path.isdir(yolov8_labels_test_dir):os.mkdir(yolov8_labels_test_dir)
clear_hidden_files(yolov8_labels_test_dir)train_file = open(os.path.join(wd, "yolov8_train.txt"), 'w')
test_file = open(os.path.join(wd, "yolov8_val.txt"), 'w')
train_file.close()
test_file.close()
train_file = open(os.path.join(wd, "yolov8_train.txt"), 'a')
test_file = open(os.path.join(wd, "yolov8_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 datasetif os.path.exists(annotation_path):train_file.write(image_path + '\n')convert_annotation(nameWithoutExtention) # convert labelcopyfile(image_path, yolov8_images_train_dir + voc_path)copyfile(label_path, yolov8_labels_train_dir + label_name)else: # test datasetif os.path.exists(annotation_path):test_file.write(image_path + '\n')convert_annotation(nameWithoutExtention) # convert labelcopyfile(image_path, yolov8_images_test_dir + voc_path)copyfile(label_path, yolov8_labels_test_dir + label_name)
train_file.close()
test_file.close()二、修改配置文件 mydata.yaml 以及 my_yolov8s.yaml
修改图片路径为步骤一生成的路径，更改names为自己数据集的类别名。
修改 nc为自己数据的类别数。
三、YOLOV8训练
python detect_train.py这里只运行了10个 epoch结果保存在 run/detect/train
训练的检测样例如下：
四、YOLOV8测试
yolo predict model=runs/detect/train/weights/best.pt source="BloodImage_00098.jpg"
