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])/2.0
    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('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').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, "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 dataset
        if os.path.exists(annotation_path):
            train_file.write(image_path + '\n')
            convert_annotation(nameWithoutExtention) # convert label
            copyfile(image_path, yolov8_images_train_dir + voc_path)
            copyfile(label_path, yolov8_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, 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"