YOLOv5 分类模型 数据集加载

YOLOv5 分类模型 数据集加载

flyfish

数据集的加载 python实现,不使用torch库

简化实现

py 复制代码
import os
import os.path
from typing import Any, Callable, cast, Dict, List, Optional, Tuple, Union


class DatasetFolder:

    def __init__(
        self,
        root: str,

    ) -> None:
        self.root=root
        classes, class_to_idx = self.find_classes(self.root)
        samples = self.make_dataset(self.root, class_to_idx)
        
        self.classes = classes
        self.class_to_idx = class_to_idx
        self.samples = samples
        self.targets = [s[1] for s in samples]


    @staticmethod
    def make_dataset(
        directory: str,
        class_to_idx: Optional[Dict[str, int]] = None,

    ) -> List[Tuple[str, int]]:
 
        directory = os.path.expanduser(directory)

        if class_to_idx is None:
            _, class_to_idx = self.find_classes(directory)
        elif not class_to_idx:
            raise ValueError("'class_to_index' must have at least one entry to collect any samples.")



        instances = []
        available_classes = set()
        for target_class in sorted(class_to_idx.keys()):
            class_index = class_to_idx[target_class]
            target_dir = os.path.join(directory, target_class)
            if not os.path.isdir(target_dir):
                continue
            for root, _, fnames in sorted(os.walk(target_dir, followlinks=True)):
                for fname in sorted(fnames):
                    path = os.path.join(root, fname)
                    if 1:#验证:
                        item = path, class_index
                        instances.append(item)

                        if target_class not in available_classes:
                            available_classes.add(target_class)

        empty_classes = set(class_to_idx.keys()) - available_classes
        if empty_classes:
            msg = f"Found no valid file for the classes {', '.join(sorted(empty_classes))}. "


        return instances

    def find_classes(self, directory: str) -> Tuple[List[str], Dict[str, int]]:
 
        classes = sorted(entry.name for entry in os.scandir(directory) if entry.is_dir())
        if not classes:
            raise FileNotFoundError(f"Couldn't find any class folder in {directory}.")

        class_to_idx = {cls_name: i for i, cls_name in enumerate(classes)}
        return classes, class_to_idx


    def __getitem__(self, index: int) -> Tuple[Any, Any]:

        path, target = self.samples[index]
        sample = self.loader(path)


        return sample, target

    def __len__(self) -> int:
        return len(self.samples)





dataset =  DatasetFolder(root="/media/a/flyfish/test");

print(dataset)
print("dataset.targets:",dataset.targets)
print("dataset.classes:",dataset.classes)
print("samples:",dataset.samples)

find_classes 将标签索引和标签内容对应

0,1,2是标签索引
'n01440764', 'n01443537', 'n01484850'是类别名字也是文件夹名字

按照升序排序

复制代码
dataset.targets: [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2]
dataset.classes: ['n01440764', 'n01443537', 'n01484850']

样本中一个是图像文件的绝对路径,后面的是标签

复制代码
samples: [('/media/a/flyfish/test/n01440764/ILSVRC2012_val_00000293.JPEG', 0),
          ('/media/a/flyfish/test/n01440764/ILSVRC2012_val_00002138.JPEG', 0),
          ('/media/a/flyfish/test/n01440764/ILSVRC2012_val_00003014.JPEG', 0),
          ('/media/a/flyfish/test/n01440764/ILSVRC2012_val_00006697.JPEG', 0),
          ('/media/a/flyfish/test/n01443537/ILSVRC2012_val_00000236.JPEG', 1),
          ('/media/a/flyfish/test/n01443537/ILSVRC2012_val_00000262.JPEG', 1),
          ('/media/a/flyfish/test/n01443537/ILSVRC2012_val_00000307.JPEG', 1),
          ('/media/a/flyfish/test/n01443537/ILSVRC2012_val_00000994.JPEG', 1),
          ('/media/a/flyfish/test/n01484850/ILSVRC2012_val_00002338.JPEG', 2),
          ('/media/a/flyfish/test/n01484850/ILSVRC2012_val_00002752.JPEG', 2),
          ('/media/a/flyfish/test/n01484850/ILSVRC2012_val_00004311.JPEG', 2),
          ('/media/a/flyfish/test/n01484850/ILSVRC2012_val_00004329.JPEG', 2)]

可以功能丰富一些,例如检测文件的扩展名是否是支持的图像文件

py 复制代码
IMG_EXTENSIONS = (".jpg", ".jpeg", ".png", ".ppm", ".bmp", ".pgm", ".tif", ".tiff", ".webp")

def has_file_allowed_extension(filename: str, extensions: Union[str, Tuple[str, ...]]) -> bool:
    """检查文件是否为允许的扩展名
    """
    return filename.lower().endswith(extensions if isinstance(extensions, str) else tuple(extensions))

def is_image_file(filename: str) -> bool:
    return has_file_allowed_extension(filename, IMG_EXTENSIONS)

测试

py 复制代码
r=is_image_file("/media/a/flyfish/data/imagewoof/val/n02086240/1.jpeg");

print(r)#True

r=is_image_file("/media/a/flyfish/data/imagewoof/val/n02086240/1.txt");

print(r)#False
相关推荐
毕竟是shy哥3 小时前
计算YOLO数据集中每个类的目标数
算法·yolo·机器学习
仙女修炼史12 小时前
gradcam在yolo中的应用
人工智能·python·yolo
码农幻想梦15 小时前
yolov8入门篇
yolo
AI视觉网奇16 小时前
拓竹打印机获取相机图片
yolo·3d打印
YOLO数据集集合19 小时前
中国城市建筑功能矢量数据集 | 建筑功能 矢量数据 城市规划 空间分析 Shapefile 多分类 GIS数据集8017期
人工智能·分类·数据挖掘·空间分析·城市规划·中国城市建筑·功能矢量
qq_25294131682 天前
航拍羊群目标检测数据集 | 航拍羊群 智慧畜牧业 动物检测 无人机巡检5010期
人工智能·yolo·目标检测·计算机视觉·无人机·羊群识别·羊群
java1234_小锋3 天前
YOLO26 计算机视觉 - 训练自己的 YOLO26 模型
人工智能·yolo·机器学习·计算机视觉·yolo26
向哆哆3 天前
打击罂粟种植检测数据集分享(适用于YOLO系列深度学习分类检测任务)
深度学习·yolo·目标检测·分类
空堂与归4 天前
图像识别总是准确率低?用卷积神经网络CNN实现高效分类
人工智能·深度学习·分类·cnn
jay神4 天前
一文讲清楚YOLOv26模型
人工智能·深度学习·yolo·机器学习·计算机视觉