【Yolov7】Crea tu propio conjunto de datos

1. Preparar datos

2. Etiquetado

Labelimg se utiliza aquí para la anotación de datos (el uso de labelimg no se explica aquí)

Una vez completada la anotación, se obtienen los resultados de la anotación, una classes.txto varias imágenes.标注信息txt

3. Divida el conjunto de datos

Primero, simplemente cree algunas carpetas y archivos, de la siguiente manera:

primero, saque clases.txt por separado y luego use el script Python para dividir rápidamente el conjunto de datos.

# 将图片和标注数据按比例切分为 训练集和测试集
import shutil
import random
import os

# 原始路径
image_original_path = "图像文件夹路径"
label_original_path = "标注结果的路径"		// 该路径下不要有classes.txt

cur_path = os.getcwd()

# 训练集路径
train_image_path = os.path.join(cur_path, "images/train/")
train_label_path = os.path.join(cur_path, "labels/train/")

# 验证集路径
val_image_path = os.path.join(cur_path, "images/val/")
val_label_path = os.path.join(cur_path, "labels/val/")

# 测试集路径
test_image_path = os.path.join(cur_path, "images/test/")
test_label_path = os.path.join(cur_path, "labels/test/")

# 训练集目录
list_train = os.path.join(cur_path, "train.txt")
list_val = os.path.join(cur_path, "val.txt")
list_test = os.path.join(cur_path, "test.txt")

train_percent = 0.8
val_percent = 0.1
test_percent = 0.1


def del_file(path):
    for i in os.listdir(path):
        file_data = path + "\\" + i
        os.remove(file_data)


def mkdir():
    if not os.path.exists(train_image_path):
        os.makedirs(train_image_path)
    else:
        del_file(train_image_path)
    if not os.path.exists(train_label_path):
        os.makedirs(train_label_path)
    else:
        del_file(train_label_path)

    if not os.path.exists(val_image_path):
        os.makedirs(val_image_path)
    else:
        del_file(val_image_path)
    if not os.path.exists(val_label_path):
        os.makedirs(val_label_path)
    else:
        del_file(val_label_path)

    if not os.path.exists(test_image_path):
        os.makedirs(test_image_path)
    else:
        del_file(test_image_path)
    if not os.path.exists(test_label_path):
        os.makedirs(test_label_path)
    else:
        del_file(test_label_path)


def clearfile():
    if os.path.exists(list_train):
        os.remove(list_train)
    if os.path.exists(list_val):
        os.remove(list_val)
    if os.path.exists(list_test):
        os.remove(list_test)


def main():
    mkdir()
    clearfile()

    file_train = open(list_train, 'w')
    file_val = open(list_val, 'w')
    file_test = open(list_test, 'w')

    total_txt = os.listdir(label_original_path)
    num_txt = len(total_txt)
    list_all_txt = range(num_txt)

    num_train = int(num_txt * train_percent)
    num_val = int(num_txt * val_percent)
    num_test = num_txt - num_train - num_val

    train = random.sample(list_all_txt, num_train)
    # train从list_all_txt取出num_train个元素
    # 所以list_all_txt列表只剩下了这些元素
    val_test = [i for i in list_all_txt if not i in train]
    # 再从val_test取出num_val个元素，val_test剩下的元素就是test
    val = random.sample(val_test, num_val)

    print("训练集数目：{}, 验证集数目：{}, 测试集数目：{}".format(len(train), len(val), len(val_test) - len(val)))
    for i in list_all_txt:
        name = total_txt[i][:-4]

        srcImage = image_original_path + name + '.jpg'
        srcLabel = label_original_path + name + ".txt"

        if i in train:
            dst_train_Image = train_image_path + name + '.jpg'
            dst_train_Label = train_label_path + name + '.txt'
            shutil.copyfile(srcImage, dst_train_Image)
            shutil.copyfile(srcLabel, dst_train_Label)
            file_train.write(dst_train_Image + '\n')
        elif i in val:
            dst_val_Image = val_image_path + name + '.jpg'
            dst_val_Label = val_label_path + name + '.txt'
            shutil.copyfile(srcImage, dst_val_Image)
            shutil.copyfile(srcLabel, dst_val_Label)
            file_val.write(dst_val_Image + '\n')
        else:
            dst_test_Image = test_image_path + name + '.jpg'
            dst_test_Label = test_label_path + name + '.txt'
            shutil.copyfile(srcImage, dst_test_Image)
            shutil.copyfile(srcLabel, dst_test_Label)
            file_test.write(dst_test_Image + '\n')

    file_train.close()
    file_val.close()
    file_test.close()

if __name__ == "__main__":
    main()

Una vez completada la partición, el conjunto de datos debería verse así

• 
  Hay tres carpetas debajo de la carpeta de imágenes , cada carpeta tiene las imágenes correspondientes.
  
  
• 
  Hay tres carpetas debajo de la carpeta de etiquetas , cada carpeta tiene los datos de anotación correspondientes.
  
  
• El archivo train.txt
  guarda la ruta de la imagen de entrenamiento y
  
  se crea el conjunto de datos.