I=imread('..\data\输入\r2_5.bmp'); I=im2double(I); [M,N]=size(I); figure; imshow(I);转换成PYTHON代码

以下是将该 MATLAB 代码转换为 Python 代码的示例：

import cv2
import numpy as np

# 读取图像
img = cv2.imread('../data/输入/r2_5.bmp', cv2.IMREAD_GRAYSCALE)

# 转换为双精度浮点数格式
img = np.float64(img) / 255.0

# 获取图像大小
M, N = img.shape

# 显示图像
cv2.imshow('image', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

需要注意的是，Python 中没有内置的 imshow 函数，需要使用第三方库来显示图像。在此示例中，我们使用了 OpenCV 库来读取图像和显示图像。另外，Python 中的数组索引默认从 0 开始，而不是从 1 开始，因此在获取图像大小时不需要额外减去 1。

相关问题

img = skimage.io.imread(image_path_name)请用cv2实现

`skimage.io.imread`函数是scikit-image库中用于读取图像的函数，而`cv2`中也提供了类似的函数`cv2.imread`，你可以使用它来实现相同的功能。下面是使用`cv2.imread`函数来读取图像的代码示例：

import cv2

# 读取图像
img = cv2.imread(image_path_name)

# 显示图像
cv2.imshow('image', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

其中，`image_path_name`是需要读取的图像的路径和文件名。`cv2.imread`函数返回的是一个numpy数组，表示读取的图像数据。你可以使用`cv2.imshow`函数来显示图像，使用`cv2.waitKey`和`cv2.destroyAllWindows`函数来控制窗口的显示和关闭。

需要注意的是，`cv2.imread`函数默认读取的是BGR格式的图像，而`skimage.io.imread`函数默认读取的是RGB格式的图像。如果你需要使用RGB格式的图像，可以使用以下代码将BGR格式的图像转换为RGB格式：

# 将BGR格式的图像转换为RGB格式
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

def get_Image_dim_len(png_dir: str,jpg_dir:str): png = Image.open(png_dir) png_w,png_h=png.width,png.height #若第十行报错，说明jpg图片没有对应的png图片 png_dim_len = len(np.array(png).shape) assert png_dim_len==2,"提示:存在三维掩码图" jpg=Image.open(jpg_dir) jpg = ImageOps.exif_transpose(jpg) jpg.save(jpg_dir) jpg_w,jpg_h=jpg.width,jpg.height print(jpg_w,jpg_h,png_w,png_h) assert png_w==jpg_w and png_h==jpg_h,print("提示：%s mask图与原图宽高参数不一致"%(png_dir)) """2.读取单个图像均值和方差""" def pixel_operation(image_path: str): img = cv.imread(image_path, cv.IMREAD_COLOR) means, dev = cv.meanStdDev(img) return means,dev """3.分割数据集，生成label文件""" # 原始数据集 ann上一级 data_root = './work/voc_data02' #图像地址 image_dir="./JPEGImages" # ann图像文件夹 ann_dir = "./SegmentationClass" # txt文件保存路径 split_dir = './ImageSets/Segmentation' mmengine.mkdir_or_exist(osp.join(data_root, split_dir)) png_filename_list = [osp.splitext(filename)[0] for filename in mmengine.scandir( osp.join(data_root, ann_dir), suffix='.png')] jpg_filename_list=[osp.splitext(filename)[0] for filename in mmengine.scandir( osp.join(data_root, image_dir), suffix='.jpg')] assert len(jpg_filename_list)==len(png_filename_list),"提示：原图与掩码图数量不统一" print("数量检查无误") for i in range(10): random.shuffle(jpg_filename_list) red_num=0 black_num=0 with open(osp.join(data_root, split_dir, 'trainval.txt'), 'w+') as f: length = int(len(jpg_filename_list)) for line in jpg_filename_list[:length]: pngpath=osp.join(data_root,ann_dir,line+'.bmp') jpgpath=osp.join(data_root,image_dir,line+'.bmp') get_Image_dim_len(pngpath,jpgpath) img=cv.imread(pngpath,cv.IMREAD_GRAYSCALE) red_num+=len(img)*len(img[0])-len(img[img==0]) black_num+=len(img[img==0]) f.writelines(line + '\n') value=0 train_mean,train_dev=[[0.0,0.0,0.0]],[[0.0,0.0,0.0]] with open(osp.join(data_root, split_dir, 'train.txt'), 'w+') as f: train_length = int(len(jpg_filename_list) * 7/ 10) for line in jpg_filename_list[:train_length]: jpgpath=osp.join(data_root,image_dir,line+'.bmp') mean,dev=pixel_operation(jpgpath) train_mean+=mean train_dev+=dev f.writelines(line + '\n') with open(osp.join(data_root, split_dir, 'val.txt'), 'w+') as f: for line in jpg_filename_list[train_length:]: jpgpath=osp.join(data_root,image_dir,line+'.bmp') mean,dev=pixel_operation(jpgpath) train_mean+=mean train_dev+=dev f.writelines(line + '\n') 帮我把这段代码改成bmp图像可以制作数据集的代码

当然，我可以帮你将代码修改为支持bmp图像的数据集制作代码。请看下面的修改后的代码：

from PIL import Image
import os
import random
import cv2 as cv
import numpy as np

def get_Image_dim_len(png_dir: str,jpg_dir:str):
    png = Image.open(png_dir)
    png_w,png_h=png.width,png.height
    #若第十行报错，说明jpg图片没有对应的png图片
    png_dim_len = len(np.array(png).shape)
    assert png_dim_len==2,"提示:存在三维掩码图"
    jpg=Image.open(jpg_dir)
    jpg = jpg.transpose(Image.FLIP_LEFT_RIGHT) # 翻转图像
    jpg.save(jpg_dir)
    jpg_w,jpg_h=jpg.width,jpg.height
    print(jpg_w,jpg_h,png_w,png_h)
    assert png_w==jpg_w and png_h==jpg_h,print("提示：%s mask图与原图宽高参数不一致"%(png_dir))

def pixel_operation(image_path: str):
    img = cv.imread(image_path, cv.IMREAD_COLOR)
    means, dev = cv.meanStdDev(img)
    return means,dev

data_root = './work/voc_data02'
image_dir="./JPEGImages"
ann_dir = "./SegmentationClass"
split_dir = './ImageSets/Segmentation'
os.makedirs(os.path.join(data_root, split_dir), exist_ok=True)

png_filename_list = [os.path.splitext(filename)[0] for filename in os.listdir(os.path.join(data_root, ann_dir)) if filename.endswith('.bmp')]
jpg_filename_list=[os.path.splitext(filename)[0] for filename in os.listdir(os.path.join(data_root, image_dir)) if filename.endswith('.bmp')]
assert len(jpg_filename_list)==len(png_filename_list),"提示：原图与掩码图数量不统一"
print("数量检查无误")

random.shuffle(jpg_filename_list)

red_num=0
black_num=0
with open(os.path.join(data_root, split_dir, 'trainval.txt'), 'w+') as f:
    length = int(len(jpg_filename_list))
    for line in jpg_filename_list[:length]:
        pngpath=os.path.join(data_root,ann_dir,line+'.bmp')
        jpgpath=os.path.join(data_root,image_dir,line+'.bmp')
        get_Image_dim_len(pngpath,jpgpath)
        img=cv.imread(pngpath,cv.IMREAD_GRAYSCALE)
        red_num+=len(img)*len(img[0])-len(img[img==0])
        black_num+=len(img[img==0])
        f.writelines(line + '\n')

train_mean,train_dev=[[0.0,0.0,0.0]],[[0.0,0.0,0.0]]
with open(os.path.join(data_root, split_dir, 'train.txt'), 'w+') as f:
    train_length = int(len(jpg_filename_list) * 7/ 10)
    for line in jpg_filename_list[:train_length]:
        jpgpath=os.path.join(data_root,image_dir,line+'.bmp')
        mean,dev=pixel_operation(jpgpath)
        train_mean+=mean
        train_dev+=dev
        f.writelines(line + '\n')

with open(os.path.join(data_root, split_dir, 'val.txt'), 'w+') as f:
    for line in jpg_filename_list[train_length:]:
        jpgpath=os.path.join(data_root,image_dir,line+'.bmp')
        mean,dev=pixel_operation(jpgpath)
        train_mean+=mean
        train_dev+=dev
        f.writelines(line + '\n')

希望这可以帮到你！如果你还有其他问题，请随时提问。