Python处理mat文件的三种方式小结

import scipy.io as scio
data = scio.loadmat(‘example.mat')
data[‘loss']

import h5py
data=h5py.loadmat('example.mat')   
data['loss'][:]

import mat73
data=mat73.loadmat(path)   
data['loss']

Microsoft Access Table Shortcut (.mat)

from scipy.io import loadmat  
m = loadmat(r"E:\data\yolov5_dbb\ITC_VD_Training_Testing_set\Training\GT/00064.mat")
 
daaa=m.keys()
print(daaa)
 
print(m['x00064'])

import pandas as pd
 
df = pd.DataFrame(m['H_BETA'])
 
df.head()

import glob 
import cv2
import numpy as np
 
r_path=r'C:\File\2021-11'
 
files=glob.glob(r_path+"/*.mat")
from scipy.io import loadmat 
 
# 数据矩阵转图片的函数
def MatrixToImage(data):
    # data = data * 255
    new_im = data.astype(np.uint8)
    return new_im 
 
for file in files:
 
    if "2018" in file:
        continue
    print(file)
    m = loadmat(file)
 
    daaa = m.keys()
 
    data=m['groundTruth']
 
    print(data)
 
    new_im = MatrixToImage(data)
    cv2.imshow("asdf",new_im)
    cv2.waiTKEy()
 
    # print(data)