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How do I convert a folder of images to a npy file?

I have a folder containing images of gestures. But to make it work on my code I need to change it to X.npy and Y.npy. I looked for many questions regarding this kinda problems but still in the dark. How do I evaluate this? How do I convert the folder to create npy dataset of my own? Is there any code for this or any converter?

I found a piece of code for this purpose on github.
from PIL import Image
import os, sys
import cv2
import numpy as np
'''
Converts all images in a directory to '.npy' format.
Use np.save and np.load to save and load the images.
Use it for training your neural networks in ML/DL projects.
'''
# Path to image directory
path = "/path/to/image/directory/"
dirs = os.listdir( path )
dirs.sort()
x_train=[]
def load_dataset():
# Append images to a list
for item in dirs:
if os.path.isfile(path+item):
im = Image.open(path+item).convert("RGB")
im = np.array(im)
x_train.append(im)
if __name__ == "__main__":
load_dataset()
# Convert and save the list of images in '.npy' format
imgset=np.array(x_train)
np.save("imgds.npy",imgset)

You can refer to the code snippet in the following GitHub repo, that I found in google to convert a folder of images to a npy file:
https://gist.github.com/anilsathyan7/ffb35601483ac46bd72790fde55f5c04
Here in this case entire images in the folder are converted into NumPy array and are appended in a list named x_train.To convert and save this list of images in a single '.npy' format file, we can use the same code snippet:
imgset=np.array(x_train)
np.save("imgds.npy",imgset)
To convert and save this list of images in multiple '.npy' format files, use below code snippet :
imgset=np.array(x_train,dtype=object)
for i in range(len(imgset)):
np.save("imgds"+str(i)+".npy",imgset[i])

How to save/extract dataset from hdf5 and convert into TiFF?

I am trying to import CT scan data into ImageJ/FIJI (There is HDF5 plugin in ImageJ/Fiji, however the synchrotron CT data has so large datasets.. so it was failed to open). The scan data (Image dataset) is saved as dataset into the hdf5 file. So I have to extract image dataset from the hdf5 file, then converted it into the Tiff file.
HdF5 File path is "F:/New_ESRF/SNT_BTO4/SNT_BTO4_S1/SNT_BTO4_S1_1_1pag_db0005_vol.hdf5"
Herein, 'SNT_BTO4_S1_1_1pag_db0005_vol.hdf5' is divided into several datasets, and the image dataset is in here:/entry0000/reconstruction/results/data
At the moment, I accessed to the image dataset using h5py. However, after that, I am in stuck to extract/save the dataset separately from the hdf5 file.
Which code is required to extract the image dataset from the hdf5 file?
After that, I am thinking of using from PIL to Image then convert the image into Tiff file. Can I get any advice on the code for this?
import numpy as np
import h5py
filename = "F:/New_ESRF/SNT_BTO4/SNT_BTO4_S1/SNT_BTO4_S1_1_1pag_db0005_vol.hdf5"
with h5py.File(filename,'r') as hdf:
base_items = list (hdf.items())
print('#Items in the base directory:', base_items)
#entry0000
G1 = hdf.get ('entry0000')
G1_items = list (G1.items())
print('#Items in entry0000', G1_items)
#reconstruction
G11 = G1.get ('/entry0000/reconstruction')
G11_items = list (G11.items())
print('#Items in reconstruction', G11_items)
#results_data
G12 = G11.get ('/entry0000/reconstruction/results')
G12_items = list (G12.items())
print('#Items in results', G12_items)

Extracting image data from an HDF5 file and converting to an image is a "relatively straight forward" 2 step process:
Access the data in the HDF5 file
Convert to an image with cv2 (or PIL)
A simple example is available here: How to extract individual JPEG images from a HDF5 file.
You can apply the same process to your file. Here is some pseudo-code. It's not complete because you don't show the shape of the image dataset (and the shape affects how to read the data). Also, you didn't say how many images are in dataset /entry0000/reconstruction/results/data --- does it have a single image or multiple images. If multiple images, which axis is the image counter?
import h5py
import cv2 ## for image conversion
filename = "F:/New_ESRF/SNT_BTO4/SNT_BTO4_S1/SNT_BTO4_S1_1_1pag_db0005_vol.hdf5"
with h5py.File(filename,'r') as hdf:
# get image dataset
img_ds = hdf['/entry0000/reconstruction/results/data']
print(f'Image Dataset info: Shape={img_ds.shape},Dtype={img_ds.dtype}')
## following depends on dataset shape/schema
## code below assumes images are along axis=0
for i in range(img_ds.shape[0]):
cv2.imwrite(f'test_img_{i:03}.tiff',img_ds[i,:]) # uses slice notation
# alternately load to a numpy array first
img_arr = img_ds[i,:] # slice notation gets [i,:,:,:]
cv2.imwrite(f'test_img_{i:03}.tiff',img_arr)
Note: you don't need to use .get() to get a dataset. You can simply reference the dataset path. Also, when you use a group object, use the relative path from the dataset to the group, not the absolute path. (You should modify your code to reflect these changes.) For example, the following are equivalent
G1 = hdf['entry0000']
## is the same as G1 = hdf.get('entry0000')
G11 = hdf['entry0000/reconstruction']
## is the same as G11 = hdf.get('entry0000/reconstruction')
## OR referencing G1 group object:
G11 = G1['reconstruction']
## is the same as G11 = G1.get('reconstruction')

How to read images using csv in python

I have a csv file that contains three columns namely (image file names, labels(in form of 0,1...), and class names). While another folder contains all the images. I want to read the images using this csv file and use them further for the task of image classification using deep learning models on python.

You can try this:
import pandas as pd
import cv2
import numpy as np
from pathlib import Path
df = pd.read_csv('path_to_csv.csv')
path = Path('path/to/img/dir') # relative path is OK
dataset = df.apply(lambda file_name, label:
return (cv2.imread(path/file_name), label),
axis=1)
dataset = np.asarray(dataset)
Now you'll have dataset that is a numpy matrix with images as column 1 and labels as column 2
I'm using OpenCV to import image. Of course you can use others like pillow too, but OpenCV is faster than pillow

How to load an image dataset in scikit-learn?

I have collected a group of images that I want to train a model on.
How do I load the image dataset? I have a folder of training data with two folders in it denoting the two different kinds of objects. How would I go about loading this data set and then training a model?

this might help you to load your dataset into data variable from a single folder of images
import cv2
import os
import numpy as np
path = 'path to your dataset'
list_of_files = os.listdir(path)
data = np.empty(0)
for i in list_of_files:
x = cv2.imread(os.path.join(path+i))
data.append(x)

How to convert multiple mha images to nii

I would like to convert about 300 .mha images into .nii format. Which are under subfolders . I have successfully converted one image but in order to go each folder and convert, its a tidious task. Please suggest me code that is functioning with sitk library
import SimpleITK as sitk
import matplotlib.pyplot as plt
import numpy as np
import os
OUTPUT_DIR = '/home/user/Downloads/'
Image = sitk.ReadImage('Input dir')
print(Image.GetPixelIDTypeAsString())
sitk.WriteImage(Image, os.path.join(OUTPUT_DIR, 'Flair.nii'))

Use python's os.walk method to get all the files in a directory tree. Then select out only the ones with the '.mha' suffix.
Here's an example of how it works:
https://www.tutorialspoint.com/python/os_walk.htm
Or you can try and decipher the os library documentation:
https://docs.python.org/3/library/os.html