How to load many images eficiently from folder using openCV

Question

I try to create my own image datasets for machine learning.

The workflow I thought is the following :

①Load all image files as an array in the folder.

②Label the loaded images

③Split loaded image files to image_data and label_data.

④Finally, split image_data to image_train_data and image_test_data and split label_data to label_train_data and label_test_data.

However, it doesn't go well in the first step(①).

How can I load all image data efficiently?

And if you implement an image data set for machine learning according to this workflow, how you handle it?

I wrote following code.

cat_im = cv2.imread("C:\\Users\\path\\cat1.jpg")

But, Am I forced writing \\cat1.jpg , \\cat2.jpg ,\\cat3.jpg.....?

Answer 1

## you can find all images like extenstion
import os,cv2
import glob
all_images_path= glob.glob('some_folder\images\*png') ## it gives path of images as list   

## then you can loop over all files
loaded_images  = []
for image_path in all_images_path:
    image = cv2.imread(image_path)
    loaded_images.append(image)

## lets assume your labels are just name of files and its like cat1.png,cat2.png etc
labels = []
for image_path in all_images_path:
    labels.append(os.basename(image_path))