text_plain_output_1.png
from sklearn.preprocessing import MultiLabelBinarizer

import cv2 as cv

import numpy as np

import os

import re

X = []
Y = []
input_shape = (96, 96, 3)
path_to_subset = f'../input/apparel-images-dataset/'
for folder in os.listdir(path_to_subset):
    for image in os.listdir(os.path.join(path_to_subset, folder)):
        path_to_image = os.path.join(path_to_subset, folder, image)
        image = cv.imread(path_to_image)
        image = cv.resize(image, (input_shape[1], input_shape[0]))
        label = re.findall('\\w+\\_\\w+', path_to_image)[0].split('_')
        X.append(image)
        Y.append(label)
X = np.array(X) / 255.0
Y = np.array(Y)
mlb = MultiLabelBinarizer()
Y = mlb.fit_transform(Y)

print(mlb.classes_)
print(Y[0])