image_output_1.png
import cv2

import matplotlib.image as mpimg

import matplotlib.pyplot as plt

import os

import cv2
import numpy as np
import os
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from sklearn.metrics import roc_curve, auc, accuracy_score, precision_score, recall_score, f1_score, roc_auc_score
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, roc_auc_score, roc_curve, confusion_matrix
import seaborn as sns
EPOCHS = 10
IMG_WIDTH = 30
IMG_HEIGHT = 30
NUM_CATEGORIES = 43
TEST_SIZE = 0.4

data_dir = '/kaggle/input/gstrb-dataset/gtsrb'
IMAGES = []
LABELS = []
for folder in os.listdir(data_dir):
    folder_path = os.path.join(data_dir, folder)
    if os.path.isdir(folder_path):
        for image_file in os.listdir(folder_path):
            image = cv2.imread(os.path.join(folder_path, image_file), cv2.IMREAD_ANYCOLOR)
            image = cv2.resize(image, (IMG_WIDTH, IMG_HEIGHT), interpolation=cv2.INTER_AREA)
            IMAGES.append(image)
            LABELS.append(int(folder))

image = mpimg.imread('/kaggle/input/gstrb-dataset/gtsrb/0/00000_00004.ppm')
plt.imshow(image)
plt.axis('off')
plt.show()