Scan a directory of images to ensure that they are not corrupt for training purposes. Can swap load_image to your local version.

test_images.py

import os
import argparse
import tensorflow as tf

parser = argparse.ArgumentParser()

parser.add_argument("-d", "--directory", dest='directory', required=True, help="the base directory for training data")
args = parser.parse_args()

directory = args.directory

# iterate over files in
# that directory
for filename in os.listdir(directory):
    f = os.path.join(directory, filename)
    # checking if it is a file
    if os.path.isfile(f):
        try:
            img = tf.keras.utils.load_img(f, target_size=(224, 224))
        except OSError as err:
            print("file:", filename)
            print("OS error:", err)
        except Exception as err:
            print("file:", f)
            print("Unexpected: ",err, "type:", type(err))

    #img = image.load_img(img_path, target_size=(height, width))
print('done')