Cats and Dogs Kaggle dataset corrupt jpeg images

I’m just trying to train the Kaggle Cats and Dogs dataset but I keep getting the error

tensorflow.python.framework.errors_impl.InvalidArgumentError: Input size should match (header_size + row_size * abs_height) but they differ by 2

[[{{node decode_image/DecodeImage}}]] [Op:IteratorGetNext]

Along with:

Corrupt JPEG data: 242 extraneous bytes before marker 0xd9

Corrupt JPEG data: 217 extraneous bytes before marker 0xd9

Corrupt JPEG data: 133 extraneous bytes before marker 0xd9

Even though I implemented 3 different methods to check for and remove corrupt image files… So I have no idea why I’m still getting this error. Please help!

CODE:

#check for corrupted files (method 1)
from pathlib import Path
import imghdr
classes = [‘Cat’,’Dog’]
image_extensions = [“.png”, “.jpg”] # add there all your images file extensions
for item in classes:
data_dir = f’kagglecatsanddogs_5340/PetImages/{item}’
img_type_accepted_by_tf = [“bmp”, “gif”, “jpeg”, “png”]
for filepath in Path(data_dir).rglob(“*”):
if filepath.suffix.lower() in image_extensions:
img_type = imghdr.what(filepath)
if img_type is None:
print(f”{filepath} is not an image”)
# remove image
os.remove(filepath)
print(f”successfully removed {filepath}”)
elif img_type not in img_type_accepted_by_tf:
print(f”{filepath} is a {img_type}, not accepted by TensorFlow”)
try:
img = Image.open(filepath) # open the image file
img.verify() # verify that it is, in fact an image
# print(‘valid image’)
except Exception:
print(‘Bad file:’, filepath)
# method 2
import glob
img_paths = glob.glob(os.path.join(‘kagglecatsanddogs_5340/PetImages/Dog’,’*/*.*’)) # assuming you point to the directory containing the label folders.
bad_paths = []
for image_path in img_paths:
try:
img_bytes = tf.io.read_file(image_path)
decoded_img = tf.decode_image(img_bytes)
except Exception:
print(f”Found bad path {image_path}”)
bad_paths.append(image_path)

print(f”{image_path}: OK”)
print(“BAD PATHS:”)
for bad_path in bad_paths:
print(f”{bad_path}”)
# method 3
num_skipped = 0
for folder_name in (“Cat”, “Dog”):
folder_path = os.path.join(“kagglecatsanddogs_5340/PetImages”, folder_name)
for fname in os.listdir(folder_path):
fpath = os.path.join(folder_path, fname)
try:
fobj = open(fpath, “rb”)
is_jfif = tf.compat.as_bytes(“JFIF”) in fobj.peek(10)
finally:
fobj.close()
if not is_jfif:
num_skipped += 1
# Delete corrupted image
os.remove(fpath)
print(“Deleted %d images” % num_skipped)

def normalize(x,y):
x = tf.cast(x,tf.float32) / 255.0
return x, y
def convert_to_categorical(input):
if input == 1:
return “Dog”
else:
return “Cat”
def to_list(ds):
ds_list = []
for sample in ds:
image, label = sample
ds_list.append((image, label))
return ds_list

# load dataset
directory = ‘archive/PetImages’
ds_train = tf.keras.utils.image_dataset_from_directory(
directory,
labels=’inferred’,
label_mode=’binary’,
color_mode=’rgb’,
batch_size=1,
shuffle=False,
validation_split=0.3,
subset=’training’,
image_size=(180,180)
)
ds_test = tf.keras.utils.image_dataset_from_directory(
directory,
labels=’inferred’,
label_mode=’binary’,
color_mode=’rgb’,
batch_size=1,
shuffle=False,
validation_split=0.3,
subset=’validation’,
image_size=(180,180)
)

# normalize data
ds_train.map(normalize)
ds_test.map(normalize)
# plot 10 random images from training set
num = len(ds_train)
ds_train_list = to_list(ds_train)
for i in range(1,11):
random_index = np.random.randint(num)
img, label = ds_train_list[random_index]
label = convert_to_categorical(np.array(label))
img = np.reshape(img,(300,300,3))
plt.subplot(2,5,i)
plt.imshow(img)
plt.title(label)
plt.savefig(‘figures/example_images.png’)