Tensorflow Keras error: Unknown image file format. One of JPEG, PNG, GIF, BMP required

Question

i'm training a classifier and i made sure all the pictures are jpg but still, this error occurs: InvalidArgumentError: Unknown image file format. One of JPEG, PNG, GIF, BMP required. [[{{node decode_image/DecodeImage}}]] [[IteratorGetNext]] [Op:__inference_train_function_1481]

i tried training on a smaller dataset and also they were all jpg and there was no problem

this is the code:

import numpy as np
import tensorflow as tf
from tensorflow import keras

dataset = keras.preprocessing.image_dataset_from_directory(
  '/content/drive/MyDrive/fi_dataset/train', batch_size=64, image_size=(200, 200))

dense = keras.layers.Dense(units=16)
inputs = keras.Input(shape=(None, None, 3))

from tensorflow.keras import layers

x = CenterCrop(height=150, width=150)(inputs)
x = Rescaling(scale=1.0 / 255)(x)

x = layers.Conv2D(filters=32, kernel_size=(3, 3), activation="relu")(x)
x = layers.MaxPooling2D(pool_size=(3, 3))(x)
x = layers.Conv2D(filters=32, kernel_size=(3, 3), activation="relu")(x)
x = layers.MaxPooling2D(pool_size=(3, 3))(x)
x = layers.Conv2D(filters=32, kernel_size=(3, 3), activation="relu")(x)

x = layers.GlobalAveragePooling2D()(x)

num_classes = 1
outputs = layers.Dense(num_classes, activation="sigmoid")(x)

model = keras.Model(inputs=inputs, outputs=outputs)

data = np.random.randint(0, 256, size=(64, 200, 200, 3)).astype("float32")
processed_data = model(data)

model.compile(optimizer='adam',
              loss='binary_crossentropy',
               metrics=[keras.metrics.binary_accuracy],)

history=model.fit(dataset, epochs=10)

Answer 1

Actually it might have an extension name jpg but be in say a tiff format. To take it a stepfurther you can add some code ...

If you want to check for a type of image, not an extension name, then try this modificated version of code above:

import os
import cv2
import imghdr

def check_images( s_dir, ext_list):
    bad_images=[]
    bad_ext=[]
    s_list= os.listdir(s_dir)
    for klass in s_list:
        klass_path=os.path.join (s_dir, klass)
        print ('processing class directory ', klass)
        if os.path.isdir(klass_path):
            file_list=os.listdir(klass_path)
            for f in file_list:               
                f_path=os.path.join (klass_path,f)
                tip = imghdr.what(f_path)
                if ext_list.count(tip) == 0:
                  bad_images.append(f_path)
                if os.path.isfile(f_path):
                    try:
                        img=cv2.imread(f_path)
                        shape=img.shape
                    except:
                        print('file ', f_path, ' is not a valid image file')
                        bad_images.append(f_path)
                else:
                    print('*** fatal error, you a sub directory ', f, ' in class directory ', klass)
        else:
            print ('*** WARNING*** you have files in ', s_dir, ' it should only contain sub directories')
    return bad_images, bad_ext

source_dir =r'c:\temp\people\storage'
good_exts=['jpg', 'png', 'jpeg', 'gif', 'bmp' ] # list of acceptable extensions
bad_file_list, bad_ext_list=check_images(source_dir, good_exts)
if len(bad_file_list) !=0:
    print('improper image files are listed below')
    for i in range (len(bad_file_list)):
        print (bad_file_list[i])
else:
    print(' no improper image files were found')

Python has many modules in its standard library, and one that helps here is imghdr. It lets you identify what is the image type that is contained in a file, byte stream or path-like object. The imghdr can recognize the following image types: rgb, gif, pbm, pgm, ppm, tiff, rast, xbm, jpeg / jpg, bmp, png, webp and exr.