Unidentified image error

PIL.UnidentifiedImageError: cannot identify image file: .ai #4392

Pillow seems to not be able to load .ai file using Pillow 7.0

The text was updated successfully, but these errors were encountered:

Pillow doesn’t support AI format. Here’s the list of supported formats:

@hugovk I’m getting this error on a PNG image, that should be supported right?

@hugovk ah ok, the error happens for a PNG image with size 0 bytes !

Getting»UnidentifiedImageError» error while executing the following code
Using Pillow 7.1.2

======================================
UnidentifiedImageError Traceback (most recent call last)
in ()
1 import matplotlib.pyplot as plt
—-> 2 img = Image.open(response.raw)
3 #plt.imshow(img)

/usr/local/lib/python3.6/dist-packages/PIL/Image.py in open(fp, mode)
2894 warnings.warn(message)
2895 raise UnidentifiedImageError(
-> 2896 «cannot identify image file %r» % (filename if filename else fp)
2897 )
2898

UnidentifiedImageError: cannot identify image file

@sumeysh I’m unable to replicate your problem. Could you make check that you’re actually receiving a valid image from requests? I would suggest running this code and taking a look at out.jpg

Getting UnidentifiedImageError: cannot identify image file
while executing this code

@anis-agwan Please open a new issue, thank you.

how to rectify the error @anis-agwan

@krovel the error from @anis-agwan is currently under discussion in #4678. However, if are not having a problem with keras_preprocessing and have a just generic UnidentifiedImageError , please open a new issue, supplying the image and a short self-contained example of the code you are using.

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Источник

OSError: cannot identify image file png #3287

What did you do?

It’s random, but sometimes I have images that are not being read correctly and displaying this error. It can be different common file formats such as png, jpg, jpeg, etc and it displays this error. I am running python 3.7 on a Windows 10.

The text was updated successfully, but these errors were encountered:

Could you post a self-contained code example? For instance, in the code that you have provided so far, I don’t have a value for self.website_cache[entry][0].raw_content .

At the moment, the closest way I can replicate your situation is to fetch data from the URL you have provided, put the value into a BytesIO object, and load it into an image.

I am able to run this code without any problems. Are you able to do so? If you are, then we would need more code to be able to replicate the issue.

This might be hard to test, but here is one image that fail to load. I save the input in wb mode in python there. Maybe it has to do with the way it is reading the raw content from the web. I did open the file in Gimp and it seem to load just fine.

Using the following code you provided I was able to still get the same error message:
File «G:TestNewfoldertest.py», line 6, in
im = Image.open(b)
File «C:UsersOwnerAppDataLocalProgramsPythonPython37libsite-packagesPILImage.py», line 2622, in open
% (filename if filename else fp))
OSError: cannot identify image file

Here is the mod code I did use:

Thanks. The image you have attached does load in Gimp, but does not load in Preview on macOS, or in Chrome. So it is malformed in some way, and we would need to figure out how to get Pillow to accept that data.

image has extra 15 bytes in the beginning and it seems to be truncated. Works that way:

Cool, thanks @kkopachev. So @Mradr, do you think that there is a problem in your code that is creating the extra 15 bytes?

@kkopachev, thanks
@radarhere, no not with in my code that is creating the extra bytes that I know of it. It should the right. I mean if gimp can open it — it can’t be «extra» otherwise the image wouldn’t load, no? That tells me it’s a different format type of some sort would it not? I been shaving the images that fail to load as well to check out later. If it was all of them I would agree it’s my code, but it’s only been a few from a few different sites. I will test them with this code and see what happens. I’m sure it’s extra data, but there would have to be a way to read for that in this case instead of «try/excepting» it all the time for the extra data.

https://www.w3.org/TR/PNG-Structure.html — ‘The first eight bytes of a PNG file always contain the following (decimal) values: 137 80 78 71 13 10 26 10’.

Running the following —

gives ’31 139 8 0 0 0 0 0 0 3 0 59 64 196 191 137 80 78 71 13 10 26 10′. So your file has extra bytes at the beginning, meaning it does not conform to the specification. Gimp is being flexible in it’s interpretation of your file.

@Mradr was this resolved? I’m facing the same issue, with some images I downloaded from the web working and others are giving the same error.

@nadzimo I’d be surprised if part of your problem was also extra bytes at the beginning of images. Try #1510 (comment) to see if you are dealing with truncated images. If not, then I would recommend opening a new issue.

@Mradr did you ever find a problem in your code? The alternative is that this issue is you making a request for Pillow to search through provided image data until it recognises the start of an image.

Okay, without any more responses, I am closing this issue, as directly accessing the image at the URL works. The only thing Pillow doesn’t do in this issue is read a malformed file, which may have been malformed as a result of a bug unrelated to Pillow. Feel free to comment again if you would like to pursue this further.

Источник

Issue

I am trying to train my model (Image classification) using Tensorflow. I keep getting an error when I try to run the following cell:

    hist = model.fit(
        train_generator, 
        epochs=100,
        verbose=1,
        steps_per_epoch=steps_per_epoch,
        validation_data=valid_generator,
        validation_steps=val_steps_per_epoch).history

Error is:

Epoch 1/100
27/31 [=========================>....] - ETA: 1s - loss: 0.7309 - acc: 0.6181
---------------------------------------------------------------------------
UnknownError                              Traceback (most recent call last)
<ipython-input-36-b1c104100211> in <module>
      2 val_steps_per_epoch = np.ceil(valid_generator.samples/valid_generator.batch_size)
      3 
----> 4 hist = model.fit(
      5     train_generator,
      6     epochs=100,

/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
   1098                 _r=1):
   1099               callbacks.on_train_batch_begin(step)
-> 1100               tmp_logs = self.train_function(iterator)
   1101               if data_handler.should_sync:
   1102                 context.async_wait()

/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
    826     tracing_count = self.experimental_get_tracing_count()
    827     with trace.Trace(self._name) as tm:
--> 828       result = self._call(*args, **kwds)
    829       compiler = "xla" if self._experimental_compile else "nonXla"
    830       new_tracing_count = self.experimental_get_tracing_count()

/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
    853       # In this case we have created variables on the first call, so we run the
    854       # defunned version which is guaranteed to never create variables.
--> 855       return self._stateless_fn(*args, **kwds)  # pylint: disable=not-callable
    856     elif self._stateful_fn is not None:
    857       # Release the lock early so that multiple threads can perform the call

/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in __call__(self, *args, **kwargs)
   2940       (graph_function,
   2941        filtered_flat_args) = self._maybe_define_function(args, kwargs)
-> 2942     return graph_function._call_flat(
   2943         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access
   2944 

/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
   1916         and executing_eagerly):
   1917       # No tape is watching; skip to running the function.
-> 1918       return self._build_call_outputs(self._inference_function.call(
   1919           ctx, args, cancellation_manager=cancellation_manager))
   1920     forward_backward = self._select_forward_and_backward_functions(

/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in call(self, ctx, args, cancellation_manager)
    553       with _InterpolateFunctionError(self):
    554         if cancellation_manager is None:
--> 555           outputs = execute.execute(
    556               str(self.signature.name),
    557               num_outputs=self._num_outputs,

/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     57   try:
     58     ctx.ensure_initialized()
---> 59     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
     60                                         inputs, attrs, num_outputs)
     61   except core._NotOkStatusException as e:

UnknownError:  UnidentifiedImageError: cannot identify image file <_io.BytesIO object at 0x7fc88d55c9a0>
Traceback (most recent call last):

  File "/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/ops/script_ops.py", line 249, in __call__
    ret = func(*args)

  File "/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py", line 620, in wrapper
    return func(*args, **kwargs)

  File "/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 891, in generator_py_func
    values = next(generator_state.get_iterator(iterator_id))

  File "/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/data_adapter.py", line 807, in wrapped_generator
    for data in generator_fn():

  File "/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/data_adapter.py", line 933, in generator_fn
    yield x[i]

  File "/opt/anaconda3/lib/python3.8/site-packages/keras_preprocessing/image/iterator.py", line 65, in __getitem__
    return self._get_batches_of_transformed_samples(index_array)

  File "/opt/anaconda3/lib/python3.8/site-packages/keras_preprocessing/image/iterator.py", line 227, in _get_batches_of_transformed_samples
    img = load_img(filepaths[j],

  File "/opt/anaconda3/lib/python3.8/site-packages/keras_preprocessing/image/utils.py", line 114, in load_img
    img = pil_image.open(io.BytesIO(f.read()))

  File "/opt/anaconda3/lib/python3.8/site-packages/PIL/Image.py", line 2943, in open
    raise UnidentifiedImageError(

PIL.UnidentifiedImageError: cannot identify image file <_io.BytesIO object at 0x7fc88d55c9a0>


     [[{{node PyFunc}}]]
     [[IteratorGetNext]] [Op:__inference_train_function_24233]

Function call stack:
train_function

I tried changing from loss=’categorical_crossentropy’ to loss=’binary_crossentropy’ but still the issue persists. I wish to train the model but the Epoch keeps getting stuck.

Edit:

The train generator function and where it is used is as follows:

IMAGE_SHAPE = (224, 224)
TRAINING_DATA_DIR = str(data_root)


datagen_kwargs = dict(rescale=1./255, validation_split=.20)
valid_datagen = tf.keras.preprocessing.image.ImageDataGenerator(**datagen_kwargs)
valid_generator = valid_datagen.flow_from_directory(
    TRAINING_DATA_DIR, 
    subset="validation", 
    shuffle=True,
    target_size=IMAGE_SHAPE
)

train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(**datagen_kwargs)
train_generator = train_datagen.flow_from_directory(
    TRAINING_DATA_DIR, 
    subset="training",
    shuffle=True,
    target_size=IMAGE_SHAPE)


for image_batch, label_batch in train_generator:
  break
image_batch.shape, label_batch.shape

Output: ((32, 224, 224, 3), (32, 2))

print (train_generator.class_indices)

labels = 'n'.join(sorted(train_generator.class_indices.keys()))

with open('labels.txt', 'w') as f:
  f.write(labels)

Output: {‘off’: 0, ‘on’: 1}

Solution

There was an issue with one of the img that was causing an issue and was pointed out by @Lescurel. To view the img you can run the following:

import PIL
from pathlib import Path
from PIL import UnidentifiedImageError

path = Path("INSERT PATH HERE").rglob("*.jpeg")
for img_p in path:
    try:
        img = PIL.Image.open(img_p)
    except PIL.UnidentifiedImageError:
            print(img_p)

You can also do the same for png or other formats. If there is an issue with your image, it will list it as soon as you run it

Answered By — EverydayDeveloper

.
├── test
│   ├── Cat
│   └── Dog
└── train
    ├── Cat
    └── Dog

batch_size = 16

# Data augmentation and preprocess
train_datagen = ImageDataGenerator(rescale=1./255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    validation_split=0.20) # set validation split

# Train dataset
train_generator = train_datagen.flow_from_directory(
    'PetImages/train',
    target_size=(244, 244),
    batch_size=batch_size,
    class_mode='binary',
    subset='training') # set as training data

# Validation dataset
validation_generator = train_datagen.flow_from_directory(
    'PetImages/train',
    target_size=(244, 244),
    batch_size=batch_size,
    class_mode='binary',
    subset='validation') # set as validation data

test_datagen = ImageDataGenerator(rescale=1./255)
# Test dataset
test_datagen = test_datagen.flow_from_directory(
    'PetImages/test')

history = model.fit(train_generator,
                    validation_data=validation_generator,
                    epochs=5)

Epoch 1/5
1150/1150 [==============================] - ETA: 0s - loss: 0.0505 - accuracy: 0.9906

2 ответа

Лучший ответ

import os
from PIL import Image
folder_path = 'dataimg'
extensions = []
for fldr in os.listdir(folder_path):
    sub_folder_path = os.path.join(folder_path, fldr)
    for filee in os.listdir(sub_folder_path):
        file_path = os.path.join(sub_folder_path, filee)
        print('** Path: {}  **'.format(file_path), end="r", flush=True)
        im = Image.open(file_path)
        rgb_im = im.convert('RGB')
        if filee.split('.')[1] not in extensions:
            extensions.append(filee.split('.')[1])
    

import os
import cv2
bad_list=[]
dir=r'c:'PetImages'
subdir_list=os.listdir(dir) # create a list of the sub directories in the directory ie train or test
for d in subdir_list:  # iterate through the sub directories train and test
    dpath=os.path.join (dir, d) # create path to sub directory
    if d in ['test', 'train']:
        class_list=os.listdir(dpath) # list of classes ie dog or cat
       # print (class_list)
        for klass in class_list: # iterate through the two classes
            class_path=os.path.join(dpath, klass) # path to class directory
            #print(class_path)
            file_list=os.listdir(class_path) # create list of files in class directory
            for f in file_list: # iterate through the files
                fpath=os.path.join (class_path,f)
                index=f.rfind('.') # find index of period infilename
                ext=f[index+1:] # get the files extension
                if ext  not in ['jpg', 'png', 'bmp', 'gif']:
                    print(f'file {fpath}  has an invalid extension {ext}')
                    bad_list.append(fpath)                    
                else:
                    try:
                        img=cv2.imread(fpath)
                        size=img.shape
                    except:
                        print(f'file {fpath} is not a valid image file ')
                        bad_list.append(fpath)
                       
print (bad_list)
                    
    
  

I’m working on GCP cloud functions and intend to write a functions which combines two images. But I’, getting the following error when I invoke the function:

Traceback (most recent call last): File
«/env/local/lib/python3.7/site-packages/google/cloud/functions/worker.py»,
line 346, in run_http_function result =
_function_handler.invoke_user_function(flask.request) File «/env/local/lib/python3.7/site-packages/google/cloud/functions/worker.py»,
line 217, in invoke_user_function return
call_user_function(request_or_event) File
«/env/local/lib/python3.7/site-packages/google/cloud/functions/worker.py»,
line 210, in call_user_function return
self._user_function(request_or_event) File «/user_code/main.py», line
74, in execute newIntro= generateIntroImage(nameMappings[‘stdName’],
nameMappings[‘stdPicture’], nameMappings[‘logo’],
nameMappings[‘stdYear’], nameMappings[‘font’]) File
«/user_code/main.py», line 12, in generateIntroImage
images.append(Image.open(logo)) File
«/env/local/lib/python3.7/site-packages/PIL/Image.py», line 2862, in
open «cannot identify image file %r» % (filename if filename else fp)
PIL.UnidentifiedImageError: cannot identify image file ‘/tmp/logo.jpg’

I have ran this function on my local machine and it works as expected but when I deploy it on GCP, it gives this error and crashes. Here’s my function:

from PIL import Image
from PIL import ImageFont
from PIL import ImageDraw

def generateIntroImage(stdName, stdPicture, logo, year, typeFace):
    images = [Image.open(x) for x in [stdPicture, logo]]
    widths, heights = zip(*(i.size for i in images))
    total_width = sum(widths)
    max_height = max(heights)
    new_im = Image.new('RGB', (total_width, max_height))
    x_offset = 0
    for im in images:
        new_im.paste(im, (x_offset,0))
        x_offset += im.size[0]

    font= ImageFont.truetype(typeFace, 70)
    draw= ImageDraw.Draw(new_im)
    draw.text((0, 0), stdName+"'s " +year+" Year Book", (0,0,0),font= font)
    fileName= "/tmp/test.jpg"
    new_im.save(fileName)
    return fileName

These images are .jpg and .png files. Any idea what could be wrong?