PIL.UnidentifiedImageError: cannot identify image file <_io.BytesIO object
PIL.UnidentifiedImageError: cannot identify image file <_io.BytesIO object
我正在尝试使用 Tensorflow 训练我的模型（图像分类）。当我尝试 运行 以下单元格时，我总是收到错误消息：
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
错误是：
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
我尝试将 loss='categorical_crossentropy' 更改为 loss='binary_crossentropy' 但问题仍然存在。我想训练模型，但 Epoch 总是卡住。
编辑：
train generator函数及使用位置如下：
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
输出：((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)
输出：{'off'：0，'on'：1}
@Lescurel 指出了导致问题的其中一个 img 问题。要查看 img，您可以 运行 以下内容：
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)
您也可以对 png 或其他格式执行相同的操作。如果您的图片有问题，它会在您 运行 后立即列出
类似于@EverydayDeveloper，但使用 glob 来保存所有带有 class 的图像路径。
import PIL
from PIL import UnidentifiedImageError
import glob
imgs_ = glob.glob("/home/ubuntu/imageTrain_dobby/SKJEWELLERY/classification/dataset/jewellery_dataset/train/*/*.jpg")
for img in imgs_:
try:
img = PIL.Image.open(img)
except PIL.UnidentifiedImageError:
print(img)
在 mac 上的 外部硬盘驱动器 上访问训练数据集时，我遇到了同样的问题。 flow_from_directory 函数返回的文件比我放置在文件夹中的文件多。
将整个训练数据集移动到 本地驱动器 解决了问题。
我在尝试调用 Image.open() 时遇到了同样的错误，但我是在通过多部分请求从 flutter 客户端应用程序接收到的字节上调用此函数的。
问题是我没有在客户端包含文件名参数：
MultipartFile.fromBytes('image', await image.readAsBytes(),
filename: image.name)
添加filename: image.name后，Image.open能够识别图像文件。
我正在尝试使用 Tensorflow 训练我的模型（图像分类）。当我尝试 运行 以下单元格时，我总是收到错误消息：
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
错误是：
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
我尝试将 loss='categorical_crossentropy' 更改为 loss='binary_crossentropy' 但问题仍然存在。我想训练模型，但 Epoch 总是卡住。
编辑：
train generator函数及使用位置如下：
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
输出：((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)
输出：{'off'：0，'on'：1}
@Lescurel 指出了导致问题的其中一个 img 问题。要查看 img，您可以 运行 以下内容：
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)
您也可以对 png 或其他格式执行相同的操作。如果您的图片有问题，它会在您 运行 后立即列出
类似于@EverydayDeveloper，但使用 glob 来保存所有带有 class 的图像路径。
import PIL
from PIL import UnidentifiedImageError
import glob
imgs_ = glob.glob("/home/ubuntu/imageTrain_dobby/SKJEWELLERY/classification/dataset/jewellery_dataset/train/*/*.jpg")
for img in imgs_:
try:
img = PIL.Image.open(img)
except PIL.UnidentifiedImageError:
print(img)
在 mac 上的 外部硬盘驱动器 上访问训练数据集时，我遇到了同样的问题。 flow_from_directory 函数返回的文件比我放置在文件夹中的文件多。
将整个训练数据集移动到 本地驱动器 解决了问题。
我在尝试调用 Image.open() 时遇到了同样的错误，但我是在通过多部分请求从 flutter 客户端应用程序接收到的字节上调用此函数的。
问题是我没有在客户端包含文件名参数：
MultipartFile.fromBytes('image', await image.readAsBytes(),
filename: image.name)
添加filename: image.name后，Image.open能够识别图像文件。