PNG TO TFRecord: error : has type numpy.ndarray, but expected one of: bytes
0
I'm doing a .png file to tfrecord.
def _bytes_feature(value):
"""Wrapper for inserting bytes features into Example proto."""
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
convert to example file:
def _convert_to_example(filename, image_buffer, label, text, height, width):
example = tf.train.Example(features=tf.train.Features(feature={
'image/height': _int64_feature(height),
'image/width': _int64_feature(width),
........
'image/encoded': _bytes_feature(image_buffer)})) #Error
return example
decode_png file, not sure this part
def decode_png(image_data):
img_str = image_data.tostring()
reconstructed_img_1d = np.fromstring(img_str, dtype=np.uint8)
reconstructed_img = reconstructed_img_1d.reshape(image_data.shape)
return reconstructed_img
process_image file:
def _process_image(filename):
# Read the image file.
with open(filename, 'r') as f:
image_data = io.imread(f)
# Decode the RGB PNG.
image = decode_png(image_data)
# Check that image converted to RGB
assert len(image.shape) == 3
height = image.shape[0]
width = image.shape[1]
assert image.shape[2] == 3
return image_data, height, width
Main, process image files batch:
for i in files_in_shard:
filename = filenames[i]
label = labels[i]
text = texts[i]
image_buffer, height, width = _process_image(filename)
example = _convert_to_example(filename, image_buffer, label,
text, height, width)
writer.write(example.SerializeToString())
shard_counter += 1
counter += 1
But, there is a error that TypeError: array([[[223, 198, 219],
[215, 185, 209],
[207, 174, 201],
...,
[230 has type numpy.ndarray, but expected one of: bytes
How should I deal with this? Any help would be great.
Thank you
python tensorflow png tfrecord
asked Nov 19 '18 at 20:36
ZhuoZhuo
2516
add a comment |
0
I'm doing a .png file to tfrecord.
def _bytes_feature(value):
"""Wrapper for inserting bytes features into Example proto."""
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
convert to example file:
def _convert_to_example(filename, image_buffer, label, text, height, width):
example = tf.train.Example(features=tf.train.Features(feature={
'image/height': _int64_feature(height),
'image/width': _int64_feature(width),
........
'image/encoded': _bytes_feature(image_buffer)})) #Error
return example
decode_png file, not sure this part
def decode_png(image_data):
img_str = image_data.tostring()
reconstructed_img_1d = np.fromstring(img_str, dtype=np.uint8)
reconstructed_img = reconstructed_img_1d.reshape(image_data.shape)
return reconstructed_img
process_image file:
def _process_image(filename):
# Read the image file.
with open(filename, 'r') as f:
image_data = io.imread(f)
# Decode the RGB PNG.
image = decode_png(image_data)
# Check that image converted to RGB
assert len(image.shape) == 3
height = image.shape[0]
width = image.shape[1]
assert image.shape[2] == 3
return image_data, height, width
Main, process image files batch:
for i in files_in_shard:
filename = filenames[i]
label = labels[i]
text = texts[i]
image_buffer, height, width = _process_image(filename)
example = _convert_to_example(filename, image_buffer, label,
text, height, width)
writer.write(example.SerializeToString())
shard_counter += 1
counter += 1
But, there is a error that TypeError: array([[[223, 198, 219],
[215, 185, 209],
[207, 174, 201],
...,
[230 has type numpy.ndarray, but expected one of: bytes
How should I deal with this? Any help would be great.
Thank you
python tensorflow png tfrecord
asked Nov 19 '18 at 20:36
ZhuoZhuo
2516
add a comment |
0
0
0
I'm doing a .png file to tfrecord.
def _bytes_feature(value):
"""Wrapper for inserting bytes features into Example proto."""
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
convert to example file:
def _convert_to_example(filename, image_buffer, label, text, height, width):
example = tf.train.Example(features=tf.train.Features(feature={
'image/height': _int64_feature(height),
'image/width': _int64_feature(width),
........
'image/encoded': _bytes_feature(image_buffer)})) #Error
return example
decode_png file, not sure this part
def decode_png(image_data):
img_str = image_data.tostring()
reconstructed_img_1d = np.fromstring(img_str, dtype=np.uint8)
reconstructed_img = reconstructed_img_1d.reshape(image_data.shape)
return reconstructed_img
process_image file:
def _process_image(filename):
# Read the image file.
with open(filename, 'r') as f:
image_data = io.imread(f)
# Decode the RGB PNG.
image = decode_png(image_data)
# Check that image converted to RGB
assert len(image.shape) == 3
height = image.shape[0]
width = image.shape[1]
assert image.shape[2] == 3
return image_data, height, width
Main, process image files batch:
for i in files_in_shard:
filename = filenames[i]
label = labels[i]
text = texts[i]
image_buffer, height, width = _process_image(filename)
example = _convert_to_example(filename, image_buffer, label,
text, height, width)
writer.write(example.SerializeToString())
shard_counter += 1
counter += 1
But, there is a error that TypeError: array([[[223, 198, 219],
[215, 185, 209],
[207, 174, 201],
...,
[230 has type numpy.ndarray, but expected one of: bytes
How should I deal with this? Any help would be great.
Thank you
python tensorflow png tfrecord
asked Nov 19 '18 at 20:36
ZhuoZhuo
2516
I'm doing a .png file to tfrecord.
def _bytes_feature(value):
"""Wrapper for inserting bytes features into Example proto."""
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
convert to example file:
def _convert_to_example(filename, image_buffer, label, text, height, width):
example = tf.train.Example(features=tf.train.Features(feature={
'image/height': _int64_feature(height),
'image/width': _int64_feature(width),
........
'image/encoded': _bytes_feature(image_buffer)})) #Error
return example
decode_png file, not sure this part
def decode_png(image_data):
img_str = image_data.tostring()
reconstructed_img_1d = np.fromstring(img_str, dtype=np.uint8)
reconstructed_img = reconstructed_img_1d.reshape(image_data.shape)
return reconstructed_img
process_image file:
def _process_image(filename):
# Read the image file.
with open(filename, 'r') as f:
image_data = io.imread(f)
# Decode the RGB PNG.
image = decode_png(image_data)
# Check that image converted to RGB
assert len(image.shape) == 3
height = image.shape[0]
width = image.shape[1]
assert image.shape[2] == 3
return image_data, height, width
Main, process image files batch:
for i in files_in_shard:
filename = filenames[i]
label = labels[i]
text = texts[i]
image_buffer, height, width = _process_image(filename)
example = _convert_to_example(filename, image_buffer, label,
text, height, width)
writer.write(example.SerializeToString())
shard_counter += 1
counter += 1
But, there is a error that TypeError: array([[[223, 198, 219],
[215, 185, 209],
[207, 174, 201],
...,
[230 has type numpy.ndarray, but expected one of: bytes
How should I deal with this? Any help would be great.
Thank you
python tensorflow png tfrecord
python tensorflow png tfrecord
asked Nov 19 '18 at 20:36
ZhuoZhuo
2516
asked Nov 19 '18 at 20:36
ZhuoZhuo
2516
asked Nov 19 '18 at 20:36
ZhuoZhuo
2516
asked Nov 19 '18 at 20:36
ZhuoZhuo
2516
asked Nov 19 '18 at 20:36
ZhuoZhuo
2516
2516
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
0
Ok I got it... logic pro
def decode_png(image_data):
img_str = image_data.tostring()
reconstructed_img_1d = np.fromstring(img_str, dtype=np.uint8)
reconstructed_img = reconstructed_img_1d.reshape(image_data.shape)
return img_str
def _process_image(filename):
# Read the image file.
with open(filename, 'r') as f:
image_data = io.imread(f)
# Decode the RGB PNG.
img_str = image_data.tostring()
height = image_data.shape[0]
width = image_data.shape[1]
return img_str, height, width
answered Nov 19 '18 at 20:59
ZhuoZhuo
2516
add a comment |
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1 Answer
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1 Answer
1
active
oldest
votes
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oldest
votes
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oldest
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0
Ok I got it... logic pro
def decode_png(image_data):
img_str = image_data.tostring()
reconstructed_img_1d = np.fromstring(img_str, dtype=np.uint8)
reconstructed_img = reconstructed_img_1d.reshape(image_data.shape)
return img_str
def _process_image(filename):
# Read the image file.
with open(filename, 'r') as f:
image_data = io.imread(f)
# Decode the RGB PNG.
img_str = image_data.tostring()
height = image_data.shape[0]
width = image_data.shape[1]
return img_str, height, width
answered Nov 19 '18 at 20:59
ZhuoZhuo
2516
add a comment |
0
Ok I got it... logic pro
def decode_png(image_data):
img_str = image_data.tostring()
reconstructed_img_1d = np.fromstring(img_str, dtype=np.uint8)
reconstructed_img = reconstructed_img_1d.reshape(image_data.shape)
return img_str
def _process_image(filename):
# Read the image file.
with open(filename, 'r') as f:
image_data = io.imread(f)
# Decode the RGB PNG.
img_str = image_data.tostring()
height = image_data.shape[0]
width = image_data.shape[1]
return img_str, height, width
answered Nov 19 '18 at 20:59
ZhuoZhuo
2516
add a comment |
0
0
0
Ok I got it... logic pro
def decode_png(image_data):
img_str = image_data.tostring()
reconstructed_img_1d = np.fromstring(img_str, dtype=np.uint8)
reconstructed_img = reconstructed_img_1d.reshape(image_data.shape)
return img_str
def _process_image(filename):
# Read the image file.
with open(filename, 'r') as f:
image_data = io.imread(f)
# Decode the RGB PNG.
img_str = image_data.tostring()
height = image_data.shape[0]
width = image_data.shape[1]
return img_str, height, width
answered Nov 19 '18 at 20:59
ZhuoZhuo
2516
Ok I got it... logic pro
def decode_png(image_data):
img_str = image_data.tostring()
reconstructed_img_1d = np.fromstring(img_str, dtype=np.uint8)
reconstructed_img = reconstructed_img_1d.reshape(image_data.shape)
return img_str
def _process_image(filename):
# Read the image file.
with open(filename, 'r') as f:
image_data = io.imread(f)
# Decode the RGB PNG.
img_str = image_data.tostring()
height = image_data.shape[0]
width = image_data.shape[1]
return img_str, height, width
answered Nov 19 '18 at 20:59
ZhuoZhuo
2516
answered Nov 19 '18 at 20:59
ZhuoZhuo
2516
answered Nov 19 '18 at 20:59
ZhuoZhuo
2516
answered Nov 19 '18 at 20:59
ZhuoZhuo
2516
2516
add a comment |
add a comment |
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