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diff --git a/image_utils.py b/image_utils.py
index b4b77db..d2cb9a3 100644
--- a/image_utils.py
+++ b/image_utils.py
@@ -1,143 +1,143 @@
VGG_MEAN = [104, 117, 123]
def create_yahoo_image_loader(expand_dims=True):
"""Yahoo open_nsfw image loading mechanism
Approximation of the image loading mechanism defined in
https://github.com/yahoo/open_nsfw/blob/79f77bcd45076b000df71742a59d726aa4a36ad1/classify_nsfw.py#L40
"""
import numpy as np
import skimage
import skimage.io
from PIL import Image
from io import BytesIO
def load_image(image_path):
pimg = open(image_path, 'rb').read()
img_data = pimg
im = Image.open(BytesIO(img_data))
if im.mode != "RGB":
im = im.convert('RGB')
imr = im.resize((256, 256), resample=Image.BILINEAR)
fh_im = BytesIO()
imr.save(fh_im, format='JPEG')
fh_im.seek(0)
- image = (skimage.img_as_float(skimage.io.imread(fh_im, as_grey=False))
+ image = (skimage.img_as_float(skimage.io.imread(fh_im, as_gray=False))
.astype(np.float32))
H, W, _ = image.shape
h, w = (224, 224)
h_off = max((H - h) // 2, 0)
w_off = max((W - w) // 2, 0)
image = image[h_off:h_off + h, w_off:w_off + w, :]
# RGB to BGR
image = image[:, :, :: -1]
image = image.astype(np.float32, copy=False)
image = image * 255.0
image -= np.array(VGG_MEAN, dtype=np.float32)
if expand_dims:
image = np.expand_dims(image, axis=0)
return image
return load_image
def create_tensorflow_image_loader(session, expand_dims=True,
options=None,
run_metadata=None):
"""Tensorflow image loader
Results seem to deviate quite a bit from yahoo image loader due to
different jpeg encoders/decoders and different image resize
implementations between PIL, skimage and tensorflow
Only supports jpeg images.
Relevant tensorflow issues:
* https://github.com/tensorflow/tensorflow/issues/6720
* https://github.com/tensorflow/tensorflow/issues/12753
"""
import tensorflow as tf
def load_image(image_path):
image = tf.read_file(image_path)
image = __tf_jpeg_process(image)
if expand_dims:
image_batch = tf.expand_dims(image, axis=0)
return session.run(image_batch,
options=options,
run_metadata=run_metadata)
return session.run(image,
options=options,
run_metadata=run_metadata)
return load_image
def load_base64_tensor(_input):
import tensorflow as tf
def decode_and_process(base64):
_bytes = tf.decode_base64(base64)
_image = __tf_jpeg_process(_bytes)
return _image
# we have to do some preprocessing with map_fn, since functions like
# decode_*, resize_images and crop_to_bounding_box do not support
# processing of batches
image = tf.map_fn(decode_and_process, _input,
back_prop=False, dtype=tf.float32)
return image
def __tf_jpeg_process(data):
import tensorflow as tf
# The whole jpeg encode/decode dance is neccessary to generate a result
# that matches the original model's (caffe) preprocessing
# (as good as possible)
image = tf.image.decode_jpeg(data, channels=3,
fancy_upscaling=True,
dct_method="INTEGER_FAST")
image = tf.image.convert_image_dtype(image, tf.float32, saturate=True)
image = tf.image.resize_images(image, (256, 256),
method=tf.image.ResizeMethod.BILINEAR,
align_corners=True)
image = tf.image.convert_image_dtype(image, tf.uint8, saturate=True)
image = tf.image.encode_jpeg(image, format='', quality=75,
progressive=False, optimize_size=False,
chroma_downsampling=True,
density_unit=None,
x_density=None, y_density=None,
xmp_metadata=None)
image = tf.image.decode_jpeg(image, channels=3,
fancy_upscaling=False,
dct_method="INTEGER_ACCURATE")
image = tf.cast(image, dtype=tf.float32)
image = tf.image.crop_to_bounding_box(image, 16, 16, 224, 224)
image = tf.reverse(image, axis=[2])
image -= VGG_MEAN
return image

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