def distort_randomly_image_color(image_tensor, fast_mode=False):
"""Accepts image tensor of (width, height, 3) and returns color distorted image.
The function performs random brightness, saturation, hue, contrast change as it is performed
for inception model training in TF-Slim (you can find the link below in comments). All the
parameters of random variables were originally preserved. There are two regimes for the function
to work: fast and slow. Slow one performs only saturation and brightness random change is performed.
Parameters
----------
image_tensor : Tensor of size (width, height, 3) of tf.int32 or tf.float
Tensor with image with range [0,255]
fast_mode : boolean
Boolean value representing whether to use fast or slow mode
Returns
-------
img_float_distorted_original_range : Tensor of size (width, height, 3) of type tf.float.
Image Tensor with distorted color in [0,255] intensity range
"""
# Make the range to be in [0,1]
img_float_zero_one_range = tf.to_float(image_tensor) / 255
# Randomly distort the color of image. There are 4 ways to do it.
# Credit: TF-Slim
# https://github.com/tensorflow/models/blob/master/slim/preprocessing/inception_preprocessing.py#L224
# Most probably the inception models were trainined using this color augmentation:
# https://github.com/tensorflow/models/tree/master/slim#pre-trained-models
distorted_image = apply_with_random_selector(img_float_zero_one_range,
lambda x, ordering: distort_color(x, ordering, fast_mode=fast_mode),
num_cases=4)
img_float_distorted_original_range = distorted_image * 255
return img_float_distorted_original_range
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