in autoaugment.py [0:0]
def autocontrast(image):
"""Implements Autocontrast function from PIL using TF ops.
Args:
image: A 3D uint8 tensor.
Returns:
The image after it has had autocontrast applied to it and will be of type
uint8.
"""
def scale_channel(image):
"""Scale the 2D image using the autocontrast rule."""
# A possibly cheaper version can be done using cumsum/unique_with_counts
# over the histogram values, rather than iterating over the entire image.
# to compute mins and maxes.
lo = tf.to_float(tf.reduce_min(image))
hi = tf.to_float(tf.reduce_max(image))
# Scale the image, making the lowest value 0 and the highest value 255.
def scale_values(im):
scale = 255.0 / (hi - lo)
offset = -lo * scale
im = tf.to_float(im) * scale + offset
im = tf.clip_by_value(im, 0.0, 255.0)
return tf.cast(im, tf.uint8)
result = tf.cond(hi > lo, lambda: scale_values(image), lambda: image)
return result
# Assumes RGB for now. Scales each channel independently
# and then stacks the result.
s1 = scale_channel(image[:, :, 0])
s2 = scale_channel(image[:, :, 1])
s3 = scale_channel(image[:, :, 2])
image = tf.stack([s1, s2, s3], 2)
return image