def distort_fn_1(image=image):
"""Variant 1 of distort function."""
image = tf.image.random_brightness(image, max_delta=32. / 255.)
image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
if distort_color_in_yiq:
image = distort_image_ops.random_hsv_in_yiq(
image, lower_saturation=0.5, upper_saturation=1.5,
max_delta_hue=0.2 * math.pi)
else:
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
image = tf.image.random_hue(image, max_delta=0.2)
return image
def distort_fn_0(image=image):
"""Variant 0 of distort function."""
image = tf.image.random_brightness(image, max_delta=32. / 255.)
if distort_color_in_yiq:
image = distort_image_ops.random_hsv_in_yiq(
image, lower_saturation=0.5, upper_saturation=1.5,
max_delta_hue=0.2 * math.pi)
else:
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
image = tf.image.random_hue(image, max_delta=0.2)
image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
return image
def distort_fn_1(image=image):
"""Variant 1 of distort function."""
image = tf.image.random_brightness(image, max_delta=R_BRIGHTNESS_MAX_DELTA)
image = tf.image.random_contrast(image, lower=R_CONSTRAST_LOWER, upper=R_CONSTRAST_UPPER)
if distort_color_in_yiq:
image = distort_image_ops.random_hsv_in_yiq(
image, lower_saturation=R_SATURATION_LOWER, upper_saturation=R_SATURATION_UPPER,
max_delta_hue=R_HUE_MAX_DELTA * math.pi)
else:
image = tf.image.random_saturation(image, lower=R_SATURATION_LOWER, upper=R_SATURATION_UPPER)
image = tf.image.random_hue(image, max_delta=R_HUE_MAX_DELTA)
return image
def parse_and_preprocess_image_record(config, record, height, width,
brightness, contrast, saturation, hue,
distort, nsummary=10, increased_aug=False, random_search_aug=False):
#imgdata, label, bbox, text = deserialize_image_record(record)
#label -= 1 # Change to 0-based (don't use background class)
with tf.name_scope('preprocess_train'):
image = crop_and_resize_image(config, record, height, width, distort)
if distort:
image = tf.image.random_flip_left_right(image)
if increased_aug:
image = tf.image.random_brightness(image, max_delta=brightness)
image = distort_image_ops.random_hsv_in_yiq(image,
lower_saturation=saturation,
upper_saturation=2.0 - saturation,
max_delta_hue=hue * math.pi)
image = tf.image.random_contrast(image, lower=contrast, upper=2.0 - contrast)
tf.summary.image('distorted_color_image', tf.expand_dims(image, 0))
image = tf.clip_by_value(image, 0., 255.)
image = normalize(image)
image = tf.cast(image, tf.float16)
return image
