def filter_param_regressor(self, features):
color_curve = tf.reshape(features,
shape=(-1, self.channels,
self.cfg.curve_steps))[:, None,
None, :]
color_curve = tanh_range(*self.cfg.color_curve_range,
initial=1)(color_curve)
return color_curve
def filter_param_regressor(self, features):
log_wb_range = 0.5
mask = np.array(((0, 1, 1)), dtype=np.float32).reshape(1, 3)
print(mask.shape)
assert mask.shape == (1, 3)
features = features * mask
color_scaling = tf.exp(
tanh_range(-log_wb_range, log_wb_range)(features))
# There will be no division by zero here unless the WB range lower bound is 0
# normalize by luminance
color_scaling *= 1.0 / (1e-5 + 0.27 * color_scaling[:, 0] +
0.67 * color_scaling[:, 1] +
0.06 * color_scaling[:, 2])[:, None]
return color_scaling
def get_mask(self, img, mask_parameters):
if not self.use_masking():
print('* Masking Disabled')
return tf.ones(shape=(1, 1, 1, 1), dtype=tf.float32)
else:
print('* Masking Enabled')
with tf.name_scope(name='mask'):
# Six parameters for one filter
filter_input_range = 5
assert mask_parameters.shape[1] == self.get_num_mask_parameters()
mask_parameters = tanh_range(l=-filter_input_range,
r=filter_input_range,
initial=0)(mask_parameters)
size = list(map(int, img.shape[1:3]))
grid = np.zeros(shape=[1] + size + [2], dtype=np.float32)
shorter_edge = min(size[0], size[1])
for i in range(size[0]):
for j in range(size[1]):
grid[0, i, j, 0] = (
i +
(shorter_edge - size[0]) / 2.0) / shorter_edge - 0.5
grid[0, i, j, 1] = (
j +
(shorter_edge - size[1]) / 2.0) / shorter_edge - 0.5
grid = tf.constant(grid)
# Ax + By + C * L + D
inp = grid[:, :, :, 0, None] * mask_parameters[:, None, None, 0, None] + \
grid[:, :, :, 1, None] * mask_parameters[:, None, None, 1, None] + \
mask_parameters[:, None, None, 2, None] * (rgb2lum(img) - 0.5) + \
mask_parameters[:, None, None, 3, None] * 2
# Sharpness and inversion
inp *= self.cfg.maximum_sharpness * mask_parameters[:, None, None,
4,
None] / filter_input_range
mask = tf.sigmoid(inp)
# Strength
mask = mask * (mask_parameters[:, None, None, 5, None] /
filter_input_range * 0.5 +
0.5) * (1 - self.cfg.minimum_strength
) + self.cfg.minimum_strength
print('mask', mask.shape)
return mask
def get_mask(self, img, mask_parameters):
with tf.name_scope(name='mask'):
# Five parameters for one filter
filter_input_range = 5
assert mask_parameters.shape[1] == self.get_num_mask_parameters()
mask_parameters = tanh_range(l=-filter_input_range,
r=filter_input_range,
initial=0)(mask_parameters)
size = list(map(int, img.shape[1:3]))
grid = np.zeros(shape=[1] + size + [2], dtype=np.float32)
shorter_edge = min(size[0], size[1])
for i in range(size[0]):
for j in range(size[1]):
grid[0, i, j, 0] = (
i +
(shorter_edge - size[0]) / 2.0) / shorter_edge - 0.5
grid[0, i, j, 1] = (
j +
(shorter_edge - size[1]) / 2.0) / shorter_edge - 0.5
grid = tf.constant(grid)
# (Ax)^2 + (By)^2 + C
inp = (grid[:, :, :, 0, None] * mask_parameters[:, None, None, 0, None]) ** 2 + \
(grid[:, :, :, 1, None] * mask_parameters[:, None, None, 1, None]) ** 2 + \
mask_parameters[:, None, None, 2, None] - filter_input_range
# Sharpness and inversion
inp *= self.cfg.maximum_sharpness * mask_parameters[:, None, None,
3,
None] / filter_input_range
mask = tf.sigmoid(inp)
# Strength
mask *= mask_parameters[:, None, None, 4,
None] / filter_input_range * 0.5 + 0.5
if not self.use_masking():
print('* Masking Disabled')
mask = mask * 0 + 1
else:
print('* Masking Enabled')
print('mask', mask.shape)
return mask
def filter_param_regressor(self, features):
tone_curve = tf.reshape(features,
shape=(-1, 1, self.cfg.curve_steps))[:, None,
None, :]
tone_curve = tanh_range(*self.cfg.tone_curve_range)(tone_curve)
return tone_curve
def filter_param_regressor(self, features):
log_gamma_range = np.log(self.cfg.gamma_range)
return tf.exp(tanh_range(-log_gamma_range, log_gamma_range)(features))
def filter_param_regressor(self, features):
return tanh_range(-self.cfg.exposure_range,
self.cfg.exposure_range,
initial=0)(features)
