def tf_map_coordinates(input, coords, order=1):
"""Tensorflow verion of scipy.ndimage.map_coordinates"""
assert order == 1
coords_tl = tf.cast(tf.floor(coords), 'int32')
coords_br = tf.cast(tf.ceil(coords), 'int32')
coords_bl = tf.stack([coords_tl[:, 0], coords_br[:, 1]], axis=1)
coords_tr = tf.stack([coords_br[:, 0], coords_tl[:, 1]], axis=1)
vals_tl = tf.gather_nd(input, coords_tl)
vals_br = tf.gather_nd(input, coords_br)
vals_bl = tf.gather_nd(input, coords_bl)
vals_tr = tf.gather_nd(input, coords_tr)
h_offset = coords[:, 0] - tf.cast(coords_tl[:, 0], tf.float32)
h_int_t = (((1.0 - h_offset) * vals_tl) + (h_offset * vals_tr))
h_int_b = (((1.0 - h_offset) * vals_bl) + (h_offset * vals_br))
v_offset = coords[:, 1] - tf.cast(coords_tl[:, 1], tf.float32)
int_vals = (((1.0 - v_offset) * h_int_t) + (v_offset * h_int_b))
return int_vals
def _row_kernel(upsampled_region_size, upsample_factor, axis_offsets,
data_shape):
data_shape_float = tf.cast(data_shape, tf.float32)
row_constant = tf.cast(data_shape_float[1] * upsample_factor, tf.complex64)
row_constant = (-1j * 2 * np.pi / row_constant)
row_kernel_a = tf.range(0, upsampled_region_size, dtype=tf.float32)
row_kernel_a = tf.reshape(row_kernel_a, (1, -1))
row_kernel_a = tf.tile(row_kernel_a, (data_shape[0], 1))
row_kernel_a = tf.transpose(row_kernel_a)
row_kernel_a = row_kernel_a - axis_offsets[:, 0]
row_kernel_b = tf.range(0, data_shape_float[1], dtype=tf.float32)
row_kernel_b = fftshift1d(row_kernel_b)
row_kernel_b = tf.reshape(row_kernel_b, (1, -1))
row_kernel_b = tf.tile(row_kernel_b, (data_shape[0], 1))
row_kernel_b = row_kernel_b - tf.floor(data_shape_float[1] / 2.)
row_kernel_a = tf.expand_dims(row_kernel_a, 1)
row_kernel_b = tf.expand_dims(row_kernel_b, -1)
row_kernel = tf.transpose(row_kernel_a) * row_kernel_b
row_kernel = tf.transpose(row_kernel, perm=(0, 2, 1))
row_kernel = row_constant * tf.cast(row_kernel, tf.complex64)
row_kernel = tf.exp(row_kernel)
return row_kernel
def _col_kernel(upsampled_region_size, upsample_factor, axis_offsets,
data_shape):
data_shape_float = tf.cast(data_shape, tf.float32)
col_constant = tf.cast(data_shape_float[2] * upsample_factor, tf.complex64)
col_constant = (-1j * 2 * np.pi / col_constant)
col_kernel_a = tf.range(0, data_shape_float[2], dtype=tf.float32)
col_kernel_a = fftshift1d(col_kernel_a)
col_kernel_a = tf.reshape(col_kernel_a, (-1, 1))
col_kernel_a -= tf.floor(data_shape_float[2] / 2.)
col_kernel_a = tf.reshape(col_kernel_a, (1, -1))
col_kernel_a = tf.tile(col_kernel_a, (data_shape[0], 1))
col_kernel_b = tf.range(0, upsampled_region_size, dtype=tf.float32)
col_kernel_b = tf.reshape(col_kernel_b, (1, -1))
col_kernel_b = tf.tile(col_kernel_b, (data_shape[0], 1))
col_kernel_b = tf.transpose(col_kernel_b)
col_kernel_b -= tf.transpose(axis_offsets[:, 1])
col_kernel_b = tf.transpose(col_kernel_b)
col_kernel_a = tf.expand_dims(col_kernel_a, 1)
col_kernel_b = tf.expand_dims(col_kernel_b, -1)
col_kernel = col_kernel_a * col_kernel_b
col_kernel = tf.transpose(col_kernel, perm=(0, 2, 1))
col_kernel = col_constant * tf.cast(col_kernel, tf.complex64)
col_kernel = tf.exp(col_kernel)
return col_kernel
def batch_map_coordinates(input, coords, order=1):
"""Batch version of tf_map_coordinates"""
input_shape = tf.shape(input)
batch_size = input_shape[0]
input_size = input_shape[1]
#coords = tf.reshape(coords, (batch_size, -1, 2))
n_coords = tf.shape(coords)[1]
coords = tf.clip_by_value(coords, 0, tf.cast(input_size, 'float32') - 1)
coords_tl = tf.cast(tf.floor(coords), 'int32')
coords_br = tf.cast(tf.ceil(coords), 'int32')
coords_bl = tf.stack([coords_tl[..., 0], coords_br[..., 1]], axis=-1)
coords_tr = tf.stack([coords_br[..., 0], coords_tl[..., 1]], axis=-1)
idx = tf.range(batch_size)
idx = tf.expand_dims(idx, -1)
idx = tf.tile(idx, [1, n_coords])
idx = tf.reshape(idx, [-1])
def _get_vals_by_coords(input, coords):
coords_0_flat = tf.reshape(coords[..., 0], [-1])
coords_1_flat = tf.reshape(coords[..., 1], [-1])
indices = tf.stack([idx, coords_0_flat, coords_1_flat], axis=-1)
vals = tf.gather_nd(input, indices)
vals = tf.reshape(vals, (batch_size, n_coords))
return vals
vals_tl = _get_vals_by_coords(input, coords_tl)
vals_br = _get_vals_by_coords(input, coords_br)
vals_bl = _get_vals_by_coords(input, coords_bl)
vals_tr = _get_vals_by_coords(input, coords_tr)
h_offset = coords[..., 0] - tf.cast(coords_tl[..., 0], tf.float32)
h_int_t = (((1.0 - h_offset) * vals_tl) + (h_offset * vals_tr))
h_int_b = (((1.0 - h_offset) * vals_bl) + (h_offset * vals_br))
v_offset = coords[..., 1] - tf.cast(coords_tl[..., 1], tf.float32)
int_vals = (((1.0 - v_offset) * h_int_t) + (v_offset * h_int_b))
return int_vals
def batch_map_coordinates(input, coords, n_coords):
"""Batch version of tf_map_coordinates"""
#init_input_shape = input.get_shape()
#input = tf.reshape(input, (-1, init_input_shape[3]))
#coords = tf.reshape(coords, (-1, n_coords * 2))
input_shape = input.get_shape()
input_h = input_shape[1].value
input_w = input_shape[2].value
#batch_size = input_shape[0]
#input_size = input_shape[1]
#coords = tf.reshape(coords, (batch_size, -1, 2))
#n_coords = tf.shape(coords)[1]
coords_h = tf.clip_by_value(coords[..., :n_coords], 0,
tf.cast(input_h, 'float32') - 1)
coords_w = tf.clip_by_value(coords[..., n_coords:], 0,
tf.cast(input_w, 'float32') - 1)
coords = tf.stack([coords_h, coords_w], axis=-1)
coords_tl = tf.cast(tf.floor(coords), 'float32')
coords_br = tf.cast(tf.ceil(coords), 'float32')
coords_bl = tf.stack([coords_tl[..., 0], coords_br[..., 1]], axis=-1)
coords_tr = tf.stack([coords_br[..., 0], coords_tl[..., 1]], axis=-1)
#idx = tf.range(batch_size)
#idx = tf.expand_dims(idx, -1)
#idx = tf.tile(idx, [1, n_coords])
#idx = tf.reshape(idx, [-1])
def _get_vals_by_coords(input, coords, n_coords):
coords_shape = tf.shape(coords)
input_shape = input.get_shape()
input_w = input_shape[2].value
input_h = input_shape[1].value
channel_size = input_shape[3].value
batch_size = tf.shape(input)[0]
input = tf.transpose(input, (0, 3, 1, 2))
input = tf.reshape(input, (-1, channel_size, input_h * input_w))
indices = coords[..., 0] * input_w + coords[..., 1]
#indices = tf.expand_dims(indices, axis=1)
#indices = tf.tile(indices, [1, channel_size, 1, 1, 1])
#indices = tf.reshape(indices, (-1, channel_size, input_h * input_w * n_coords))
#indices = tf.transpose(indices, (0, 3, 1, 2))
indices = tf.reshape(indices, (-1, input_h * input_w * n_coords))
indices = tf.cast(indices, 'int32')
#indices = tf.reshape(indices, [-1])
#input = tf.reshape(input, [-1])
vals = tf.gather(input, indices[0], axis=-1)
#vals = tf.map_fn(lambda x: tf.gather(x[0], x[1], axis=-1), (input,indices), dtype=tf.float32)
vals = tf.reshape(vals, (-1, channel_size, input_h, input_w, n_coords))
return vals
vals_tl = (1 + (coords_tl[..., 0] - coords[..., 0])) * \
(1 + (coords_tl[..., 1] - coords[..., 1]))
vals_br = (1 - (coords_br[..., 0] - coords[..., 0])) * \
(1 - (coords_br[..., 1] - coords[..., 1]))
vals_bl = (1 + (coords_bl[..., 0] - coords[..., 0])) * \
(1 + (coords_bl[..., 1] - coords[..., 1]))
vals_tr = (1 - (coords_tr[..., 0] - coords[..., 0])) * \
(1 - (coords_tr[..., 1] - coords[..., 1]))
x_vals_tl = _get_vals_by_coords(input, coords_tl, n_coords)
x_vals_br = _get_vals_by_coords(input, coords_br, n_coords)
x_vals_bl = _get_vals_by_coords(input, coords_bl, n_coords)
x_vals_tr = _get_vals_by_coords(input, coords_tr, n_coords)
#h_offset = coords[..., 0] - tf.cast(coords_tl[..., 0], tf.float32)
#h_int_t = (((1.0 - h_offset) * vals_tl) + (h_offset * vals_tr))
#h_int_b = (((1.0 - h_offset) * vals_bl) + (h_offset * vals_br))
#v_offset = coords[..., 1] - tf.cast(coords_tl[..., 1], tf.float32)
#int_vals = (((1.0 - v_offset) * h_int_t) + (v_offset * h_int_b))
int_vals = tf.expand_dims(vals_tl, 1) * x_vals_tl + \
tf.expand_dims(vals_br, 1) * x_vals_br + \
tf.expand_dims(vals_bl, 1) * x_vals_bl + \
tf.expand_dims(vals_tr, 1) * x_vals_tr
return int_vals
def fix(x):
x = tf.where(x >= 0, tf.floor(x), tf.ceil(x))
return x