"""Image warping using sparse flow defined at control points."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import scipy as sp
from scipy.interpolate import interp2d
from skimage.transform import warp
def _get_grid_locations(image_height, image_width):
"""Wrapper for np.meshgrid."""
y_range = np.linspace(0, image_height - 1, image_height)
x_range = np.linspace(0, image_width - 1, image_width)
y_grid, x_grid = np.meshgrid(y_range, x_range, indexing='ij')
return np.stack((y_grid, x_grid), -1)
def _expand_to_minibatch(np_array, batch_size):
"""Tile arbitrarily-sized np_array to include new batch dimension."""
tiles = [batch_size] + [1] * np_array.ndim
return np.tile(np.expand_dims(np_array, 0), tiles)
def _get_boundary_locations(image_height, image_width, num_points_per_edge):
"""Compute evenly-spaced indices along edge of image."""
y_range = np.linspace(0, image_height - 1, num_points_per_edge + 2)
x_range = np.linspace(0, image_width - 1, num_points_per_edge + 2)
ys, xs = np.meshgrid(y_range, x_range, indexing='ij')
is_boundary = np.logical_or(
np.logical_or(xs == 0, xs == image_width - 1),
np.logical_or(ys == 0, ys == image_height - 1))
return np.stack([ys[is_boundary], xs[is_boundary]], axis=-1)
def _add_zero_flow_controls_at_boundary(control_point_locations,
control_point_flows, image_height,
image_width, boundary_points_per_edge):
# batch_size = tensor_shape.dimension_value(control_point_locations.shape[0])
batch_size = control_point_locations.shape[0]
boundary_point_locations = _get_boundary_locations(image_height, image_width,
boundary_points_per_edge)
boundary_point_flows = np.zeros([boundary_point_locations.shape[0], 2])
type_to_use = control_point_locations.dtype
# boundary_point_locations = constant_op.constant(
# _expand_to_minibatch(boundary_point_locations, batch_size),
# dtype=type_to_use)
boundary_point_locations = _expand_to_minibatch(boundary_point_locations, batch_size)
# boundary_point_flows = constant_op.constant(
# _expand_to_minibatch(boundary_point_flows, batch_size), dtype=type_to_use)
boundary_point_flows = _expand_to_minibatch(boundary_point_flows, batch_size)
# merged_control_point_locations = array_ops.concat(
# [control_point_locations, boundary_point_locations], 1)
merged_control_point_locations = np.concatenate(
[control_point_locations, boundary_point_locations], 1)
# merged_control_point_flows = array_ops.concat(
# [control_point_flows, boundary_point_flows], 1)
merged_control_point_flows = np.concatenate(
[control_point_flows, boundary_point_flows], 1)
return merged_control_point_locations, merged_control_point_flows
def sparse_image_warp_np(image,
source_control_point_locations,
dest_control_point_locations,
interpolation_order=2,
regularization_weight=0.0,
num_boundary_points=0):
# image = ops.convert_to_tensor(image)
# source_control_point_locations = ops.convert_to_tensor(
# source_control_point_locations)
# dest_control_point_locations = ops.convert_to_tensor(
# dest_control_point_locations)
control_point_flows = (
dest_control_point_locations - source_control_point_locations)
clamp_boundaries = num_boundary_points > 0
boundary_points_per_edge = num_boundary_points - 1
# batch_size, image_height, image_width, _ = image.get_shape().as_list()
batch_size, image_height, image_width, _ = list(image.shape)
# This generates the dense locations where the interpolant
# will be evaluated.
grid_locations = _get_grid_locations(image_height, image_width)
flattened_grid_locations = np.reshape(grid_locations,
[image_height * image_width, 2])
# flattened_grid_locations = constant_op.constant(
# _expand_to_minibatch(flattened_grid_locations, batch_size), image.dtype)
flattened_grid_locations = _expand_to_minibatch(flattened_grid_locations, batch_size)
if clamp_boundaries:
(dest_control_point_locations,
control_point_flows) = _add_zero_flow_controls_at_boundary(
dest_control_point_locations, control_point_flows, image_height,
image_width, boundary_points_per_edge)
# flattened_flows = interpolate_spline.interpolate_spline(
# dest_control_point_locations, control_point_flows,
# flattened_grid_locations, interpolation_order, regularization_weight)
flattened_flows = sp.interpolate.spline(
dest_control_point_locations, control_point_flows,
flattened_grid_locations, interpolation_order, regularization_weight)
# dense_flows = array_ops.reshape(flattened_flows,
# [batch_size, image_height, image_width, 2])
dense_flows = np.reshape(flattened_flows,
[batch_size, image_height, image_width, 2])
# warped_image = dense_image_warp.dense_image_warp(image, dense_flows)
warped_image = warp(image, dense_flows)
return warped_image, dense_flows
def dense_image_warp(image, flow):
# batch_size, height, width, channels = (array_ops.shape(image)[0],
# array_ops.shape(image)[1],
# array_ops.shape(image)[2],
# array_ops.shape(image)[3])
batch_size, height, width, channels = (np.shape(image)[0],
np.shape(image)[1],
np.shape(image)[2],
np.shape(image)[3])
# The flow is defined on the image grid. Turn the flow into a list of query
# points in the grid space.
# grid_x, grid_y = array_ops.meshgrid(
# math_ops.range(width), math_ops.range(height))
# stacked_grid = math_ops.cast(
# array_ops.stack([grid_y, grid_x], axis=2), flow.dtype)
# batched_grid = array_ops.expand_dims(stacked_grid, axis=0)
# query_points_on_grid = batched_grid - flow
# query_points_flattened = array_ops.reshape(query_points_on_grid,
# [batch_size, height * width, 2])
grid_x, grid_y = np.meshgrid(
np.range(width), np.range(height))
stacked_grid = np.cast(
np.stack([grid_y, grid_x], axis=2), flow.dtype)
batched_grid = np.expand_dims(stacked_grid, axis=0)
query_points_on_grid = batched_grid - flow
query_points_flattened = np.reshape(query_points_on_grid,
[batch_size, height * width, 2])
# Compute values at the query points, then reshape the result back to the
# image grid.
interpolated = interp2d(image, query_points_flattened)
interpolated = np.reshape(interpolated,
[batch_size, height, width, channels])
return interpolated
