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import numpy as np
from core.leras import nn
tf = nn.tf
class TanhPolar(nn.LayerBase):
"""
RoI Tanh-polar Transformer Network for Face Parsing in the Wild
https://github.com/hhj1897/roi_tanh_warping
"""
def __init__(self, width, height, angular_offset_deg=270, **kwargs):
self.width = width
self.height = height
warp_gridx, warp_gridy = TanhPolar._get_tanh_polar_warp_grids(width,height,angular_offset_deg=angular_offset_deg)
restore_gridx, restore_gridy = TanhPolar._get_tanh_polar_restore_grids(width,height,angular_offset_deg=angular_offset_deg)
self.warp_gridx_t = tf.constant(warp_gridx[None, ...])
self.warp_gridy_t = tf.constant(warp_gridy[None, ...])
self.restore_gridx_t = tf.constant(restore_gridx[None, ...])
self.restore_gridy_t = tf.constant(restore_gridy[None, ...])
super().__init__(**kwargs)
def warp(self, inp_t):
batch_t = tf.shape(inp_t)[0]
warp_gridx_t = tf.tile(self.warp_gridx_t, (batch_t,1,1) )
warp_gridy_t = tf.tile(self.warp_gridy_t, (batch_t,1,1) )
if nn.data_format == "NCHW":
inp_t = tf.transpose(inp_t,(0,2,3,1))
out_t = nn.bilinear_sampler(inp_t, warp_gridx_t, warp_gridy_t)
if nn.data_format == "NCHW":
out_t = tf.transpose(out_t,(0,3,1,2))
return out_t
def restore(self, inp_t):
batch_t = tf.shape(inp_t)[0]
restore_gridx_t = tf.tile(self.restore_gridx_t, (batch_t,1,1) )
restore_gridy_t = tf.tile(self.restore_gridy_t, (batch_t,1,1) )
if nn.data_format == "NCHW":
inp_t = tf.transpose(inp_t,(0,2,3,1))
inp_t = tf.pad(inp_t, [(0,0), (1, 1), (1, 0), (0, 0)], "SYMMETRIC")
out_t = nn.bilinear_sampler(inp_t, restore_gridx_t, restore_gridy_t)
if nn.data_format == "NCHW":
out_t = tf.transpose(out_t,(0,3,1,2))
return out_t
@staticmethod
def _get_tanh_polar_warp_grids(W,H,angular_offset_deg):
angular_offset_pi = angular_offset_deg * np.pi / 180.0
roi_center = np.array([ W//2, H//2], np.float32 )
roi_radii = np.array([W, H], np.float32 ) / np.pi ** 0.5
cos_offset, sin_offset = np.cos(angular_offset_pi), np.sin(angular_offset_pi)
normalised_dest_indices = np.stack(np.meshgrid(np.arange(0.0, 1.0, 1.0 / W),np.arange(0.0, 2.0 * np.pi, 2.0 * np.pi / H)), axis=-1)
radii = normalised_dest_indices[..., 0]
orientation_x = np.cos(normalised_dest_indices[..., 1])
orientation_y = np.sin(normalised_dest_indices[..., 1])
src_radii = np.arctanh(radii) * (roi_radii[0] * roi_radii[1] / np.sqrt(roi_radii[1] ** 2 * orientation_x ** 2 + roi_radii[0] ** 2 * orientation_y ** 2))
src_x_indices = src_radii * orientation_x
src_y_indices = src_radii * orientation_y
src_x_indices, src_y_indices = (roi_center[0] + cos_offset * src_x_indices - sin_offset * src_y_indices,
roi_center[1] + cos_offset * src_y_indices + sin_offset * src_x_indices)
return src_x_indices.astype(np.float32), src_y_indices.astype(np.float32)
@staticmethod
def _get_tanh_polar_restore_grids(W,H,angular_offset_deg):
angular_offset_pi = angular_offset_deg * np.pi / 180.0
roi_center = np.array([ W//2, H//2], np.float32 )
roi_radii = np.array([W, H], np.float32 ) / np.pi ** 0.5
cos_offset, sin_offset = np.cos(angular_offset_pi), np.sin(angular_offset_pi)
dest_indices = np.stack(np.meshgrid(np.arange(W), np.arange(H)), axis=-1).astype(float)
normalised_dest_indices = np.matmul(dest_indices - roi_center, np.array([[cos_offset, -sin_offset],
[sin_offset, cos_offset]]))
radii = np.linalg.norm(normalised_dest_indices, axis=-1)
normalised_dest_indices[..., 0] /= np.clip(radii, 1e-9, None)
normalised_dest_indices[..., 1] /= np.clip(radii, 1e-9, None)
radii *= np.sqrt(roi_radii[1] ** 2 * normalised_dest_indices[..., 0] ** 2 +
roi_radii[0] ** 2 * normalised_dest_indices[..., 1] ** 2) / roi_radii[0] / roi_radii[1]
src_radii = np.tanh(radii)
src_x_indices = src_radii * W + 1.0
src_y_indices = np.mod((np.arctan2(normalised_dest_indices[..., 1], normalised_dest_indices[..., 0]) /
2.0 / np.pi) * H, H) + 1.0
return src_x_indices.astype(np.float32), src_y_indices.astype(np.float32)
nn.TanhPolar = TanhPolar |