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utils.py
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utils.py
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from __future__ import division
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
def gray2rgb(im, cmap='gray'):
cmap = plt.get_cmap(cmap)
rgba_img = cmap(im.astype(np.float32))
rgb_img = np.delete(rgba_img, 3, 2)
return rgb_img
def normalize_depth_for_display(depth, pc=95, crop_percent=0, normalizer=None, cmap='gray'):
# convert to disparity
depth = 1./(depth + 1e-6)
if normalizer is not None:
depth = depth/normalizer
else:
depth = depth/(np.percentile(depth, pc) + 1e-6)
depth = np.clip(depth, 0, 1)
depth = gray2rgb(depth, cmap=cmap)
keep_H = int(depth.shape[0] * (1-crop_percent))
depth = depth[:keep_H]
depth = depth
return depth
def euler2mat(z, y, x):
"""Converts euler angles to rotation matrix
TODO: remove the dimension for 'N' (deprecated for converting all source
poses altogether)
Reference: https://github.com/pulkitag/pycaffe-utils/blob/master/rot_utils.py#L174
Args:
z: rotation angle along z axis (in radians) -- size = [B, N]
y: rotation angle along y axis (in radians) -- size = [B, N]
x: rotation angle along x axis (in radians) -- size = [B, N]
Returns:
Rotation matrix corresponding to the euler angles -- size = [B, N, 3, 3]
"""
B = tf.shape(z)[0]
N = 1
z = tf.clip_by_value(z, -np.pi, np.pi)
y = tf.clip_by_value(y, -np.pi, np.pi)
x = tf.clip_by_value(x, -np.pi, np.pi)
# Expand to B x N x 1 x 1
z = tf.expand_dims(tf.expand_dims(z, -1), -1)
y = tf.expand_dims(tf.expand_dims(y, -1), -1)
x = tf.expand_dims(tf.expand_dims(x, -1), -1)
zeros = tf.zeros([B, N, 1, 1])
ones = tf.ones([B, N, 1, 1])
cosz = tf.cos(z)
sinz = tf.sin(z)
rotz_1 = tf.concat([cosz, -sinz, zeros], axis=3)
rotz_2 = tf.concat([sinz, cosz, zeros], axis=3)
rotz_3 = tf.concat([zeros, zeros, ones], axis=3)
zmat = tf.concat([rotz_1, rotz_2, rotz_3], axis=2)
cosy = tf.cos(y)
siny = tf.sin(y)
roty_1 = tf.concat([cosy, zeros, siny], axis=3)
roty_2 = tf.concat([zeros, ones, zeros], axis=3)
roty_3 = tf.concat([-siny,zeros, cosy], axis=3)
ymat = tf.concat([roty_1, roty_2, roty_3], axis=2)
cosx = tf.cos(x)
sinx = tf.sin(x)
rotx_1 = tf.concat([ones, zeros, zeros], axis=3)
rotx_2 = tf.concat([zeros, cosx, -sinx], axis=3)
rotx_3 = tf.concat([zeros, sinx, cosx], axis=3)
xmat = tf.concat([rotx_1, rotx_2, rotx_3], axis=2)
rotMat = tf.matmul(tf.matmul(xmat, ymat), zmat)
return rotMat
def pose_vec2mat(vec):
"""Converts 6DoF parameters to transformation matrix
Args:
vec: 6DoF parameters in the order of tx, ty, tz, rx, ry, rz -- [B, 6]
Returns:
A transformation matrix -- [B, 4, 4]
"""
batch_size, _ = vec.get_shape().as_list()
translation = tf.slice(vec, [0, 0], [-1, 3])
translation = tf.expand_dims(translation, -1)
rx = tf.slice(vec, [0, 3], [-1, 1])
ry = tf.slice(vec, [0, 4], [-1, 1])
rz = tf.slice(vec, [0, 5], [-1, 1])
rot_mat = euler2mat(rz, ry, rx)
rot_mat = tf.squeeze(rot_mat, axis=[1])
filler = tf.constant([0.0, 0.0, 0.0, 1.0], shape=[1, 1, 4])
filler = tf.tile(filler, [batch_size, 1, 1])
transform_mat = tf.concat([rot_mat, translation], axis=2)
transform_mat = tf.concat([transform_mat, filler], axis=1)
return transform_mat
def pixel2cam(depth, pixel_coords, intrinsics, is_homogeneous=True):
"""Transforms coordinates in the pixel frame to the camera frame.
Args:
depth: [batch, height, width]
pixel_coords: homogeneous pixel coordinates [batch, 3, height, width]
intrinsics: camera intrinsics [batch, 3, 3]
is_homogeneous: return in homogeneous coordinates
Returns:
Coords in the camera frame [batch, 3 (4 if homogeneous), height, width]
"""
batch, height, width = depth.get_shape().as_list()
depth = tf.reshape(depth, [batch, 1, -1])
pixel_coords = tf.reshape(pixel_coords, [batch, 3, -1])
cam_coords = tf.matmul(tf.matrix_inverse(intrinsics), pixel_coords) * depth
if is_homogeneous:
ones = tf.ones([batch, 1, height*width])
cam_coords = tf.concat([cam_coords, ones], axis=1)
cam_coords = tf.reshape(cam_coords, [batch, -1, height, width])
return cam_coords
def cam2pixel(cam_coords, proj):
"""Transforms coordinates in a camera frame to the pixel frame.
Args:
cam_coords: [batch, 4, height, width]
proj: [batch, 4, 4]
Returns:
Pixel coordinates projected from the camera frame [batch, height, width, 2]
"""
batch, _, height, width = cam_coords.get_shape().as_list()
cam_coords = tf.reshape(cam_coords, [batch, 4, -1])
unnormalized_pixel_coords = tf.matmul(proj, cam_coords)
x_u = tf.slice(unnormalized_pixel_coords, [0, 0, 0], [-1, 1, -1])
y_u = tf.slice(unnormalized_pixel_coords, [0, 1, 0], [-1, 1, -1])
z_u = tf.slice(unnormalized_pixel_coords, [0, 2, 0], [-1, 1, -1])
x_n = x_u / (z_u + 1e-10)
y_n = y_u / (z_u + 1e-10)
pixel_coords = tf.concat([x_n, y_n], axis=1)
pixel_coords = tf.reshape(pixel_coords, [batch, 2, height, width])
return tf.transpose(pixel_coords, perm=[0, 2, 3, 1])
def meshgrid(batch, height, width, is_homogeneous=True):
"""Construct a 2D meshgrid.
Args:
batch: batch size
height: height of the grid
width: width of the grid
is_homogeneous: whether to return in homogeneous coordinates
Returns:
x,y grid coordinates [batch, 2 (3 if homogeneous), height, width]
"""
x_t = tf.matmul(tf.ones(shape=tf.stack([height, 1])),
tf.transpose(tf.expand_dims(
tf.linspace(-1.0, 1.0, width), 1), [1, 0]))
y_t = tf.matmul(tf.expand_dims(tf.linspace(-1.0, 1.0, height), 1),
tf.ones(shape=tf.stack([1, width])))
x_t = (x_t + 1.0) * 0.5 * tf.cast(width - 1, tf.float32)
y_t = (y_t + 1.0) * 0.5 * tf.cast(height - 1, tf.float32)
if is_homogeneous:
ones = tf.ones_like(x_t)
coords = tf.stack([x_t, y_t, ones], axis=0)
else:
coords = tf.stack([x_t, y_t], axis=0)
coords = tf.tile(tf.expand_dims(coords, 0), [batch, 1, 1, 1])
return coords
def projective_inverse_warp(img, depth, pose, intrinsics):
"""Inverse warp a source image to the target image plane based on projection.
Args:
img: the source image [batch, height_s, width_s, 3]
depth: depth map of the target image [batch, height_t, width_t]
pose: target to source camera transformation matrix [batch, 6], in the
order of tx, ty, tz, rx, ry, rz
intrinsics: camera intrinsics [batch, 3, 3]
Returns:
Source image inverse warped to the target image plane [batch, height_t,
width_t, 3]
"""
batch, height, width, _ = img.get_shape().as_list()
# Convert pose vector to matrix
pose = pose_vec2mat(pose)
# Construct pixel grid coordinates
pixel_coords = meshgrid(batch, height, width)
# Convert pixel coordinates to the camera frame
cam_coords = pixel2cam(depth, pixel_coords, intrinsics)
# Construct a 4x4 intrinsic matrix (TODO: can it be 3x4?)
filler = tf.constant([0.0, 0.0, 0.0, 1.0], shape=[1, 1, 4])
filler = tf.tile(filler, [batch, 1, 1])
intrinsics = tf.concat([intrinsics, tf.zeros([batch, 3, 1])], axis=2)
intrinsics = tf.concat([intrinsics, filler], axis=1)
# Get a 4x4 transformation matrix from 'target' camera frame to 'source'
# pixel frame.
proj_tgt_cam_to_src_pixel = tf.matmul(intrinsics, pose)
src_pixel_coords = cam2pixel(cam_coords, proj_tgt_cam_to_src_pixel)
output_img = bilinear_sampler(img, src_pixel_coords)
return output_img
def bilinear_sampler(imgs, coords):
"""Construct a new image by bilinear sampling from the input image.
Points falling outside the source image boundary have value 0.
Args:
imgs: source image to be sampled from [batch, height_s, width_s, channels]
coords: coordinates of source pixels to sample from [batch, height_t,
width_t, 2]. height_t/width_t correspond to the dimensions of the output
image (don't need to be the same as height_s/width_s). The two channels
correspond to x and y coordinates respectively.
Returns:
A new sampled image [batch, height_t, width_t, channels]
"""
def _repeat(x, n_repeats):
rep = tf.transpose(
tf.expand_dims(tf.ones(shape=tf.stack([
n_repeats,
])), 1), [1, 0])
rep = tf.cast(rep, 'float32')
x = tf.matmul(tf.reshape(x, (-1, 1)), rep)
return tf.reshape(x, [-1])
with tf.name_scope('image_sampling'):
coords_x, coords_y = tf.split(coords, [1, 1], axis=3)
inp_size = imgs.get_shape()
coord_size = coords.get_shape()
out_size = coords.get_shape().as_list()
out_size[3] = imgs.get_shape().as_list()[3]
coords_x = tf.cast(coords_x, 'float32')
coords_y = tf.cast(coords_y, 'float32')
x0 = tf.floor(coords_x)
x1 = x0 + 1
y0 = tf.floor(coords_y)
y1 = y0 + 1
y_max = tf.cast(tf.shape(imgs)[1] - 1, 'float32')
x_max = tf.cast(tf.shape(imgs)[2] - 1, 'float32')
zero = tf.zeros([1], dtype='float32')
x0_safe = tf.clip_by_value(x0, zero, x_max)
y0_safe = tf.clip_by_value(y0, zero, y_max)
x1_safe = tf.clip_by_value(x1, zero, x_max)
y1_safe = tf.clip_by_value(y1, zero, y_max)
## bilinear interp weights, with points outside the grid having weight 0
# wt_x0 = (x1 - coords_x) * tf.cast(tf.equal(x0, x0_safe), 'float32')
# wt_x1 = (coords_x - x0) * tf.cast(tf.equal(x1, x1_safe), 'float32')
# wt_y0 = (y1 - coords_y) * tf.cast(tf.equal(y0, y0_safe), 'float32')
# wt_y1 = (coords_y - y0) * tf.cast(tf.equal(y1, y1_safe), 'float32')
wt_x0 = x1_safe - coords_x
wt_x1 = coords_x - x0_safe
wt_y0 = y1_safe - coords_y
wt_y1 = coords_y - y0_safe
## indices in the flat image to sample from
dim2 = tf.cast(inp_size[2], 'float32')
dim1 = tf.cast(inp_size[2] * inp_size[1], 'float32')
base = tf.reshape(
_repeat(
tf.cast(tf.range(coord_size[0]), 'float32') * dim1,
coord_size[1] * coord_size[2]),
[out_size[0], out_size[1], out_size[2], 1])
base_y0 = base + y0_safe * dim2
base_y1 = base + y1_safe * dim2
idx00 = tf.reshape(x0_safe + base_y0, [-1])
idx01 = x0_safe + base_y1
idx10 = x1_safe + base_y0
idx11 = x1_safe + base_y1
## sample from imgs
imgs_flat = tf.reshape(imgs, tf.stack([-1, inp_size[3]]))
imgs_flat = tf.cast(imgs_flat, 'float32')
im00 = tf.reshape(tf.gather(imgs_flat, tf.cast(idx00, 'int32')), out_size)
im01 = tf.reshape(tf.gather(imgs_flat, tf.cast(idx01, 'int32')), out_size)
im10 = tf.reshape(tf.gather(imgs_flat, tf.cast(idx10, 'int32')), out_size)
im11 = tf.reshape(tf.gather(imgs_flat, tf.cast(idx11, 'int32')), out_size)
w00 = wt_x0 * wt_y0
w01 = wt_x0 * wt_y1
w10 = wt_x1 * wt_y0
w11 = wt_x1 * wt_y1
output = tf.add_n([
w00 * im00, w01 * im01,
w10 * im10, w11 * im11
])
return output