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layers.py
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layers.py
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import numpy as np
import tensorflow as tf
from tensorflow.contrib import slim
def rand_warp(images, out_size, max_warp=0.5, name='rand_hom'):
num_batch = tf.shape(images)[0]
y = tf.lin_space(-1., 1., 2)
x = tf.lin_space(-1., 1., 2)
py, px = tf.meshgrid(y, x)
pts_orig = tf.tile(tf.concat([tf.reshape(px, [1, -1, 1]),
tf.reshape(py, [1, -1, 1])],
axis=-1), [num_batch, 1, 1])
x = pts_orig[:,:,0:1]
y = pts_orig[:,:,1:2]
rx1 = tf.random.uniform([num_batch, 2, 1], -1., -1.+ max_warp)
rx2 = tf.random.uniform([num_batch, 2, 1], 1.- max_warp, 1.)
rx = tf.concat([rx1, rx2], axis=1)
ry1 = tf.random.uniform([num_batch, 2, 1], -1., -1.+max_warp)
ry2 = tf.random.uniform([num_batch, 2, 1], 1.-max_warp, 1.)
ry = tf.reshape(tf.concat([ry1, ry2], axis=2), [num_batch, 4, 1])
pts_warp = tf.concat([rx, ry], axis=2)
h = estimate_hom(pts_orig, pts_warp)
return hom_warp(images, out_size, h)
def hom_warp(images, out_size, h, name='hom_warp'):
def _repeat(x, n_repeats):
with tf.variable_scope('_repeat'):
rep = tf.transpose(
tf.expand_dims(tf.ones(shape=tf.stack([n_repeats, ])), 1), [1, 0])
rep = tf.cast(rep, 'int32')
x = tf.matmul(tf.reshape(x, (-1, 1)), rep)
return tf.reshape(x, [-1])
def _interpolate(im, x, y, out_size):
with tf.variable_scope('_interpolate'):
# constants
num_batch = tf.shape(im)[0]
height = tf.shape(im)[1]
width = tf.shape(im)[2]
channels = tf.shape(im)[3]
x = tf.cast(x, 'float32')
y = tf.cast(y, 'float32')
height_f = tf.cast(height, 'float32')
width_f = tf.cast(width, 'float32')
out_height = out_size[0]
out_width = out_size[1]
zero = tf.zeros([], dtype='int32')
max_y = tf.cast(tf.shape(im)[1] - 1, 'int32')
max_x = tf.cast(tf.shape(im)[2] - 1, 'int32')
# scale indices from [-1, 1] to [0, width/height]
x = (x + 1.0)*(width_f) / 2.0
y = (y + 1.0)*(height_f) / 2.0
# do sampling
x0 = tf.cast(tf.floor(x), 'int32')
x1 = x0 + 1
y0 = tf.cast(tf.floor(y), 'int32')
y1 = y0 + 1
x0 = tf.clip_by_value(x0, zero, max_x)
x1 = tf.clip_by_value(x1, zero, max_x)
y0 = tf.clip_by_value(y0, zero, max_y)
y1 = tf.clip_by_value(y1, zero, max_y)
dim2 = width
dim1 = width*height
base = _repeat(tf.range(num_batch)*dim1, out_height*out_width)
base_y0 = base + y0*dim2
base_y1 = base + y1*dim2
idx_a = base_y0 + x0
idx_b = base_y1 + x0
idx_c = base_y0 + x1
idx_d = base_y1 + x1
# use indices to lookup pixels in the flat image and restore
# channels dim
im_flat = tf.reshape(im, tf.stack([-1, channels]))
im_flat = tf.cast(im_flat, 'float32')
Ia = tf.gather(im_flat, idx_a)
Ib = tf.gather(im_flat, idx_b)
Ic = tf.gather(im_flat, idx_c)
Id = tf.gather(im_flat, idx_d)
# and finally calculate interpolated values
x0_f = tf.cast(x0, 'float32')
x1_f = tf.cast(x1, 'float32')
y0_f = tf.cast(y0, 'float32')
y1_f = tf.cast(y1, 'float32')
wa = tf.expand_dims(((x1_f-x) * (y1_f-y)), 1)
wb = tf.expand_dims(((x1_f-x) * (y-y0_f)), 1)
wc = tf.expand_dims(((x-x0_f) * (y1_f-y)), 1)
wd = tf.expand_dims(((x-x0_f) * (y-y0_f)), 1)
output = tf.add_n([wa*Ia, wb*Ib, wc*Ic, wd*Id])
return output
def _transform(images, out_size):
with tf.variable_scope('_transform'):
shape = tf.shape(images)
num_batch = tf.shape(images)[0]
num_channels = images.get_shape()[3]
out_width = out_size[1]
out_height = out_size[0]
x = tf.linspace(-1., 1., out_width)
y = tf.linspace(-1., 1., out_height)
x, y = tf.meshgrid(x, y)
grid = tf.expand_dims(tf.concat([tf.expand_dims(tf.reshape(x,[-1]),0),
tf.expand_dims(tf.reshape(y,[-1]),0)], 0), 0)
grid = tf.tile(grid, tf.stack([num_batch, 1, 1]))
grid_hom = tf.concat([grid, tf.ones([num_batch, 1, tf.shape(grid)[-1]])], axis=1)
W = tf.shape(images)[2]
H = tf.shape(images)[1]
W = tf.cast(W, tf.float32)
H = tf.cast(H, tf.float32)
#####################
grid_warp = tf.matmul(h, grid_hom)
grid_warp = grid_warp[:,:2,:] / grid_warp[:,2:3,:]
x_s = grid_warp[:, 0, :]
y_s = grid_warp[:, 1, :]
x_s_flat = tf.reshape(x_s, [-1])
y_s_flat = tf.reshape(y_s, [-1])
input_transformed = _interpolate(
images, x_s_flat, y_s_flat,
out_size)
output = tf.reshape(input_transformed,
tf.stack([num_batch, out_height, out_width, num_channels]))
return output
with tf.variable_scope(name):
output = _transform(images, out_size)
return output
def estimate_hom(src, dst):
rx = src[:,:,0:1]
ry = src[:,:,1:2]
x = dst[:,:,0:1]
y = dst[:,:,1:2]
num_batch = tf.shape(src)[0]
num_pts = tf.shape(src)[1]
_0 = tf.zeros([num_batch, num_pts, 3])
_1 = tf.ones([num_batch, num_pts, 1])
A_even_rows = tf.concat([-rx, -ry, -_1, _0, rx*x, ry*x, x], axis=-1)
A_odd_rows = tf.concat([_0, -rx, -ry, -_1, rx*y, ry*y, y], axis=-1)
A = tf.concat([A_even_rows, A_odd_rows], axis=-1)
A = tf.reshape(A, [num_batch, 2*num_pts, 9])
_, _, V = tf.svd(A, full_matrices=True)
return tf.reshape(V[:,:,-1], [num_batch, 3, 3])
if __name__ == '__main__':
import cv2
from matplotlib import pyplot as plt
from time import time
from sys import argv
im = cv2.cvtColor(cv2.imread(argv[1]),
cv2.COLOR_BGR2RGB) / 255.
h = im.shape[0]
w = im.shape[1]
x = tf.expand_dims(tf.placeholder_with_default(im.astype(np.float32), im.shape), 0)
y = rand_warp(x, im.shape[:2])
y = tf.clip_by_value(y+.5, 0.0, 1.)
with tf.Session() as sess:
t = time()
p = sess.run(y)
print("Took", 1000*(time()-t), "ms")
plt.subplot(2,1,1)
plt.imshow(im)
plt.subplot(2,1,2)
plt.imshow(np.squeeze(p))
plt.show()