from __future__ import division
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
imgs = [[[[255, 0, 0], [0, 255, 0], [0, 0, 255]],
[[255, 0, 0], [0, 255, 0], [0, 0, 255]],
[[255, 0, 0], [0, 255, 0], [0, 0, 255]],
[[255, 0, 0], [0, 255, 0], [0, 0, 255]]],
[[[255, 0, 0], [0, 255, 0], [0, 0, 255]],
[[255, 0, 0], [0, 255, 0], [0, 0, 255]],
[[255, 0, 0], [0, 255, 0], [0, 0, 255]],
[[255, 0, 0], [0, 255, 0], [0, 0, 255]]]]
imgs = tf.reshape(imgs, [2, 4, 3, 3])
coords = [[[[0.2, 0.2], [1.3, 0.2], [1.8, 2.2]],
[[0.5, 2], [1.3, 2], [0.2, 1.6]],
[[0.2, 0.2], [1.3, 0.2], [1.8, 2.2]],
[[0.5, 2], [1.3, 2], [0.2, 1.6]]],
[[[-1.2, 0.2], [1.3, 0.2], [1.8, 2.2]],
[[0.5, 2], [1.3, 2], [0.2, 1.6]],
[[0.2, 0.2], [1.3, 0.2], [1.8, 2.2]],
[[0.5, 2], [1.3, 2], [0.2, 1.6]]]]
coords = tf.reshape(coords, [2, 4, 3, 2])
cam_coords = [[[[1, 2, 3, 1], [2, 3, 4, 1], [3, 4, 5, 1]],
[[4, 5, 6, 1], [7, 8, 9, 1], [10, 11, 12, 1]],
[[1, 2, 3, 1], [2, 3, 4, 1], [3, 4, 5, 1]],
[[4, 5, 6, 1], [7, 8, 9, 1], [10, 11, 12, 1]]],
[[[1, 2, 3, 1], [2, 3, 4, 1], [3, 4, 5, 1]],
[[4, 5, 6, 1], [7, 8, 9, 1], [10, 11, 12, 1]],
[[2, 2, 3, 1], [3, 3, 4, 1], [4, 4, 5, 1]],
[[5, 5, 6, 1], [8, 8, 9, 1], [11, 11, 12, 1]]]]
cam_coords = tf.reshape(cam_coords, [2, 4, 3, 4])
cam_coords = tf.transpose(cam_coords, perm=[0, 3, 1, 2])
# def bilinear_sampler(imgs, coords, cam_coords_T):
# imgs = tf.tile(imgs, multiples=[1, 100, 50, 1])
# coords = tf.tile(coords, multiples=[1, 100, 50, 1])
# cam_coords_T = tf.tile(cam_coords_T, multiples=[1, 1, 100, 50])
def _repeat(x, n_repeats): # x = tf.cast(tf.range(4), 'float32') * 53248 n_repeats = 53248。
rep = tf.transpose(
tf.expand_dims(tf.ones(shape=tf.stack([
n_repeats,
])), 1), [1,
0]) # 最后得到[1,53248]大小的全一矩阵。tf.stack其作用类似于tf.concat，都是拼接两个张量，而不同之处在于，tf.concat拼接的是两个shape完全相同的张量，并且产生的张量的阶数不会发生变化，而tf.stack则会在新的张量阶上拼接，产生的张量的阶数将会增加
rep = tf.cast(rep, 'float32')
x = tf.matmul(tf.reshape(x, (-1, 1)),
rep) # reshape为一列，得到[[ 0.][ 53248.][106496.][159744.]]*rep，最后得到shape=(4, 53248)的矩阵。
return tf.reshape(x, [-1]) # 最后又化为一列Tensor("Reshape_1:0", shape=(212992,), dtype=float32)
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#判断是否相等，相等为1，不相等为0.
## 以下方式没有提高效果的原因是令重建的对应像素值为0了，但是原图像对应位置还有值，计算误差单纯为原图像的像素值。
# 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')
mask_p = tf.logical_and( # 如果四个值都在图像中说明投影点没有落到图像外。这个mask和相邻帧即投影来点的图像大小相等。
tf.logical_and(x0 >= zero, x1 <= x_max),
tf.logical_and(y0 >= zero, y1 <= y_max))
mask_p = tf.to_float(mask_p)
mask_p = tf.tile(mask_p, multiples=[1, 1, 1, 3])
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]) # tf.reshape(_repeat(tf.cast(tf.range(4), 'float32') * 128 * 416, 128 * 416), [4, 128, 416, 1])
# 上面最后得base=Tensor("Reshape_2:0", shape=(4, 128, 416, 1), dtype=float32)。中间有[ 0.][ 53248.][106496.][159744.]四种数。
base_y0 = base + y0_safe * dim2
base_y1 = base + y1_safe * dim2 # 考虑进有4个batch，所以不同batch要加上不同的基数。
idx00 = tf.reshape(x0_safe + base_y0, [-1]) # 加上基数之后构成了四个像素值的索引。
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) # 每一个输出都有对应的四个像素点的值参与运算。
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 ######这里横轴和纵轴的距离乘机就算距离了。
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
])
# 以下为自定义代码
cam_coords = tf.transpose(cam_coords, perm=[0, 2, 3, 1])
batch, height, width, channels = imgs.get_shape().as_list()
cam_coords = cam_coords[:, :, :, 0:-1]
cam_coords = tf.cast(cam_coords, 'float32')
euclidean = tf.sqrt(tf.reduce_sum(tf.square(cam_coords), 3))
euclidean = tf.reshape(euclidean, [batch, -1])
xy00 = tf.concat([x0, y0], axis=3)
for i in range(1):
euclideani = euclidean[i, :]
euclideani = tf.reshape(euclideani, [-1, 1])
xy00_batchi = xy00[i, :, :, :] # 将横纵坐标合在一起，取batch1.
xy00_batchi = tf.reshape(xy00_batchi, [-1, 2])
xy00_batchi = tf.cast(xy00_batchi, tf.int32)
mask0 = tf.ones(shape=[height * width], dtype='float32')
euclideani_tr = tf.transpose(euclideani, perm=[1, 0])
changdu = 4
batch_h1 = 3
for h1 in range(batch_h1):
xy00_batchi_tile1 = tf.tile(tf.expand_dims(xy00_batchi[h1*changdu:(h1+1)*changdu], 1), multiples=[1, height * width, 1])
xy00_batchi_tile2 = tf.tile(tf.expand_dims(xy00_batchi, 0), multiples=[changdu, 1, 1])
xy00_batchi_equal = tf.equal(xy00_batchi_tile1, xy00_batchi_tile2)
xy00_batchi_equal = tf.reduce_all(xy00_batchi_equal, axis=2)
xy00_batchi_equal = tf.reshape(xy00_batchi_equal, shape=[changdu, height * width])
#
euclideani_tile = tf.tile(euclideani[h1*changdu:(h1+1)*changdu, :], multiples=[1, height * width])
euclideani_greater = tf.greater(euclideani_tile,euclideani_tr)
xy00_batchi_equal_euclideani_greater = tf.logical_and(xy00_batchi_equal, euclideani_greater)
xy00_batchi_equal_euclideani_greater_or = tf.reduce_any(xy00_batchi_equal_euclideani_greater, axis=1)
xy00_batchi_equal_euclideani_greater_or = tf.to_float(xy00_batchi_equal_euclideani_greater_or)
mask0_part1 = mask0[0:h1*changdu]
mask0_part3 = mask0[(h1+1)*changdu:]
mask0_part2 = mask0[h1*changdu:(h1+1)*changdu] - xy00_batchi_equal_euclideani_greater_or
mask0 = tf.concat([mask0_part1, mask0_part2, mask0_part3], axis=0)
mask0 = tf.clip_by_value(mask0, 0, 1)
mask0 = tf.reshape(mask0, [height, width])
mask0 = tf.expand_dims(mask0, 0)
# if i == 0:
# mask0_stack = mask0
# else:
# mask0_stack = tf.concat([mask0_stack, mask0], axis=0)
# mask0_stack = tf.tile(tf.expand_dims(mask0_stack, 3), multiples=[1, 1, 1, 3])
# return output, mask_p, mask0_stack
# output, mask_p, mask0_stack = bilinear_sampler(imgs, coords, cam_coords)
# imgs = tf.cast(imgs, 'float32')
# imgs_mask = imgs*mask1_stack
with tf.Session() as sess:
print(sess.run(xy00_batchi_equal_euclideani_greater_or))
print(xy00_batchi_equal_euclideani_greater_or)
print(mask0)
# # print(sess.run(output))
print(sess.run(mask0))
# print(sess.run(imgs_mask))
# print(mask_p)
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