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models.py
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import tensorflow as tf
from tensorflow.python.platform import flags
from nets.pose_net import pose_exp_net
from monodepth_model import disp_godard
from nets.pwc_flow import construct_model_pwc_full, feature_pyramid_flow
from nets.pwc_disp import feature_pyramid_disp
from optical_flow_warp_fwd import transformerFwd
from optical_flow_warp_old import transformer_old
from monodepth_dataloader import get_multi_scale_intrinsics
from utils import inverse_warp, inverse_warp_new
from loss_utils import SSIM, deprocess_image, preprocess_image,\
cal_grad2_error_mask, charbonnier_loss, cal_grad2_error
opt = flags.FLAGS
class Model_stereo(object):
def __init__(self,
image1=None,
image2=None,
image1r=None,
image2r=None,
cam2pix=None,
pix2cam=None,
reuse_scope=False,
scope=None):
summaries = []
with tf.variable_scope(scope, reuse=reuse_scope):
feature1_disp = feature_pyramid_disp(image1, reuse=False)
feature1r_disp = feature_pyramid_disp(image1r, reuse=True)
pred_disp, stereo_smooth_loss = disp_godard(
image1,
image1r,
feature1_disp,
feature1r_disp,
opt,
is_training=True)
pred_depth = [1. / d for d in pred_disp]
self.loss = stereo_smooth_loss
summaries.append(tf.summary.scalar("total_loss", self.loss))
summaries.append(
tf.summary.scalar("stereo_smooth_loss", stereo_smooth_loss))
tf.summary.image("pred_disp", pred_disp[0][:, :, :, 0:1])
s = 0
tf.summary.image('scale%d_depth_image' % s,
pred_depth[s][:, :, :, 0:1])
tf.summary.image('scale%d_right_disparity_image' % s,
pred_disp[s][:, :, :, 1:2])
#
self.summ_op = tf.summary.merge(summaries)
class Model_eval_stereo(object):
def __init__(self, scope=None):
with tf.variable_scope(scope, reuse=True):
input_uint8_1 = tf.placeholder(
tf.uint8, [1, opt.img_height, opt.img_width, 3],
name='raw_input_1')
input_uint8_1r = tf.placeholder(
tf.uint8, [1, opt.img_height, opt.img_width, 3],
name='raw_input_1r')
input_uint8_2 = tf.placeholder(
tf.uint8, [1, opt.img_height, opt.img_width, 3],
name='raw_input_2')
input_uint8_2r = tf.placeholder(
tf.uint8, [1, opt.img_height, opt.img_width, 3],
name='raw_input_2r')
input_intrinsic = tf.placeholder(tf.float32, [3, 3])
input_1 = preprocess_image(input_uint8_1)
input_2 = preprocess_image(input_uint8_2)
input_1r = preprocess_image(input_uint8_1r)
input_2r = preprocess_image(input_uint8_2r)
feature1_disp = feature_pyramid_disp(input_1, reuse=True)
feature1r_disp = feature_pyramid_disp(input_1r, reuse=True)
pred_disp = disp_godard(
input_1,
input_1r,
feature1_disp,
feature1r_disp,
opt,
is_training=False)
self.input_1 = input_uint8_1
self.input_2 = input_uint8_2
self.input_r = input_uint8_1r
self.input_2r = input_uint8_2r
self.input_intrinsic = input_intrinsic
self.pred_disp = pred_disp[0][:, :, :, 0:1]
# Placeholder created for interface consistency
self.pred_flow_optical = tf.constant(0.0)
self.pred_flow_rigid = tf.constant(0.0)
self.pred_disp2 = tf.constant(0.0)
self.pred_mask = tf.constant(0.0)
class Model_flow(object):
def __init__(self,
image1=None,
image2=None,
image1r=None,
image2r=None,
cam2pix=None,
pix2cam=None,
reuse_scope=False,
scope=None):
summaries = []
batch_size, H, W, color_channels = map(int, image1.get_shape()[0:4])
with tf.variable_scope(scope, reuse=reuse_scope):
feature1 = feature_pyramid_flow(image1, reuse=False)
feature2 = feature_pyramid_flow(image2, reuse=True)
optical_flows = construct_model_pwc_full(image1, image2, feature1,
feature2)
with tf.variable_scope(scope, reuse=True):
optical_flows_rev = construct_model_pwc_full(image2, image1,
feature2, feature1)
occu_masks = [
tf.clip_by_value(
transformerFwd(
tf.ones(
shape=[batch_size, H / (2**s), W / (2**s), 1],
dtype='float32'),
flowr, [H / (2**s), W / (2**s)]),
clip_value_min=0.0,
clip_value_max=1.0)
for s, flowr in enumerate(optical_flows_rev)
]
pixel_loss_depth = 0
pixel_loss_optical = 0
exp_loss = 0
flow_smooth_loss = 0
tgt_image_all = []
src_image_all = []
proj_image_depth_all = []
proj_error_depth_all = []
exp_mask_stack_all = []
flyout_map_all = []
for s in range(opt.num_scales):
# Scale the source and target images for computing loss at the
# according scale.
curr_tgt_image = tf.image.resize_area(
image1,
[int(opt.img_height / (2**s)), int(opt.img_width / (2**s))])
curr_src_image = tf.image.resize_area(
image2,
[int(opt.img_height / (2**s)), int(opt.img_width / (2**s))])
occu_mask = occu_masks[s]
occu_mask_avg = tf.reduce_mean(occu_mask)
curr_proj_image_optical = transformer_old(
curr_src_image, optical_flows[s], [H / (2**s), W / (2**s)])
curr_proj_error_optical = tf.abs(curr_proj_image_optical -
curr_tgt_image)
pixel_loss_optical += (1.0 - opt.ssim_weight) * tf.reduce_mean(
curr_proj_error_optical * occu_mask) / occu_mask_avg
curr_flyout_map = occu_mask
if opt.ssim_weight > 0:
pixel_loss_optical += opt.ssim_weight * tf.reduce_mean(
SSIM(curr_proj_image_optical * occu_mask, curr_tgt_image *
occu_mask)) / occu_mask_avg
flow_smooth_loss += opt.flow_smooth_weight * cal_grad2_error(
optical_flows[s] / 20.0, curr_tgt_image, 1.0)
tgt_image_all.append(curr_tgt_image)
src_image_all.append(curr_src_image)
proj_image_depth_all.append(curr_proj_image_optical)
proj_error_depth_all.append(curr_proj_error_optical)
flyout_map_all.append(curr_flyout_map)
self.loss = (pixel_loss_optical + flow_smooth_loss)
summaries.append(tf.summary.scalar("total_loss", self.loss))
summaries.append(
tf.summary.scalar("pixel_loss_depth", pixel_loss_depth))
summaries.append(
tf.summary.scalar("pixel_loss_optical", pixel_loss_optical))
summaries.append(tf.summary.scalar("exp_loss", exp_loss))
tf.summary.image('scale%d_target_image' % s, \
deprocess_image(tgt_image_all[s]))
tf.summary.image('scale%d_src_image' % s, \
deprocess_image(src_image_all[s]))
tf.summary.image('scale_projected_image',
deprocess_image(proj_image_depth_all[s]))
tf.summary.image('scale_proj_error_error', proj_error_depth_all[s])
tf.summary.image('scale_flyout_mask', flyout_map_all[s])
self.summ_op = tf.summary.merge(summaries)
class Model_eval_flow(object):
def __init__(self, scope=None):
with tf.variable_scope(scope, reuse=True):
input_uint8_1 = tf.placeholder(
tf.uint8, [1, opt.img_height, opt.img_width, 3],
name='raw_input_1')
input_uint8_1r = tf.placeholder(
tf.uint8, [1, opt.img_height, opt.img_width, 3],
name='raw_input_1r')
input_uint8_2 = tf.placeholder(
tf.uint8, [1, opt.img_height, opt.img_width, 3],
name='raw_input_2')
input_uint8_2r = tf.placeholder(
tf.uint8, [1, opt.img_height, opt.img_width, 3],
name='raw_input_2r')
input_intrinsic = tf.placeholder(tf.float32, [3, 3])
cam2pix, pix2cam = get_multi_scale_intrinsics(input_intrinsic,
opt.num_scales)
cam2pix = tf.expand_dims(cam2pix, axis=0)
pix2cam = tf.expand_dims(pix2cam, axis=0)
input_1 = preprocess_image(input_uint8_1)
input_2 = preprocess_image(input_uint8_2)
input_1r = preprocess_image(input_uint8_1r)
input_2r = preprocess_image(input_uint8_2r)
feature1 = feature_pyramid_flow(input_1, reuse=True)
feature2 = feature_pyramid_flow(input_2, reuse=True)
optical_flows = construct_model_pwc_full(input_1, input_2,
feature1, feature2)
self.input_1 = input_uint8_1
self.input_2 = input_uint8_2
self.input_r = input_uint8_1r
self.input_2r = input_uint8_2r
self.input_intrinsic = input_intrinsic
self.pred_flow_optical = optical_flows[0]
# Placeholder created for interface consistency
self.pred_flow_rigid = tf.constant(0.0)
self.pred_disp = tf.constant(0.0)
self.pred_disp2 = tf.constant(0.0)
self.pred_mask = tf.constant(0.0)
class Model_depth(object):
def __init__(self,
image1=None,
image2=None,
image1r=None,
image2r=None,
cam2pix=None,
pix2cam=None,
reuse_scope=False,
scope=None):
summaries = []
batch_size, H, W, color_channels = map(int, image1.get_shape()[0:4])
with tf.variable_scope(scope, reuse=reuse_scope):
feature1_flow = feature_pyramid_flow(image1, reuse=False)
feature2_flow = feature_pyramid_flow(image2, reuse=True)
feature1_disp = feature_pyramid_disp(image1, reuse=False)
feature1r_disp = feature_pyramid_disp(image1r, reuse=True)
pred_disp, stereo_smooth_loss = disp_godard(
image1,
image1r,
feature1_disp,
feature1r_disp,
opt,
is_training=True)
pred_depth = [1. / d for d in pred_disp]
pred_poses = pose_exp_net(image1, image2)
optical_flows_rev = construct_model_pwc_full(
image2, image1, feature2_flow, feature1_flow)
occu_masks = [
tf.clip_by_value(
transformerFwd(
tf.ones(
shape=[batch_size, H / (2**s), W / (2**s), 1],
dtype='float32'),
flowr, [H / (2**s), W / (2**s)]),
clip_value_min=0.0,
clip_value_max=1.0)
for s, flowr in enumerate(optical_flows_rev)
]
pixel_loss_depth = 0
pixel_loss_optical = 0
exp_loss = 0
flow_smooth_loss = 0
tgt_image_all = []
src_image_all = []
proj_image_depth_all = []
proj_error_depth_all = []
exp_mask_stack_all = []
flyout_map_all = []
for s in range(opt.num_scales):
# Scale the source and target images for computing loss at the
# according scale.
curr_tgt_image = tf.image.resize_area(
image1,
[int(opt.img_height / (2**s)), int(opt.img_width / (2**s))])
curr_src_image = tf.image.resize_area(
image2,
[int(opt.img_height / (2**s)), int(opt.img_width / (2**s))])
depth_flow, pose_mat = inverse_warp(
pred_depth[s][:, :, :, 0:1],
pred_poses,
cam2pix[:, s, :, :], ## [batchsize, scale, 3, 3]
pix2cam[:, s, :, :])
occu_mask = occu_masks[s]
occu_mask_avg = tf.reduce_mean(occu_mask)
curr_proj_image_depth = transformer_old(curr_src_image, depth_flow,
[H / (2**s), W / (2**s)])
curr_proj_error_depth = tf.abs(curr_proj_image_depth -
curr_tgt_image)
pixel_loss_depth += (1.0 - opt.ssim_weight) * tf.reduce_mean(
curr_proj_error_depth * occu_mask) / occu_mask_avg
curr_flyout_map = occu_mask
if opt.ssim_weight > 0:
pixel_loss_depth += opt.ssim_weight * tf.reduce_mean(
SSIM(curr_proj_image_depth * occu_mask, curr_tgt_image *
occu_mask)) / occu_mask_avg
tgt_image_all.append(curr_tgt_image)
src_image_all.append(curr_src_image)
proj_image_depth_all.append(curr_proj_image_depth)
proj_error_depth_all.append(curr_proj_error_depth)
flyout_map_all.append(curr_flyout_map)
self.loss = (10.0 * pixel_loss_depth + stereo_smooth_loss)
summaries.append(tf.summary.scalar("total_loss", self.loss))
summaries.append(
tf.summary.scalar("pixel_loss_depth", pixel_loss_depth))
summaries.append(
tf.summary.scalar("pixel_loss_optical", pixel_loss_optical))
summaries.append(tf.summary.scalar("exp_loss", exp_loss))
summaries.append(
tf.summary.scalar("stereo_smooth_loss", stereo_smooth_loss))
tf.summary.image("pred_disp", pred_disp[0][:, :, :, 0:1])
# for s in range(opt.num_scales):
s = 0
tf.summary.histogram("pose_0-2", pred_poses[:, 0:3])
tf.summary.histogram("pose_3-5", pred_poses[:, 3:6])
tf.summary.image('scale%d_depth_image' % s,
pred_depth[s][:, :, :, 0:1])
tf.summary.image('scale%d_right_disparity_image' % s,
pred_disp[s][:, :, :, 1:2])
tf.summary.image('scale%d_target_image' % s, \
deprocess_image(tgt_image_all[s]))
tf.summary.image('scale%d_src_image' % s, \
deprocess_image(src_image_all[s]))
tf.summary.image('scale_projected_image',
deprocess_image(proj_image_depth_all[s]))
tf.summary.image('scale_proj_error_error', proj_error_depth_all[s])
tf.summary.image('scale_flyout_mask', flyout_map_all[s])
self.summ_op = tf.summary.merge(summaries)
class Model_eval_depth(object):
def __init__(self, scope=None):
with tf.variable_scope(scope, reuse=True):
input_uint8_1 = tf.placeholder(
tf.uint8, [1, opt.img_height, opt.img_width, 3],
name='raw_input_1')
input_uint8_1r = tf.placeholder(
tf.uint8, [1, opt.img_height, opt.img_width, 3],
name='raw_input_1r')
input_uint8_2 = tf.placeholder(
tf.uint8, [1, opt.img_height, opt.img_width, 3],
name='raw_input_2')
input_uint8_2r = tf.placeholder(
tf.uint8, [1, opt.img_height, opt.img_width, 3],
name='raw_input_2r')
input_intrinsic = tf.placeholder(tf.float32, [3, 3])
cam2pix, pix2cam = get_multi_scale_intrinsics(input_intrinsic,
opt.num_scales)
cam2pix = tf.expand_dims(cam2pix, axis=0)
pix2cam = tf.expand_dims(pix2cam, axis=0)
input_1 = preprocess_image(input_uint8_1)
input_2 = preprocess_image(input_uint8_2)
input_1r = preprocess_image(input_uint8_1r)
input_2r = preprocess_image(input_uint8_2r)
feature1_disp = feature_pyramid_disp(input_1, reuse=True)
feature1r_disp = feature_pyramid_disp(input_1r, reuse=True)
feature1_flow = feature_pyramid_flow(input_1, reuse=True)
feature2_flow = feature_pyramid_flow(input_2, reuse=True)
pred_disp = disp_godard(
input_1,
input_1r,
feature1_disp,
feature1r_disp,
opt,
is_training=False)
pred_poses = pose_exp_net(input_1, input_2)
optical_flows = construct_model_pwc_full(
input_1, input_2, feature1_flow, feature2_flow)
s = 0
depth_flow, pose_mat = inverse_warp(
1.0 / pred_disp[s][:, :, :, 0:1],
pred_poses,
cam2pix[:, s, :, :], ## [batchsize, scale, 3, 3]
pix2cam[:, s, :, :])
self.input_1 = input_uint8_1
self.input_2 = input_uint8_2
self.input_r = input_uint8_1r
self.input_2r = input_uint8_2r
self.input_intrinsic = input_intrinsic
self.pred_flow_rigid = depth_flow
self.pred_flow_optical = optical_flows[0]
self.pred_disp = pred_disp[0][:, :, :, 0:1]
self.pred_pose_mat = pose_mat[0, :, :]
# Placeholder created for interface consistency
self.pred_disp2 = tf.constant(0.0)
self.pred_mask = tf.constant(0.0)
class Model_depthflow(object):
def __init__(self,
image1=None,
image2=None,
image1r=None,
image2r=None,
cam2pix=None,
pix2cam=None,
reuse_scope=False,
scope=None):
summaries = []
batch_size, H, W, color_channels = map(int, image1.get_shape()[0:4])
with tf.variable_scope(scope, reuse=reuse_scope):
feature1_flow = feature_pyramid_flow(image1, reuse=False)
feature2_flow = feature_pyramid_flow(image2, reuse=True)
feature1_disp = feature_pyramid_disp(image1, reuse=False)
feature1r_disp = feature_pyramid_disp(image1r, reuse=True)
pred_disp, stereo_smooth_loss = disp_godard(
image1,
image1r,
feature1_disp,
feature1r_disp,
opt,
is_training=True)
pred_depth = [1. / d for d in pred_disp]
pred_poses = pose_exp_net(image1, image2)
optical_flows_rev = construct_model_pwc_full(
image2, image1, feature2_flow, feature1_flow)
with tf.variable_scope(scope, reuse=True):
feature2_disp = feature_pyramid_disp(image2, reuse=True)
feature2r_disp = feature_pyramid_disp(image2r, reuse=True)
pred_disp_rev = disp_godard(
image2,
image2r,
feature2_disp,
feature2r_disp,
opt,
is_training=False)
optical_flows = construct_model_pwc_full(
image1, image2, feature1_flow, feature2_flow)
occu_masks = [
tf.clip_by_value(
transformerFwd(
tf.ones(
shape=[batch_size, H / (2**s), W / (2**s), 1],
dtype='float32'),
flowr, [H / (2**s), W / (2**s)]),
clip_value_min=0.0,
clip_value_max=1.0)
for s, flowr in enumerate(optical_flows_rev)
]
_, pose_mat, _, _ = inverse_warp_new(
1.0 / pred_disp[0][:, :, :, 0:1], 1.0 /
pred_disp_rev[0][:, :, :, 0:1], pred_poses, cam2pix[:, 0, :, :],
pix2cam[:, 0, :, :], optical_flows[0], occu_masks[0])
pixel_loss_depth = 0
pixel_loss_optical = 0
exp_loss = 0
flow_smooth_loss = 0
flow_consist_loss = 0
tgt_image_all = []
src_image_all = []
proj_image_depth_all = []
proj_error_depth_all = []
flyout_map_all = []
for s in range(opt.num_scales):
occu_mask = occu_masks[s]
# Scale the source and target images for computing loss at the
# according scale.
curr_tgt_image = tf.image.resize_area(
image1,
[int(opt.img_height / (2**s)), int(opt.img_width / (2**s))])
curr_src_image = tf.image.resize_area(
image2,
[int(opt.img_height / (2**s)), int(opt.img_width / (2**s))])
depth_flow, pose_mat = inverse_warp(
pred_depth[s][:, :, :, 0:1],
tf.stop_gradient(pose_mat),
cam2pix[:, s, :, :], ## [batchsize, scale, 3, 3]
pix2cam[:, s, :, :])
depth_flow_orig, _ = inverse_warp(
tf.stop_gradient(pred_depth[s][:, :, :, 0:1]),
pred_poses,
cam2pix[:, s, :, :], ## [batchsize, scale, 3, 3]
pix2cam[:, s, :, :])
flow_diff = tf.sqrt(
tf.reduce_sum(
tf.square(depth_flow - optical_flows[s]),
axis=3,
keep_dims=True))
flow_diff_mask = tf.cast(
flow_diff < (opt.flow_diff_threshold / 2**s), tf.float32)
occu_region = tf.cast(occu_mask < 0.5, tf.float32)
ref_exp_mask = tf.clip_by_value(
flow_diff_mask + occu_region,
clip_value_min=0.0,
clip_value_max=1.0)
occu_mask_avg = tf.reduce_mean(occu_mask)
curr_proj_image_depth = transformer_old(curr_src_image, depth_flow,
[H / (2**s), W / (2**s)])
curr_proj_error_depth = tf.abs(curr_proj_image_depth -
curr_tgt_image) * ref_exp_mask
pixel_loss_depth += (1.0 - opt.ssim_weight) * tf.reduce_mean(
curr_proj_error_depth * occu_mask) / occu_mask_avg
curr_proj_image_depth_orig = transformer_old(
curr_src_image, depth_flow_orig, [H / (2**s), W / (2**s)])
curr_proj_error_depth_orig = tf.abs(curr_proj_image_depth_orig -
curr_tgt_image) * ref_exp_mask
pixel_loss_depth += (1.0 - opt.ssim_weight) * tf.reduce_mean(
curr_proj_error_depth_orig * occu_mask) / occu_mask_avg
curr_proj_image_optical = transformer_old(
curr_src_image, optical_flows[s], [H / (2**s), W / (2**s)])
curr_proj_error_optical = tf.abs(curr_proj_image_optical -
curr_tgt_image)
pixel_loss_optical += (1.0 - opt.ssim_weight) * tf.reduce_mean(
curr_proj_error_optical * occu_mask) / occu_mask_avg
curr_flyout_map = occu_mask
if opt.ssim_weight > 0:
pixel_loss_depth += opt.ssim_weight * tf.reduce_mean(
SSIM(curr_proj_image_depth * occu_mask * ref_exp_mask,
curr_tgt_image * occu_mask *
ref_exp_mask)) / occu_mask_avg
pixel_loss_depth += opt.ssim_weight * tf.reduce_mean(
SSIM(curr_proj_image_depth_orig * occu_mask * ref_exp_mask,
curr_tgt_image * occu_mask *
ref_exp_mask)) / occu_mask_avg
pixel_loss_optical += opt.ssim_weight * tf.reduce_mean(
SSIM(curr_proj_image_optical * occu_mask, curr_tgt_image *
occu_mask)) / occu_mask_avg
#
flow_smooth_loss += opt.flow_smooth_weight * cal_grad2_error_mask(
optical_flows[s] / 20.0, curr_tgt_image, 1.0,
1.0 - ref_exp_mask)
depth_flow_stop = tf.stop_gradient(depth_flow)
flow_consist_loss += opt.flow_consist_weight * charbonnier_loss(
depth_flow_stop - optical_flows[s], ref_exp_mask)
tgt_image_all.append(curr_tgt_image)
src_image_all.append(curr_src_image)
proj_image_depth_all.append(curr_proj_image_depth)
proj_error_depth_all.append(curr_proj_error_depth)
flyout_map_all.append(curr_flyout_map)
self.loss = (
10.0 * pixel_loss_depth + stereo_smooth_loss
) + pixel_loss_optical + flow_smooth_loss + flow_consist_loss
summaries.append(tf.summary.scalar("total_loss", self.loss))
summaries.append(
tf.summary.scalar("pixel_loss_depth", pixel_loss_depth))
summaries.append(
tf.summary.scalar("pixel_loss_optical", pixel_loss_optical))
summaries.append(tf.summary.scalar("exp_loss", exp_loss))
summaries.append(
tf.summary.scalar("stereo_smooth_loss", stereo_smooth_loss))
tf.summary.image("pred_disp", pred_disp[0][:, :, :, 0:1])
s = 0
tf.summary.histogram("pose_0-2", pred_poses[:, 0:3])
tf.summary.histogram("pose_3-5", pred_poses[:, 3:6])
tf.summary.image('scale%d_depth_image' % s,
pred_depth[s][:, :, :, 0:1])
tf.summary.image('scale%d_right_disparity_image' % s,
pred_disp[s][:, :, :, 1:2])
tf.summary.image('scale%d_target_image' % s, \
deprocess_image(tgt_image_all[s]))
tf.summary.image('scale%d_src_image' % s, \
deprocess_image(src_image_all[s]))
tf.summary.image('scale_projected_image',
deprocess_image(proj_image_depth_all[s]))
tf.summary.image('scale_proj_error_error', proj_error_depth_all[s])
tf.summary.image('scale_flyout_mask', flyout_map_all[s])
self.summ_op = tf.summary.merge(summaries)
class Model_eval_depthflow(object):
def __init__(self, scope=None):
with tf.variable_scope(scope, reuse=True):
input_uint8_1 = tf.placeholder(
tf.uint8, [1, opt.img_height, opt.img_width, 3],
name='raw_input_1')
input_uint8_1r = tf.placeholder(
tf.uint8, [1, opt.img_height, opt.img_width, 3],
name='raw_input_1r')
input_uint8_2 = tf.placeholder(
tf.uint8, [1, opt.img_height, opt.img_width, 3],
name='raw_input_2')
input_uint8_2r = tf.placeholder(
tf.uint8, [1, opt.img_height, opt.img_width, 3],
name='raw_input_2r')
input_intrinsic = tf.placeholder(tf.float32, [3, 3])
cam2pix, pix2cam = get_multi_scale_intrinsics(input_intrinsic,
opt.num_scales)
cam2pix = tf.expand_dims(cam2pix, axis=0)
pix2cam = tf.expand_dims(pix2cam, axis=0)
input_1 = preprocess_image(input_uint8_1)
input_2 = preprocess_image(input_uint8_2)
input_1r = preprocess_image(input_uint8_1r)
input_2r = preprocess_image(input_uint8_2r)
feature1_disp = feature_pyramid_disp(input_1, reuse=True)
feature1r_disp = feature_pyramid_disp(input_1r, reuse=True)
feature2_disp = feature_pyramid_disp(input_2, reuse=True)
feature2r_disp = feature_pyramid_disp(input_2r, reuse=True)
feature1_flow = feature_pyramid_flow(input_1, reuse=True)
feature2_flow = feature_pyramid_flow(input_2, reuse=True)
pred_disp = disp_godard(
input_1,
input_1r,
feature1_disp,
feature1r_disp,
opt,
is_training=False)
pred_disp_rev = disp_godard(
input_2,
input_2r,
feature2_disp,
feature2r_disp,
opt,
is_training=False)
pred_poses = pose_exp_net(input_1, input_2)
optical_flows = construct_model_pwc_full(
input_1, input_2, feature1_flow, feature2_flow)
optical_flows_rev = construct_model_pwc_full(
input_2, input_1, feature2_flow, feature1_flow)
s = 0
occu_mask = tf.clip_by_value(
transformerFwd(
tf.ones(
shape=[
1, opt.img_height // (2**s),
opt.img_width // (2**s), 1
],
dtype='float32'),
optical_flows_rev[s],
[opt.img_height // (2**s), opt.img_width // (2**s)]),
clip_value_min=0.0,
clip_value_max=1.0)
depth_flow, pose_mat, disp1_trans, small_mask = inverse_warp_new(
1.0 / pred_disp[0][:, :, :, 0:1],
1.0 / pred_disp_rev[0][:, :, :, 0:1], pred_poses,
cam2pix[:, 0, :, :], pix2cam[:, 0, :, :], optical_flows[0],
occu_mask)
flow_diff = tf.sqrt(
tf.reduce_sum(
tf.square(depth_flow - optical_flows[0]),
axis=3,
keep_dims=True))
flow_diff_mask = tf.cast(flow_diff < (opt.flow_diff_threshold),
tf.float32)
occu_region = tf.cast(occu_mask < 0.5, tf.float32)
ref_exp_mask = tf.clip_by_value(
flow_diff_mask + occu_region,
clip_value_min=0.0,
clip_value_max=1.0)
self.input_1 = input_uint8_1
self.input_2 = input_uint8_2
self.input_r = input_uint8_1r
self.input_2r = input_uint8_2r
self.input_intrinsic = input_intrinsic
self.pred_pose_mat = pose_mat[0, :, :]
self.pred_flow_rigid = depth_flow
self.pred_flow_optical = optical_flows[0]
self.pred_disp = pred_disp[0][:, :, :, 0:1]
self.pred_disp2 = disp1_trans*0.0 + \
transformer_old(pred_disp_rev[0][:,:,:,0:1], optical_flows[0], [opt.img_height, opt.img_width])*(1.0-0.0)
self.pred_mask = 1.0 - ref_exp_mask