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test_reflection.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import os
import tensorflow as tf
from warp_utils import dense_image_warp
import cv2
from model import Decomposition_Net_Translation, ImageReconstruction_reflection
FLAGS = tf.app.flags.FLAGS
# Define necessary FLAGS
tf.app.flags.DEFINE_integer('batch_size', 1,
"""The number of samples in each batch.""")
tf.app.flags.DEFINE_string('test_dataset_name', 'imgs/00000',
"""Where the test sequences are.""")
tf.app.flags.DEFINE_string('img_type', 'png',
"""Image types.""")
tf.app.flags.DEFINE_float('test_ratio', 1.0,
"""Rescaling factor for the testing sequence.""")
tf.app.flags.DEFINE_string('ckpt_path', 'temp_online_ckpt/model.ckpt-1000',
"""If specified, restore this pretrained model """
"""before beginning any training.""")
tf.app.flags.DEFINE_string('output_dir', 'output',
"""Output folder.""")
tf.app.flags.DEFINE_string('GPU_ID', '',
"""GPU ID""")
GPU_ID = FLAGS.GPU_ID
os.environ["CUDA_VISIBLE_DEVICES"] = GPU_ID
import sys
sys.path.insert(1, 'tfoptflow/tfoptflow/')
from copy import deepcopy
from model_pwcnet import ModelPWCNet, _DEFAULT_PWCNET_TEST_OPTIONS
nn_opts = deepcopy(_DEFAULT_PWCNET_TEST_OPTIONS)
nn_opts['verbose'] = True
nn_opts['ckpt_path'] = 'tfoptflow/tfoptflow/models/pwcnet-lg-6-2-multisteps-chairsthingsmix/pwcnet.ckpt-595000'
nn_opts['batch_size'] = 1
nn_opts['gpu_devices'] = ['/device:CPU:0']
nn_opts['controller'] = '/device:CPU:0'
nn_opts['use_dense_cx'] = True
nn_opts['use_res_cx'] = True
nn_opts['pyr_lvls'] = 6
nn_opts['flow_pred_lvl'] = 2
I0 = cv2.imread(FLAGS.test_dataset_name+'_I0.'+FLAGS.img_type).astype(np.float32)[..., ::-1] / 255.0
I1 = cv2.imread(FLAGS.test_dataset_name+'_I1.'+FLAGS.img_type).astype(np.float32)[..., ::-1] / 255.0
I2 = cv2.imread(FLAGS.test_dataset_name+'_I2.'+FLAGS.img_type).astype(np.float32)[..., ::-1] / 255.0
I3 = cv2.imread(FLAGS.test_dataset_name+'_I3.'+FLAGS.img_type).astype(np.float32)[..., ::-1] / 255.0
I4 = cv2.imread(FLAGS.test_dataset_name+'_I4.'+FLAGS.img_type).astype(np.float32)[..., ::-1] / 255.0
ORIGINAL_H = I0.shape[0]
ORIGINAL_W = I0.shape[1]
RESIZED_H = int(np.ceil(float(ORIGINAL_H) * FLAGS.test_ratio / 16.0))*16
RESIZED_W = int(np.ceil(float(ORIGINAL_W) * FLAGS.test_ratio / 16.0))*16
print(RESIZED_H)
print(RESIZED_W)
I0 = np.expand_dims(cv2.resize(I0, dsize=(RESIZED_W, RESIZED_H), interpolation=cv2.INTER_CUBIC), 0)
I1 = np.expand_dims(cv2.resize(I1, dsize=(RESIZED_W, RESIZED_H), interpolation=cv2.INTER_CUBIC), 0)
I2 = np.expand_dims(cv2.resize(I2, dsize=(RESIZED_W, RESIZED_H), interpolation=cv2.INTER_CUBIC), 0)
I3 = np.expand_dims(cv2.resize(I3, dsize=(RESIZED_W, RESIZED_H), interpolation=cv2.INTER_CUBIC), 0)
I4 = np.expand_dims(cv2.resize(I4, dsize=(RESIZED_W, RESIZED_H), interpolation=cv2.INTER_CUBIC), 0)
CROP_PATCH_H = RESIZED_H
CROP_PATCH_W = RESIZED_W
def flow_to_img(flow):
flow_magnitude = tf.sqrt(1e-6 + flow[..., 0]**2.0 + flow[..., 1]**2.0)
flow_angle = tf.atan2(flow[..., 0], flow[..., 1])
hsv_0 = ((flow_angle / np.pi)+1.0)/2.0
hsv_1 = (flow_magnitude - tf.reduce_min(flow_magnitude, axis=[1, 2], keepdims=True)) / (1e-6 + tf.reduce_max(flow_magnitude, axis=[1, 2], keepdims=True) - tf.reduce_min(flow_magnitude, axis=[1, 2], keepdims=True))
hsv_2 = tf.ones(tf.shape(hsv_0))
hsv = tf.stack([hsv_0, hsv_1, hsv_2], -1)
rgb = tf.image.hsv_to_rgb(hsv)
return rgb
def warp(I, F):
return tf.reshape(dense_image_warp(I, tf.stack([-F[..., 1], -F[..., 0]], -1)), [FLAGS.batch_size, CROP_PATCH_H, CROP_PATCH_W, 3])
with tf.Graph().as_default():
fused_frame0 = tf.placeholder(tf.float32, [1, CROP_PATCH_H, CROP_PATCH_W, 3])
fused_frame1 = tf.placeholder(tf.float32, [1, CROP_PATCH_H, CROP_PATCH_W, 3])
fused_frame2 = tf.placeholder(tf.float32, [1, CROP_PATCH_H, CROP_PATCH_W, 3])
fused_frame3 = tf.placeholder(tf.float32, [1, CROP_PATCH_H, CROP_PATCH_W, 3])
fused_frame4 = tf.placeholder(tf.float32, [1, CROP_PATCH_H, CROP_PATCH_W, 3])
fused_frame0_small = tf.image.resize_bilinear(fused_frame0, [192, 320])
fused_frame1_small = tf.image.resize_bilinear(fused_frame1, [192, 320])
fused_frame2_small = tf.image.resize_bilinear(fused_frame2, [192, 320])
fused_frame3_small = tf.image.resize_bilinear(fused_frame3, [192, 320])
fused_frame4_small = tf.image.resize_bilinear(fused_frame4, [192, 320])
def PWC_full(F0, F1, F2, F3, F4, B0, B1, B2, B3, B4, lvl_h, lvl_w, pwc_h, pwc_w):
ratio_h = float(lvl_h) / float(pwc_h)
ratio_w = float(lvl_w) / float(pwc_w)
nn = ModelPWCNet(mode='test', options=nn_opts)
nn.print_config()
F0 = tf.image.resize_bilinear(F0, (pwc_h, pwc_w))
F1 = tf.image.resize_bilinear(F1, (pwc_h, pwc_w))
F2 = tf.image.resize_bilinear(F2, (pwc_h, pwc_w))
F3 = tf.image.resize_bilinear(F3, (pwc_h, pwc_w))
F4 = tf.image.resize_bilinear(F4, (pwc_h, pwc_w))
B0 = tf.image.resize_bilinear(B0, (pwc_h, pwc_w))
B1 = tf.image.resize_bilinear(B1, (pwc_h, pwc_w))
B2 = tf.image.resize_bilinear(B2, (pwc_h, pwc_w))
B3 = tf.image.resize_bilinear(B3, (pwc_h, pwc_w))
B4 = tf.image.resize_bilinear(B4, (pwc_h, pwc_w))
"""intensity normalization"""
F_max = tf.reduce_max(tf.concat([F0, F1, F2, F3, F4], -1), [1, 2, 3], keepdims=True)
B_max = tf.reduce_max(tf.concat([B0, B1, B2, B3, B4], -1), [1, 2, 3], keepdims=True)
F_min = tf.reduce_min(tf.concat([F0, F1, F2, F3, F4], -1), [1, 2, 3], keepdims=True)
B_min = tf.reduce_min(tf.concat([B0, B1, B2, B3, B4], -1), [1, 2, 3], keepdims=True)
F0 = (F0 - F_min) / tf.maximum((F_max - F_min), 1e-10)
F1 = (F1 - F_min) / tf.maximum((F_max - F_min), 1e-10)
F2 = (F2 - F_min) / tf.maximum((F_max - F_min), 1e-10)
F3 = (F3 - F_min) / tf.maximum((F_max - F_min), 1e-10)
F4 = (F4 - F_min) / tf.maximum((F_max - F_min), 1e-10)
B0 = (B0 - B_min) / tf.maximum((B_max - B_min), 1e-10)
B1 = (B1 - B_min) / tf.maximum((B_max - B_min), 1e-10)
B2 = (B2 - B_min) / tf.maximum((B_max - B_min), 1e-10)
B3 = (B3 - B_min) / tf.maximum((B_max - B_min), 1e-10)
B4 = (B4 - B_min) / tf.maximum((B_max - B_min), 1e-10)
tmp_list = []
tmp_list.append(tf.stack([F0, F1], 1))
tmp_list.append(tf.stack([F0, F2], 1))
tmp_list.append(tf.stack([F0, F3], 1))
tmp_list.append(tf.stack([F0, F4], 1))
tmp_list.append(tf.stack([F1, F0], 1))
tmp_list.append(tf.stack([F1, F2], 1))
tmp_list.append(tf.stack([F1, F3], 1))
tmp_list.append(tf.stack([F1, F4], 1))
tmp_list.append(tf.stack([F2, F0], 1))
tmp_list.append(tf.stack([F2, F1], 1))
tmp_list.append(tf.stack([F2, F3], 1))
tmp_list.append(tf.stack([F2, F4], 1))
tmp_list.append(tf.stack([F3, F0], 1))
tmp_list.append(tf.stack([F3, F1], 1))
tmp_list.append(tf.stack([F3, F2], 1))
tmp_list.append(tf.stack([F3, F4], 1))
tmp_list.append(tf.stack([F4, F0], 1))
tmp_list.append(tf.stack([F4, F1], 1))
tmp_list.append(tf.stack([F4, F2], 1))
tmp_list.append(tf.stack([F4, F3], 1))
tmp_list.append(tf.stack([B0, B1], 1))
tmp_list.append(tf.stack([B0, B2], 1))
tmp_list.append(tf.stack([B0, B3], 1))
tmp_list.append(tf.stack([B0, B4], 1))
tmp_list.append(tf.stack([B1, B0], 1))
tmp_list.append(tf.stack([B1, B2], 1))
tmp_list.append(tf.stack([B1, B3], 1))
tmp_list.append(tf.stack([B1, B4], 1))
tmp_list.append(tf.stack([B2, B0], 1))
tmp_list.append(tf.stack([B2, B1], 1))
tmp_list.append(tf.stack([B2, B3], 1))
tmp_list.append(tf.stack([B2, B4], 1))
tmp_list.append(tf.stack([B3, B0], 1))
tmp_list.append(tf.stack([B3, B1], 1))
tmp_list.append(tf.stack([B3, B2], 1))
tmp_list.append(tf.stack([B3, B4], 1))
tmp_list.append(tf.stack([B4, B0], 1))
tmp_list.append(tf.stack([B4, B1], 1))
tmp_list.append(tf.stack([B4, B2], 1))
tmp_list.append(tf.stack([B4, B3], 1))
PWC_input = tf.concat(tmp_list, 0) # [batch_size*20, 2, H, W, 3]
PWC_input = tf.reshape(PWC_input, [FLAGS.batch_size * 40, 2, pwc_h, pwc_w, 3])
pred_labels, _ = nn.nn(PWC_input, reuse=tf.AUTO_REUSE)
print(pred_labels)
pred_labels = tf.image.resize_bilinear(pred_labels, (lvl_h, lvl_w))
"""
0: W
1: H
"""
ratio_tensor = tf.expand_dims(tf.expand_dims(tf.expand_dims(tf.convert_to_tensor(np.asarray([ratio_w, ratio_h]), dtype=tf.float32), 0), 0), 0)
FF01 = pred_labels[FLAGS.batch_size * 0:FLAGS.batch_size * 1] * ratio_tensor
FF02 = pred_labels[FLAGS.batch_size * 1:FLAGS.batch_size * 2] * ratio_tensor
FF03 = pred_labels[FLAGS.batch_size * 2:FLAGS.batch_size * 3] * ratio_tensor
FF04 = pred_labels[FLAGS.batch_size * 3:FLAGS.batch_size * 4] * ratio_tensor
FF10 = pred_labels[FLAGS.batch_size * 4:FLAGS.batch_size * 5] * ratio_tensor
FF12 = pred_labels[FLAGS.batch_size * 5:FLAGS.batch_size * 6] * ratio_tensor
FF13 = pred_labels[FLAGS.batch_size * 6:FLAGS.batch_size * 7] * ratio_tensor
FF14 = pred_labels[FLAGS.batch_size * 7:FLAGS.batch_size * 8] * ratio_tensor
FF20 = pred_labels[FLAGS.batch_size * 8:FLAGS.batch_size * 9] * ratio_tensor
FF21 = pred_labels[FLAGS.batch_size * 9:FLAGS.batch_size * 10] * ratio_tensor
FF23 = pred_labels[FLAGS.batch_size * 10:FLAGS.batch_size * 11] * ratio_tensor
FF24 = pred_labels[FLAGS.batch_size * 11:FLAGS.batch_size * 12] * ratio_tensor
FF30 = pred_labels[FLAGS.batch_size * 12:FLAGS.batch_size * 13] * ratio_tensor
FF31 = pred_labels[FLAGS.batch_size * 13:FLAGS.batch_size * 14] * ratio_tensor
FF32 = pred_labels[FLAGS.batch_size * 14:FLAGS.batch_size * 15] * ratio_tensor
FF34 = pred_labels[FLAGS.batch_size * 15:FLAGS.batch_size * 16] * ratio_tensor
FF40 = pred_labels[FLAGS.batch_size * 16:FLAGS.batch_size * 17] * ratio_tensor
FF41 = pred_labels[FLAGS.batch_size * 17:FLAGS.batch_size * 18] * ratio_tensor
FF42 = pred_labels[FLAGS.batch_size * 18:FLAGS.batch_size * 19] * ratio_tensor
FF43 = pred_labels[FLAGS.batch_size * 19:FLAGS.batch_size * 20] * ratio_tensor
FB01 = pred_labels[FLAGS.batch_size * 20:FLAGS.batch_size * 21] * ratio_tensor
FB02 = pred_labels[FLAGS.batch_size * 21:FLAGS.batch_size * 22] * ratio_tensor
FB03 = pred_labels[FLAGS.batch_size * 22:FLAGS.batch_size * 23] * ratio_tensor
FB04 = pred_labels[FLAGS.batch_size * 23:FLAGS.batch_size * 24] * ratio_tensor
FB10 = pred_labels[FLAGS.batch_size * 24:FLAGS.batch_size * 25] * ratio_tensor
FB12 = pred_labels[FLAGS.batch_size * 25:FLAGS.batch_size * 26] * ratio_tensor
FB13 = pred_labels[FLAGS.batch_size * 26:FLAGS.batch_size * 27] * ratio_tensor
FB14 = pred_labels[FLAGS.batch_size * 27:FLAGS.batch_size * 28] * ratio_tensor
FB20 = pred_labels[FLAGS.batch_size * 28:FLAGS.batch_size * 29] * ratio_tensor
FB21 = pred_labels[FLAGS.batch_size * 29:FLAGS.batch_size * 30] * ratio_tensor
FB23 = pred_labels[FLAGS.batch_size * 30:FLAGS.batch_size * 31] * ratio_tensor
FB24 = pred_labels[FLAGS.batch_size * 31:FLAGS.batch_size * 32] * ratio_tensor
FB30 = pred_labels[FLAGS.batch_size * 32:FLAGS.batch_size * 33] * ratio_tensor
FB31 = pred_labels[FLAGS.batch_size * 33:FLAGS.batch_size * 34] * ratio_tensor
FB32 = pred_labels[FLAGS.batch_size * 34:FLAGS.batch_size * 35] * ratio_tensor
FB34 = pred_labels[FLAGS.batch_size * 35:FLAGS.batch_size * 36] * ratio_tensor
FB40 = pred_labels[FLAGS.batch_size * 36:FLAGS.batch_size * 37] * ratio_tensor
FB41 = pred_labels[FLAGS.batch_size * 37:FLAGS.batch_size * 38] * ratio_tensor
FB42 = pred_labels[FLAGS.batch_size * 38:FLAGS.batch_size * 39] * ratio_tensor
FB43 = pred_labels[FLAGS.batch_size * 39:FLAGS.batch_size * 40] * ratio_tensor
return FF01, FF02, FF03, FF04, \
FF10, FF12, FF13, FF14, \
FF20, FF21, FF23, FF24, \
FF30, FF31, FF32, FF34, \
FF40, FF41, FF42, FF43, \
FB01, FB02, FB03, FB04, \
FB10, FB12, FB13, FB14, \
FB20, FB21, FB23, FB24, \
FB30, FB31, FB32, FB34, \
FB40, FB41, FB42, FB43
model = Decomposition_Net_Translation(CROP_PATCH_H//16, CROP_PATCH_W//16, False, False, False)
FF01, FF02, FF03, FF04, \
FF10, FF12, FF13, FF14, \
FF20, FF21, FF23, FF24, \
FF30, FF31, FF32, FF34, \
FF40, FF41, FF42, FF43, \
FB01, FB02, FB03, FB04, \
FB10, FB12, FB13, FB14, \
FB20, FB21, FB23, FB24, \
FB30, FB31, FB32, FB34, \
FB40, FB41, FB42, FB43 = model.inference(fused_frame0, fused_frame1, fused_frame2, fused_frame3, fused_frame4)
"""image"""
model4 = ImageReconstruction_reflection(FLAGS.batch_size, CROP_PATCH_H, CROP_PATCH_W, level=4)
F0_pred_4, F1_pred_4, F2_pred_4, F3_pred_4, F4_pred_4, \
B0_pred_4, B1_pred_4, B2_pred_4, B3_pred_4, B4_pred_4 = model4._build_model(tf.concat([fused_frame0,
fused_frame1,
fused_frame2,
fused_frame3,
fused_frame4], 3),
None, None, None, None, None,
None, None, None, None, None,
FF01, FF02, FF03, FF04,
FF10, FF12, FF13, FF14,
FF20, FF21, FF23, FF24,
FF30, FF31, FF32, FF34,
FF40, FF41, FF42, FF43,
FB01, FB02, FB03, FB04,
FB10, FB12, FB13, FB14,
FB20, FB21, FB23, FB24,
FB30, FB31, FB32, FB34,
FB40, FB41, FB42, FB43)
"""upsample (no resize in model)"""
F0_pred_4_up = tf.image.resize_bilinear(F0_pred_4, (CROP_PATCH_H // (2 ** 3), CROP_PATCH_W // (2 ** 3)))
F1_pred_4_up = tf.image.resize_bilinear(F1_pred_4, (CROP_PATCH_H // (2 ** 3), CROP_PATCH_W // (2 ** 3)))
F2_pred_4_up = tf.image.resize_bilinear(F2_pred_4, (CROP_PATCH_H // (2 ** 3), CROP_PATCH_W // (2 ** 3)))
F3_pred_4_up = tf.image.resize_bilinear(F3_pred_4, (CROP_PATCH_H // (2 ** 3), CROP_PATCH_W // (2 ** 3)))
F4_pred_4_up = tf.image.resize_bilinear(F4_pred_4, (CROP_PATCH_H // (2 ** 3), CROP_PATCH_W // (2 ** 3)))
B0_pred_4_up = tf.image.resize_bilinear(B0_pred_4, (CROP_PATCH_H // (2 ** 3), CROP_PATCH_W // (2 ** 3)))
B1_pred_4_up = tf.image.resize_bilinear(B1_pred_4, (CROP_PATCH_H // (2 ** 3), CROP_PATCH_W // (2 ** 3)))
B2_pred_4_up = tf.image.resize_bilinear(B2_pred_4, (CROP_PATCH_H // (2 ** 3), CROP_PATCH_W // (2 ** 3)))
B3_pred_4_up = tf.image.resize_bilinear(B3_pred_4, (CROP_PATCH_H // (2 ** 3), CROP_PATCH_W // (2 ** 3)))
B4_pred_4_up = tf.image.resize_bilinear(B4_pred_4, (CROP_PATCH_H // (2 ** 3), CROP_PATCH_W // (2 ** 3)))
FF01, FF02, FF03, FF04, \
FF10, FF12, FF13, FF14, \
FF20, FF21, FF23, FF24, \
FF30, FF31, FF32, FF34, \
FF40, FF41, FF42, FF43, \
FB01, FB02, FB03, FB04, \
FB10, FB12, FB13, FB14, \
FB20, FB21, FB23, FB24, \
FB30, FB31, FB32, FB34, \
FB40, FB41, FB42, FB43 = PWC_full(F0_pred_4_up, F1_pred_4_up, F2_pred_4_up, F3_pred_4_up, F4_pred_4_up,
B0_pred_4_up, B1_pred_4_up, B2_pred_4_up, B3_pred_4_up, B4_pred_4_up,
CROP_PATCH_H // (2 ** 3), CROP_PATCH_W // (2 ** 3),
int(np.ceil(float(CROP_PATCH_H // (2 ** 3)) / 64.0)) * 64,
int(np.ceil(float(CROP_PATCH_W // (2 ** 3)) / 64.0)) * 64)
"""3"""
model3 = ImageReconstruction_reflection(FLAGS.batch_size, CROP_PATCH_H, CROP_PATCH_W, level=3)
F0_pred_3, F1_pred_3, F2_pred_3, F3_pred_3, F4_pred_3, \
B0_pred_3, B1_pred_3, B2_pred_3, B3_pred_3, B4_pred_3 = model3._build_model(tf.concat([fused_frame0,
fused_frame1,
fused_frame2,
fused_frame3,
fused_frame4], 3),
F0_pred_4_up, F1_pred_4_up,
F2_pred_4_up,
F3_pred_4_up, F4_pred_4_up,
B0_pred_4_up, B1_pred_4_up,
B2_pred_4_up,
B3_pred_4_up, B4_pred_4_up,
FF01, FF02, FF03, FF04,
FF10, FF12, FF13, FF14,
FF20, FF21, FF23, FF24,
FF30, FF31, FF32, FF34,
FF40, FF41, FF42, FF43,
FB01, FB02, FB03, FB04,
FB10, FB12, FB13, FB14,
FB20, FB21, FB23, FB24,
FB30, FB31, FB32, FB34,
FB40, FB41, FB42, FB43)
F0_pred_3_up = tf.image.resize_bilinear(F0_pred_3, (CROP_PATCH_H // (2 ** 2), CROP_PATCH_W // (2 ** 2)))
F1_pred_3_up = tf.image.resize_bilinear(F1_pred_3, (CROP_PATCH_H // (2 ** 2), CROP_PATCH_W // (2 ** 2)))
F2_pred_3_up = tf.image.resize_bilinear(F2_pred_3, (CROP_PATCH_H // (2 ** 2), CROP_PATCH_W // (2 ** 2)))
F3_pred_3_up = tf.image.resize_bilinear(F3_pred_3, (CROP_PATCH_H // (2 ** 2), CROP_PATCH_W // (2 ** 2)))
F4_pred_3_up = tf.image.resize_bilinear(F4_pred_3, (CROP_PATCH_H // (2 ** 2), CROP_PATCH_W // (2 ** 2)))
B0_pred_3_up = tf.image.resize_bilinear(B0_pred_3, (CROP_PATCH_H // (2 ** 2), CROP_PATCH_W // (2 ** 2)))
B1_pred_3_up = tf.image.resize_bilinear(B1_pred_3, (CROP_PATCH_H // (2 ** 2), CROP_PATCH_W // (2 ** 2)))
B2_pred_3_up = tf.image.resize_bilinear(B2_pred_3, (CROP_PATCH_H // (2 ** 2), CROP_PATCH_W // (2 ** 2)))
B3_pred_3_up = tf.image.resize_bilinear(B3_pred_3, (CROP_PATCH_H // (2 ** 2), CROP_PATCH_W // (2 ** 2)))
B4_pred_3_up = tf.image.resize_bilinear(B4_pred_3, (CROP_PATCH_H // (2 ** 2), CROP_PATCH_W // (2 ** 2)))
FF01, FF02, FF03, FF04, \
FF10, FF12, FF13, FF14, \
FF20, FF21, FF23, FF24, \
FF30, FF31, FF32, FF34, \
FF40, FF41, FF42, FF43, \
FB01, FB02, FB03, FB04, \
FB10, FB12, FB13, FB14, \
FB20, FB21, FB23, FB24, \
FB30, FB31, FB32, FB34, \
FB40, FB41, FB42, FB43 = PWC_full(F0_pred_3_up, F1_pred_3_up, F2_pred_3_up, F3_pred_3_up, F4_pred_3_up,
B0_pred_3_up, B1_pred_3_up, B2_pred_3_up, B3_pred_3_up, B4_pred_3_up,
CROP_PATCH_H // (2 ** 2), CROP_PATCH_W // (2 ** 2),
int(np.ceil(float(CROP_PATCH_H // (2 ** 2)) / 64.0)) * 64,
int(np.ceil(float(CROP_PATCH_W // (2 ** 2)) / 64.0)) * 64)
"""2"""
model2 = ImageReconstruction_reflection(FLAGS.batch_size, CROP_PATCH_H, CROP_PATCH_W, level=2)
F0_pred_2, F1_pred_2, F2_pred_2, F3_pred_2, F4_pred_2, \
B0_pred_2, B1_pred_2, B2_pred_2, B3_pred_2, B4_pred_2 = model2._build_model(tf.concat([fused_frame0,
fused_frame1,
fused_frame2,
fused_frame3,
fused_frame4], 3),
F0_pred_3_up, F1_pred_3_up,
F2_pred_3_up,
F3_pred_3_up, F4_pred_3_up,
B0_pred_3_up, B1_pred_3_up,
B2_pred_3_up,
B3_pred_3_up, B4_pred_3_up,
FF01, FF02, FF03, FF04,
FF10, FF12, FF13, FF14,
FF20, FF21, FF23, FF24,
FF30, FF31, FF32, FF34,
FF40, FF41, FF42, FF43,
FB01, FB02, FB03, FB04,
FB10, FB12, FB13, FB14,
FB20, FB21, FB23, FB24,
FB30, FB31, FB32, FB34,
FB40, FB41, FB42, FB43)
F0_pred_2_up = tf.image.resize_bilinear(F0_pred_2, (CROP_PATCH_H // (2 ** 1), CROP_PATCH_W // (2 ** 1)))
F1_pred_2_up = tf.image.resize_bilinear(F1_pred_2, (CROP_PATCH_H // (2 ** 1), CROP_PATCH_W // (2 ** 1)))
F2_pred_2_up = tf.image.resize_bilinear(F2_pred_2, (CROP_PATCH_H // (2 ** 1), CROP_PATCH_W // (2 ** 1)))
F3_pred_2_up = tf.image.resize_bilinear(F3_pred_2, (CROP_PATCH_H // (2 ** 1), CROP_PATCH_W // (2 ** 1)))
F4_pred_2_up = tf.image.resize_bilinear(F4_pred_2, (CROP_PATCH_H // (2 ** 1), CROP_PATCH_W // (2 ** 1)))
B0_pred_2_up = tf.image.resize_bilinear(B0_pred_2, (CROP_PATCH_H // (2 ** 1), CROP_PATCH_W // (2 ** 1)))
B1_pred_2_up = tf.image.resize_bilinear(B1_pred_2, (CROP_PATCH_H // (2 ** 1), CROP_PATCH_W // (2 ** 1)))
B2_pred_2_up = tf.image.resize_bilinear(B2_pred_2, (CROP_PATCH_H // (2 ** 1), CROP_PATCH_W // (2 ** 1)))
B3_pred_2_up = tf.image.resize_bilinear(B3_pred_2, (CROP_PATCH_H // (2 ** 1), CROP_PATCH_W // (2 ** 1)))
B4_pred_2_up = tf.image.resize_bilinear(B4_pred_2, (CROP_PATCH_H // (2 ** 1), CROP_PATCH_W // (2 ** 1)))
FF01, FF02, FF03, FF04, \
FF10, FF12, FF13, FF14, \
FF20, FF21, FF23, FF24, \
FF30, FF31, FF32, FF34, \
FF40, FF41, FF42, FF43, \
FB01, FB02, FB03, FB04, \
FB10, FB12, FB13, FB14, \
FB20, FB21, FB23, FB24, \
FB30, FB31, FB32, FB34, \
FB40, FB41, FB42, FB43 = PWC_full(F0_pred_2_up, F1_pred_2_up, F2_pred_2_up, F3_pred_2_up, F4_pred_2_up,
B0_pred_2_up, B1_pred_2_up, B2_pred_2_up, B3_pred_2_up, B4_pred_2_up,
CROP_PATCH_H // (2 ** 1), CROP_PATCH_W // (2 ** 1),
int(np.ceil(float(CROP_PATCH_H // (2 ** 1)) / 64.0)) * 64,
int(np.ceil(float(CROP_PATCH_W // (2 ** 1)) / 64.0)) * 64)
"""1"""
model1 = ImageReconstruction_reflection(FLAGS.batch_size, CROP_PATCH_H, CROP_PATCH_W, level=1)
F0_pred_1, F1_pred_1, F2_pred_1, F3_pred_1, F4_pred_1, \
B0_pred_1, B1_pred_1, B2_pred_1, B3_pred_1, B4_pred_1 = model1._build_model(tf.concat([fused_frame0,
fused_frame1,
fused_frame2,
fused_frame3,
fused_frame4], 3),
F0_pred_2_up, F1_pred_2_up,
F2_pred_2_up,
F3_pred_2_up, F4_pred_2_up,
B0_pred_2_up, B1_pred_2_up,
B2_pred_2_up,
B3_pred_2_up, B4_pred_2_up,
FF01, FF02, FF03, FF04,
FF10, FF12, FF13, FF14,
FF20, FF21, FF23, FF24,
FF30, FF31, FF32, FF34,
FF40, FF41, FF42, FF43,
FB01, FB02, FB03, FB04,
FB10, FB12, FB13, FB14,
FB20, FB21, FB23, FB24,
FB30, FB31, FB32, FB34,
FB40, FB41, FB42, FB43)
F0_pred_1_up = tf.image.resize_bilinear(F0_pred_1, (CROP_PATCH_H // (2 ** 0), CROP_PATCH_W // (2 ** 0)))
F1_pred_1_up = tf.image.resize_bilinear(F1_pred_1, (CROP_PATCH_H // (2 ** 0), CROP_PATCH_W // (2 ** 0)))
F2_pred_1_up = tf.image.resize_bilinear(F2_pred_1, (CROP_PATCH_H // (2 ** 0), CROP_PATCH_W // (2 ** 0)))
F3_pred_1_up = tf.image.resize_bilinear(F3_pred_1, (CROP_PATCH_H // (2 ** 0), CROP_PATCH_W // (2 ** 0)))
F4_pred_1_up = tf.image.resize_bilinear(F4_pred_1, (CROP_PATCH_H // (2 ** 0), CROP_PATCH_W // (2 ** 0)))
B0_pred_1_up = tf.image.resize_bilinear(B0_pred_1, (CROP_PATCH_H // (2 ** 0), CROP_PATCH_W // (2 ** 0)))
B1_pred_1_up = tf.image.resize_bilinear(B1_pred_1, (CROP_PATCH_H // (2 ** 0), CROP_PATCH_W // (2 ** 0)))
B2_pred_1_up = tf.image.resize_bilinear(B2_pred_1, (CROP_PATCH_H // (2 ** 0), CROP_PATCH_W // (2 ** 0)))
B3_pred_1_up = tf.image.resize_bilinear(B3_pred_1, (CROP_PATCH_H // (2 ** 0), CROP_PATCH_W // (2 ** 0)))
B4_pred_1_up = tf.image.resize_bilinear(B4_pred_1, (CROP_PATCH_H // (2 ** 0), CROP_PATCH_W // (2 ** 0)))
FF01, FF02, FF03, FF04, \
FF10, FF12, FF13, FF14, \
FF20, FF21, FF23, FF24, \
FF30, FF31, FF32, FF34, \
FF40, FF41, FF42, FF43, \
FB01, FB02, FB03, FB04, \
FB10, FB12, FB13, FB14, \
FB20, FB21, FB23, FB24, \
FB30, FB31, FB32, FB34, \
FB40, FB41, FB42, FB43 = PWC_full(F0_pred_1_up, F1_pred_1_up, F2_pred_1_up, F3_pred_1_up, F4_pred_1_up,
B0_pred_1_up, B1_pred_1_up, B2_pred_1_up, B3_pred_1_up, B4_pred_1_up,
CROP_PATCH_H // (2 ** 0), CROP_PATCH_W // (2 ** 0),
int(np.ceil(float(CROP_PATCH_H // (2 ** 0)) / 64.0)) * 64,
int(np.ceil(float(CROP_PATCH_W // (2 ** 0)) / 64.0)) * 64)
"""0"""
model0 = ImageReconstruction_reflection(FLAGS.batch_size, CROP_PATCH_H, CROP_PATCH_W, level=0)
F0_pred_0, F1_pred_0, F2_pred_0, F3_pred_0, F4_pred_0, \
B0_pred_0, B1_pred_0, B2_pred_0, B3_pred_0, B4_pred_0 = model0._build_model(tf.concat([fused_frame0,
fused_frame1,
fused_frame2,
fused_frame3,
fused_frame4], 3),
F0_pred_1_up, F1_pred_1_up,
F2_pred_1_up,
F3_pred_1_up, F4_pred_1_up,
B0_pred_1_up, B1_pred_1_up,
B2_pred_1_up,
B3_pred_1_up, B4_pred_1_up,
FF01, FF02, FF03, FF04,
FF10, FF12, FF13, FF14,
FF20, FF21, FF23, FF24,
FF30, FF31, FF32, FF34,
FF40, FF41, FF42, FF43,
FB01, FB02, FB03, FB04,
FB10, FB12, FB13, FB14,
FB20, FB21, FB23, FB24,
FB30, FB31, FB32, FB34,
FB40, FB41, FB42, FB43)
sess = tf.Session()
saver2 = tf.train.Saver(var_list=[v for v in tf.all_variables() if "pwcnet" in v.name])
saver2.restore(sess, nn_opts['ckpt_path'])
saver4 = tf.train.Saver(var_list=[v for v in tf.all_variables() if "pwcnet" not in v.name])
saver4.restore(sess, FLAGS.ckpt_path)
import cv2
out_path = FLAGS.output_dir + '/'
F2, B2 = sess.run([F2_pred_0, B2_pred_0], feed_dict={fused_frame0: I0, fused_frame1: I1, fused_frame2: I2, fused_frame3: I3, fused_frame4: I4})
cv2.imwrite(out_path + FLAGS.test_dataset_name[-5:]+'F2_norm.png', np.clip(np.round(cv2.resize(F2[0, :, :, ::-1]/np.max(F2[0, :, :, ::-1]), dsize=(ORIGINAL_W, ORIGINAL_H), interpolation=cv2.INTER_CUBIC) * 255.0), 0.0, 255.0).astype(np.uint8))
cv2.imwrite(out_path + FLAGS.test_dataset_name[-5:]+'B2_norm.png', np.clip(np.round(cv2.resize(B2[0, :, :, ::-1]/np.max(B2[0, :, :, ::-1]), dsize=(ORIGINAL_W, ORIGINAL_H), interpolation=cv2.INTER_CUBIC) * 255.0), 0.0, 255.0).astype(np.uint8))
cv2.imwrite(out_path + FLAGS.test_dataset_name[-5:]+'F2.png', np.clip(np.round(cv2.resize(F2[0, :, :, ::-1], dsize=(ORIGINAL_W, ORIGINAL_H), interpolation=cv2.INTER_CUBIC) * 255.0), 0.0, 255.0).astype(np.uint8))
cv2.imwrite(out_path + FLAGS.test_dataset_name[-5:]+'B2.png', np.clip(np.round(cv2.resize(B2[0, :, :, ::-1], dsize=(ORIGINAL_W, ORIGINAL_H), interpolation=cv2.INTER_CUBIC) * 255.0), 0.0, 255.0).astype(np.uint8))