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hdrcnn_predict.py
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hdrcnn_predict.py
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"""
" License:
" -----------------------------------------------------------------------------
" Copyright (c) 2017, Gabriel Eilertsen.
" All rights reserved.
"
" Redistribution and use in source and binary forms, with or without
" modification, are permitted provided that the following conditions are met:
"
" 1. Redistributions of source code must retain the above copyright notice,
" this list of conditions and the following disclaimer.
"
" 2. Redistributions in binary form must reproduce the above copyright notice,
" this list of conditions and the following disclaimer in the documentation
" and/or other materials provided with the distribution.
"
" 3. Neither the name of the copyright holder nor the names of its contributors
" may be used to endorse or promote products derived from this software
" without specific prior written permission.
"
" THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
" IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
" ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
" LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
" CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
" SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
" INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
" CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
" ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
" POSSIBILITY OF SUCH DAMAGE.
" -----------------------------------------------------------------------------
"
" Description: TensorFlow prediction script, for reconstructing HDR images
from single expousure LDR images.
" Author: Gabriel Eilertsen, [email protected]
" Date: Aug 2017
"""
import os, sys
import tensorflow as tf
import tensorlayer as tl
import numpy as np
import network, img_io
eps = 1e-5
def print_(str, color='', bold=False):
if color == 'w':
sys.stdout.write('\033[93m')
elif color == "e":
sys.stdout.write('\033[91m')
elif color == "m":
sys.stdout.write('\033[95m')
if bold:
sys.stdout.write('\033[1m')
sys.stdout.write(str)
sys.stdout.write('\033[0m')
sys.stdout.flush()
# Settings, using TensorFlow arguments
FLAGS = tf.flags.FLAGS
tf.flags.DEFINE_integer("width", "1024", "Reconstruction image width")
tf.flags.DEFINE_integer("height", "768", "Reconstruction image height")
tf.flags.DEFINE_string("im_dir", "data", "Path to image directory or an individual image")
tf.flags.DEFINE_string("out_dir", "out", "Path to output directory")
tf.flags.DEFINE_string("params", "hdrcnn_params.npz", "Path to trained CNN weights")
tf.flags.DEFINE_float("scaling", "1.0", "Pre-scaling, which is followed by clipping, in order to remove compression artifacts close to highlights")
tf.flags.DEFINE_float("gamma", "1.0", "Gamma/exponential curve applied before, and inverted after, prediction. This can be used to control the boost of reconstructed pixels.")
# Round to be multiple of 32, so that autoencoder pooling+upsampling
# yields same size as input image
sx = int(np.maximum(32, np.round(FLAGS.width/32.0)*32))
sy = int(np.maximum(32, np.round(FLAGS.height/32.0)*32))
if sx != FLAGS.width or sy != FLAGS.height:
print_("Warning: ", 'w', True)
print_("prediction size has been changed from %dx%d pixels to %dx%d\n"%(FLAGS.width, FLAGS.height, sx, sy), 'w')
print_(" pixels, to comply with autoencoder pooling and up-sampling.\n\n", 'w')
# Info
print_("\n\n\t-------------------------------------------------------------------\n", 'm')
print_("\t HDR image reconstruction from a single exposure using deep CNNs\n\n", 'm')
print_("\t Prediction settings\n", 'm')
print_("\t -------------------\n", 'm')
print_("\t Input image directory/file: %s\n" % FLAGS.im_dir, 'm')
print_("\t Output directory: %s\n" % FLAGS.out_dir, 'm')
print_("\t CNN weights: %s\n" % FLAGS.params, 'm')
print_("\t Prediction resolution: %dx%d pixels\n" % (sx, sy), 'm')
if FLAGS.scaling > 1.0:
print_("\t Pre-scaling: %0.4f\n" % FLAGS.scaling, 'm')
if FLAGS.gamma > 1.0 + eps or FLAGS.gamma < 1.0 - eps:
print_("\t Gamma: %0.4f\n" % FLAGS.gamma, 'm')
print_("\t-------------------------------------------------------------------\n\n\n", 'm')
# Single frame
frames = [FLAGS.im_dir]
# If directory is supplied, get names of all files in the path
if os.path.isdir(FLAGS.im_dir):
frames = [os.path.join(FLAGS.im_dir, name)
for name in sorted(os.listdir(FLAGS.im_dir))
if os.path.isfile(os.path.join(FLAGS.im_dir, name))]
# Placeholder for image input
x = tf.placeholder(tf.float32, shape=[1, sy, sx, 3])
# HDR reconstruction autoencoder model
print_("Network setup:\n")
net = network.model(x)
# The CNN prediction (this also includes blending with input image x)
y = network.get_final(net, x)
# TensorFlow session for running inference
sess = tf.InteractiveSession()
# Load trained CNN weights
print_("\nLoading trained parameters from '%s'..."%FLAGS.params)
load_params = tl.files.load_npz(name=FLAGS.params)
tl.files.assign_params(sess, load_params, net)
print_("\tdone\n")
if not os.path.exists(FLAGS.out_dir):
os.makedirs(FLAGS.out_dir)
print_("\nStarting prediction...\n\n")
k = 0
for i in range(len(frames)):
print("Frame %d: '%s'"%(i,frames[i]))
try:
# Read frame
print_("\tReading...")
x_buffer = img_io.readLDR(frames[i], (sy,sx), True, FLAGS.scaling)
print_("\tdone")
print_("\t(Saturation: %0.2f%%)\n" % (100.0*(x_buffer>=1).sum()/x_buffer.size), 'm')
# Run prediction.
# The gamma value is used to allow for boosting/reducing the intensity of
# the reconstructed highlights. If y = f(x) is the reconstruction, the gamma
# g alters this according to y = f(x^(1/g))^g
print_("\tInference...")
feed_dict = {x: np.power(np.maximum(x_buffer, 0.0), 1.0/FLAGS.gamma)}
y_predict = sess.run([y], feed_dict=feed_dict)
y_predict = np.power(np.maximum(y_predict, 0.0), FLAGS.gamma)
print_("\tdone\n")
# Gamma corrected output
y_gamma = np.power(np.maximum(y_predict, 0.0), 0.5)
# Write to disc
print_("\tWriting...")
k += 1;
img_io.writeLDR(x_buffer, '%s/%06d_in.png' % (FLAGS.out_dir, k), -3)
img_io.writeLDR(y_gamma, '%s/%06d_out.png' % (FLAGS.out_dir, k), -3)
img_io.writeEXR(y_predict, '%s/%06d_out.exr' % (FLAGS.out_dir, k))
print_("\tdone\n")
except img_io.IOException as e:
print_("\n\t\tWarning! ", 'w', True)
print_("%s\n"%e, 'w')
except Exception as e:
print_("\n\t\tError: ", 'e', True)
print_("%s\n"%e, 'e')
print_("Done!\n")
sess.close()