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test_Sony.py
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test_Sony.py
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#uniform content loss + adaptive threshold + per_class_input + recursive G
#improvement upon cqf37
from __future__ import division
import os,time,scipy.io
import tensorflow as tf
import tensorflow.contrib.slim as slim
from tensorflow.contrib.layers.python.layers import initializers
import numpy as np
import pdb
import rawpy
import glob
input_dir = './dataset/Sony/short/'
gt_dir = './dataset/Sony/long/'
checkpoint_dir = './checkpoint/Sony/'
result_dir = './result_Sony/'
#get train and test IDs
train_fns = glob.glob(gt_dir + '0*.ARW')
train_ids = []
for i in range(len(train_fns)):
_, train_fn = os.path.split(train_fns[i])
train_ids.append(int(train_fn[0:5]))
test_fns = glob.glob(gt_dir + '/1*.ARW')
test_ids = []
for i in range(len(test_fns)):
_, test_fn = os.path.split(test_fns[i])
test_ids.append(int(test_fn[0:5]))
ps = 512 #patch size for training
save_freq = 500
DEBUG = 0
if DEBUG == 1:
save_freq = 2
train_ids = train_ids[0:5]
test_ids = test_ids[0:5]
def lrelu(x):
return tf.maximum(x*0.2,x)
def upsample_and_concat(x1, x2, output_channels, in_channels):
pool_size = 2
deconv_filter = tf.Variable(tf.truncated_normal( [pool_size, pool_size, output_channels, in_channels], stddev=0.02))
deconv = tf.nn.conv2d_transpose(x1, deconv_filter, tf.shape(x2) , strides=[1, pool_size, pool_size, 1] )
deconv_output = tf.concat([deconv, x2],3)
deconv_output.set_shape([None, None, None, output_channels*2])
return deconv_output
def network(input):
conv1=slim.conv2d(input,32,[3,3], rate=1, activation_fn=lrelu,scope='g_conv1_1')
conv1=slim.conv2d(conv1,32,[3,3], rate=1, activation_fn=lrelu,scope='g_conv1_2')
pool1=slim.max_pool2d(conv1, [2, 2], padding='SAME' )
conv2=slim.conv2d(pool1,64,[3,3], rate=1, activation_fn=lrelu,scope='g_conv2_1')
conv2=slim.conv2d(conv2,64,[3,3], rate=1, activation_fn=lrelu,scope='g_conv2_2')
pool2=slim.max_pool2d(conv2, [2, 2], padding='SAME' )
conv3=slim.conv2d(pool2,128,[3,3], rate=1, activation_fn=lrelu,scope='g_conv3_1')
conv3=slim.conv2d(conv3,128,[3,3], rate=1, activation_fn=lrelu,scope='g_conv3_2')
pool3=slim.max_pool2d(conv3, [2, 2], padding='SAME' )
conv4=slim.conv2d(pool3,256,[3,3], rate=1, activation_fn=lrelu,scope='g_conv4_1')
conv4=slim.conv2d(conv4,256,[3,3], rate=1, activation_fn=lrelu,scope='g_conv4_2')
pool4=slim.max_pool2d(conv4, [2, 2], padding='SAME' )
conv5=slim.conv2d(pool4,512,[3,3], rate=1, activation_fn=lrelu,scope='g_conv5_1')
conv5=slim.conv2d(conv5,512,[3,3], rate=1, activation_fn=lrelu,scope='g_conv5_2')
up6 = upsample_and_concat( conv5, conv4, 256, 512 )
conv6=slim.conv2d(up6, 256,[3,3], rate=1, activation_fn=lrelu,scope='g_conv6_1')
conv6=slim.conv2d(conv6,256,[3,3], rate=1, activation_fn=lrelu,scope='g_conv6_2')
up7 = upsample_and_concat( conv6, conv3, 128, 256 )
conv7=slim.conv2d(up7, 128,[3,3], rate=1, activation_fn=lrelu,scope='g_conv7_1')
conv7=slim.conv2d(conv7,128,[3,3], rate=1, activation_fn=lrelu,scope='g_conv7_2')
up8 = upsample_and_concat( conv7, conv2, 64, 128 )
conv8=slim.conv2d(up8, 64,[3,3], rate=1, activation_fn=lrelu,scope='g_conv8_1')
conv8=slim.conv2d(conv8,64,[3,3], rate=1, activation_fn=lrelu,scope='g_conv8_2')
up9 = upsample_and_concat( conv8, conv1, 32, 64 )
conv9=slim.conv2d(up9, 32,[3,3], rate=1, activation_fn=lrelu,scope='g_conv9_1')
conv9=slim.conv2d(conv9,32,[3,3], rate=1, activation_fn=lrelu,scope='g_conv9_2')
conv10=slim.conv2d(conv9,12,[1,1], rate=1, activation_fn=None, scope='g_conv10')
out = tf.depth_to_space(conv10,2)
return out
def pack_raw(raw):
#pack Bayer image to 4 channels
im = raw.raw_image_visible.astype(np.float32)
im = np.maximum(im - 512,0)/ (16383 - 512) #subtract the black level
im = np.expand_dims(im,axis=2)
img_shape = im.shape
H = img_shape[0]
W = img_shape[1]
out = np.concatenate((im[0:H:2,0:W:2,:],
im[0:H:2,1:W:2,:],
im[1:H:2,1:W:2,:],
im[1:H:2,0:W:2,:]), axis=2)
return out
sess=tf.Session()
in_image=tf.placeholder(tf.float32,[None,None,None,4])
gt_image=tf.placeholder(tf.float32,[None,None,None,3])
out_image=network(in_image)
saver=tf.train.Saver()
sess.run(tf.global_variables_initializer())
ckpt=tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt:
print('loaded '+ckpt.model_checkpoint_path)
saver.restore(sess,ckpt.model_checkpoint_path)
if not os.path.isdir(result_dir + 'final/'):
os.makedirs(result_dir + 'final/')
for test_id in test_ids:
#test the first image in each sequence
in_files = glob.glob(input_dir + '%05d_00*.ARW'%test_id)
for k in range(len(in_files)):
in_path = in_files[k]
_, in_fn = os.path.split(in_path)
print(in_fn)
gt_files = glob.glob(gt_dir + '%05d_00*.ARW'%test_id)
gt_path = gt_files[0]
_, gt_fn = os.path.split(gt_path)
in_exposure = float(in_fn[9:-5])
gt_exposure = float(gt_fn[9:-5])
ratio = min(gt_exposure/in_exposure,300)
raw = rawpy.imread(in_path)
input_full = np.expand_dims(pack_raw(raw),axis=0) *ratio
im = raw.postprocess(use_camera_wb=True, half_size=False, no_auto_bright=True, output_bps=16)
#scale_full = np.expand_dims(np.float32(im/65535.0),axis = 0)*ratio
scale_full = np.expand_dims(np.float32(im/65535.0),axis = 0)
gt_raw = rawpy.imread(gt_path)
im = gt_raw.postprocess(use_camera_wb=True, half_size=False, no_auto_bright=True, output_bps=16)
gt_full = np.expand_dims(np.float32(im/65535.0),axis = 0)
input_full = np.minimum(input_full,1.0)
output =sess.run(out_image,feed_dict={in_image: input_full})
output = np.minimum(np.maximum(output,0),1)
output = output[0,:,:,:]
gt_full = gt_full[0,:,:,:]
scale_full = scale_full[0,:,:,:]
scale_full = scale_full*np.mean(gt_full)/np.mean(scale_full) # scale the low-light image to the same mean of the groundtruth
scipy.misc.toimage(output*255, high=255, low=0, cmin=0, cmax=255).save(result_dir + 'final/%5d_00_%d_out.png'%(test_id,ratio))
scipy.misc.toimage(scale_full*255, high=255, low=0, cmin=0, cmax=255).save(result_dir + 'final/%5d_00_%d_scale.png'%(test_id,ratio))
scipy.misc.toimage(gt_full*255, high=255, low=0, cmin=0, cmax=255).save(result_dir + 'final/%5d_00_%d_gt.png'%(test_id,ratio))