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draw_inference.py
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draw_inference.py
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from __future__ import division, print_function
import os, scipy.io
import tensorflow as tf
import tensorflow.contrib.slim as slim
import numpy as np
import rawpy
import glob
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, black_level, bit_depth=16383):
# pack Bayer image to 4 channels
im = raw.raw_image_visible.astype(np.float32)
im = np.maximum(im -black_level, 0) / (bit_depth - 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
def infrence(input_path, platform):
# Setting parameters based on platform
if platform == 'ios':
black_level = 528
ratio = 50
bit_depth = 16383
else:
black_level = 0
ratio = 180
bit_depth = 35535
# Loading tf model
checkpoint_dir = './model/'
result_dir = './result/'
sess = tf.Session()
in_image = tf.placeholder(tf.float32, [None, None, None, 4])
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):
os.makedirs(result_dir)
raw = rawpy.imread(input_path)
input_full = np.expand_dims(pack_raw(raw, black_level,
bit_depth), axis=0) * ratio
im = raw.postprocess(use_camera_wb=True, half_size=False,
no_auto_bright=True, output_bps=16)
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, :, :, :]
scipy.misc.toimage(output * 255, high=255, low=0, cmin=0, cmax=255).save(
result_dir + platform + '.png')
return result_dir + platform + '.png'
if __name__ == '__main__':
print(infrence('/home/saahil/rps/Hackvento2k19/Learning-to-See-in-the-Dark/Iphone8/3.dng',
'ios'))