# uniform content loss + adaptive threshold + per_class_input + recursive G
# improvement upon cqf37
from __future__ import division
import os, scipy.io
import tensorflow as tf
import tensorflow.contrib.slim as slim
import numpy as np
import rawpy
import glob
from model import *

input_dir = './dataset/Sony/short/'
gt_dir = './dataset/Sony/long/'
checkpoint_dir = './checkpoint/Sony/'
result_dir = './result_Sony/'

# get test IDs
test_fns = glob.glob(gt_dir + '/1*.ARW')
test_ids = [int(os.path.basename(test_fn)[0:5]) for test_fn in test_fns]

DEBUG = 0
if DEBUG == 1:
    save_freq = 2
    test_ids = test_ids[0:5]






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

def pack_raw_gt(raw):
    # pack Bayer image to 4 channels，SID数据集是14位，所以这里是16383
    im = raw.raw_image_visible.astype(np.float32)
    im = np.maximum(im, 0) / (16383)  # 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, 4])
out_image = denoise_net(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.basename(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.basename(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

        scale_full = np.expand_dims(np.float32(im/16383.0),axis = 0)*ratio

        gt_raw = rawpy.imread(gt_path)
        gt_full = np.expand_dims(np.float32(im / 16383.0), 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))