Cov_version.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\tensorFlow\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Conv2D, UpSampling2D, InputLayer, Conv2DTranspose\n",
    "from keras.layers import Activation, Dense, Dropout, Flatten\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.models import Sequential\n",
    "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
    "from skimage.color import rgb2lab, lab2rgb, rgb2gray, xyz2lab\n",
    "from skimage.io import imsave\n",
    "import numpy as np\n",
    "import os\n",
    "import random\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get images\n",
    "image = img_to_array(load_img('train/timg3.jpg'))\n",
    "image = np.array(image, dtype=float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\tensorFlow\\lib\\site-packages\\skimage\\transform\\_warps.py:84: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.\n",
      "  warn(\"The default mode, 'constant', will be changed to 'reflect' in \"\n"
     ]
    }
   ],
   "source": [
    "from skimage import transform,data\n",
    "image = transform.resize(image, (400, 400))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = rgb2lab(1.0/255*image)[:,:,0]\n",
    "Y = rgb2lab(1.0/255*image)[:,:,1:]\n",
    "Y /= 128\n",
    "X = X.reshape(1, 400, 400, 1)\n",
    "Y = Y.reshape(1, 400, 400, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building the neural network\n",
    "model = Sequential()\n",
    "model.add(InputLayer(input_shape=(None, None, 1)))\n",
    "model.add(Conv2D(8, (3, 3), activation='relu', padding='same', strides=2))\n",
    "model.add(Conv2D(8, (3, 3), activation='relu', padding='same'))\n",
    "model.add(Conv2D(16, (3, 3), activation='relu', padding='same'))\n",
    "model.add(Conv2D(16, (3, 3), activation='relu', padding='same', strides=2))\n",
    "model.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n",
    "model.add(Conv2D(32, (3, 3), activation='relu', padding='same', strides=2))\n",
    "model.add(UpSampling2D((2, 2)))\n",
    "model.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n",
    "model.add(UpSampling2D((2, 2)))\n",
    "model.add(Conv2D(16, (3, 3), activation='relu', padding='same'))\n",
    "model.add(UpSampling2D((2, 2)))\n",
    "model.add(Conv2D(2, (3, 3), activation='tanh', padding='same'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finish model\n",
    "model.compile(optimizer='adam',loss='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "1/1 [==============================] - 2s 2s/step - loss: 0.3964\n",
      "Epoch 2/1000\n",
      "1/1 [==============================] - 1s 854ms/step - loss: 0.3483\n",
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      "Epoch 4/1000\n",
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      "Epoch 5/1000\n",
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      "Epoch 6/1000\n",
      "1/1 [==============================] - 1s 854ms/step - loss: 0.0174\n",
      "Epoch 7/1000\n",
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      "Epoch 8/1000\n",
      "1/1 [==============================] - 1s 774ms/step - loss: 0.0154\n",
      "Epoch 9/1000\n",
      "1/1 [==============================] - 1s 811ms/step - loss: 0.0112\n",
      "Epoch 10/1000\n",
      "1/1 [==============================] - 1s 714ms/step - loss: 0.0092\n",
      "Epoch 11/1000\n",
      "1/1 [==============================] - 1s 701ms/step - loss: 0.0085\n",
      "Epoch 12/1000\n",
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      "Epoch 16/1000\n",
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      "Epoch 31/1000\n",
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      "1/1 [==============================] - 1s 861ms/step - loss: 0.0046\n",
      "Epoch 33/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 0.0045\n",
      "Epoch 34/1000\n",
      "1/1 [==============================] - 1s 968ms/step - loss: 0.0044\n",
      "Epoch 35/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 0.0043\n",
      "Epoch 36/1000\n",
      "1/1 [==============================] - 1s 852ms/step - loss: 0.0043\n",
      "Epoch 37/1000\n",
      "1/1 [==============================] - 1s 812ms/step - loss: 0.0042\n",
      "Epoch 38/1000\n",
      "1/1 [==============================] - 1s 878ms/step - loss: 0.0041\n",
      "Epoch 39/1000\n",
      "1/1 [==============================] - 1s 862ms/step - loss: 0.0041\n",
      "Epoch 40/1000\n",
      "1/1 [==============================] - 1s 998ms/step - loss: 0.0040\n",
      "Epoch 41/1000\n",
      "1/1 [==============================] - 1s 828ms/step - loss: 0.0040\n",
      "Epoch 42/1000\n",
      "1/1 [==============================] - 1s 764ms/step - loss: 0.0039\n",
      "Epoch 43/1000\n",
      "1/1 [==============================] - 1s 752ms/step - loss: 0.0038\n",
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      "1/1 [==============================] - 1s 890ms/step - loss: 0.0037\n",
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      "1/1 [==============================] - 1s 774ms/step - loss: 0.0037\n",
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      "1/1 [==============================] - 1s 830ms/step - loss: 0.0036\n",
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      "Epoch 84/1000\n",
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      "Epoch 87/1000\n",
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      "Epoch 88/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 0.0025\n",
      "Epoch 89/1000\n",
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      "Epoch 90/1000\n",
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      "Epoch 92/1000\n",
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      "Epoch 93/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 0.0024\n",
      "Epoch 94/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 0.0024\n",
      "Epoch 95/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 0.0024\n",
      "Epoch 96/1000\n",
      "1/1 [==============================] - 2s 2s/step - loss: 0.0024\n",
      "Epoch 97/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 0.0024\n",
      "Epoch 98/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 0.0023\n",
      "Epoch 99/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 0.0023\n",
      "Epoch 100/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 0.0023\n",
      "Epoch 101/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 0.0023\n",
      "Epoch 102/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 0.0023\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 103/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 0.0023\n",
      "Epoch 104/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 0.0022\n",
      "Epoch 105/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 0.0022\n",
      "Epoch 106/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 0.0022\n",
      "Epoch 107/1000\n",
      "1/1 [==============================] - 2s 2s/step - loss: 0.0022\n",
      "Epoch 108/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 0.0022\n",
      "Epoch 109/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 0.0022\n",
      "Epoch 110/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 0.0022\n",
      "Epoch 111/1000\n",
      "1/1 [==============================] - 2s 2s/step - loss: 0.0021\n",
      "Epoch 112/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 0.0021\n",
      "Epoch 113/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 0.0021\n",
      "Epoch 114/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 0.0021\n",
      "Epoch 115/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 0.0021\n",
      "Epoch 116/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 0.0021\n",
      "Epoch 117/1000\n",
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      "Epoch 118/1000\n",
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      "Epoch 119/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 0.0020\n",
      "Epoch 120/1000\n",
      "1/1 [==============================] - 2s 2s/step - loss: 0.0020\n",
      "Epoch 121/1000\n",
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      "Epoch 122/1000\n",
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      "Epoch 124/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 0.0020\n",
      "Epoch 125/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 0.0020\n",
      "Epoch 126/1000\n",
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      "Epoch 127/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 0.0019\n",
      "Epoch 128/1000\n",
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      "Epoch 170/1000\n",
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      "Epoch 171/1000\n",
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     ]
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     ]
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     "text": [
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     ]
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     "text": [
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     ]
    },
    {
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     "text": [
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     ]
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     ]
    },
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     "text": [
      "Epoch 897/1000\n",
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      "1/1 [==============================] - 1s 1s/step - loss: 4.5037e-04\n",
      "Epoch 946/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.4994e-04\n",
      "Epoch 947/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.4967e-04\n",
      "Epoch 948/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.4915e-04\n",
      "Epoch 949/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.4873e-04\n",
      "Epoch 950/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.4832e-04\n",
      "Epoch 951/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.4786e-04\n",
      "Epoch 952/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.4739e-04\n",
      "Epoch 953/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.4705e-04\n",
      "Epoch 954/1000\n",
      "1/1 [==============================] - 2s 2s/step - loss: 4.4664e-04\n",
      "Epoch 955/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.4623e-04\n",
      "Epoch 956/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.4585e-04\n",
      "Epoch 957/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.4556e-04\n",
      "Epoch 958/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.4509e-04\n",
      "Epoch 959/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.4470e-04\n",
      "Epoch 960/1000\n",
      "1/1 [==============================] - 2s 2s/step - loss: 4.4447e-04\n",
      "Epoch 961/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.4447e-04\n",
      "Epoch 962/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.4481e-04\n",
      "Epoch 963/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.4680e-04\n",
      "Epoch 964/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.5444e-04\n",
      "Epoch 965/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.6096e-04\n",
      "Epoch 966/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.7032e-04\n",
      "Epoch 967/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.8388e-04\n",
      "Epoch 968/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.9029e-04\n",
      "Epoch 969/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.9617e-04\n",
      "Epoch 970/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.8385e-04\n",
      "Epoch 971/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.6184e-04\n",
      "Epoch 972/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.4516e-04\n",
      "Epoch 973/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.4742e-04\n",
      "Epoch 974/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.6525e-04\n",
      "Epoch 975/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.6629e-04\n",
      "Epoch 976/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.5799e-04\n",
      "Epoch 977/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.4818e-04\n",
      "Epoch 978/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.4070e-04\n",
      "Epoch 979/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.4684e-04\n",
      "Epoch 980/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.5110e-04\n",
      "Epoch 981/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.5084e-04\n",
      "Epoch 982/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.4416e-04\n",
      "Epoch 983/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.3791e-04\n",
      "Epoch 984/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.3940e-04\n",
      "Epoch 985/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.4273e-04\n",
      "Epoch 986/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.4477e-04\n",
      "Epoch 987/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.4436e-04\n",
      "Epoch 988/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.3957e-04\n",
      "Epoch 989/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.3649e-04\n",
      "Epoch 990/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.3684e-04\n",
      "Epoch 991/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.3798e-04\n",
      "Epoch 992/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.3985e-04\n",
      "Epoch 993/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.3970e-04\n",
      "Epoch 994/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.3827e-04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 995/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.3700e-04\n",
      "Epoch 996/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.3571e-04\n",
      "Epoch 997/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.3312e-04\n",
      "Epoch 998/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.3404e-04\n",
      "Epoch 999/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.3477e-04\n",
      "Epoch 1000/1000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4.3397e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2290a213eb8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x=X, \n",
    "    y=Y,\n",
    "    batch_size=1,\n",
    "    epochs=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 895ms/step\n",
      "0.00043616537004709244\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\tensorFlow\\lib\\site-packages\\skimage\\util\\dtype.py:122: UserWarning: Possible precision loss when converting from float64 to uint8\n",
      "  .format(dtypeobj_in, dtypeobj_out))\n",
      "D:\\Anaconda3\\envs\\tensorFlow\\lib\\site-packages\\skimage\\util\\dtype.py:122: UserWarning: Possible precision loss when converting from float64 to uint16\n",
      "  .format(dtypeobj_in, dtypeobj_out))\n"
     ]
    }
   ],
   "source": [
    "print(model.evaluate(X, Y, batch_size=1))\n",
    "output = model.predict(X)\n",
    "output *= 128\n",
    "# Output colorizations\n",
    "cur = np.zeros((400, 400, 3))\n",
    "cur[:,:,0] = X[0][:,:,0]\n",
    "cur[:,:,1:] = output[0]\n",
    "imsave(\"img_result.png\", lab2rgb(cur))\n",
    "imsave(\"img_gray_version.png\", rgb2gray(lab2rgb(cur)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 上色"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skimage import transform,data\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_x = img_to_array(load_img('test/img1.jpg'))\n",
    "image_x = np.array(image_x, dtype=float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "图片原始维度为(500,500)\n"
     ]
    }
   ],
   "source": [
    "ll_1 = len(image_x[1])\n",
    "ll_2 = len(image_x[2])\n",
    "print(\"图片原始维度为({},{})\".format(ll_1, ll_2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\tensorFlow\\lib\\site-packages\\skimage\\transform\\_warps.py:84: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.\n",
      "  warn(\"The default mode, 'constant', will be changed to 'reflect' in \"\n"
     ]
    }
   ],
   "source": [
    "dst = transform.resize(image_x, (400, 400))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#画图\n",
    "plt.figure('resize')\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.title('before resize')\n",
    "plt.imshow(image_x,plt.cm.gray)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.title('before resize')\n",
    "plt.imshow(dst,plt.cm.gray)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_2 = rgb2lab(1.0/255*dst)[:,:,0]\n",
    "Y_2 = rgb2lab(1.0/255*dst)[:,:,1:]\n",
    "Y_2 /= 128\n",
    "X_2 = X_2.reshape(1, 400, 400, 1)\n",
    "Y_2 = Y_2.reshape(1, 400, 400, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "output = model.predict(X_2)\n",
    "output *= 128\n",
    "# Output colorizations\n",
    "cur = np.zeros((400, 400, 3))\n",
    "cur[:,:,0] = X_2[0][:,:,0]\n",
    "cur[:,:,1:] = output[0]\n",
    "#imsave(\"img_result_2.png\", lab2rgb(cur))\n",
    "#imsave(\"img_gray_version_2.png\", rgb2gray(lab2rgb(cur)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\tensorFlow\\lib\\site-packages\\skimage\\transform\\_warps.py:84: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.\n",
      "  warn(\"The default mode, 'constant', will be changed to 'reflect' in \"\n",
      "D:\\Anaconda3\\envs\\tensorFlow\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: img_result_44.png is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "D:\\Anaconda3\\envs\\tensorFlow\\lib\\site-packages\\skimage\\util\\dtype.py:122: UserWarning: Possible precision loss when converting from float64 to uint8\n",
      "  .format(dtypeobj_in, dtypeobj_out))\n",
      "D:\\Anaconda3\\envs\\tensorFlow\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: img_gray_version_44.png is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "D:\\Anaconda3\\envs\\tensorFlow\\lib\\site-packages\\skimage\\util\\dtype.py:122: UserWarning: Possible precision loss when converting from float64 to uint16\n",
      "  .format(dtypeobj_in, dtypeobj_out))\n"
     ]
    }
   ],
   "source": [
    "#把维度拉回来\n",
    "dst=transform.resize(cur, (ll_1, ll_2))\n",
    "imsave(\"img_result_1.png\", lab2rgb(dst))\n",
    "imsave(\"img_gray_version_1.png\", rgb2gray(lab2rgb(dst)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}