DL_Colorization_CNN_v1_0.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "### Deep Learning Project\n",
        "\n",
        "# Colorization of GrayScale Image using CNN model\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "jdY9Vi4W08TZ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ras1sY2ZWKgs"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import sys\n",
        "import random\n",
        "import warnings\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import cv2\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from tqdm import tqdm\n",
        "from itertools import chain\n",
        "import skimage\n",
        "from PIL import Image\n",
        "from skimage.io import imread, imshow, imread_collection, concatenate_images\n",
        "from skimage.transform import resize\n",
        "from skimage.util import crop, pad\n",
        "from skimage.morphology import label\n",
        "from skimage.color import rgb2gray, gray2rgb, rgb2lab, lab2rgb\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.applications.inception_resnet_v2 import InceptionResNetV2, preprocess_input\n",
        "from tensorflow.keras.models import Model, load_model, Sequential, model_from_json\n",
        "from tensorflow.keras.layers import Input, Dense, UpSampling2D, RepeatVector, Reshape\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.layers import Dropout, Lambda\n",
        "from tensorflow.keras.layers import Conv2D, Conv2DTranspose\n",
        "from tensorflow.keras.layers import MaxPooling2D\n",
        "from tensorflow.keras.layers import Embedding\n",
        "from tensorflow.keras.layers import concatenate\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard, ReduceLROnPlateau\n",
        "from tensorflow.keras import backend as K\n",
        "\n",
        "\n",
        "warnings.filterwarnings('ignore', category=UserWarning, module='skimage')\n",
        "seed = 42\n",
        "random.seed = seed\n",
        "np.random.seed = seed\n",
        "\n",
        "# tf.compat.v1.disable_v2_behavior()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HrQzZmJtdQD2"
      },
      "source": [
        "Getting Dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jApY2wB0eo9-",
        "outputId": "f4ae51fe-d004-4938-a207-a9464825b994"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'dl-proj'...\n",
            "remote: Enumerating objects: 22280, done.\u001b[K\n",
            "remote: Counting objects: 100% (22/22), done.\u001b[K\n",
            "remote: Compressing objects: 100% (14/14), done.\u001b[K\n",
            "remote: Total 22280 (delta 7), reused 22 (delta 7), pack-reused 22258\u001b[K\n",
            "Receiving objects: 100% (22280/22280), 527.58 MiB | 14.03 MiB/s, done.\n",
            "Resolving deltas: 100% (23/23), done.\n",
            "Checking out files: 100% (23821/23821), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://{token}@github.com/regulusv/dl-proj.git"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Read in Data\n",
        "The dataset I am using is ~2000 classic paintings which we will remove the color from and attempt to teach a nearal network to recolorize them.\n",
        "\n",
        "data source\n",
        "https://www.kaggle.com/thedownhill/art-images-drawings-painting-sculpture-engraving\n"
      ],
      "metadata": {
        "id": "1Txoq1iREn64"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, we would process with Dataset_portrait "
      ],
      "metadata": {
        "id": "m8lIM6pGGJZ4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "TRAIN_PATH = '/content/dl-proj/dataset_art/training_set/painting/'"
      ],
      "metadata": {
        "id": "UOy4j83BFvY9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# show image\n",
        "img = cv2.imread(TRAIN_PATH+os.listdir(TRAIN_PATH)[0])\n",
        "# img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n",
        "# img = cv2.resize(img, (256, 256))\n",
        "plt.imshow(img)\n",
        "img.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "6MU3zvwbv8r6",
        "outputId": "e33f36af-5f80-4ecc-ebd6-3f8f5b5c8c32"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(360, 290, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# data size\n",
        "image_num = len([name for name in os.listdir(TRAIN_PATH)])\n",
        "print(\"images count:\", image_num)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q9FyCWhwHHNN",
        "outputId": "0872d851-4122-400f-c305-69d599698326"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "images count: 2129\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# image dimentions \n",
        "IMG_WIDTH = 256\n",
        "IMG_HEIGHT = 256\n",
        "IMG_CHANNELS = 3\n",
        "INPUT_SHAPE=(IMG_HEIGHT, IMG_WIDTH, 1)\n",
        "\n",
        "train_ids = next(os.walk(TRAIN_PATH))[2][:100]\n",
        "print(\"how many train_id count? \", len(train_ids))\n",
        "print(train_ids[:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ee9SvRC0RrFn",
        "outputId": "3b19f854-1ad9-41b9-de39-bc28d5783c1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "how many train_id count?  100\n",
            "['1167.jpg', '0876.jpg', '1451.jpg', '1713.jpg', '0758.jpg']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# put images into np array, while some images are broken, we would skip them\n",
        "X_train = np.zeros((0, IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.uint8)\n",
        "X_train_arr = []\n",
        "missing_count = 0\n",
        "print(\"np.array shape: \", X_train.shape)\n",
        "\n",
        "print('Getting training images ... ')\n",
        "sys.stdout.flush()\n",
        "\n",
        "for n, id_ in tqdm(enumerate(train_ids), total=len(train_ids)):\n",
        "    path = TRAIN_PATH + id_+''\n",
        "    try:\n",
        "        img = imread(path)\n",
        "        img = resize(img, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True)\n",
        "        X_train_arr.append(img)\n",
        "    except:\n",
        "        # print(\"Img has problem: \"+path)\n",
        "        missing_count += 1\n",
        "\n",
        "X_train = np.array(X_train_arr, dtype=np.uint8)\n",
        "X_train = X_train.astype('float32') / 255.\n",
        "print(\"Total missing: \"+ str(missing_count))\n",
        "print(\"X_train shape: \", X_train.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BUKj5LCs_Ntp",
        "outputId": "1089f15a-7864-4a80-8d92-b9e837f5c600"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "np.array shape:  (0, 256, 256, 3)\n",
            "Getting training images ... \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 100/100 [00:01<00:00, 67.59it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total missing: 5\n",
            "X_train shape:  (95, 256, 256, 3)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Display a sample image in X_train\n",
        "imshow(X_train[0])\n",
        "plt.show()\n",
        "X_train[0].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "yD6WpUtqD1AD",
        "outputId": "cff3751e-e59b-49cf-d986-3b15648c564e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(256, 256, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train/Test Set\n",
        "\n",
        "Just getting a sample of 20 images to test the model when it is done."
      ],
      "metadata": {
        "id": "NJxZ00c7MKlY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test = train_test_split(X_train, test_size=20, random_state=seed)"
      ],
      "metadata": {
        "id": "Z02MMPWrMJQ_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Resnet classifier\n",
        "\n",
        "model is expected to have Combination of  of an autoencoder and resnet classifier. The best an autoencoder by itself is just shade everything in a brownish tone. The model uses an resnet classifier to give the neural network an \"idea\" of what things should be colored."
      ],
      "metadata": {
        "id": "Z9PZoYxmM9uX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# # Bring resnet\n",
        "# import requests\n",
        "\n",
        "# url = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.7/inception_resnet_v2_weights_tf_dim_ordering_tf_kernels.h5'\n",
        "# r = requests.get(url, allow_redirects=True)\n",
        "# with open('inception_resnet_v2_weights_tf_dim_ordering_tf_kernels.h5', 'wb') as f:\n",
        "#   f.write(r.content)"
      ],
      "metadata": {
        "id": "WNMW7L3JOa98"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# inception = InceptionResNetV2(weights=None, include_top=True)\n",
        "# # print(type(inception))\n",
        "# inception.load_weights('inception_resnet_v2_weights_tf_dim_ordering_tf_kernels.h5')\n",
        "# inception.graph = tf.compat.v1.get_default_graph()"
      ],
      "metadata": {
        "id": "BXUHXTR1M8fu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create the Model\n",
        "\n",
        "Per work from Richard Zhang (2016), the model is a combination of an autoencoder and resnet classifier would have better results. The best an autoencoder by itself is just shade everything in a brownish tone. The model uses an resnet classifier to give the neural network an \"idea\" of what things should be colored. While as we tried, TF 2.0+ has some imcompatiblity while adopting InceptionResNetV2 within CNN here, we would leave adopting InceptionResNetV2 work in the future."
      ],
      "metadata": {
        "id": "alHusmamMW5a"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dgodms_-lt-w"
      },
      "source": [
        "construct CNN model, referring paper:\n",
        "\n",
        "![title](http://richzhang.github.io/colorization/resources/images/net_diagram.jpg)\n",
        "\n",
        "Reference:\n",
        "\n",
        "Zhang, Richard, Phillip Isola, and Alexei A. Efros. \"Colorful image colorization.\" European conference on computer vision. Springer, Cham, 2016.\n",
        "\n",
        "http://richzhang.github.io/colorization/"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def Colorize():\n",
        "  #CNN model\n",
        "  model = Sequential()\n",
        "\n",
        "  #Input Layer\n",
        "  model.add(Conv2D(64, (3, 3), input_shape=(256, 256, 1), activation='relu', padding='same'))\n",
        "\n",
        "  #Hidden Layers\n",
        "  model.add(Conv2D(64, (3, 3), activation='relu', padding='same', strides=2))\n",
        "  model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))\n",
        "  model.add(Conv2D(128, (3, 3), activation='relu', padding='same', strides=2))\n",
        "  model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))\n",
        "  model.add(Conv2D(256, (3, 3), activation='relu', padding='same', strides=2))\n",
        "  model.add(Conv2D(512, (3, 3), activation='relu', padding='same'))\n",
        "  model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))\n",
        "  model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))\n",
        "  model.add(UpSampling2D((2, 2)))\n",
        "  model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))\n",
        "  model.add(UpSampling2D((2, 2)))\n",
        "  model.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n",
        "  model.add(Conv2D(2, (3, 3), activation='tanh', padding='same'))\n",
        "  model.add(UpSampling2D((2, 2)))\n",
        "  return model\n",
        "\n"
      ],
      "metadata": {
        "id": "j-sRqLTOXvyo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Colorize()\n",
        "#Compiling the CNN\n",
        "model.compile(optimizer='rmsprop', loss='mse', metrics = ['accuracy'])\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-4nRuVdWnbV2",
        "outputId": "0ba6de7e-1f72-4718-eb61-643e85a19102"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_24 (Conv2D)          (None, 256, 256, 64)      640       \n",
            "                                                                 \n",
            " conv2d_25 (Conv2D)          (None, 128, 128, 64)      36928     \n",
            "                                                                 \n",
            " conv2d_26 (Conv2D)          (None, 128, 128, 128)     73856     \n",
            "                                                                 \n",
            " conv2d_27 (Conv2D)          (None, 64, 64, 128)       147584    \n",
            "                                                                 \n",
            " conv2d_28 (Conv2D)          (None, 64, 64, 256)       295168    \n",
            "                                                                 \n",
            " conv2d_29 (Conv2D)          (None, 32, 32, 256)       590080    \n",
            "                                                                 \n",
            " conv2d_30 (Conv2D)          (None, 32, 32, 512)       1180160   \n",
            "                                                                 \n",
            " conv2d_31 (Conv2D)          (None, 32, 32, 256)       1179904   \n",
            "                                                                 \n",
            " conv2d_32 (Conv2D)          (None, 32, 32, 128)       295040    \n",
            "                                                                 \n",
            " up_sampling2d_6 (UpSampling  (None, 64, 64, 128)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_33 (Conv2D)          (None, 64, 64, 64)        73792     \n",
            "                                                                 \n",
            " up_sampling2d_7 (UpSampling  (None, 128, 128, 64)     0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_34 (Conv2D)          (None, 128, 128, 32)      18464     \n",
            "                                                                 \n",
            " conv2d_35 (Conv2D)          (None, 128, 128, 2)       578       \n",
            "                                                                 \n",
            " up_sampling2d_8 (UpSampling  (None, 256, 256, 2)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3,892,194\n",
            "Trainable params: 3,892,194\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data Generator\n",
        "\n"
      ],
      "metadata": {
        "id": "mI-UX1_AMs8I"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are two techniques to predict colored image from its gray scaled form:\n",
        "\n",
        "Turn the RGB image into LAB image, then separate the L value and ab value from the image and then train the model to predict the ab value.\n",
        "Turn the RGB image into LUV image, then separate the L value and UV value from the image and then train the model to predict the UV value.\n",
        "Here we have used **LAB** value to colorize the image, where L stands for lightness, and a and b for the color spectra green–red and blue–yellow.\n",
        "\n",
        "Convert the rgb values of the input image to the range of 0 to 1,\n",
        "\n",
        "1.0/255 indicates that we are using a 24-bit RGB color space. It means that we are using numbers between 0–255 for each color channel.\n",
        "\n",
        "img_lab = 1.0/225*img_lab resize the lightness channel to network input size.\n",
        "\n",
        "The true color values range between -128 and 128. This is the default interval  in the LAB color space. By dividing them by 128, they too fall within the -1 to 1 interval."
      ],
      "metadata": {
        "id": "-YilY2C2wmyL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Image transformer\n",
        "datagen = ImageDataGenerator(\n",
        "        shear_range=0.2,\n",
        "        zoom_range=0.2,\n",
        "        rotation_range=20,\n",
        "        horizontal_flip=True)\n",
        "\n",
        "# Generate training data\n",
        "batch_size = 10\n",
        "def image_a_b_gen(batch_size):\n",
        "    for batch in datagen.flow(X_train, batch_size=batch_size):\n",
        "        lab_batch = rgb2lab(batch)\n",
        "        X_batch = lab_batch[:,:,:,0]\n",
        "        Y_batch = lab_batch[:,:,:,1:] / 128\n",
        "        yield (X_batch.reshape(X_batch.shape+(1,)), Y_batch)\n"
      ],
      "metadata": {
        "id": "8H-ddv4EMrSq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train the Model"
      ],
      "metadata": {
        "id": "a980djddYDgn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train model      \n",
        "tensorboard = TensorBoard(log_dir=\"/output/beta_run\")\n",
        "trainedmodel = model.fit_generator(\n",
        "    image_a_b_gen(batch_size), \n",
        "    callbacks=[tensorboard],\n",
        "    epochs=30, \n",
        "    steps_per_epoch=30)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ADtWkfXct7Wh",
        "outputId": "83d2ebc5-5118-4831-ac13-6413b0356ec3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  import sys\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "30/30 [==============================] - 7s 206ms/step - loss: 0.3772 - accuracy: 0.4112\n",
            "Epoch 2/30\n",
            "30/30 [==============================] - 6s 200ms/step - loss: 0.0088 - accuracy: 0.8064\n",
            "Epoch 3/30\n",
            "30/30 [==============================] - 6s 202ms/step - loss: 0.0073 - accuracy: 0.8345\n",
            "Epoch 4/30\n",
            "30/30 [==============================] - 6s 203ms/step - loss: 0.0073 - accuracy: 0.8414\n",
            "Epoch 5/30\n",
            "30/30 [==============================] - 6s 206ms/step - loss: 0.0070 - accuracy: 0.8467\n",
            "Epoch 6/30\n",
            "30/30 [==============================] - 6s 201ms/step - loss: 0.0076 - accuracy: 0.8286\n",
            "Epoch 7/30\n",
            "30/30 [==============================] - 7s 239ms/step - loss: 0.0069 - accuracy: 0.8450\n",
            "Epoch 8/30\n",
            "30/30 [==============================] - 6s 202ms/step - loss: 0.0077 - accuracy: 0.8393\n",
            "Epoch 9/30\n",
            "30/30 [==============================] - 6s 206ms/step - loss: 0.0068 - accuracy: 0.8429\n",
            "Epoch 10/30\n",
            "30/30 [==============================] - 6s 204ms/step - loss: 0.0068 - accuracy: 0.8469\n",
            "Epoch 11/30\n",
            "30/30 [==============================] - 6s 200ms/step - loss: 0.0063 - accuracy: 0.8389\n",
            "Epoch 12/30\n",
            "30/30 [==============================] - 6s 204ms/step - loss: 0.0066 - accuracy: 0.8427\n",
            "Epoch 13/30\n",
            "30/30 [==============================] - 6s 208ms/step - loss: 0.0062 - accuracy: 0.8489\n",
            "Epoch 14/30\n",
            "30/30 [==============================] - 6s 202ms/step - loss: 0.0095 - accuracy: 0.8153\n",
            "Epoch 15/30\n",
            "30/30 [==============================] - 6s 206ms/step - loss: 0.0064 - accuracy: 0.8444\n",
            "Epoch 16/30\n",
            "30/30 [==============================] - 7s 218ms/step - loss: 0.0058 - accuracy: 0.8457\n",
            "Epoch 17/30\n",
            "30/30 [==============================] - 6s 208ms/step - loss: 0.0062 - accuracy: 0.8418\n",
            "Epoch 18/30\n",
            "30/30 [==============================] - 6s 201ms/step - loss: 0.0062 - accuracy: 0.8409\n",
            "Epoch 19/30\n",
            "30/30 [==============================] - 6s 204ms/step - loss: 0.0058 - accuracy: 0.8419\n",
            "Epoch 20/30\n",
            "30/30 [==============================] - 6s 201ms/step - loss: 0.0063 - accuracy: 0.8366\n",
            "Epoch 21/30\n",
            "30/30 [==============================] - 6s 209ms/step - loss: 0.0062 - accuracy: 0.8381\n",
            "Epoch 22/30\n",
            "30/30 [==============================] - 6s 201ms/step - loss: 0.0061 - accuracy: 0.8415\n",
            "Epoch 23/30\n",
            "30/30 [==============================] - 6s 203ms/step - loss: 0.0058 - accuracy: 0.8432\n",
            "Epoch 24/30\n",
            "30/30 [==============================] - 6s 202ms/step - loss: 0.0061 - accuracy: 0.8322\n",
            "Epoch 25/30\n",
            "16/30 [===============>..............] - ETA: 2s - loss: 0.0060 - accuracy: 0.8389"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Summarize log for accuracy\n",
        "plt.plot(trainedmodel.history['accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# Summarize log for loss\n",
        "plt.plot(trainedmodel.history['loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "qfsuNmU0YLgN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save model\n",
        "model_json = model.to_json()\n",
        "with open(\"model.json\", \"w\") as json_file:\n",
        "    json_file.write(model_json)\n",
        "model.save_weights(\"model_color_art.h5\")\n"
      ],
      "metadata": {
        "id": "z-xBGlGuxIuT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load json and create model\n",
        "json_file = open('model.json', 'r')\n",
        "loaded_model_json = json_file.read()\n",
        "json_file.close()\n",
        "loaded_model = model_from_json(loaded_model_json)\n",
        "\n",
        "# Load weights into new model\n",
        "loaded_model.load_weights(\"model_color_art.h5\")"
      ],
      "metadata": {
        "id": "kFSrjJfDvhS6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_model.summary()\n"
      ],
      "metadata": {
        "id": "89vXvGM6x0rD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sample the Results"
      ],
      "metadata": {
        "id": "4hT0Bdt7v74I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sample = X_test\n",
        "color_me = gray2rgb(rgb2gray(sample))\n",
        "color_me = rgb2lab(color_me)[:,:,:,0]\n",
        "color_me = color_me.reshape(color_me.shape+(1,))\n",
        "\n",
        "output = model.predict(color_me)\n",
        "output = output * 128\n",
        "\n",
        "decoded_imgs = np.zeros((len(output),256, 256, 3))\n",
        "\n",
        "for i in range(len(output)):\n",
        "    cur = np.zeros((256, 256, 3))\n",
        "    cur[:,:,0] = color_me[i][:,:,0]\n",
        "    cur[:,:,1:] = output[i]\n",
        "    decoded_imgs[i] = lab2rgb(cur)\n",
        "    cv2.imwrite(\"img_\"+str(i)+\".jpg\", lab2rgb(cur))"
      ],
      "metadata": {
        "id": "rLLxfWkVv-Rg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20, 6))\n",
        "for i in range(10):\n",
        "    # grayscale\n",
        "    plt.subplot(3, 10, i + 1)\n",
        "    plt.imshow(rgb2gray(X_test)[i].reshape(256, 256))\n",
        "    plt.gray()\n",
        "    plt.axis('off')\n",
        " \n",
        "    # recolorization\n",
        "    plt.subplot(3, 10, i + 1 +10)\n",
        "    plt.imshow(decoded_imgs[i].reshape(256, 256,3))\n",
        "    plt.axis('off')\n",
        "    \n",
        "    # original\n",
        "    plt.subplot(3, 10, i + 1 + 20)\n",
        "    plt.imshow(X_test[i].reshape(256, 256,3))\n",
        "    plt.axis('off')\n",
        " \n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "SXHw11gbv_58"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        " Now we test model to colorize another type of pictures: landscape"
      ],
      "metadata": {
        "id": "cTB3uo1I1TC_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "TRAIN_PATH = '/content/dl-proj/Dataset_landscape /color/'"
      ],
      "metadata": {
        "id": "_v0w9wzx1X0-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# show image\n",
        "img = cv2.imread(TRAIN_PATH+os.listdir(TRAIN_PATH)[3])\n",
        "# img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n",
        "img = cv2.resize(img, (256, 256))\n",
        "plt.imshow(img)\n",
        "img.shape"
      ],
      "metadata": {
        "id": "_IdYZj0P10wu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# put images into np array, while some images are broken, we would skip them\n",
        "X_train = np.zeros((0, IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.uint8)\n",
        "X_train_arr = []\n",
        "missing_count = 0\n",
        "print(\"np.array shape: \", X_train.shape)\n",
        "\n",
        "print('Getting training images ... ')\n",
        "sys.stdout.flush()\n",
        "\n",
        "for n, id_ in tqdm(enumerate(train_ids), total=len(train_ids)):\n",
        "    path = TRAIN_PATH + id_+''\n",
        "    try:\n",
        "        img = imread(path)\n",
        "        img = resize(img, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True)\n",
        "        X_train_arr.append(img)\n",
        "    except:\n",
        "        # print(\"Img has problem: \"+path)\n",
        "        missing_count += 1\n",
        "\n",
        "X_train = np.array(X_train_arr, dtype=np.uint8)\n",
        "X_train = X_train.astype('float32') / 255.\n",
        "print(\"Total missing: \"+ str(missing_count))\n",
        "print(\"X_train shape: \", X_train.shape)"
      ],
      "metadata": {
        "id": "wCD7PvuB1_ty"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test = train_test_split(X_train, test_size=20, random_state=seed)"
      ],
      "metadata": {
        "id": "kukLAH5r2Hp5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Samping results in picture: landscape"
      ],
      "metadata": {
        "id": "d10007w42Kya"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def test_sample(X_test, model):\n",
        "  sample = X_test\n",
        "  color_me = gray2rgb(rgb2gray(sample))\n",
        "  color_me = rgb2lab(color_me)[:,:,:,0]\n",
        "  color_me = color_me.reshape(color_me.shape+(1,))\n",
        "\n",
        "  output = model.predict(color_me)\n",
        "  output = output * 128\n",
        "\n",
        "  decoded_imgs = np.zeros((len(output),256, 256, 3))\n",
        "\n",
        "  for i in range(len(output)):\n",
        "      cur = np.zeros((256, 256, 3))\n",
        "      cur[:,:,0] = color_me[i][:,:,0]\n",
        "      cur[:,:,1:] = output[i]\n",
        "      decoded_imgs[i] = lab2rgb(cur)\n",
        "      cv2.imwrite(\"img_\"+str(i)+\".jpg\", lab2rgb(cur))\n",
        "  return decoded_imgs"
      ],
      "metadata": {
        "id": "l7MvXIoo2Ka-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_res(decoded_imgs, X_test):\n",
        "  plt.figure(figsize=(20, 6))\n",
        "  for i in range(10):\n",
        "      # grayscale\n",
        "      plt.subplot(3, 10, i + 1)\n",
        "      plt.imshow(rgb2gray(X_test)[i].reshape(256, 256))\n",
        "      plt.gray()\n",
        "      plt.axis('off')\n",
        "  \n",
        "      # recolorization\n",
        "      plt.subplot(3, 10, i + 1 +10)\n",
        "      plt.imshow(decoded_imgs[i].reshape(256, 256,3))\n",
        "      plt.axis('off')\n",
        "      \n",
        "      # original\n",
        "      plt.subplot(3, 10, i + 1 + 20)\n",
        "      plt.imshow(X_test[i].reshape(256, 256,3))\n",
        "      plt.axis('off')\n",
        "  \n",
        "  plt.tight_layout()\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "RM_fpOJB3T2T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "decoded_imgs = test_sample(X_test, model)"
      ],
      "metadata": {
        "id": "1BQuf2ay3WBo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_res(decoded_imgs, X_test)"
      ],
      "metadata": {
        "id": "QNJ02Bcd2f7D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we see, the predicted results are not acceptable,\n",
        "\n",
        "So we. would retrain the CNN model using photos of landscape."
      ],
      "metadata": {
        "id": "dWVJeyMJ2ohD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Re - train model      \n",
        "model_landscape = Colorize()\n",
        "#Compiling the CNN\n",
        "model_landscape.compile(optimizer='rmsprop', loss='mse', metrics = ['accuracy'])\n",
        "model_landscape.summary()"
      ],
      "metadata": {
        "id": "g-QrohtZ2sfx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 10\n",
        "tensorboard = TensorBoard(log_dir=\"/output/beta_run\")\n",
        "trainedmodel = model_landscape.fit_generator(\n",
        "    image_a_b_gen(batch_size), \n",
        "    callbacks=[tensorboard],\n",
        "    epochs=60, \n",
        "    steps_per_epoch=30)"
      ],
      "metadata": {
        "id": "PqWC0MibtanY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save model\n",
        "model_landscape_json = model_landscape.to_json()\n",
        "with open(\"model_landscape.json\", \"w\") as json_file:\n",
        "    json_file.write(model_landscape_json)\n",
        "model_landscape.save_weights(\"model_color_landscape.h5\")\n",
        "\n",
        "# Load json and create model\n",
        "json_file = open('model_landscape.json', 'r')\n",
        "loaded_model_landscape_json = json_file.read()\n",
        "json_file.close()\n",
        "loaded_model_landscape = model_from_json(loaded_model_landscape_json)\n",
        "\n",
        "# Load weights into new model\n",
        "loaded_model_landscape.load_weights(\"model_color_landscape.h5\")"
      ],
      "metadata": {
        "id": "QFa9JBhPqVKh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "decoded_imgs = test_sample(X_test, model_landscape)"
      ],
      "metadata": {
        "id": "vQKLpoaA27ss"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_res(decoded_imgs, X_test)"
      ],
      "metadata": {
        "id": "1PoZYb9f29HU"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "machine_shape": "hm",
      "name": "DL_Colorization_v1.0.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}