CNNforRS_Regression_Lookmee_MIST2024.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyOa2mBX0GLENA03K7gZTb7O",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lookmeebbear/GeoProg_MIST2024/blob/main/CNNforRS_Regression_Lookmee_MIST2024.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**SAR to NDVI Conversion -- Mission Impossible ??**\n",
        "\n",
        "Thepchai Srinoi\n",
        "\n",
        "Department of Survey Engineering\n",
        "\n",
        "Chulalongkorn University"
      ],
      "metadata": {
        "id": "ttV0_T5-OkR8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "ปกติ ภาพเชิงทัศน์จะมีปัญหาในภูมิภาคร้อนชื้น มรสุม เมฆมักปกคลุมบ่อย (พบได้ในภาคใต้ของประเทศไทย) เวลาจะหา NDVI ก็จะยากมาก แต่ภาพซาร์ไร้ปัญหา แต่ไม่ได้บอกตรงๆ ถ้าเราหา SAR NDVI ได้ก็จะเติมเต็มข้อมูลได้ ทางออกของปัญหาน่าจะใช้ Convolution Neural Network สร้างภาพจาก Ascending/Descending VV VH Sentinel 1A/B แปลงเป็น NDVI ปลอมๆ ทดแทนไปก่อน"
      ],
      "metadata": {
        "id": "8p9-DqCnQqFy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "SAR Ascending ขาขึ้น 10 ธันวา 2022\n",
        "\n",
        "Descending ขาลง 14 ธันวา 2022\n",
        "\n",
        "Optical 12 ธันวา 2022\n",
        "\n",
        "------------------------------------------------------------\n",
        "Ascending\n",
        "S1A_IW_GRDH_1SDV_20221210T112900_20221210T112925_046269_058A71_5C94\n",
        "\n",
        "Decending\n",
        "S1A_IW_GRDH_1SDV_20221214T230941_20221214T231006_046334_058CAA_076F\n",
        "\n",
        "Optical 20221212T034139_20221212T034733_T47PPR\n",
        "\n",
        "-------------------------------------------------------------\n"
      ],
      "metadata": {
        "id": "c-Yqi6frQtDT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "ลง library ที่จำเป็น ทั้งการเปิดภาพ แสดงผลสถาปัตยกรรม การทำแบบจำลองแยกส่วน"
      ],
      "metadata": {
        "id": "lLKIN7o5PHDz"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5excX3dMOjlz",
        "outputId": "b8ee34b3-fb30-491b-c870-804dcb5bd801"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting rasterio\n",
            "  Downloading rasterio-1.3.9-cp310-cp310-manylinux2014_x86_64.whl (20.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m20.6/20.6 MB\u001b[0m \u001b[31m69.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting visualkeras\n",
            "  Downloading visualkeras-0.0.2-py3-none-any.whl (12 kB)\n",
            "Collecting segmentation-models\n",
            "  Downloading segmentation_models-1.0.1-py3-none-any.whl (33 kB)\n",
            "Collecting affine (from rasterio)\n",
            "  Downloading affine-2.4.0-py3-none-any.whl (15 kB)\n",
            "Requirement already satisfied: attrs in /usr/local/lib/python3.10/dist-packages (from rasterio) (23.2.0)\n",
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            "Collecting snuggs>=1.4.1 (from rasterio)\n",
            "  Downloading snuggs-1.4.7-py3-none-any.whl (5.4 kB)\n",
            "Requirement already satisfied: click-plugins in /usr/local/lib/python3.10/dist-packages (from rasterio) (1.1.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from rasterio) (67.7.2)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from visualkeras) (9.4.0)\n",
            "Collecting aggdraw>=1.3.11 (from visualkeras)\n",
            "  Downloading aggdraw-1.3.18-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (993 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m993.7/993.7 kB\u001b[0m \u001b[31m66.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting keras-applications<=1.0.8,>=1.0.7 (from segmentation-models)\n",
            "  Downloading Keras_Applications-1.0.8-py3-none-any.whl (50 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.7/50.7 kB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting image-classifiers==1.0.0 (from segmentation-models)\n",
            "  Downloading image_classifiers-1.0.0-py3-none-any.whl (19 kB)\n",
            "Collecting efficientnet==1.0.0 (from segmentation-models)\n",
            "  Downloading efficientnet-1.0.0-py3-none-any.whl (17 kB)\n",
            "Requirement already satisfied: scikit-image in /usr/local/lib/python3.10/dist-packages (from efficientnet==1.0.0->segmentation-models) (0.19.3)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.10/dist-packages (from keras-applications<=1.0.8,>=1.0.7->segmentation-models) (3.9.0)\n",
            "Requirement already satisfied: pyparsing>=2.1.6 in /usr/local/lib/python3.10/dist-packages (from snuggs>=1.4.1->rasterio) (3.1.1)\n",
            "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from scikit-image->efficientnet==1.0.0->segmentation-models) (1.11.4)\n",
            "Requirement already satisfied: networkx>=2.2 in /usr/local/lib/python3.10/dist-packages (from scikit-image->efficientnet==1.0.0->segmentation-models) (3.2.1)\n",
            "Requirement already satisfied: imageio>=2.4.1 in /usr/local/lib/python3.10/dist-packages (from scikit-image->efficientnet==1.0.0->segmentation-models) (2.31.6)\n",
            "Requirement already satisfied: tifffile>=2019.7.26 in /usr/local/lib/python3.10/dist-packages (from scikit-image->efficientnet==1.0.0->segmentation-models) (2023.12.9)\n",
            "Requirement already satisfied: PyWavelets>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-image->efficientnet==1.0.0->segmentation-models) (1.5.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from scikit-image->efficientnet==1.0.0->segmentation-models) (23.2)\n",
            "Installing collected packages: snuggs, aggdraw, affine, visualkeras, rasterio, keras-applications, image-classifiers, efficientnet, segmentation-models\n",
            "Successfully installed affine-2.4.0 aggdraw-1.3.18 efficientnet-1.0.0 image-classifiers-1.0.0 keras-applications-1.0.8 rasterio-1.3.9 segmentation-models-1.0.1 snuggs-1.4.7 visualkeras-0.0.2\n"
          ]
        }
      ],
      "source": [
        "!pip install rasterio visualkeras segmentation-models"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "นำเข้าเครื่องมือที่จำเป็น"
      ],
      "metadata": {
        "id": "W4IGCjObPEzT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# processing and reading images\n",
        "import zipfile\n",
        "import requests\n",
        "import io\n",
        "from PIL import Image\n",
        "from skimage.color import rgb2gray\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "import rasterio\n",
        "from rasterio.plot import show, show_hist\n",
        "import visualkeras\n",
        "\n",
        "# tensor processing\n",
        "import numpy as np\n",
        "import os\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "\n",
        "# plotting\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.utils import plot_model\n",
        "\n",
        "# modeling\n",
        "from sklearn.model_selection import train_test_split\n",
        "import tensorflow as tf\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D, BatchNormalization, Activation\n",
        "from keras.optimizers import RMSprop\n",
        "from keras.applications import vgg16\n",
        "from keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint\n",
        "\n",
        "# evaluation metrics\n",
        "from sklearn.metrics import classification_report\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "HmHcHdjnPBNu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "ดาวน์โหลดข้อมูลทำแบบจำลอง"
      ],
      "metadata": {
        "id": "EQi2xK97PO5i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# https://drive.google.com/file/d/1SyUMkQKLknil7G5m7s5ej01vQdO48WAc/view?usp=drive_link\n",
        "!gdown https://drive.google.com/uc?id=1SyUMkQKLknil7G5m7s5ej01vQdO48WAc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mytm2S2fPPYy",
        "outputId": "c98ffcf7-d1c0-4334-bd30-57a659fe4b4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From (original): https://drive.google.com/uc?id=1SyUMkQKLknil7G5m7s5ej01vQdO48WAc\n",
            "From (redirected): https://drive.google.com/uc?id=1SyUMkQKLknil7G5m7s5ej01vQdO48WAc&confirm=t&uuid=2ea0fd0a-7ef9-4c54-93c2-deca7dc2d621\n",
            "To: /content/Lookmee_NDVI_CNN.zip\n",
            "100% 471M/471M [00:10<00:00, 44.1MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "แตกไฟล์"
      ],
      "metadata": {
        "id": "VvlG7f58PeV6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip Lookmee_NDVI_CNN.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mIZ7v5HpPk8b",
        "outputId": "be8fdf2f-29b3-4c70-e908-9373dacf7ef2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  Lookmee_NDVI_CNN.zip\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ลองนับจำนวนข้อมูล"
      ],
      "metadata": {
        "id": "NMIAx2QmPtyr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import fnmatch\n",
        "\n",
        "dir_path = '/content/mypatch_SNDVI/'\n",
        "count = len(fnmatch.filter(os.listdir(dir_path), '*.*'))\n",
        "print('File Count:', count)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v6YeNsF_PuIR",
        "outputId": "e3c221ae-210e-4908-a8b4-aa0542d73a4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "File Count: 4743\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "นำภาพเข้าชุดอาร์เรย์ของข้อมูล input และ output"
      ],
      "metadata": {
        "id": "pTcEO15pQLCt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "XImages = np.zeros([count, 64, 64, 4], dtype=\"float32\")\n",
        "YImages = np.zeros([count, 64, 64, 1], dtype=\"float32\")\n",
        "\n",
        "for j in range(1,count) :\n",
        "  with rasterio.open( dir_path + 'patchSNDVI_' + str(j) + '.tiff') as img:\n",
        "    tmp = np.moveaxis(img.read(), 0, 2)\n",
        "    XImages[j-1] = tmp[:,:,0:4]\n",
        "    YImages[j-1] = tmp[:,:,4:5]\n"
      ],
      "metadata": {
        "id": "iZk-sir8PzHa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "สุ่มเปิดภาพดาวเทียม และ ภาพ label"
      ],
      "metadata": {
        "id": "JZD3LH10Qe7R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import matplotlib.pyplot as plt\n",
        "random_image_id = random.randint(0, count)\n",
        "plt.figure(figsize=(16,8))\n",
        "\n",
        "plt.subplot(231)\n",
        "plt.title(\"Ascending VV\")\n",
        "plt.imshow(XImages[random_image_id][:,:,0], cmap = 'Greys_r', vmin=0, vmax=1)\n",
        "plt.colorbar(shrink=0.5)\n",
        "\n",
        "plt.subplot(232)\n",
        "plt.title(\"Ascending VH\")\n",
        "plt.imshow(XImages[random_image_id][:,:,1], cmap = 'Greys_r', vmin=0, vmax=0.25)\n",
        "plt.colorbar(shrink=0.5)\n",
        "\n",
        "plt.subplot(234)\n",
        "plt.title(\"Descending VV\")\n",
        "plt.imshow(XImages[random_image_id][:,:,2], cmap = 'Greys_r', vmin=0, vmax=1)\n",
        "plt.colorbar(shrink=0.5)\n",
        "\n",
        "plt.subplot(235)\n",
        "plt.title(\"Descending VH\")\n",
        "plt.imshow(XImages[random_image_id][:,:,3], cmap = 'Greys_r', vmin=0, vmax=0.25)\n",
        "plt.colorbar(shrink=0.5)\n",
        "\n",
        "plt.subplot(233)\n",
        "plt.title(\"Sentinel2 NDVI\")\n",
        "plt.imshow(YImages[random_image_id][:,:,0], cmap='RdYlGn', vmin=0, vmax=0.5)\n",
        "plt.colorbar(shrink=0.5)\n",
        "\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 612
        },
        "id": "-hG2iuhMQdgR",
        "outputId": "545f38b1-ca03-4bae-a298-21c976f8716b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 10 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "แบ่ง train/test dataset"
      ],
      "metadata": {
        "id": "A4xeoeKZR6TB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(XImages, YImages, train_size = 0.5, random_state=42)"
      ],
      "metadata": {
        "id": "xtHpJTwiRsta"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.shape)\n",
        "print(X_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lbzG6wWOR9G4",
        "outputId": "724a1a7f-cb23-4d0d-e1e0-1f596db42eeb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2371, 64, 64, 4)\n",
            "(2372, 64, 64, 4)\n",
            "(2371, 64, 64, 1)\n",
            "(2372, 64, 64, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "แปลงจาก NAN เป็น 0 ให้หมด รอบหน้าต้องหาทางทำให้ NAN หายไปตั้งแต่เริ่ม"
      ],
      "metadata": {
        "id": "hw-0rj-aSfqI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = np.where(np.isfinite( X_train ), X_train, 0)\n",
        "X_test = np.where(np.isfinite( X_test ), X_test, 0)\n",
        "y_train = np.where(np.isfinite( y_train ), y_train, 0)\n",
        "y_test = np.where(np.isfinite( y_test ), y_test, 0)"
      ],
      "metadata": {
        "id": "m0wMuYk_SeXg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "ลง เครื่องมือ ที่เกี่ยวข้องกับการสร้าง สถาปัตยกรรมของแบบจำลอง"
      ],
      "metadata": {
        "id": "w3-7nO0nSll1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Model\n",
        "from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, Conv2DTranspose\n",
        "from keras.layers import concatenate, BatchNormalization, Dropout, Lambda"
      ],
      "metadata": {
        "id": "sRgeq1pQSETD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "ตัวอย่างที่น่าสนใจ มั้ง Building Autoencoder from image denoising\n",
        "\n",
        "https://colab.research.google.com/github/iagml/iagml.github.io/blob/master/assets/uploads/image_denoising.ipynb#scrollTo=NAqXl7JOTcq4"
      ],
      "metadata": {
        "id": "ZkBSA_NxgvO5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "สร้าง สถาปัตยกรรมนิวรอนเส็งเคร็ง UNET ขึ้นมา"
      ],
      "metadata": {
        "id": "cpv__GWaXXf-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def multi_unet_model(n_classes=12, image_height=64, image_width=64, image_channels=1):\n",
        "\n",
        "  inputs = Input((image_height, image_width, image_channels))\n",
        "\n",
        "  source_input = inputs\n",
        "\n",
        "  c1 = Conv2D(16, (3,3), activation=\"relu\", kernel_initializer=\"he_normal\", padding=\"same\")(source_input)\n",
        "  c1 = Dropout(0.2)(c1)\n",
        "  c1 = Conv2D(16, (3,3), activation=\"relu\", kernel_initializer=\"he_normal\", padding=\"same\")(c1)\n",
        "  p1 = MaxPooling2D((2,2))(c1)\n",
        "\n",
        "  c2 = Conv2D(32, (3,3), activation=\"relu\", kernel_initializer=\"he_normal\", padding=\"same\")(p1)\n",
        "  c2 = Dropout(0.2)(c2)\n",
        "  c2 = Conv2D(32, (3,3), activation=\"relu\", kernel_initializer=\"he_normal\", padding=\"same\")(c2)\n",
        "  p2 = MaxPooling2D((2,2))(c2)\n",
        "\n",
        "  c3 = Conv2D(64, (3,3), activation=\"relu\", kernel_initializer=\"he_normal\", padding=\"same\")(p2)\n",
        "  c3 = Dropout(0.2)(c3)\n",
        "  c3 = Conv2D(64, (3,3), activation=\"relu\", kernel_initializer=\"he_normal\", padding=\"same\")(c3)\n",
        "  p3 = MaxPooling2D((2,2))(c3)\n",
        "\n",
        "  c4 = Conv2D(128, (3,3), activation=\"relu\", kernel_initializer=\"he_normal\", padding=\"same\")(p3)\n",
        "  c4 = Dropout(0.2)(c4)\n",
        "  c4 = Conv2D(128, (3,3), activation=\"relu\", kernel_initializer=\"he_normal\", padding=\"same\")(c4)\n",
        "  p4 = MaxPooling2D((2,2))(c4)\n",
        "\n",
        "  c5 = Conv2D(256, (3,3), activation=\"relu\", kernel_initializer=\"he_normal\", padding=\"same\")(p4)\n",
        "  c5 = Dropout(0.2)(c5)\n",
        "  c5 = Conv2D(256, (3,3), activation=\"relu\", kernel_initializer=\"he_normal\", padding=\"same\")(c5)\n",
        "\n",
        "  u6 = Conv2DTranspose(128, (2,2), strides=(2,2), padding=\"same\")(c5)\n",
        "  u6 = concatenate([u6, c4])\n",
        "  c6 = Conv2D(128, (3,3), activation=\"relu\", kernel_initializer=\"he_normal\", padding=\"same\")(u6)\n",
        "  c6 = Dropout(0.2)(c6)\n",
        "  c6 = Conv2D(128, (3,3), activation=\"relu\", kernel_initializer=\"he_normal\", padding=\"same\")(c6)\n",
        "\n",
        "  u7 = Conv2DTranspose(64, (2,2), strides=(2,2), padding=\"same\")(c6)\n",
        "  u7 = concatenate([u7, c3])\n",
        "  c7 = Conv2D(64, (3,3), activation=\"relu\", kernel_initializer=\"he_normal\", padding=\"same\")(u7)\n",
        "  c7 = Dropout(0.2)(c7)\n",
        "  c7 = Conv2D(64, (3,3), activation=\"relu\", kernel_initializer=\"he_normal\", padding=\"same\")(c7)\n",
        "\n",
        "  u8 = Conv2DTranspose(32, (2,2), strides=(2,2), padding=\"same\")(c7)\n",
        "  u8 = concatenate([u8, c2])\n",
        "  c8 = Conv2D(32, (3,3), activation=\"relu\", kernel_initializer=\"he_normal\", padding=\"same\")(u8)\n",
        "  c8 = Dropout(0.2)(c8)\n",
        "  c8 = Conv2D(32, (3,3), activation=\"relu\", kernel_initializer=\"he_normal\", padding=\"same\")(c8)\n",
        "\n",
        "  u9 = Conv2DTranspose(16, (2,2), strides=(2,2), padding=\"same\")(c8)\n",
        "  u9 = concatenate([u9, c1], axis=3)\n",
        "  c9 = Conv2D(16, (3,3), activation=\"relu\", kernel_initializer=\"he_normal\", padding=\"same\")(u9)\n",
        "  c9 = Dropout(0.2)(c9)\n",
        "  c9 = Conv2D(16, (3,3), activation=\"relu\", kernel_initializer=\"he_normal\", padding=\"same\")(c9)\n",
        "\n",
        "  outputs = Conv2D(n_classes, (1,1), activation=\"linear\")(c9)\n",
        "\n",
        "  model = Model(inputs=[inputs], outputs=[outputs])\n",
        "  #visualkeras.layered_view(model, legend=True )\n",
        "  #model.summary()\n",
        "  return model"
      ],
      "metadata": {
        "id": "odfxvM3fWTbI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "สร้าง สถาปัตยกรรมนิวรอนเส็งเคร็ง LOOKMEE ขึ้นมา"
      ],
      "metadata": {
        "id": "47fAv7pDUV6K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def lookmee_model(n_classes=1, image_height=64, image_width=64, image_channels=4):\n",
        "\n",
        "  inputs = Input((image_height, image_width, image_channels))\n",
        "\n",
        "  source_input = inputs\n",
        "\n",
        "  x = Conv2D(filters = 32, kernel_size = (3, 3), activation = 'relu', padding = 'same')(source_input)\n",
        "  x = MaxPooling2D(pool_size = (2, 2), padding = 'same')(x)\n",
        "  x = Conv2D(filters = 32, kernel_size = (3, 3), activation = 'relu', padding = 'same')(x)\n",
        "  x = MaxPooling2D(pool_size = (2, 2), padding = 'same')(x)\n",
        "  x = Conv2D(filters = 32, kernel_size = (3, 3), activation = 'relu', padding = 'same')(x)\n",
        "  x = MaxPooling2D(pool_size = (2, 2), padding = 'same')(x)\n",
        "  x = Conv2D(filters = 32, kernel_size = (3, 3), activation = 'relu', padding = 'same')(x)\n",
        "  #x = MaxPooling2D(pool_size = (2, 2), padding = 'same')(x)\n",
        "  #x = Conv2D(filters = 32, kernel_size = (3, 3), activation = 'relu', padding = 'same')(x)\n",
        "  encoded = MaxPooling2D(pool_size = (2, 2), padding = 'same')(x)\n",
        "\n",
        "  x = Conv2D(filters =  32, kernel_size = (3, 3), activation = 'relu', padding = 'same')(encoded)\n",
        "  #x = UpSampling2D(size = (2, 2))(x)\n",
        "  #x = Conv2D(filters =  32, kernel_size = (3, 3), activation = 'relu', padding = 'same')(x)\n",
        "  x = UpSampling2D(size = (2, 2))(x)\n",
        "  x = Conv2D(filters =  32, kernel_size = (3, 3), activation = 'relu', padding = 'same')(x)\n",
        "  x = UpSampling2D(size = (2, 2))(x)\n",
        "  x = Conv2D(filters = 32, kernel_size = (3, 3), activation = 'relu', padding = 'same')(x)\n",
        "  x = UpSampling2D(size = (2, 2))(x)\n",
        "  x = Conv2D(filters = 32, kernel_size = (3, 3), activation = 'relu', padding = 'same')(x)\n",
        "  x = UpSampling2D(size = (2, 2))(x)\n",
        "  decoded = Conv2D(filters = 1, kernel_size = (2, 2), activation = 'linear', padding = 'same')(x)\n",
        "\n",
        "  model = Model(inputs=[inputs], outputs=[decoded])\n",
        "  #visualkeras.layered_view(model, legend=True )\n",
        "  #model.summary()\n",
        "  return model"
      ],
      "metadata": {
        "id": "_UlYbedASIRo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_lookmee_learning_model():\n",
        "  return lookmee_model(n_classes=1,\n",
        "                          image_height=64,\n",
        "                          image_width=64,\n",
        "                          image_channels=4)\n",
        "\n",
        "def get_unet_learning_model():\n",
        "  return multi_unet_model(n_classes=1,\n",
        "                          image_height=64,\n",
        "                          image_width=64,\n",
        "                          image_channels=4)\n",
        "\n",
        "model = get_unet_learning_model()\n",
        "visualkeras.layered_view(model, legend=True )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "Ze4CFHpOSKSH",
        "outputId": "152fd025-aec7-414f-ae06-37fb58228e2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGBA size=1348x378>"
            ],
            "image/png": 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          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "เผื่อ save เผื่อจะสั่งหยุดเลย เผื่อต้องปรับ parameter ระหว่างทาง"
      ],
      "metadata": {
        "id": "IgwO5DSiUgQi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_file = 'unet.h5'\n",
        "early_stop = keras.callbacks.EarlyStopping(monitor = 'val_loss', # what is the metric to measure\n",
        "                              patience = 15, # how many epochs to continue running the model after seeing an increase in val_loss\n",
        "                              restore_best_weights = True) # update the model with the best-seen weights?\n",
        "\n",
        "reduce_LR = keras.callbacks.ReduceLROnPlateau(monitor = 'val_loss',\n",
        "                                              factor = 0.1,\n",
        "                                              patience = 5) # to make sure early_stop is what stops the model\n",
        "\n",
        "checkpoint = keras.callbacks.ModelCheckpoint(filepath = model_file,\n",
        "                                             monitor = 'val_loss',\n",
        "                                             save_best_only = True)\n",
        "\n",
        "callback_list = [reduce_LR, early_stop, checkpoint]"
      ],
      "metadata": {
        "id": "echbtb3GSNiI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    optimizer='adam',\n",
        "    loss='mean_absolute_error',\n",
        "    metrics=[tf.keras.metrics.MeanAbsoluteError(name='MAE')])\n"
      ],
      "metadata": {
        "id": "Xqt636vuSVBS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.any(np.isnan(X_test)))\n",
        "print(np.any(np.isnan(y_test)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8-6aDHVNSafN",
        "outputId": "533b8b9e-0d5f-4008-9f3b-99a1ef196d7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "False\n",
            "False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_history = model.fit(X_train, y_train,\n",
        "                          batch_size=16,\n",
        "                          verbose=1,\n",
        "                          epochs=200,\n",
        "                          validation_data=(X_test, y_test),\n",
        "                          shuffle=True,\n",
        "                          callbacks = callback_list)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pX9rT7GcUMHm",
        "outputId": "c9366625-2f9b-4a02-b0f0-10b940ccc23e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "149/149 [==============================] - 20s 40ms/step - loss: 0.1837 - MAE: 0.1837 - val_loss: 0.1262 - val_MAE: 0.1262 - lr: 0.0010\n",
            "Epoch 2/200\n",
            "  1/149 [..............................] - ETA: 2s - loss: 0.1330 - MAE: 0.1330"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "149/149 [==============================] - 3s 21ms/step - loss: 0.1229 - MAE: 0.1229 - val_loss: 0.1218 - val_MAE: 0.1218 - lr: 0.0010\n",
            "Epoch 3/200\n",
            "149/149 [==============================] - 4s 30ms/step - loss: 0.1171 - MAE: 0.1171 - val_loss: 0.1181 - val_MAE: 0.1181 - lr: 0.0010\n",
            "Epoch 4/200\n",
            "149/149 [==============================] - 3s 21ms/step - loss: 0.1135 - MAE: 0.1135 - val_loss: 0.1099 - val_MAE: 0.1099 - lr: 0.0010\n",
            "Epoch 5/200\n",
            "149/149 [==============================] - 3s 20ms/step - loss: 0.1108 - MAE: 0.1108 - val_loss: 0.1132 - val_MAE: 0.1132 - lr: 0.0010\n",
            "Epoch 6/200\n",
            "149/149 [==============================] - 3s 21ms/step - loss: 0.1086 - MAE: 0.1086 - val_loss: 0.1054 - val_MAE: 0.1054 - lr: 0.0010\n",
            "Epoch 7/200\n",
            "149/149 [==============================] - 4s 26ms/step - loss: 0.1068 - MAE: 0.1068 - val_loss: 0.1062 - val_MAE: 0.1062 - lr: 0.0010\n",
            "Epoch 8/200\n",
            "149/149 [==============================] - 4s 24ms/step - loss: 0.1054 - MAE: 0.1054 - val_loss: 0.1032 - val_MAE: 0.1032 - lr: 0.0010\n",
            "Epoch 9/200\n",
            "149/149 [==============================] - 3s 21ms/step - loss: 0.1044 - MAE: 0.1044 - val_loss: 0.1023 - val_MAE: 0.1023 - lr: 0.0010\n",
            "Epoch 10/200\n",
            "149/149 [==============================] - 3s 20ms/step - loss: 0.1032 - MAE: 0.1032 - val_loss: 0.1052 - val_MAE: 0.1052 - lr: 0.0010\n",
            "Epoch 11/200\n",
            "149/149 [==============================] - 4s 24ms/step - loss: 0.1026 - MAE: 0.1026 - val_loss: 0.0999 - val_MAE: 0.0999 - lr: 0.0010\n",
            "Epoch 12/200\n",
            "149/149 [==============================] - 4s 28ms/step - loss: 0.1013 - MAE: 0.1013 - val_loss: 0.0995 - val_MAE: 0.0995 - lr: 0.0010\n",
            "Epoch 13/200\n",
            "149/149 [==============================] - 4s 27ms/step - loss: 0.1010 - MAE: 0.1010 - val_loss: 0.1007 - val_MAE: 0.1007 - lr: 0.0010\n",
            "Epoch 14/200\n",
            "149/149 [==============================] - 3s 23ms/step - loss: 0.1003 - MAE: 0.1003 - val_loss: 0.1003 - val_MAE: 0.1003 - lr: 0.0010\n",
            "Epoch 15/200\n",
            "149/149 [==============================] - 4s 29ms/step - loss: 0.0998 - MAE: 0.0998 - val_loss: 0.1007 - val_MAE: 0.1007 - lr: 0.0010\n",
            "Epoch 16/200\n",
            "149/149 [==============================] - 3s 20ms/step - loss: 0.0995 - MAE: 0.0995 - val_loss: 0.1006 - val_MAE: 0.1006 - lr: 0.0010\n",
            "Epoch 17/200\n",
            "149/149 [==============================] - 3s 20ms/step - loss: 0.0992 - MAE: 0.0992 - val_loss: 0.1040 - val_MAE: 0.1040 - lr: 0.0010\n",
            "Epoch 18/200\n",
            "149/149 [==============================] - 3s 21ms/step - loss: 0.0976 - MAE: 0.0976 - val_loss: 0.0978 - val_MAE: 0.0978 - lr: 1.0000e-04\n",
            "Epoch 19/200\n",
            "149/149 [==============================] - 4s 25ms/step - loss: 0.0972 - MAE: 0.0972 - val_loss: 0.0972 - val_MAE: 0.0972 - lr: 1.0000e-04\n",
            "Epoch 20/200\n",
            "149/149 [==============================] - 3s 23ms/step - loss: 0.0970 - MAE: 0.0970 - val_loss: 0.0979 - val_MAE: 0.0979 - lr: 1.0000e-04\n",
            "Epoch 21/200\n",
            "149/149 [==============================] - 3s 20ms/step - loss: 0.0970 - MAE: 0.0970 - val_loss: 0.0979 - val_MAE: 0.0979 - lr: 1.0000e-04\n",
            "Epoch 22/200\n",
            "149/149 [==============================] - 3s 20ms/step - loss: 0.0969 - MAE: 0.0969 - val_loss: 0.0975 - val_MAE: 0.0975 - lr: 1.0000e-04\n",
            "Epoch 23/200\n",
            "149/149 [==============================] - 3s 21ms/step - loss: 0.0968 - MAE: 0.0968 - val_loss: 0.0980 - val_MAE: 0.0980 - lr: 1.0000e-04\n",
            "Epoch 24/200\n",
            "149/149 [==============================] - 4s 26ms/step - loss: 0.0967 - MAE: 0.0967 - val_loss: 0.0982 - val_MAE: 0.0982 - lr: 1.0000e-04\n",
            "Epoch 25/200\n",
            "149/149 [==============================] - 3s 20ms/step - loss: 0.0964 - MAE: 0.0964 - val_loss: 0.0976 - val_MAE: 0.0976 - lr: 1.0000e-05\n",
            "Epoch 26/200\n",
            "149/149 [==============================] - 3s 20ms/step - loss: 0.0964 - MAE: 0.0964 - val_loss: 0.0975 - val_MAE: 0.0975 - lr: 1.0000e-05\n",
            "Epoch 27/200\n",
            "149/149 [==============================] - 3s 20ms/step - loss: 0.0964 - MAE: 0.0964 - val_loss: 0.0973 - val_MAE: 0.0973 - lr: 1.0000e-05\n",
            "Epoch 28/200\n",
            "149/149 [==============================] - 4s 27ms/step - loss: 0.0963 - MAE: 0.0963 - val_loss: 0.0978 - val_MAE: 0.0978 - lr: 1.0000e-05\n",
            "Epoch 29/200\n",
            "149/149 [==============================] - 3s 20ms/step - loss: 0.0963 - MAE: 0.0963 - val_loss: 0.0976 - val_MAE: 0.0976 - lr: 1.0000e-05\n",
            "Epoch 30/200\n",
            "149/149 [==============================] - 3s 20ms/step - loss: 0.0963 - MAE: 0.0963 - val_loss: 0.0975 - val_MAE: 0.0975 - lr: 1.0000e-06\n",
            "Epoch 31/200\n",
            "149/149 [==============================] - 3s 20ms/step - loss: 0.0963 - MAE: 0.0963 - val_loss: 0.0975 - val_MAE: 0.0975 - lr: 1.0000e-06\n",
            "Epoch 32/200\n",
            "149/149 [==============================] - 3s 22ms/step - loss: 0.0962 - MAE: 0.0962 - val_loss: 0.0976 - val_MAE: 0.0976 - lr: 1.0000e-06\n",
            "Epoch 33/200\n",
            "149/149 [==============================] - 4s 25ms/step - loss: 0.0963 - MAE: 0.0963 - val_loss: 0.0976 - val_MAE: 0.0976 - lr: 1.0000e-06\n",
            "Epoch 34/200\n",
            "149/149 [==============================] - 3s 20ms/step - loss: 0.0962 - MAE: 0.0962 - val_loss: 0.0976 - val_MAE: 0.0976 - lr: 1.0000e-06\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ดูผลลัผธ์"
      ],
      "metadata": {
        "id": "Jx9baEppUsdv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history_a = model_history\n",
        "loss = history_a.history['MAE']\n",
        "val_loss = history_a.history['val_MAE']\n",
        "epochs = range(1, len(loss) + 1)\n",
        "\n",
        "plt.plot(epochs, loss, 'b', label=\"Training Loss\")\n",
        "plt.plot(epochs, val_loss, 'r', label=\"Validation Loss\")\n",
        "plt.title(\"Training Vs Validation Loss\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "LR = history_a.history['lr']\n",
        "plt.plot(epochs, LR , 'g', label=\"Learning Rate\")\n",
        "plt.title(\"Learning Rate\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Learning Rate\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "2CmGPsLEUszP",
        "outputId": "de27724a-70e3-42e0-fc9c-7fa3f54ed0e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "เก็บภาพที่เค้าทำนายไว้"
      ],
      "metadata": {
        "id": "TEJKLIlHV5wF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "adXcTLOsVn0n",
        "outputId": "3118629a-5359-4781-a867-4de60ee15a21"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "75/75 [==============================] - 2s 5ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "แสดงภาพผลลัผธ์ คั่นกู จุ๊กกู๊"
      ],
      "metadata": {
        "id": "sc6ouMjpV9_N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "test_image_number = random.randint(0, len(X_test))\n",
        "\n",
        "test_image = X_test[test_image_number]\n",
        "ground_truth_image = y_test[test_image_number]\n",
        "predicted_image = y_pred[test_image_number]\n",
        "\n",
        "\n",
        "plt.figure(figsize=(24,8))\n",
        "\n",
        "plt.subplot(241)\n",
        "plt.title(\"Ascending VV\")\n",
        "plt.imshow(test_image[:,:,0], cmap='Greys_r', vmin=0, vmax=1)\n",
        "plt.colorbar(shrink=0.5)\n",
        "\n",
        "plt.subplot(242)\n",
        "plt.title(\"Ascending VH\")\n",
        "plt.imshow(test_image[:,:,1], cmap='Greys_r', vmin=0, vmax=0.1)\n",
        "plt.colorbar(shrink=0.5)\n",
        "\n",
        "plt.subplot(243)\n",
        "plt.title(\"Descending VV\")\n",
        "plt.imshow(test_image[:,:,2], cmap='Greys_r', vmin=0, vmax=1)\n",
        "plt.colorbar(shrink=0.5)\n",
        "\n",
        "plt.subplot(244)\n",
        "plt.title(\"Descending VH\")\n",
        "plt.imshow(test_image[:,:,3], cmap='Greys_r', vmin=0, vmax=0.1)\n",
        "plt.colorbar(shrink=0.5)\n",
        "\n",
        "plt.subplot(246)\n",
        "plt.title(\"Original NDVI\")\n",
        "plt.imshow(ground_truth_image, cmap='RdYlGn', vmin=0, vmax=0.5)\n",
        "plt.colorbar(shrink=0.5)\n",
        "\n",
        "plt.subplot(247)\n",
        "plt.title(\"SAR_NDVI\")\n",
        "plt.imshow(predicted_image, cmap='RdYlGn', vmin= 0, vmax=0.5)\n",
        "plt.colorbar(shrink=0.5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "2B9Tie9KVrzm",
        "outputId": "34529eed-8a0a-4fb4-a426-e97ff224bbc4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.colorbar.Colorbar at 0x79b65598a2f0>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2400x800 with 12 Axes>"
            ],
            "image/png": 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}