arabicmodelling.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6u6Ch3XKC9um",
        "outputId": "5275737c-8066-4275-bea7-d677b9f70bfa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/drive/MyDrive/hw/arabic/ADAB DATABASE.zip\n",
            "   creating: ADAB DATABASE/\n",
            "  inflating: ADAB DATABASE/set_1.7z  \n",
            "  inflating: ADAB DATABASE/set_2.7z  \n",
            "  inflating: ADAB DATABASE/set_3.7z  \n"
          ]
        }
      ],
      "source": [
        "!unzip '/content/drive/MyDrive/hw/arabic/ADAB DATABASE.zip'"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!7z x '/content/ADAB DATABASE/set_1.7z' \n",
        "!7z x '/content/ADAB DATABASE/set_2.7z' \n",
        "!7z x '/content/ADAB DATABASE/set_3.7z' "
      ],
      "metadata": {
        "id": "30jr1mvmGyde"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "im = len(os.listdir('/content/set_1/images'))\n",
        "ink = len(os.listdir('/content/set_1/inkml'))\n",
        "upx = len(os.listdir('/content/set_1/upx'))"
      ],
      "metadata": {
        "id": "Heg-YbKSKzQ6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ink == upx"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9xY1tZJUhthm",
        "outputId": "a3c53c46-fe58-43e5-a813-020ce3e1c7ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "file_1 = os.listdir('/content/set_1/inkml')[2]\n",
        "f = open('/content/set_1/inkml/'+file_1)\n",
        "x = f.read()\n",
        "f.close()\n",
        "# print(x)"
      ],
      "metadata": {
        "id": "VPSK3cSOiR4y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import xml.etree.ElementTree as ET\n",
        "tree = ET.fromstring(x)\n",
        "[tuple(map(int,(i.split()[0],i.split()[1]))) for i in tree[2].text.split(',')] # from 2 till end"
      ],
      "metadata": {
        "id": "lThngdpcihV3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "file_1 = os.listdir('/content/set_1/upx')[2]\n",
        "f = open('/content/set_1/upx/'+file_1)\n",
        "x = f.read()\n",
        "f.close()\n",
        "# print(x)"
      ],
      "metadata": {
        "id": "XaAy_zsvkBR1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import xml.etree.ElementTree as ET\n",
        "tree = ET.fromstring(x)\n",
        "tree[2][0][0][0].attrib['value']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "-91uzN8PkT7R",
        "outputId": "0dfb5b15-9f6c-4b24-b19a-688012e853b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'تاشين'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import xml.etree.ElementTree as ET\n",
        "data_strokes = []\n",
        "for k in [1,2,3]:\n",
        "  for i in os.listdir('/content/set_{}/inkml'.format(k)):\n",
        "    f = open('/content/set_{}/inkml/'.format(k)+i)\n",
        "    x = f.read()\n",
        "    f.close()\n",
        "    tree = ET.fromstring(x)\n",
        "    temp = []\n",
        "    for i in tree[2:]:\n",
        "      temp.append([tuple(map(int,(i.split()[0],i.split()[1]))) for i in i.text.split(',')])\n",
        "    # [tuple(map(int,(i.split()[0],i.split()[1]))) for i in tree[2].text.split(',')] # from 2 till end\n",
        "    data_strokes.append(temp)"
      ],
      "metadata": {
        "id": "GNnzYe7cl2jE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import xml.etree.ElementTree as ET\n",
        "data_keys = []\n",
        "for k in [1,2,3]:\n",
        "  for i in os.listdir('/content/set_{}/upx'.format(k)):\n",
        "    f = open('/content/set_{}/upx/'.format(k)+i)\n",
        "    x = f.read()\n",
        "    f.close()\n",
        "    tree = ET.fromstring(x)\n",
        "    data_keys.append(tree[2][0][0][0].attrib['value'])"
      ],
      "metadata": {
        "id": "xD1CuQhunJKN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(data_keys))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R3WR5C2ms6pA",
        "outputId": "a4dd50f8-b94b-4f5f-a1e8-f38ae5787d53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "15158\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/X-rayLaser/pytorch-handwriting-synthesis-toolkit\n",
        "!cd pytorch-handwriting-synthesis-toolkit && pip install -r requirements.txt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "65o2rrq8qdXQ",
        "outputId": "e3a9aa11-d752-47a8-f24f-a31ba01625d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'pytorch-handwriting-synthesis-toolkit'...\n",
            "remote: Enumerating objects: 1013, done.\u001b[K\n",
            "remote: Counting objects: 100% (167/167), done.\u001b[K\n",
            "remote: Compressing objects: 100% (12/12), done.\u001b[K\n",
            "remote: Total 1013 (delta 158), reused 159 (delta 155), pack-reused 846\u001b[K\n",
            "Receiving objects: 100% (1013/1013), 98.79 MiB | 24.51 MiB/s, done.\n",
            "Resolving deltas: 100% (661/661), done.\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting Pillow==9.0.1\n",
            "  Downloading Pillow-9.0.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 4.3 MB 9.7 MB/s \n",
            "\u001b[?25hCollecting numpy==1.19.5\n",
            "  Downloading numpy-1.19.5-cp37-cp37m-manylinux2010_x86_64.whl (14.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 14.8 MB 52.3 MB/s \n",
            "\u001b[?25hCollecting torch==1.8.1\n",
            "  Downloading torch-1.8.1-cp37-cp37m-manylinux1_x86_64.whl (804.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 804.1 MB 2.6 kB/s \n",
            "\u001b[?25hCollecting matplotlib==3.3.4\n",
            "  Downloading matplotlib-3.3.4-cp37-cp37m-manylinux1_x86_64.whl (11.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 11.5 MB 29.9 MB/s \n",
            "\u001b[?25hCollecting torchvision==0.9.1\n",
            "  Downloading torchvision-0.9.1-cp37-cp37m-manylinux1_x86_64.whl (17.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 17.4 MB 9.0 MB/s \n",
            "\u001b[?25hCollecting requests==2.25.1\n",
            "  Downloading requests-2.25.1-py2.py3-none-any.whl (61 kB)\n",
            "\u001b[K     |████████████████████████████████| 61 kB 10.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: h5py==3.1.0 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 7)) (3.1.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch==1.8.1->-r requirements.txt (line 3)) (4.1.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.3.4->-r requirements.txt (line 4)) (3.0.9)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.3.4->-r requirements.txt (line 4)) (0.11.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.3.4->-r requirements.txt (line 4)) (1.4.4)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.3.4->-r requirements.txt (line 4)) (2.8.2)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests==2.25.1->-r requirements.txt (line 6)) (3.0.4)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests==2.25.1->-r requirements.txt (line 6)) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests==2.25.1->-r requirements.txt (line 6)) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests==2.25.1->-r requirements.txt (line 6)) (2022.6.15)\n",
            "Requirement already satisfied: cached-property in /usr/local/lib/python3.7/dist-packages (from h5py==3.1.0->-r requirements.txt (line 7)) (1.5.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib==3.3.4->-r requirements.txt (line 4)) (1.15.0)\n",
            "Installing collected packages: numpy, torch, Pillow, torchvision, requests, matplotlib\n",
            "  Attempting uninstall: numpy\n",
            "    Found existing installation: numpy 1.21.6\n",
            "    Uninstalling numpy-1.21.6:\n",
            "      Successfully uninstalled numpy-1.21.6\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 1.12.1+cu113\n",
            "    Uninstalling torch-1.12.1+cu113:\n",
            "      Successfully uninstalled torch-1.12.1+cu113\n",
            "  Attempting uninstall: Pillow\n",
            "    Found existing installation: Pillow 7.1.2\n",
            "    Uninstalling Pillow-7.1.2:\n",
            "      Successfully uninstalled Pillow-7.1.2\n",
            "  Attempting uninstall: torchvision\n",
            "    Found existing installation: torchvision 0.13.1+cu113\n",
            "    Uninstalling torchvision-0.13.1+cu113:\n",
            "      Successfully uninstalled torchvision-0.13.1+cu113\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.23.0\n",
            "    Uninstalling requests-2.23.0:\n",
            "      Successfully uninstalled requests-2.23.0\n",
            "  Attempting uninstall: matplotlib\n",
            "    Found existing installation: matplotlib 3.2.2\n",
            "    Uninstalling matplotlib-3.2.2:\n",
            "      Successfully uninstalled matplotlib-3.2.2\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "xarray-einstats 0.2.2 requires numpy>=1.21, but you have numpy 1.19.5 which is incompatible.\n",
            "torchtext 0.13.1 requires torch==1.12.1, but you have torch 1.8.1 which is incompatible.\n",
            "torchaudio 0.12.1+cu113 requires torch==1.12.1, but you have torch 1.8.1 which is incompatible.\n",
            "tensorflow 2.8.2+zzzcolab20220719082949 requires numpy>=1.20, but you have numpy 1.19.5 which is incompatible.\n",
            "jax 0.3.17 requires numpy>=1.20, but you have numpy 1.19.5 which is incompatible.\n",
            "cmdstanpy 1.0.7 requires numpy>=1.21, but you have numpy 1.19.5 which is incompatible.\u001b[0m\n",
            "Successfully installed Pillow-9.0.1 matplotlib-3.3.4 numpy-1.19.5 requests-2.25.1 torch-1.8.1 torchvision-0.9.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/pytorch-handwriting-synthesis-toolkit\n",
        "!python prepare_data.py ADAB 'ADAB'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CWoi3Tvqo8Nm",
        "outputId": "41d430c2-eb5e-4433-f400-e818f212713f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/pytorch-handwriting-synthesis-toolkit\n",
            "Calculating maximum length of training set sequences.\n",
            "Checked 10000 sequences\n",
            "Maximum length is 1081 points\n",
            "Prepared 10000 examples\n",
            "Computing mean: processed 10000 out of 10000 examples\n",
            "Computed mean\n",
            "Computing std: processed 10000 out of 10000 examples\n",
            "Computed std\n",
            "Prepared training data\n",
            "Prepared 5000 examples\n",
            "Computing mean: processed 5000 out of 5158 examples\n",
            "Computed mean\n",
            "Computing std: processed 5000 out of 5158 examples\n",
            "Computed std\n",
            "Prepared validation data\n",
            "Building charset: processed 10000 of 10000 examples\n",
            "Charset is saved to ADAB/charset.txt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cp -R /content/pytorch-handwriting-synthesis-toolkit/ADAB /content/drive/MyDrive/hw"
      ],
      "metadata": {
        "id": "VqR54sm2xuZH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/pytorch-handwriting-synthesis-toolkit\n",
        "!python train.py /content/drive/MyDrive/hw/ADAB /content/drive/MyDrive/hw/ADAB_models"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vlWRrk25o8J9",
        "outputId": "84bd44f7-5539-4f72-feac-5d6b6f4126ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/pytorch-handwriting-synthesis-toolkit\n",
            "Using device cuda:0\n",
            "Could not find a model\n",
            "Training handwriting synthesis model with options: training set size 10000, validation set size 5158, batch size 32, max sequence length 1081,sampling interval (in # iterations): 100\n",
            "Could not find a model\n",
            "Epoch    0 299/313 batches. Loss -142.41 nats. MSE 0.6722. SSE 2.0166./content/pytorch-handwriting-synthesis-toolkit/handwriting_synthesis/utils.py:236: RuntimeWarning: invalid value encountered in float_scalars\n",
            "  colors = [str(t / temperatures.sum()) for t in temperatures]\n",
            "Epoch    0 Loss -154.71 nats, Val. loss -411.55 nats.  MSE 0.6679. SSE 2.0037.  Val. MSE 0.5860. Val. SSE 1.7579.\n",
            "Epoch    1 Loss -426.14 nats, Val. loss -436.71 nats.  MSE 0.5851. SSE 1.7552.  Val. MSE 0.5753. Val. SSE 1.7258.\n",
            "Epoch    2 Loss -457.09 nats, Val. loss -459.99 nats.  MSE 0.5817. SSE 1.7450.  Val. MSE 0.5156. Val. SSE 1.5467.\n",
            "Epoch    3 Loss -480.26 nats, Val. loss -486.53 nats.  MSE 0.4786. SSE 1.4357.  Val. MSE 0.4606. Val. SSE 1.3818.\n",
            "Epoch    4 Loss -503.12 nats, Val. loss -483.56 nats.  MSE 0.4487. SSE 1.3460.  Val. MSE 0.4287. Val. SSE 1.2862.\n",
            "Epoch    5 234/313 batches. Loss -519.63 nats. MSE 0.4336. SSE 1.3008./content/pytorch-handwriting-synthesis-toolkit/handwriting_synthesis/utils.py:224: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
            "  fig, axes = plt.subplots(2)\n",
            "Epoch    5 Loss -536.91 nats, Val. loss -585.93 nats.  MSE 0.4320. SSE 1.2960.  Val. MSE 0.4254. Val. SSE 1.2763.\n",
            "Epoch    6 Loss -538.98 nats, Val. loss -630.05 nats.  MSE 0.4252. SSE 1.2757.  Val. MSE 0.4242. Val. SSE 1.2725.\n",
            "Epoch    7 Loss -627.77 nats, Val. loss -761.72 nats.  MSE 0.4375. SSE 1.3124.  Val. MSE 0.4420. Val. SSE 1.3259.\n",
            "Epoch    8 Loss -660.50 nats, Val. loss -700.99 nats.  MSE 0.4501. SSE 1.3502.  Val. MSE 0.4567. Val. SSE 1.3700.\n",
            "Epoch    9 168/313 batches. Loss -653.66 nats. MSE 0.4721. SSE 1.4162."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/pytorch-handwriting-synthesis-toolkit\n",
        "!python synthesize.py /content/drive/MyDrive/hw/ADAB_models/Epoch_6 '' -b 0 --samples_dir=samples2 --trials=5"
      ],
      "metadata": {
        "id": "aJwFpuN9o8HH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "759035c3-5632-41f9-d6fa-10bea25d8c90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/pytorch-handwriting-synthesis-toolkit\n",
            "Done 1 / 5\n",
            "Done 2 / 5\n",
            "Done 3 / 5\n",
            "Done 4 / 5\n",
            "Done 5 / 5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_kn6C0ulo8ET"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0LhVcUBYo8BO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mck49Zz_o7-T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "g50WRnj9o774"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "O6z-2Yw7o744"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Cm23jk9ko72C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "img = plt.imread('/content/set_1/images/1231874526312.tif')\n",
        "plt.imshow(img[:, :, 0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 184
        },
        "id": "UdyhQ3ZOP1Pl",
        "outputId": "a4d114e1-03f2-42bc-8fff-1fb280645b2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f388f5cf690>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "s = \"&#x628;&#x626;&#x631; &#x645;&#x631;&#x648;&#x651;&#x629;\"\n",
        "s = list(map(int,s.replace(\"&#x\",\"\").replace(\" \",\"\").split(';')[:-1]))\n",
        "for i in s:\n",
        "  print(chr(int('0x{}'.format(i), 16)), end = \"\")\n",
        "  time.sleep(0.5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jB7LmPHfQQQD",
        "outputId": "02933db0-5de7-4e05-9954-8cf0c812890f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "بئرمروّة"
          ]
        }
      ]
    }
  ]
}