create_human_knowledge.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "from selenium import webdriver\n",
        "from selenium.webdriver.chrome.options import Options\n",
        "from selenium.webdriver.common.by import By\n",
        "from selenium.webdriver.common.keys import Keys"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "from io import BytesIO\n",
        "import base64\n",
        "import numpy as np\n",
        "from PIL import Image, ImageOps"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "url = \"https://doodle-jump.io/game/doodle-jump/\"\n",
        "options = Options()\n",
        "#options.add_argument(\"--headless\")\n",
        "options.add_argument(\"--no-sandbox\")\n",
        "options.add_argument(\"--window-size=644,474\")\n",
        "#options.add_argument(\"--window-size=200,300\")\n",
        "#driver = webdriver.Chrome(\"chromedriver\", options=options)\n",
        "\n",
        "play = np.load(\"play_s.npy\")\n",
        "menu = np.load(\"menu_s.npy\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "def get_screenshot(driver):\n",
        "    image_b64= driver.get_screenshot_as_base64()\n",
        "    screen = Image.open(BytesIO(base64.b64decode(image_b64))).crop((200,0,400,300))\n",
        "    # elem_canvas = driver.find_element(By.XPATH, \"/html/body/canvas\")\n",
        "    # screen = Image.open(BytesIO(elem_canvas.screenshot_as_png))\n",
        "    return screen"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "from pynput import keyboard\n",
        "from pynput.keyboard import Key\n",
        "from threading import Thread \n",
        "from time import sleep\n",
        "import time\n",
        "\n",
        "def on_press(key):\n",
        "    global current_key\n",
        "    current_key = key\n",
        "\n",
        "def on_release(key):\n",
        "    pass\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "def get_screenshot_grayscale(driver):\n",
        "    return ImageOps.grayscale(get_screenshot(driver))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [],
      "source": [
        "from selenium.webdriver.common.action_chains import ActionChains\n",
        "\n",
        "def start_game(driver):\n",
        "    def __contains_play_button():\n",
        "        loading_frame = np.array(get_screenshot_grayscale(driver)\n",
        "            .crop((40, 72, 110, 97)))\n",
        "        return np.array_equal(loading_frame, play)\n",
        "\n",
        "    while True:\n",
        "        driver.get(url)\n",
        "        i = 0\n",
        "        while i < 50 and not __contains_play_button():\n",
        "            print('wait')\n",
        "            sleep(0.1)\n",
        "        i += 1\n",
        "        if i < 50:\n",
        "            elem_canvas = driver.find_element(By.XPATH, \"/html/body/canvas\")\n",
        "            action_chains = ActionChains(driver)\n",
        "            action_chains.move_to_element(elem_canvas).move_by_offset(-10, -75).click().perform()\n",
        "        return\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [],
      "source": [
        "def game_over(driver):\n",
        "    frame = get_screenshot_grayscale(driver)\n",
        "    new_menu_frame = np.array(frame.crop((120, 210, 200, 250)))\n",
        "    return np.array_equal(new_menu_frame, menu)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [],
      "source": [
        "from threading import Thread\n",
        "import os\n",
        "\n",
        "def store_episode(states, actions):\n",
        "    max = 0\n",
        "    for f in os.listdir('./human_db'):\n",
        "        e_n = int(f.split('.')[-2])\n",
        "        if e_n > max:\n",
        "            max = e_n\n",
        "    \n",
        "    # np.save(f'./human_db/states.{max + 1}', states)\n",
        "    # np.save(f'./human_db/actions.{max + 1}', actions)\n",
        "    np.savez_compressed(f'./human_db/episode.{max+1}', states=states, actions=actions)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "wait\n",
            "wait\n",
            "wait\n",
            "wait\n",
            "wait\n",
            "wait\n",
            "wait\n",
            "duration: 91.23271131515503\n"
          ]
        }
      ],
      "source": [
        "driver = webdriver.Chrome(\"chromedriver\", options=options)\n",
        "\n",
        "current_key = None\n",
        "early_end = None\n",
        "\n",
        "listener = keyboard.Listener(on_press=on_press, on_release=on_release)\n",
        "listener.start()\n",
        "\n",
        "steps = 215\n",
        "states = np.zeros((steps, 2, 300, 200), dtype=np.single)\n",
        "actions = np.ones(steps, dtype=np.int_) * -1\n",
        "\n",
        "def capture_step(driver, i):\n",
        "    global current_key\n",
        "    global early_end\n",
        "\n",
        "    for j in range(2):\n",
        "        states[i,j] = np.array(get_screenshot_grayscale(driver), dtype=np.single)\n",
        "\n",
        "    # 0: self.dJWB.move_left,\n",
        "    # 1: self.dJWB.move_right,\n",
        "    # 2: self.dJWB.stay_still,\n",
        "    # 3: self.dJWB.shoot\n",
        "    if current_key == Key.left:\n",
        "        actions[i] = 0\n",
        "    elif current_key == Key.right:\n",
        "        actions[i] = 1\n",
        "    elif current_key == None:\n",
        "        actions[i] = 2\n",
        "    elif current_key == Key.up:\n",
        "        actions[i] = 3\n",
        "\n",
        "    if current_key == Key.esc:\n",
        "        early_end = i\n",
        "        \n",
        "    current_key = None\n",
        "\n",
        "early_end = None\n",
        "\n",
        "start_game(driver)\n",
        "sleep(0.5)\n",
        "#    while not game_over(driver):\n",
        "start = time.time()\n",
        "\n",
        "for i in range(steps):\n",
        "    if early_end:\n",
        "        break\n",
        "\n",
        "    t = Thread(target=capture_step, args=(driver, i))\n",
        "    t.start()\n",
        "#    capture_step()\n",
        "    sleep(0.7)\n",
        "end = time.time()\n",
        "print(f'duration: {end - start}')\n",
        "driver.close()\n",
        "\n",
        "states = states / 255\n",
        "\n",
        "if early_end:\n",
        "    states_t = np.empty((early_end, 2, 300, 200), dtype=np.single)\n",
        "    states_t[:early_end] = states[:early_end]\n",
        "    states = states_t\n",
        "\n",
        "    actions_t = np.empty(early_end)\n",
        "    actions_t[:early_end] = actions[:early_end]\n",
        "    actions = actions_t\n",
        "\n",
        "listener.stop()\n",
        "\n",
        "store_episode(states, actions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2. 2. 1. 0. 0. 1. 2. 0. 0. 1. 1. 2. 0. 2. 0. 1. 2. 2. 2. 0. 2. 2. 2. 2.\n",
            " 0. 1. 1. 1. 0. 1. 1. 2. 0. 0. 2. 1. 1. 2. 1. 2. 0. 0. 2. 1. 0. 2. 1. 1.\n",
            " 2. 0. 2. 0. 2. 2. 0. 1. 2. 2. 3. 0. 2. 1. 2. 0. 1. 1. 0. 0. 1. 2. 0. 1.\n",
            " 2. 0. 2. 0. 1. 2. 1. 0. 1. 2. 2. 0. 2. 1. 1. 2. 1. 2. 2. 2. 0. 0. 1. 2.\n",
            " 1. 1. 2. 0. 2. 0. 0. 2. 2. 2. 1. 2. 0. 0. 2. 2. 2. 2. 2. 2. 1. 1. 0. 1.\n",
            " 2. 2. 2. 0. 1. 2. 2. 2. 2. 2. 0. 1. 2. 1. 0. 1. 0. 2. 3. 3. 3. 2. 2. 2.\n",
            " 2.]\n",
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            "\n",
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          ]
        }
      ],
      "source": [
        "et = np.load('./human_db/episode.6.npz')\n",
        "print(et['actions'])\n",
        "print(et['states'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [],
      "source": [
        "# driver = webdriver.Chrome(\"chromedriver\", options=options)\n",
        "\n",
        "# driver.get(url)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [],
      "source": [
        "# np.save(\"play_s.npy\", np.array(get_screenshot_grayscale(driver).crop((40, 72, 110, 97))))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [],
      "source": [
        "# np.save(\"menu_s.npy\", np.array(get_screenshot_grayscale(driver).crop((120, 210, 200, 250))))"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.3"
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  "nbformat": 4,
  "nbformat_minor": 0
}