main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Max\\AppData\\Roaming\\Python\\Python37\\site-packages\\ipykernel\\pylab\\config.py:70: DeprecationWarning: InlineBackend._figure_formats_changed is deprecated in traitlets 4.1: use @observe and @unobserve instead.\n",
      "  def _figure_formats_changed(self, name, old, new):\n"
     ]
    }
   ],
   "source": [
    "import logging\n",
    "import sys\n",
    "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
    "\n",
    "from tmrl.custom.utils.window import WindowInterface\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from model import BotDemineur\n",
    "import torch\n",
    "\n",
    "import cv2\n",
    "import pyautogui\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Max\\AppData\\Roaming\\Python\\Python37\\site-packages\\ipykernel\\ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "wi = WindowInterface(\"Démineur\")\n",
    "wi.move_and_resize(w=500,h=320)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DemineurInterface():\n",
    "    \n",
    "    def __init__(self):\n",
    "        \n",
    "        self.windowInterface = WindowInterface(\"Démineur\")\n",
    "        self.grid = Grid()\n",
    "    \n",
    "    def grab_image(self):\n",
    "        \n",
    "        img = self.windowInterface.screenshot()[:,:,:3]\n",
    "        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "        \n",
    "        return img\n",
    "    \n",
    "    def reset(self):\n",
    "        pass\n",
    "    \n",
    "    def get_observations(self):\n",
    "        \n",
    "        img = self.grab_image()\n",
    "        data = self.grid.extract_from_image(img)\n",
    "        \n",
    "        return data        \n",
    "    \n",
    "    def send_control(self, x, y):\n",
    "        pyautogui.click(x, y)\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Grid():\n",
    "    \n",
    "    def __init__(self, rows=16, cols=30):\n",
    "\n",
    "        self.rows = rows\n",
    "        self.cols = cols\n",
    "        self.case_width = 16\n",
    "        self.case_height = 16\n",
    "        self.first_case_x = 55\n",
    "        self.first_case_y = 12\n",
    "        \n",
    "        self.grid = np.zeros((self.rows, self.cols))\n",
    "        \n",
    "        # Define click position for each case on the grid\n",
    "        self.click_offsets = (101, 13)\n",
    "        \n",
    "        X = np.floor(np.linspace(self.click_offsets[0] + self.case_height // 2, self.click_offsets[0] + self.rows * self.case_height, self.rows))\n",
    "        Y = np.floor(np.linspace(self.click_offsets[1] + self.case_width // 2, self.click_offsets[1] + self.cols * self.case_width, self.cols))\n",
    "\n",
    "        self.click_pos = np.stack(np.meshgrid(Y,X), axis=-1)\n",
    "        \n",
    "        # Load cases templates\n",
    "        self.load_cases()\n",
    "        \n",
    "        \n",
    "    def load_cases(self):\n",
    "        \n",
    "        casesNames = os.listdir(\"images/cases\")\n",
    "        \n",
    "        self.cases_templates = np.zeros((len(casesNames), self.case_width, self.case_height, 3))\n",
    "        self.cases_labels = []\n",
    "        \n",
    "        for i in range(len(casesNames)):\n",
    "            \n",
    "            case = cv2.imread(\"images/cases/\" + casesNames[i])[:,:,:3]\n",
    "            \n",
    "            self.cases_templates[i,...] = case[:,:,::-1]\n",
    "            self.cases_labels.append(casesNames[i].rsplit('_', 1)[0])\n",
    "        \n",
    "        \n",
    "    def extract_from_image(self, img):\n",
    "        \"\"\" Convert the grid on the image into a python object \"\"\"\n",
    "        \n",
    "        for i in range(self.rows):\n",
    "            for j in range(self.cols):\n",
    "                \n",
    "                x = self.first_case_x + i * self.case_height\n",
    "                y = self.first_case_y + j * self.case_width\n",
    "                \n",
    "                case_extract = img[x:x + self.case_height, y:y + self.case_width]\n",
    "\n",
    "                case_extract = np.tile(case_extract[None,...], [len(self.cases_labels), 1, 1, 1])\n",
    "                \n",
    "                diff = np.sum((self.cases_templates - case_extract)**2, axis=(1,2,3))\n",
    "                \n",
    "                self.grid[i,j] = np.argmin(diff)\n",
    "\n",
    "        return self.grid\n",
    "\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' grid = Grid()\\n\\nprint(grid.cases_labels)\\n\\ninput = torch.unsqueeze(torch.unsqueeze(torch.Tensor(grid.extract_from_image(img)), 0), 0)\\n\\nmodel = BotDemineur(16,30,14)\\n\\nprint(model(input))\\n\\nplt.imshow(grid.extract_from_image(img)) '"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\" grid = Grid()\n",
    "\n",
    "print(grid.cases_labels)\n",
    "\n",
    "input = torch.unsqueeze(torch.unsqueeze(torch.Tensor(grid.extract_from_image(img)), 0), 0)\n",
    "\n",
    "model = BotDemineur(16,30,14)\n",
    "\n",
    "print(model(input))\n",
    "\n",
    "plt.imshow(grid.extract_from_image(img)) \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "di = DemineurInterface()\n",
    "img = di.grab_image()\n",
    "plt.imshow(img)\n",
    "\n",
    "cv2.imwrite(\"images/grid.jpg\", img[:,:,::-1])\n",
    "# 12,55\n",
    "# 27, 70\n",
    "\n",
    "\n",
    "# 72, 16\n",
    "# 95, 39\n",
    "#cv2.imwrite(\"images/defeat.png\", img[15:41, 71:97][:,:,::-1])\n",
    "#img[15:41, 71:97].shape # 26 x 26\n",
    "\n",
    "# 239, 15 top-left coord for status icon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16, 16, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1400b2cfb88>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i,j = 2,1\n",
    "\n",
    "w,h = 16,16\n",
    "x, y= 55,12\n",
    "\n",
    "case = img[x + i * w: x + (i + 1) * w, y + j * h: y + (j + 1) * h]\n",
    "print(case.shape)\n",
    "\n",
    "plt.imshow(case)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv2.imwrite('img/8_case.png', cv2.cvtColor(case, cv2.COLOR_RGB2BGR))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from PIL import ImageGrab\n",
    "\n",
    "im = ImageGrab.grab()\n",
    "imArr = np.asarray(im)\n",
    "\n",
    "cv2.imwrite(\"images/test.png\", imArr)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "10a8eb115b09bdbd6c7631d731335dfcaee8f0adfd9a993fb551a7cf535d459a"
  },
  "kernelspec": {
   "display_name": "Python 3.7.8 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}