notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pathlib import PurePath, Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "from typing import List, Tuple\n",
    "from time import sleep, perf_counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2024-03-03 18:19:12.116\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mvision_detector\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m59\u001b[0m - \u001b[1mWindow size: self.w=816 self.h=639\u001b[0m\n",
      "\u001b[32m2024-03-03 18:19:12.118\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mvision_detector\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m70\u001b[0m - \u001b[1mWindow size after crop: self.w=800 self.h=601\u001b[0m\n",
      "\u001b[32m2024-03-03 18:19:12.132\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mgame_controller\u001b[0m:\u001b[36m_start_delay\u001b[0m:\u001b[36m256\u001b[0m - \u001b[1mStarting in 5 seconds...\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "from vision_detector import VisionDetector\n",
    "from game_controller import GameController\n",
    "\n",
    "vision = VisionDetector()\n",
    "game = GameController(vision)\n",
    "\n",
    "\n",
    "game.calibrate_game_settings()\n",
    "\n",
    "# frame = vision.capture_frame()\n",
    "\n",
    "# # game_system_settings_btn_center = (400, 240)\n",
    "# gss_cam_closer_btn_center = (385, 315)\n",
    "# gss_fog_light_btn_center = (475, 342)\n",
    "\n",
    "# # game_game_settings_btn_center = (400, 270)\n",
    "# ggs_hide_chat_btn_center = (425, 165)\n",
    "# ggs_name_items_only = (490, 200)\n",
    "\n",
    "# # game.calibrate_game_settings()\n",
    "\n",
    "# ax = plt.gca()\n",
    "# ax.xaxis.set_minor_locator(plt.MultipleLocator(50))  # Set x minor ticks at 0.1 intervals\n",
    "# ax.yaxis.set_minor_locator(plt.MultipleLocator(50))  # Set y minor ticks at 0.05 intervals\n",
    "# plt.grid(which='both', alpha=0.3, color='green')\n",
    "# plt.imshow(frame)\n",
    "\n",
    "# # plt.plot([game_system_settings_btn_center[0]], [game_system_settings_btn_center[1]], marker='x', markersize=10, color=\"red\")\n",
    "# # plt.plot([gss_cam_closer_btn_center[0]], [gss_cam_closer_btn_center[1]], marker='x', markersize=10, color=\"red\")\n",
    "# # plt.plot([gss_fog_light_btn_center[0]], [gss_fog_light_btn_center[1]], marker='x', markersize=10, color=\"pink\")\n",
    "\n",
    "# # plt.plot([game_game_settings_btn_center[0]], [game_game_settings_btn_center[1]], marker='x', markersize=10, color=\"green\")\n",
    "# plt.plot([ggs_hide_chat_btn_center[0]], [ggs_hide_chat_btn_center[1]], marker='x', markersize=10, color=\"red\")\n",
    "# plt.plot([ggs_name_items_only[0]], [ggs_name_items_only[1]], marker='x', markersize=10, color=\"pink\")\n",
    "\n",
    "# plt.show()\n",
    "\n",
    "# assert False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from vision_detector import VisionDetector\n",
    "\n",
    "# vision = VisionDetector()\n",
    "\n",
    "# frame = vision.capture_frame()\n",
    "\n",
    "# exp_circles_roi_center = (210, 580)\n",
    "# # exp_circles_roi_center_global = vision.get_global_pos(exp_circles_roi_center)\n",
    "\n",
    "# plt.imshow(frame)\n",
    "# plt.plot([exp_circles_roi_center[0]], [exp_circles_roi_center[1]], marker='x', markersize=15, color=\"red\")\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "NameError",
     "evalue": "name 'frame' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[5], line 4\u001b[0m\n\u001b[0;32m      1\u001b[0m plt\u001b[39m.\u001b[39mimshow(vision\u001b[39m.\u001b[39mtarget_templates[\u001b[39m\"\u001b[39m\u001b[39mzaloguj_button\u001b[39m\u001b[39m\"\u001b[39m])\n\u001b[0;32m      2\u001b[0m plt\u001b[39m.\u001b[39mshow()\n\u001b[1;32m----> 4\u001b[0m login_btn_detected, conf, login_btn_center \u001b[39m=\u001b[39m vision\u001b[39m.\u001b[39mdetect_login_button(frame)\n\u001b[0;32m      5\u001b[0m login_btn_global_center \u001b[39m=\u001b[39m vision\u001b[39m.\u001b[39mget_global_pos(login_btn_center)\n\u001b[0;32m      7\u001b[0m first_load_credentials_btn_center \u001b[39m=\u001b[39m (\u001b[39m440\u001b[39m, \u001b[39m360\u001b[39m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'frame' is not defined"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2024-03-03 18:27:35.968\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mgame_controller\u001b[0m:\u001b[36mexit\u001b[0m:\u001b[36m356\u001b[0m - \u001b[1mExiting...\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "plt.imshow(vision.target_templates[\"zaloguj_button\"])\n",
    "plt.show()\n",
    "\n",
    "login_btn_detected, conf, login_btn_center = vision.detect_login_button(frame)\n",
    "login_btn_global_center = vision.get_global_pos(login_btn_center)\n",
    "\n",
    "first_load_credentials_btn_center = (440, 360)\n",
    "\n",
    "plt.title(f\"{login_btn_detected=}\\n{login_btn_global_center=}\\n{conf=:.4f}\")\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.xaxis.set_minor_locator(plt.MultipleLocator(10))  # Set x minor ticks at 0.1 intervals\n",
    "ax.yaxis.set_minor_locator(plt.MultipleLocator(10))  # Set y minor ticks at 0.05 intervals\n",
    "plt.grid(which='both', alpha=0.3, color='pink')\n",
    "\n",
    "plt.plot([login_btn_center[0]], [login_btn_center[1]], marker=\"x\", markersize=20, linewidth=1, color=\"red\")\n",
    "for cred_idx in range(6):\n",
    "    plt.plot([first_load_credentials_btn_center[0]], [first_load_credentials_btn_center[1] + cred_idx * 40], marker=\"x\", markersize=10, linewidth=1, color=\"pink\")\n",
    "\n",
    "plt.imshow(frame)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import os\n",
    "\n",
    "# game_exe_path = r\"D:\\Gry\\ValiumAkademia\\valium.exe\"\n",
    "# game_dir = os.path.dirname(game_exe_path)  # Assumes the game's working directory is its location.\n",
    "\n",
    "# def open_game():\n",
    "#     # Set the working directory to the game's directory and run as admin.\n",
    "#     command = fr'Powershell -Command \"&{{Set-Location -Path {game_dir}; Start-Process \"{game_exe_path}\" -Verb RunAs}}\"'\n",
    "#     os.system(command)\n",
    "\n",
    "# open_game()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from game_controller import GameController\n",
    "\n",
    "game = GameController(vision_detector=vision, start_delay=3, saved_credentials_idx=1)\n",
    "\n",
    "game.login()\n",
    "game.exit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "butelka_dywizji_template_fpath = r\"C:\\Users\\jedrz\\OneDrive\\Documents\\metin2_vision_bot\\data\\target_templates\\items\\butelka_dywizji.png\"\n",
    "butelka_dywizji_template = cv2.imread(butelka_dywizji_template_fpath, cv2.IMREAD_UNCHANGED)[...,:3]  # with drop alpha channel\n",
    "butelka_dywizji_template_gray = cv2.cvtColor(butelka_dywizji_template, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "template_w, template_h = butelka_dywizji_template.shape[:2][::-1]\n",
    "\n",
    "plt.figure(figsize=(1, 3))\n",
    "plt.imshow(butelka_dywizji_template)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# chatowe\n",
    "\n",
    "def detect_empty_slots_and_centroids(image_path):\n",
    "    image = cv2.imread(image_path)\n",
    "    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "    # Threshold the image to get the slots as white on a black background.\n",
    "    _, binary_image = cv2.threshold(gray_image, 15, 255, cv2.THRESH_BINARY_INV)\n",
    "\n",
    "    # Find contours\n",
    "    contours, _ = cv2.findContours(binary_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "\n",
    "    # Filter out contours that don't match the size and shape of an inventory slot\n",
    "    slot_contours = []\n",
    "    for contour in contours:\n",
    "        # Calculate contour area and filter out small areas\n",
    "        area = cv2.contourArea(contour)\n",
    "        # if area < 400 or area > 1000:  # Inventory slots should fall within this range\n",
    "        #     continue\n",
    "        if area < 400 or area > 1800:  # Inventory slots should fall within this range\n",
    "            continue\n",
    "        \n",
    "\n",
    "        # Get the bounding rectangle for each contour\n",
    "        x, y, w, h = cv2.boundingRect(contour)\n",
    "\n",
    "        # Inventory slots are more or less square, with the width and height being similar\n",
    "        if 0.8 < w/h < 1.2:\n",
    "            # Check if the slot is in the lower right part (which is the inventory area)\n",
    "            if (630 < x) and (210 < y < 550):\n",
    "                slot_contours.append(contour)\n",
    "\n",
    "    # Calculate centroids for the filtered contours\n",
    "    centroids = []\n",
    "    for contour in slot_contours:\n",
    "        M = cv2.moments(contour)\n",
    "        if M[\"m00\"] != 0:\n",
    "            cX = int(M[\"m10\"] / M[\"m00\"])\n",
    "            cY = int(M[\"m01\"] / M[\"m00\"])\n",
    "            centroids.append((cX, cY))\n",
    "\n",
    "    # Draw detected slot contours and centroids on the image\n",
    "    annotated_image = image.copy()\n",
    "    cv2.drawContours(annotated_image, slot_contours, -1, (0, 255, 0), 2)\n",
    "    for (cX, cY) in centroids:\n",
    "        cv2.circle(annotated_image, (cX, cY), 5, (255, 0, 0), -1)\n",
    "\n",
    "    return annotated_image, centroids\n",
    "\n",
    "# Detect empty slots and calculate centroids\n",
    "annotated_image_with_centroids, centroids = detect_empty_slots_and_centroids(img_fpath)\n",
    "plt.imshow(annotated_image_with_centroids)\n",
    "\n",
    "slot1_center = (650, 215)\n",
    "slot2_center = (690, 215)\n",
    "slot3_center = (730, 215)\n",
    "slot4_center = (770, 215)\n",
    "confirmation_btn_center = (360, 320)\n",
    "\n",
    "plt.plot([slot1_center[0]], [slot1_center[1]], marker=\"x\", markersize=10, linewidth=1, color=\"lightblue\")\n",
    "plt.plot([slot2_center[0]], [slot2_center[1]], marker=\"x\", markersize=10, linewidth=1, color=\"red\")\n",
    "plt.plot([slot3_center[0]], [slot2_center[1]], marker=\"x\", markersize=10, linewidth=1, color=\"pink\")\n",
    "plt.plot([slot4_center[0]], [slot4_center[1]], marker=\"x\", markersize=10, linewidth=1, color=\"yellow\")\n",
    "plt.plot([confirmation_btn_center[0]], [confirmation_btn_center[1]], marker=\"x\", markersize=20, linewidth=1, color=\"green\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_fpath = r\"D:\\Gry\\ValiumAkademia\\screenshot\\0226_224343.jpg\"\n",
    "fill_color = (0, 0, 0)\n",
    "thickness = -1\n",
    "\n",
    "frame = cv2.imread(img_fpath, cv2.IMREAD_COLOR)\n",
    "frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "frame = cv2.rectangle(frame, (0, 0), (260, 90), fill_color, thickness)  # mask effects roi\n",
    "frame = cv2.rectangle(frame, (0, 170), (120, 350), fill_color, thickness)  # mask quests roi\n",
    "frame = cv2.rectangle(frame, (0, 600), (800, 530), fill_color, thickness)  # mask low bar\n",
    "frame = cv2.rectangle(frame, (640, 600), (800, 0), fill_color, thickness)  # mask minimap; right bar\n",
    "frame = cv2.rectangle(frame, (100, 530), (700, 500), fill_color, thickness)  # mask rest of the chat\n",
    "\n",
    "plt.imshow(frame)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _find_by_template(frame: np.array, template_img: np.array, confidence_threshold: float) -> Tuple[bool, float, Tuple[int, int]]:\n",
    "    result = cv2.matchTemplate(frame, template_img, cv2.TM_CCOEFF_NORMED)\n",
    "    _, confidence, _, loc = cv2.minMaxLoc(result)\n",
    "    active = confidence >= confidence_threshold\n",
    "    return active, confidence, loc\n",
    "\n",
    "def drop_alpha_channel(frame: np.array) -> np.array:\n",
    "    return frame[...,:3]\n",
    "\n",
    "\n",
    "SS_DIR = Path(r\"C:\\Users\\jedrz\\OneDrive\\Documents\\metin2_vision_bot\\data\\datasets\\runo_lesne_dropped\")\n",
    "CONF = 0.95\n",
    "MIN_GRAYSCALE_THRESHOLD = 254  # only white pixels\n",
    "\n",
    "template_img = cv2.imread(r\"C:\\Users\\jedrz\\OneDrive\\Documents\\metin2_vision_bot\\data\\target_templates\\items\\runo_lesne_dropped.png\", cv2.IMREAD_UNCHANGED)\n",
    "template_img = drop_alpha_channel(template_img)\n",
    "\n",
    "\n",
    "template_gray = cv2.cvtColor(template_img, cv2.COLOR_BGR2GRAY)\n",
    "_, binarized_template_img = cv2.threshold(template_gray, MIN_GRAYSCALE_THRESHOLD, 255, cv2.THRESH_BINARY)\n",
    "template_w, template_h = binarized_template_img.shape[::-1]\n",
    "\n",
    "plt.imshow(binarized_template_img, cmap=\"gray\")\n",
    "plt.title(f\"width: {template_w}, height: {template_h}\")\n",
    "plt.show()\n",
    "\n",
    "for img_fpath in SS_DIR.iterdir():\n",
    "    img_src = cv2.imread(str(img_fpath), cv2.IMREAD_COLOR)\n",
    "    # img_rgb = cv2.cvtColor(img_src, cv2.COLOR_BGR2RGB)\n",
    "    img_gray = cv2.cvtColor(img_src, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "    _, binarized_img = cv2.threshold(img_gray, MIN_GRAYSCALE_THRESHOLD, 255, cv2.THRESH_BINARY)\n",
    "\n",
    "    active, confidence, loc = _find_by_template(binarized_img, binarized_template_img, CONF)\n",
    "    x, y = loc\n",
    "    item_center_x = x + template_w // 2\n",
    "    item_center_y = y + template_h // 2\n",
    "\n",
    "    plt.figure(figsize=(16, 9))\n",
    "    plt.title(f\"{img_fpath.name} - {active} - {confidence:.4f}\")\n",
    "    plt.imshow(binarized_img, cmap=\"gray\")\n",
    "    plt.plot([item_center_x], [item_center_y], marker=\"x\", markersize=25, markeredgecolor=\"red\", markerfacecolor=\"green\")\n",
    "    plt.axis(\"off\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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