notebooks/Semantris Block Mode.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyautogui"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pytesseract"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def take_screen_shot(region=None):\n",
    "    \"\"\"\n",
    "    region: four-integer tuple of the left, top,\n",
    "    width, and height of the region to capture\n",
    "    \"\"\"\n",
    "    \n",
    "    print('Waiting 5 seconds before screen shot')\n",
    "    print('Please change the browser window to semantris blocks mode')\n",
    "    time.sleep(5)\n",
    "    \n",
    "    return pyautogui.screenshot(region=region)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "FIGSIZE = (6,6)\n",
    "def plot_img(arr, fs=FIGSIZE, cmap='gray', title=None):\n",
    "    plt.figure(figsize=fs)\n",
    "    plt.imshow(arr, cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bgr_to_hsv_value(color):\n",
    "    \"\"\"\n",
    "    Convert a list of BGR color to HSV list\n",
    "    \"\"\"\n",
    "    \n",
    "    c = np.uint8([[color]])\n",
    "    hsv = cv2.cvtColor(c, cv2.COLOR_BGR2HSV)\n",
    "    \n",
    "    return hsv[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Improvised from the following stackoverflow answer\n",
    "https://stackoverflow.com/a/48738145/2947248\n",
    "\"\"\"\n",
    "\n",
    "import cv2\n",
    "import numpy as np\n",
    "from IPython.display import clear_output, Image, display\n",
    "from io import StringIO, BytesIO\n",
    "import PIL.Image\n",
    "\n",
    "def showarray(a, fmt='png'):\n",
    "    a = np.uint8(np.clip(a, 0, 255))\n",
    "    f = BytesIO()\n",
    "    PIL.Image.fromarray(a).save(f, fmt)\n",
    "    display(Image(data=f.getvalue()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Referred from https://code.likeagirl.io/finding-dominant-colour-on-an-image-b4e075f98097\n",
    "\"\"\"\n",
    "\n",
    "def find_histogram(clt):\n",
    "    \"\"\"\n",
    "    create a histogram with k clusters\n",
    "    :param: clt\n",
    "    :return:hist\n",
    "    \"\"\"\n",
    "    numLabels = np.arange(0, len(np.unique(clt.labels_)) + 1)\n",
    "    (hist, _) = np.histogram(clt.labels_, bins=numLabels)\n",
    "\n",
    "    hist = hist.astype(\"float\")\n",
    "    hist /= hist.sum()\n",
    "\n",
    "    return hist\n",
    "\n",
    "def plot_colors2(hist, centroids):\n",
    "    bar = np.zeros((50, 300, 3), dtype=\"uint8\")\n",
    "    startX = 0\n",
    "\n",
    "    for (percent, color) in zip(hist, centroids):\n",
    "        # plot the relative percentage of each cluster\n",
    "        endX = startX + (percent * 300)\n",
    "        cv2.rectangle(bar, (int(startX), 0), (int(endX), 50),\n",
    "                      color.astype(\"uint8\").tolist(), -1)\n",
    "        startX = endX\n",
    "\n",
    "    # return the bar chart\n",
    "    return bar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_blocks_background_color(r, g, b, area):\n",
    "    # list of background colors in blocks mode\n",
    "    blocks_background_colors_hs = [\n",
    "        [132, 8],\n",
    "        [128, 5]\n",
    "    ]\n",
    "    \n",
    "    color_hsv = bgr_to_hsv_value([b, g, r]).tolist()\n",
    "    if area > 0.2 or color_hsv[:2] in blocks_background_colors_hs:\n",
    "        return True\n",
    "    \n",
    "    return False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Screen Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Waiting 5 seconds before screen shot\n",
      "Please change the browser window to semantris blocks mode\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "screen = take_screen_shot()\n",
    "plot_img(screen)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Crop screenshot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Waiting 5 seconds before screen shot\n",
      "Please change the browser window to semantris blocks mode\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# region: four-integer tuple of the left, top\n",
    "# width, and height of the region to capture\n",
    "# for my system the screen is 1440 x 900\n",
    "screen = take_screen_shot(region=(200, 100, 1000, 700))\n",
    "plot_img(screen)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Color palette detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cut screen into half (horizontal)\n",
    "screen_frame = np.array(screen)\n",
    "screen_frame = screen_frame[int(screen_frame.shape[0] / 2):]\n",
    "\n",
    "# convert and reshape image format\n",
    "screen_frame = cv2.cvtColor(screen_frame, cv2.COLOR_RGB2BGR)\n",
    "screen_frame = cv2.cvtColor(screen_frame, cv2.COLOR_BGR2RGB)\n",
    "screen_frame = screen_frame.reshape((screen_frame.shape[0] * screen_frame.shape[1],3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=8, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Collect 8 major colors in the image using KMeans clustering \n",
    "from sklearn.cluster import KMeans\n",
    "clt = KMeans(n_clusters=8)\n",
    "clt.fit(screen_frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram representing area proportion for each color\n",
    "hist = find_histogram(clt)\n",
    "block_colors = []\n",
    "\n",
    "# plot all 8 colors\n",
    "bar = plot_colors2(hist, clt.cluster_centers_)\n",
    "plt.imshow(bar)\n",
    "plt.show()\n",
    "\n",
    "# filter out persistent colors from game background\n",
    "for i, rgb in enumerate(clt.cluster_centers_):\n",
    "    if not is_blocks_background_color(*rgb, hist[i]):\n",
    "        block_colors.append(rgb)\n",
    "        \n",
    "# plot word block colors\n",
    "bar = plot_colors2([0.25] * 4, block_colors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the 8 (K=8) colors detected in the image.\n",
    "\n",
    "Based on different characteristics, we will filter out the persistent colors used in the game background. (refer: is_blocks_background_color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(bar)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Color palette representing 4 major block colors used in the game theme."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Color tracking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame = np.array(screen)\n",
    "\n",
    "# Convert RGB to HSV\n",
    "hsv = cv2.cvtColor(frame, cv2.COLOR_RGB2HSV)\n",
    "\n",
    "# index of block color to track (random among all 4)\n",
    "block_color_index = random.randint(0, 3)\n",
    "\n",
    "# define range of a color to track a block color in HSV\n",
    "block_color_hue = bgr_to_hsv_value(block_colors[block_color_index][::-1])[0]\n",
    "lower_color_range = np.array([block_color_hue - 10, 50, 50])\n",
    "upper_color_range = np.array([block_color_hue + 10, 255, 255])\n",
    "\n",
    "# threshold the HSV image to get only blue colors\n",
    "mask_color_range = cv2.inRange(hsv, lower_color_range, upper_color_range)\n",
    "\n",
    "\"\"\"\n",
    "To track multiple colors, you can bitwise-OR\n",
    "two images with different colors\n",
    "\n",
    "res = cv2.bitwise_or(res_blue, res_purple)\n",
    "\"\"\"\n",
    "\n",
    "# Bitwise-AND mask and original images\n",
    "colored_res = cv2.bitwise_and(frame, frame, mask= mask_color_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "showarray(colored_res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Contour detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grayscale conversion\n",
    "imgray = cv2.cvtColor(colored_res, cv2.COLOR_RGB2GRAY)\n",
    "showarray(imgray)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bilateral filter is effective when you want to\n",
    "# keep the edges sharp while removing noise\n",
    "imgray = cv2.bilateralFilter(imgray, 10, 50, 50)\n",
    "showarray(imgray)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find contour in gray scale image\n",
    "_, thresh = cv2.threshold(imgray, 75, 255, 0)\n",
    "thresh = cv2.erode(thresh, None, iterations=2)\n",
    "thresh = cv2.dilate(thresh, None, iterations=2)\n",
    "\n",
    "im2, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# no of contours found in the image\n",
    "# word-rectangles attached to each other results in the same contour\n",
    "len(contours)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "perimeter 398.28427052497864\n",
      "area 7786.0\n",
      "perimeter 517.9827548265457\n",
      "area 11559.5\n",
      "perimeter 398.28427052497864\n",
      "area 7786.0\n"
     ]
    }
   ],
   "source": [
    "# finding contour with maximum area\n",
    "max_area_contour_index = 0\n",
    "max_area_contour = 0\n",
    "\n",
    "for i, contour in enumerate(contours):\n",
    "    rect = cv2.minAreaRect(contour)\n",
    "    box = cv2.boxPoints(rect)\n",
    "    box = np.int0(box)\n",
    "    imgray = cv2.drawContours(imgray, [box], 0, (0,255,0), 10)\n",
    "    \n",
    "    print('perimeter', cv2.arcLength(contour, True))\n",
    "    area = cv2.contourArea(contour)\n",
    "    print('area', area)\n",
    "    \n",
    "    if cv2.contourArea(contour) >= max_area_contour:\n",
    "        max_area_contour = area\n",
    "        max_area_contour_index = i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 11559.5)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_area_contour_index, max_area_contour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mask out the image with maximum area contour\n",
    "mask_contour = np.zeros(imgray.shape[:2], np.uint8)\n",
    "cv2.drawContours(mask_contour, [contours[max_area_contour_index]],-1, 255, -1)\n",
    "max_area_contour_image = cv2.bitwise_and(colored_res, colored_res, mask=mask_contour)\n",
    "showarray(max_area_contour_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_area_contour_image_gray = cv2.cvtColor(max_area_contour_image, cv2.COLOR_RGB2GRAY)\n",
    "(_, max_area_contour_image_bw) = cv2.threshold(\n",
    "    max_area_contour_image_gray, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU\n",
    ")\n",
    "showarray(max_area_contour_image_bw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_area_contour_image_bw_inverted = cv2.bitwise_not(max_area_contour_image_bw)\n",
    "showarray(max_area_contour_image_bw_inverted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Eye'"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Process the contour difference image through OCR/tesseract\n",
    "pytesseract.image_to_string((max_area_contour_image_bw_inverted - cv2.bitwise_not(mask_contour)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}