predict.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split board into squares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import chess\n",
    "\n",
    "from scipy.signal import find_peaks, convolve2d\n",
    "from fastai2.vision.all import *\n",
    "\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_squares(img, hticks, vticks):\n",
    "    k = 0\n",
    "    for i in range(len(hticks)-1):\n",
    "        for j in range(len(vticks)-1):\n",
    "            xmin = hticks[i]\n",
    "            xmax = hticks[i+1]\n",
    "            ymin = vticks[j]\n",
    "            ymax = vticks[j+1]\n",
    "            square = img[xmin:xmax, ymin:ymax]\n",
    "            cv2.imwrite(f'temp/{k}.png', square)\n",
    "            k+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_squares(path):\n",
    "    img = cv2.imread(path)\n",
    "\n",
    "    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "    horiz = np.array([[1,2,1],\n",
    "                         [0,0,0],\n",
    "                         [-1,-2,-1]])\n",
    "\n",
    "    vert = np.array([[1,0,-1],\n",
    "                     [2,0,-2],\n",
    "                     [1,0,-1]])\n",
    "\n",
    "    # blur\n",
    "    blurred = cv2.blur(gray,(3,3))\n",
    "\n",
    "    # horizontal and vertical Sobel\n",
    "    hgrad = convolve2d(blurred, horiz)\n",
    "    vgrad = convolve2d(blurred, vert)\n",
    "\n",
    "    # erode\n",
    "    k = np.ones((3,3), np.uint8)\n",
    "    hgrad = cv2.erode(hgrad.astype(np.uint8), k)\n",
    "    vgrad = cv2.erode(vgrad.astype(np.uint8), k)\n",
    "\n",
    "    # sum absolute values vertically and horizontally\n",
    "    vlines = np.abs(vgrad).sum(axis=0)\n",
    "    hlines = np.abs(hgrad).sum(axis=1)\n",
    "    \n",
    "    vlines = np.clip((vlines - vlines.max()/2),0,None)\n",
    "    hlines = np.clip((hlines - hlines.max()/2),0,None)\n",
    "\n",
    "    vticks = find_peaks(vlines)[0]\n",
    "    hticks = find_peaks(hlines)[0]\n",
    "    \n",
    "    if len(vticks)==9 and len(hticks)==9:\n",
    "        save_squares(img, hticks, vticks)\n",
    "    else:\n",
    "        print(f\"Board {path} didn't validate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'boards/rnbqkbr___p__np_p__p_p______p__pp_____P____PPP___PPNK__PR_BB_QNR.png'"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boards = Path('boards')\n",
    "board_path = str(boards.ls()[1])\n",
    "board_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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": [
    "img = cv2.imread(board_path)\n",
    "show(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "make_squares(board_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict on Squares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "learner = load_learner('export.pkl')\n",
    "temp = Path('temp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/svg+xml": [
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      ],
      "text/plain": [
       "Board('rnbqkbr1/2p2np1/p2p1p2/4p2p/p5P1/3PPP2/1PPNK2P/R1BB1QNR w - - 0 1')"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items = []\n",
    "for i in range(64):\n",
    "    path = Path(f'temp/{i}.png')\n",
    "    item = PILImage.create(path)\n",
    "    items.append(item)\n",
    "\n",
    "dl = learner.dls.test_dl(items)\n",
    "\n",
    "_, __, preds = learner.get_preds(dl=dl, with_decoded=True)\n",
    "classes = learner.dls.vocab\n",
    "preds = [classes[pred] for pred in preds]\n",
    "labels = ''.join([label_lib[pred] for pred in preds])\n",
    "fen = label2fen(labels)\n",
    "board = chess.Board(fen)\n",
    "board"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "empty(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
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