boards2squares.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An attempt to find boards within larger images using old school computer vision (as opposed to ML). Ultimately I could not get this to work consistently."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: opencv-python in /opt/conda/envs/fastai/lib/python3.7/site-packages (4.2.0.34)\r\n",
      "Requirement already satisfied: numpy>=1.14.5 in /opt/conda/envs/fastai/lib/python3.7/site-packages (from opencv-python) (1.18.1)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install opencv-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from IPython.display import display\n",
    "from scipy.signal import convolve2d\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "boards = os.listdir('boards')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "999"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(boards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = f'boards/{boards[0]}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f92e8180e10>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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(b)\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show(img):\n",
    "    plt.imshow(img, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "show(gray)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TF functions for reference."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based heavily on this:\n",
    "\n",
    "https://github.com/Elucidation/tensorflow_chessbot/blob/master/tensorflow_compvision.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def make_kernel(a):\n",
    "  \"\"\"Transform a 2D array into a convolution kernel\"\"\"\n",
    "  a = np.asarray(a)\n",
    "  a = a.reshape(list(a.shape) + [1,1])\n",
    "  return tf.constant(a, dtype=1)\n",
    "\n",
    "def simple_conv(x, k):\n",
    "  \"\"\"A simplified 2D convolution operation\"\"\"\n",
    "  x = tf.expand_dims(tf.expand_dims(x, 0), -1)\n",
    "  y = tf.nn.depthwise_conv2d(x, k, [1, 1, 1, 1], padding='SAME')\n",
    "  return y[0, :, :, 0]\n",
    "\n",
    "def gradientx(x):\n",
    "  \"\"\"Compute the x gradient of an array\"\"\"\n",
    "  gradient_x = make_kernel([[-1.,0., 1.],\n",
    "                            [-1.,0., 1.],\n",
    "                            [-1.,0., 1.]])\n",
    "  return simple_conv(x, gradient_x)\n",
    "\n",
    "def gradienty(x):\n",
    "  \"\"\"Compute the x gradient of an array\"\"\"\n",
    "  gradient_y = make_kernel([[-1., -1, -1],[0.,0,0], [1., 1, 1]])\n",
    "  return simple_conv(x, gradient_y)\n",
    "\n",
    "def corners(x):\n",
    "  \"\"\"Find chess square corners in an array\"\"\"\n",
    "  chess_corner = make_kernel([[-1., 0, 1],[0., 0., 0.], [1.,0, -1]])\n",
    "  return simple_conv(x, chess_corner)\n",
    "\n",
    "# Following are meant for binary images\n",
    "def dilate(x, size=3):\n",
    "  \"\"\"Dilate\"\"\"\n",
    "  kernel = make_kernel(np.ones([size,size], dtype=np.float32))\n",
    "  return tf.clip_by_value(simple_conv(x, kernel),\n",
    "                          np.float32(1),\n",
    "                          np.float32(2))-np.float32(1)\n",
    "\n",
    "def erode(x, size=3):\n",
    "  \"\"\"Erode\"\"\"\n",
    "  kernel = make_kernel(np.ones([size,size]))\n",
    "  return tf.clip_by_value(simple_conv(x, kernel),\n",
    "                          np.float32(size*size-1),\n",
    "                          np.float32(size*size))-np.float32(size*size-1)\n",
    "\n",
    "def opening(x, size=3):\n",
    "    return dilate(erode(x,size),size)\n",
    "\n",
    "def closing(x, size=3):\n",
    "    return erode(dilate(x,size),size)\n",
    "\n",
    "def skeleton(x, size=3):\n",
    "    \"\"\"Skeletonize\"\"\"\n",
    "    return tf.clip_by_value(erode(x) - opening(erode(x)),\n",
    "                            0.,\n",
    "                            1.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make horizontal and vertical Sobel filters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  1],\n",
       "       [ 0,  0,  0],\n",
       "       [-1, -2, -1]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "horiz = np.array([[1,2,1],\n",
    "                     [0,0,0],\n",
    "                     [-1,-2,-1]])\n",
    "horiz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  0, -1],\n",
       "       [ 2,  0, -2],\n",
       "       [ 1,  0, -1]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vert = np.array([[1,0,-1],\n",
    "                 [2,0,-2],\n",
    "                 [1,0,-1]])\n",
    "\n",
    "vert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "blurred = cv2.blur(gray,(3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x107f46d50>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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(blurred, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "hgrad = convolve2d(blurred, horiz)\n",
    "vgrad = convolve2d(blurred, vert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x108008f90>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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(hgrad, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x123791c90>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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(vgrad, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = np.ones((3,3), np.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "hgrad = cv2.erode(hgrad.astype(np.uint8), k)\n",
    "vgrad = cv2.erode(vgrad.astype(np.uint8), k)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sum absolute values horizontally and vertically"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "vlines = np.abs(vgrad).sum(axis=0)\n",
    "hlines = np.abs(hgrad).sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12283e390>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.plot(vlines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1229ae8d0>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.plot(hlines)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8 spikes!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12324fd10>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.plot(vlines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x123b10890>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.plot(hlines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def skeletonize_1d(arr):\n",
    "    \"\"\"return skeletonized 1d array (thin to single value, favor to the right)\"\"\"\n",
    "    _arr = arr.copy() # create a copy of array to modify without destroying original\n",
    "    # Go forwards\n",
    "    for i in range(_arr.size-1):\n",
    "        # Will right-shift if they are the same\n",
    "        if arr[i] <= _arr[i+1]:\n",
    "            _arr[i] = 0\n",
    "    \n",
    "    # Go reverse\n",
    "    for i in np.arange(_arr.size-1, 0,-1):\n",
    "        if _arr[i-1] > _arr[i]:\n",
    "            _arr[i] = 0\n",
    "    return _arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "skel = skeletonize_1d(vlines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x123c3ce90>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.plot(skel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42935"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skel.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "vticks = np.where(skel>(skel.max()/2))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 20,  65, 111, 155, 200, 246, 291, 336, 380])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vticks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 20,  65, 111, 156, 201, 246, 291, 336, 380])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skel = skeletonize_1d(hlines)\n",
    "hticks = np.where(skel>(skel.max()/2))[0]\n",
    "hticks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "_img = img.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x123d37210>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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n6ADu1WMqKM7WYb0DylAbiBQKL0cZAYXCy1FGQKHwcpQRUCi8nGI3MKgoXkRM7oox6QoAPgEleWjydoeWHxO1gynPd0AIgZSSCQu2cVdIB4fW4emonoDCacRsCCUt8TJSmpDSREZqsubdc1qZ8nwHpJSYTCaklEwZantnpMI+yggonEb2EGdZXD/r2Gm525ffGjS6E1PcQv3HFE4jqG5ul91BdVs6tA7TbSteTXb2Dijso4yAwmkc/31m7rS1nzus/IVT/ps7NJqU/PDxKIfV4Q0oI6BwCrvm2N9+vPvrYQ6pY8tK2xGC/1yuLWaAwowyAgqncO2U/ag5WRF5C8O54wcwZdp2hW4yZXLuhPOXBHsKyggonENe230d4PuhQtW8Ixznl6+4hTICCqcQVKd5Hnl5x/jTQqlyFTBki56UHYPBh1JlVbgLrSgjoHAKbUd9l0deHl6CdBDSqa/N9OadH3FI+d6CMgIKhyNNpjydXawfG2LXDZhW5r83hP12gn3s27yKBR84ZvDRG1BGQOFwwsbl390Pe6vgrwS7w5ex/bfvck8PZiEl235dwJ4NvxS4Dm8iXyMghFgghIgTQhzKlmYz3qAw86UQ4qQQ4qAQwrErQxRuz8mw2ZplY8LnFqiOOePydmKaxey3nmR16IcFqsOb0NIT+A7oeVtaVrzBBsBGy2+AXkADy2cEMMcxzVQUB+IOR+i6sU+Gzebyka266oj8U3u8QoBVX7+Xv5CXk68RkFJuAa7eltwPc5xBLH8fzZa+UJrZAZS3BCBReAHmqDp6pv8EJ9blXlWYF8v/T2fkKOWKPl8KOiaQI94gkBVvsAZwLptcrCUtF0KIEUKIPVkhzRXFnxuXTpEVBEQbkuRLMZql042pnP/niL5GSUmGMS1/OS/G0QODtsyuzatCShkqpWxtywWyonhSkBH/vGIJ3k78+dO65LMfp7BPQZ2K2Is3GAtkX6pVEzhfmAYqig+tRxRsoE8rZStULVDYuDIViiQwdrGloEbAXrzB1cDLQoglQDsgIeu1QeH5FCa6jhZKlQ2icbuuTq3DG8nXCAghFgOdgUpCiFjgPcw3v614g78DvYGTwE1giBParFAoHEi+RkBKaS8CYq54g9Ic0+ylwjYqL1RAivzxBB1ABWkpKtSKQYXCy1FGQKHwcpQRUCi8HBV3oBDEbAgl4WwU5e5sSv2uIxxefkpyAqHvPEu9pu3o/MRIygRVdngdztbBE8g6DwBD31/glPPgSpQRKCARk7uSlngZgMtHtnDu72V0nqh/DtseKckJjO5cEZMpkwNb1/Dr1+/zVcQVAkuXc1gdztbBE8h+HgBe61bd4efB1ajXgQKSFVXn1u94h5Yf+s6z1gsPzH7z5r07yKF1OFsHT6AozoOrUUagoNy+McXBG1X8AkpoSisUTtbBEyiS8+BilBEoAKbMdKQpp6dbacrElJnusDpsdTcd2QUtCh08AWefB3dAGYECED7e9p4ne+l6ObB1DVtXzc+VvmXlNxzcutYhdThbB0+gKM6DO6CMgE4y0m7kmZ9pTCl0HTNfte8o84tXbTvX1ENR6OAJOPs8uAvKCOjkZvy5fPLPFr4Se77z8svTSJHo4Ak4+Ty4C8oI6KRsjbsRdiLfCmGgzB2NCl1Hgxb2w2s3bNGx0OUXhQ6egLPPg7ugjEABsOc8o7ButLPo9dxbdvN6Pz/OIXU4WwdPoCjOgzugjIBOpDQhDLbXWAmDr0NuovOnohE2puuEEPx76nChyy8KHTwBZ58Hd0EZAZ2EvdUCacqwmSdNGYXypw+waNpofvlyPNLGO6eUkmUzx7F4xquFqsPZOngCRXEe3AVlBDSSmXaTzR900SSrVe52Xu1alU3L8vfbv2HJV6TdTNZdflHo4Amk3Ux26nlwN5QR0MiWj3tjTL7d87ptjMlXcy3EyQ9TZgaJV+M0OdKUJhNv9a2nq3xwvg6egCkzg7EP19V1HkyZtntVxQVlBDSSnpKgS/7Iqo91yf849WVd8jcStN3M2XG2Dp7Aj1Nf5mbiNc3yNxKu8uO00U5skfMpaBiy94UQ/wohIi2f3tnyJljCkB0TQvRwVsOLGmnSNy98/XSkLvkTkdt0yZsK5N7buTp4Aicit+n635qkiZMHtjuxRc6noGHIAD6XUja3fH4HEEI0BgYCwZZjZgshbAeRL0aYt9vqu4GSLpzQJf9vjM7RZilJuHJRs3hBdUjzsp2F/8Yc1rcQSEpiT0Q5r0FFQEHDkNmjH7BESpkmpfwHs9fhtoVon1uQmhCXv5ALuHox75V/2SmoDqnXLxXoOEXxoTBORV4WQgwG9gBvSCmvYQ45tiObTJ5hyDAHLXV7ytUKpscnB5xax4J9zl2GWhQ6eALOPg/uSEEHBucA9YHmwAXgU0u6CkOmUBQzCtQTkFJa+4hCiHnAGstPp4chU77o88cTdAAVn0EL6x1QRoF6AreFG38MyJo5WA0MFEIECCHqAg2AXYVrokKhcCYFDUPWWQjRHHNX/zQwEkBKeVgIsRSIBjKAl6SU3rfiRKEoRhQ0DFludyu35P8H/K8wjVIoFEWHWjHoRiScjWL92BBOrp8FwL4FL7F+bIhD6zh1aCdDW5rHb02ZGQxtKdix7ieH1qEoXnhs3IGIyV0xJl1BIvENKMVDk91/VZfw8QOgVsYJ9oSO5OEOjThUMpVjv82gdPUG1Gjdr9B1+Pr6A/Df+0qTmWF2Krrks9c5c2QvDzwxkmq1Gxa6DoU+YqJ2MOX5DgghkFIyYcE27grpUGT1e2RPIGZDKGmJl8374qUkIzWZUxu/cXWz8sW3RCkMBh82b95E/PG/+e6779izZzenIr5n6Xv9MYWNZePbbclMu1ngOkqULgtAWsoNMtKNAPj5BbD+x88Y0bEi9RL/5OfP3nCIPgptTHm+A1JKTCYTUkqmDLXv0cgZeKQRSDibexnn9bPuHyL6yIqPKFmqVK50IQT33HMPYWHrGfL8YP7+7IkClW8yZbLwo5F280eMGEGTJk14aUBnTkfvKVAdCv3c7rjEYMf1m7PwSCMQVDe3U4ygui1d0BLtJF86xeOdmzH/m3k2vdlkERoayj9HI9n4TjvddYxoG0D0ro3Ex8dTsWJFa/qVi2bHoitWrKBSpUpcv36dZ9qU1b2pSVEwTLftVSjI5rDC4JFG4PjvM3Onrf3cBS3RzvYZj/HBBx/w1FNPsXz58jxlK1euTOS+PUQu1N5tP7B1DSZTJlWqVEFKyZUrV+zKDh48mMTEREpd2ML/vfm45joU+lk45b+5NyxJyQ8fjyqyNnicEdg1Z6jdvN1fDyvClmjnzNZFrFmzhmrVqiGl5PHHc954LVq0oH///jnSgoOD8bl6hN1zhmiqY+YYs5/8uLg4KlfOP6pumzZteHHkSEY+ZWsDqcJRbFk5z2b6n8tDi6wNHmcErp3aazfvaox7vuce++0T+vTpYzMvMzOTqKgovv7661x5h6KiuHnhaL7lr5mfz7INO68fYWFhPPHEE7odnii0ce74AUyZttfSmUyZnDtRNONYHmcE8gyq6YYBN0+GzWH//v0286SU+Pr6YjD4UKlSpVz5QUFBzAudS2Js3r4IVsyemGd+vSbtbI5DDBs2jLS0NI5uW5nn8YqCUaFqrULlOwqPMwJBdZrnked+XnRj/1pMSIjtBUEDBgygdsdnqFylit3je/bsyZUTO+3mG9PyDylWve7dCENu3y8pKSns3r2bzp0751uGQj+lylXAYOP/DmAw+FCqbFCRtMPjjEDbUd/lkfdt0TVEI5UrlKNcuXLs2pVzn1WlylWIKd2RBj1HM3jQs3aPr1SpErF/LbGb//3kEbRq2ZJ//vknV55fQCAL9kl2/L7IrrPMefPmMWyYe46leAIhnWzHNGze2X4cREfjUUZAmkx5LrNdPzbE7QJrpKenk5CQwMKFCwG4ceMGBoOBlm/8TpXgzuz66hk+/jhvh5838/D+Y/D1Ze/evdStW9eaJgwGuv7nFeb+fZPMjPQ8PeuGhYUREhKi1g04gfnvDWH/n7/azNu3eRULPiga4+sxRuDiwTDCxuXf3Xe3wBo3ZQkeeeQRZs2axQcffEDp0qXpPj0Sg68/Fw+s56m+Dxaq/EHjZ1m7nAaD+XQPfP0znh5rnkYd3i4AUx6uxdPTzUuL1ZoBx7I7fBnbf/vOvj9DKdn26wL2bPjF6W3xmL0DB34Yq1k2Jnwu9bvZXzlXlHR4cxVG4G5DY37afZ0e080efi8f2YrPkaWE7tmdbxnVqt9hN8+/REm+2WPu6n86qjuN23Wl29NjABje1l+zU83i7kzT3ZgzboAmudlvPcmjL37AIyMmOa0tHmEE4g5H6JI/GTbbbYxAFrXvf9r6PTXhEolbZ3Li+HFNx1avXj1/IeCN2WHW76O7VLRuINJC4lX3dLZaHIn8c7Uu+VVfv6eMQH7EH92qS14Y3Pst6Njq6Wz9/XfN8qVLldRdx82k67rkjakF37SkyMnBbdrPLWB3BsFRuPfdoJHYnSt0yUuTCVOG0UmtKTwXD27grrvu0izv5+enq/zwn77QFGYrOxlu/P8qTqQbU/nTzipBe5hMmWQY05zUIg/pCRRkxD/l2nlKVa7j+MY4gDJlytCtWzfN8vv376dtL+3lH/o7LH+h23D208hbiD9/WrcBzjquWp1GTmiRNh+DtYCFQDXABIRKKWcKISoAPwN1MPsZHCClvCbMS89mAr2Bm8DzUsp9Tmm9hdYj5uo+xr90BSe0xDF0mKRvJL6tzmBvr32lrzu6+iyMmxehrxKFTcpWqMqbc8J1H1emgv0FY4VFS08gA3NwkX1CiDLAXiFEOPA8sFFKOVUIMR4YD4wDemH2MtwAaIc5RoH+fa86qNigvTOLVygcRqmyQTRu19XVzciBljBkF7Ke5FLKJOAI5qhC/YDvLWLfA49avvcDFkozO4Dyt7koVygUboSuMQEhRB2gBbATqCqlvABmQyGEyOqv1ACyB8nLCkV24bayChSGTAWkyB9P0AFUkJaiQrMREEKUBpYDr0opE/PwfqMpFJmUMhQItZTtfQHgFAo3QdMUoRDCD7MBWCSlzJqPu5TVzbf8zVpN4vRQZAqFwnHkawQso/3zgSNSys+yZa0GnrN8fw74NVv6YGGmPZCQ9dqgUCjcDy2vA/cBg4AoIUSkJe1tYCqwVAgxDDgLPGnJ+x3z9OBJzFOE2vxfFUNiNoSScDaKcnc2pX7XYhFlXVEAUpITCH3HvJ176PsLKBOUv3s2vbjyWtIShmwbtt/zAR6yIS+BlwrZLrdn65Qe/B2xnmbNmnHs2DFadejC/RP+cHWzFA4mJTmBmcPbceyo2Y3b3ffcwyuhOwgsXc5hdbj6WvKIZcOuIDXxMs2amUeXGzVqREoee/oVxZcNS75i2dKl1t8/L1nCxp9nObQOV19LyggUkLsb3Z3jd8OGzlnSqXAt544fsN6gACEhIZw7HpnHEfpx9bWkjEABMGWmEx2d07nn0aNHMGVq35qrKB4Eli7HwIEDrb8HDhzo0FcBd7iWlBEoAOHjWyNvc8YhpSR8fGsXtUjhDA5sXcMDTaqzZMktH45LliyhU3A1Dm5d65A63OFa8ohdhEVJRtoNPvroI5t5H374IVuMKfj4BxZxqxTO4MtXH8FkY8ff5MmTMRgMzN9bOH+V7nItqZ6ATm7Gn6Np06Y285o1a8bN+LNF3CKFs7j9Ca01Tyvuci0pI6CTwKDqHDp0yGZeVFQUJYLUXilPIS8PVI7wTuUu15IyAjrxK1mOd955x2bexIkT8QssW8QtUjiL0Z+tYtKk3L79Jk6cyJjP9fkJtIW7XEvKCOhEShM+vn4MGJDTW+yAAQPw8fVzu7gGioJz/lQ0U6dO5dy5W5tiz507x7Rp0/j3VN6h37TgLteSMgI6CXurBRnpRpZmW0ACsHTpUjLSjW4X10BRMBZNG02zSkaMRiO1at3aD1erVi2MRiNNKqSyeMarharDXa4lZQQ0kpl2ky0fdcVozNvhptFoZMtH7uU5RqGPtJvJJJ/Zw8SJ9gO5Tpo0iYSYHaTdTC5QHXqupcw053p6VkZAI9umP0zPB+/P17Ovn58f3Tt3QOYR1UfhvuaBAMgAAB0HSURBVJgyMxjf7y52/P13vrI7d+xgwqMN7MZxtIc0Zeq6lrZPtx2v0FEoI6CR1MQrmgNzDhs2jPhjfzm5RQpncHDb7yRc0b52/3r8RQ79tV5XHZePbNV1LaUkxusqXy/KCGikd+/e9OnTR5Ns3759iVn/f05ukcIZrJozkd69e2uW79GjBytnv6urjlNhs3RdSz166HQnrRNlBDSQlniZ/v376zom4d+jTmqNwpmcPX5Q17nu378/Z45p31CUlniZhPPawstlryMtyXm9AWUENJCaEMfQoUMRQmj+KIoves718OHDdZWdmmD2wqfnWho+fDipTtxerPYOaKBcrWB6fHLA1c1QFAEL9jnX5607XkuqJ6BQeDlaHI3WEkJsFkIcEUIcFkKMsaS/L4T4VwgRafn0znbMBCHESSHEMSGEc0c1FApFoShMGDKAz6WUM7ILCyEaAwOBYOAOYIMQoqGU0iET5yogRf54gg6ggrRoQd/kpG0KE4bMHv2AJVLKNCnlP5i9Drd1QFsVCoUT0DUmcFsYMoCXhRAHhRALhBBBljR7YciKPVJKzhy9FWB5++rvGNpS8OK9yomIovii2QjcHoYMc7Th+kBzzHEGP80StXF4riFXIcQIIcQeIcQe3a12AT9/9gYv3luCO5L2sGzmOADmvz8EKSWrVq7gROR2AIa1MjD5ifpE/aXcjyvsc+rQToa2NN8qpswMhrYU1t9FTYHDkEkpL0kpM6V5v+M8bnX5NYUhk1KGSilbSymd4kxt65QeXFz0POHjWxLxfqcClfHV648StX0dYHYiIaUkMTHRug7ghQ+/RwhBv0cfo0Hz+wCoXbsO0dGH+XN5qM1yiloHhXvi6+vPwoULealjWUbdXwYpJa1bt+bnz97g4hl9i4kKS4HDkN0WbvwxIMtFympgoBAiQAhRF2gA7HJck/MnIzWZGpXLEbl/H9GHD1O7ZnUyUvXt9jq4dS0JZw/xxZiHAXh42DvUbdyGcePG0WfoBAA6PDwYgN5DxluPO3P2DDVq1KBL/xdtllOUOijcl1oNQ1ixYgUPdu5Et67mGD6DBw9m68q5vP1YI1bOye3MxFlo6QlkhSF78LbpwOlCiCghxEGgC/AagJTyMLAUiAb+AF5y1MyAVg7+NIHjx8zLdhs1asSxo0c4uPhtXWX8uXIe0dGHubPWnQCULFOeCd9ux2Qy5XI5/ffaH6zfnx47kyr1mhF8b3eb5RSlDgr35bOXejBnzhzWrFnDmjVrABg9ejQ3kpORUjJzwhBeaO3D6Wjnvy1rmR3YJqUUUspmUsrmls/vUspBUsqmlvRHsgcdlVL+T0pZX0rZSEqpvx9cSILqtmDXrludj127dhFUt6WuMkZ/tooOvQZy9mxOZ49ly5Zl++rvAIjetZHx48cTF3vKmr9o2ugc8n2GTqBarXr0f2tukeugcE/On4rmvpB6VK9enfh423sC6tatS2ZmJuu+fIkXO5R0ans8csXgiXVf5oga07RpU078/oXucl7+dCVSmlgzf4o1rWXLlsx/3xxj9c/lc/n444+pW7ceAMnX4xkzZoxVds38//HR4PYMfOMza8+gqHVQuBcHtq7h3f7BfPDBBwghqFSpUp7yu3buZN+e3cwa+4TT2uRxRmDP3BdIT0+nRIkS1rTAwEDS09PZG6pvsweAj48vm36akcPF9JNPPkmGMY09G34B4MUXR3Ij4SpTnu/AF1+Yb1QpJRt+/IRhw4bRsvOjLtVB4T7MHNOXNWvWUK1aNZsxDVq0yO1SLDg4mItHnTes5nFG4MrJ3fj4+ORK9/HxIf6E/n9k8wce4eqVK4zuHMT1y+ZJjp9//pmZ2Qb6hg8fzpZV35B+45o1bUyXily/do1t27bj6x/gUh0U7sGa+f9j165ddn0JZGZmEhUVZTMvKuogP0592Snt8jgj4GjuqB+M0WgkMz2NJTNeA8zbQKN3bbS+BgQFBRH552+88MItbzHpxhSMRiMxMSdd0m6F+xH2w6e0adPGbv6rr9p3XBoUFMT+iJXOaJbnGYEK9VuRkZHb51tGRgYV6+tfktCm25OsWrWK5KQk0uJuzd/26dOHF18caf19InIbU6dOBeDongiSEhNZuXKlzbYUtQ4K12NMS6FHtwdt5qWkpCCE4NhVH6pXv8NuGdcvX7CbVxg8zp9AmxcX4Ofnh8lksi7qkVLi5+dXoH3cNe9qygudOuSITFuvXj0qVKhAWFgYu3fvBqBr1640bdqMi5cuAuDv70+JEiW4o+7dNsstSh0Uruf7ySP4fNJYypcvT1hYGG3bmtfWrV27lv5PPsWCfZK0lBucudP+EvRnn33WKW3zKCMgTSbCxrXgwoULObz7CCG4cOEC1atXp/v0/QihrwOUbJm71YsQgh6Dx+o6xlk6KFyLwdeXdu3aER8fz6uvvkrbtm3p8uCD+FZryuztyWRmpPPRoLbExtgPavL444+zP3oPdRo7tjfoMUbg4sEwDvww1u7NWq1aNaSU5htT59P0m93p+Pn5ERgYSFJSkqZjfH19+WZ3OgYf7f9iZ+qgcC2Dxs+iVKnSNG8ewl9//UXlypWp3qof3Z42TymPaBfACy+8kGcZnTp1YunHCx1uBDzmcXLwx7dyLK6xx86dO4kJ17dwx+DjS++hb1t7BPl9UlJS6D30bV0GwNk6KFyLf4mS/N+2ZF74v+08PXYmh68FWg3AyPYluHLlCqGhoXmWUaFCBWJP2J49KAwe0ROQpkweffTRPEdes2jbti3/7uhD/W4j85XNTkL8BQICAhg3bhx//fUX58+f59q1a1y7do3AwECCgoIICgoiJCSEdu3akRCvbxCnKHRQuAdd//OK9XtM1A6aNw8hKCgojyNukXpTW09UDx5hBI6snMKprUvzF7Tgb9AZMUZKjv+9lmXLlvHww/lvBIqLi+O99+6Gd/O27Nlxtg4K9+TzUV25kax9Y1jy9SsOb4NHvA7E7lqJr692ezbmlVcwZeQdBy47oW8/zf79+zQZAIAqVarw0kuj+Ofwbs11OFsHhfsR/tMXHD1yRNcxmZnpDm+HR/QEpCmTDRs2aJeXkpRr5ylVuY4m+e7Pvk61atUAuOOOGrRp05qKFStSrlw5/P39AayvBmfOnmXf3r1kZGTw5ebX3EYHhftx6O8whkau1XWMM2aFPMIItB4xlwnLTuUvaKUmZWtW0CxdN7iN0/3RO1sHhfvx2le/u7oJgIcYgYoN2ru6CYXGE3RQFE88YkxAoVAUnGLXE1C+6PPHE3QAFZ+hqFA9AYXCy9HiaLSEEGKXEOKAJQzZB5b0ukKInUKIE0KIn4UQ/pb0AMvvk5b8Os5VwbVcO7XXqWGjFQpno6UnkAY8KKUMwRxjoKcQoj0wDXMYsgbANSBrM/0w4JqU8i7gc4ucR7J+bAiTH61BgwtLOL9P31SPQuEuaHE0KqWUWUua/CwfCTwI/GJJ/x7I8qHVz/IbS/5DIvt2OA+iYaO76d69O3PnzuXSwfD8D3BDYjaEsm/BaGI2aF/dqIeU5ARmjunLzDF9Sbp22Sl1OFsHT0fTwKAQwgfYC9wFzAJigOtSyqy1q9lDjVnDkEkpM4QQCUBFwOP6zP9eTbFu931o8l8ubo1+tk7pwd8R62nWrBnHjh2jVYcu3D/BcZGTUpITmDm8HceOml2n333PPbwSuiOXy/bC4GwdvAFNA4OWSEPNMUcTagvcY0vM8tcjw5Ddzr+7V3Ez/iyhoaH07NmL7dP7urpJuklNvGz1aNyoUSNSrl9yaPkblnzFsqW39kP8vGQJG3+e5dA6nK2DN6BrdkBKeR2IANoD5YUQWT2J7KHGrGHILPnlgKs2ynJqGDJnsnPmQF7oUA6TycTw4cNZt+53biZcZsP4VmSkOH6Xl7O4u1FOr0cNGzZyaPnnjh/I4TY9JCSEc8cjHVqHs3XwBrTMDlQWQpS3fA8EumIOT74Z6G8Rew741fJ9teU3lvxNsiBuedyUk2FzmPPJJGt8ASEEu3btQghBRkY6Gyfd7+IWasOUmU50dE4vNkePHsHkwA0qgaXL5XDLNnDgQIe+ChSFDt6Alp5AdWCzJdzYbiBcSrkGGAe8LoQ4ifmdf75Ffj5Q0ZL+OjDeRpnFln82zbNe2N26dQNg1KhRfPXVVwDExMQUC4cf4eNb5/JgJKUkfLxjOmYHtq7hgSbVWbJkiTVtyZIldAquxsGtjplJcbYO3kK+A4NSyoNArogIUspT3IpEnD09FXjSIa1zM6TJxLi33rL+Dg8PRwjBjBkz6Ny5M2B2QvrPpnlu7fAjI+0GH330kc28Dz/8kC3GFHz87Tu81MKXrz5iM7jG5MmTMRgMzN+bO08PRaGDt6BWDBaSw4cPWw3ALdx7RvRm/DmaNm1qM69Zs2bcjD9rM08Peb0BOuLtsCh08BaUEdCBMBgID8+5579x48a55MrVyp3mTgQGVefQoUM286KioigRVN1mnh6Ewf6llVeeVopCB29BGQGdnLiQyB9/2J+HLlO2HK1GflOELdKPX8lyvPPOOzbzJk6ciF9g2ULXMfqzVUyaNMlm+WM+X13o8otCB29BGQGdtH91Kb169aJTp05069Ytx6dUqVI0fHIKBh8/VzczT6Q04ePrx4ABA3KkDxgwAB9fP6Qs3Ps6mMNvT506lXPnzlnTzp07x7Rp0/j3lH3f+lopCh28hWK3ldjVxGwIJTo6mnvusbVeyhw0tNu0/UXcKn2EvdXC5nv5UsvCnsLGNVg0bTQPBFdhmTGnD8RatWphNBr58MMPWTzjVf7zZsFDrTtbB29CGQGd/LN5Pq+8csxufpYvQnckM+0m2z95BKMxbwelRqORcpVr0Old7T4Ps0i7mUzymT1MXPK3XZlJkybRrn170m4mE1CytO46tnzUVbMO941djU9ASd11eBPKCOikbpdhHDn8K98uWGBdJwBw6dIlvvjiCzZs2IDtMWvXs236w/R6qBN+fnm/rvj5+dG9cwdSTJkIQ+4Q6fYwZWYwvt9dXI+/mK/szh07CKpcnRnrzukK0iJNmfR88H7NOoRN70uniRs1l++NqDEBndTvOgJj6dp0796dEiUCqVO3HlWrVadatWpM/2QGrUe470621MQrDBs2LH9BYNiwYcQf07cp6uC230m4on3t/vX4ixz6a72uOi4f2apLh5REj9u35nBUTyAf4g5t4vjqady4lvvp1rXrQyxZsoTly5fz8uhXSE5KZE/oCGt+qaBqBDW8j+D+uUfJXUHv3r3p06ePJtm+ffsy6L/3UPmejprLXzVnIr1799Ys36NHD1bOfpdmHbW1CeBU2Cz6fGv/dSw7ffv2pUePHprL9laUEciD42u/YHj3BlT55KMcMQKDgoJISkoiOvoIZcqUoUzZcjzx+GNUq1aNpKQkMjLMO6zbtWtH48aNuffeEJcPUqUlXmZw//75C2Yj4d+jpCXFE1Cmkib5s8cP8v74VzWX379/f4YPH65ZPi3xMgnnj2uWz6pj0TntOngjygjkQcPeY5i37ktqtGlCqQYvWdPPAFSCgLrQw/IQu2j5kO1aiz52miu//UrzQTOKrtF2SE2IY+jQoQwdOlTfcdcv6bqBClKH5rYkxAGg10dN+1d+UkYgD5QRyAshaNh7TIEPL1W5jttECCpXK9jpvRFnB2gpCh28ETUwqFB4OcoIKBReTrF7HVABKfLHE3QAFaRFC/omWG2jegIKhZejjEAhWPrFWIa2FAxr7eOQPfKeijH5KpELX3da+Uu/GMsLbXx5oY2vOg8FQBmBQhD24+eA2ePQV68/mo+0d3J2+2L2zn+JS1Eb2RPqHG9Lp3evJSM9nYz0dHUeCkC+YwJCiBLAFiDAIv+LlPI9IcR3wANAgkX0eSllpCXQyEygN3DTkr7PGY13NSZTpvV72+5PubAl7sud9/2H0tUasPvrYbQe4Rzfi0ePHGHEiBGYTKZCn4fdXw9DXo2hVatW7PvnOm1enM/ur4fRsm55APbu3UvbtyMA2DWlM61atcqRVhzRMjCYFYYsWQjhB2wTQqyz5I2VUv5ym3wvoIHl0w6YY/nrcVSr3ZCLZ8wr2EI6Pezi1ngv1Wo35JtvzI5cZm39vNDlhYSEEB4ejn/JcuwJHUnS+eOEnzQ/67p06WLt0WTJdenSpdB1uhItjkYlYCsMmT36AQstx+0QQpQXQlSXUl4odGvdhNQbSYzqmNNzzcudgmjZ5VFemrHcRa1yT27Gn+XAglG0aduWbR/34v4J6/I/SCNZ52Hv3r20bNkSgI6dOpHqX4VRn9z+bNKP8WZCrrTNmzcXulx3o0BhyKSUO4UQ/wX+J4SYBGwExksp08gWhsxCVogyjzACmRnpvNSpvPV348aNiY6ORkoTezetYNoLDzDumz9d2EL3Yuu0vtbBup9++okvtu2hQr3CuwTPzEjn1a6Vcw0Ebt2yhbVr1/LmiC68Far/hjUmXyMiYk+ObeJ5cfz4cc6ePWtdPl4cKVAYMiFEE2ACcDfQBqiAOQ4BeHgYsv/eVzqH66rbvQ0f27eFhHiPsHe6+GvGo0R+2gOxcRynQp9kw4TW3Ij7B0M2p6IDBgzgyvEdbHynHZGf9sR/y7tc+PE5wsa1JNOYoqu+lzqWJS011fo7IiKCiIgIAPr06UPzu6oV6Dzc9+YKenxygA0b8neocuLECUTtTsV+KbOuxUJSyutCiAigp5Qya1dMmhDiW+BNy29rGDIL2UOUZS8rFAgFEEIUm3mdzIxb0W3Cw82RiGfMmMH3339vDUDy+eg+vL/YI8dCbbLr/wZxaFcEd955Z470R/r1y/GkfuONNxCnN5OelvuGDwgoQZePdoLGzUEdOtxr/d6tWzeuXbsGwHPPPcfo0aNZvHgx9YJbM/HHgj1jKtRvbT2/9ujSpQsBfcYWqHx3QsvsQGUg3WIAssKQTct6z7fMBjwKZPl/Xg28LIRYgnlAMMFTxgNSbyTluKi7deuGlJJRo0bl2Gp89ph7+xh0NCXS43MZAIDVv/7Kxx9/zKhRo/j8889ZtmwZ58/neh4AcONGMne27Uezpz/Ot77UG0k8nM0vQnh4uPX/37btrXg4/0TvJS3lBgGBpfSq5FVo6QlUB763jAsYgKVSyjVCiE0WAyGASOBFi/zvmKcHT2KeIhzi+Ga7Bv/A3L7qgoODiY6OzpHm4+ve3oYdTecHHrCbN2HCBAwGA1evXuX777+3K+fr68vNuBhN9fkHluTclSs50rLf/Fn4+QfgH6CiEOVHYcKQPWhHXgIv2cor7hhs+Nu73QAAPPPWl0XRHLfhyNG8Pf0sWLCAIUOG5IhLeDtpaWmUqtZAU30Ggw8ff/wxU6ZMsSuzevVq/vPmFw4JdOLpFLsNRK7m03XnmPZcKxYtWpQrL81oZOQrb9G5/4s2jvRcjsWc4ebNm5Qsadurb506dfIto0rVqtz77hbNdX667hxCCDp27EhAQECOvD179lC2yp28v6R4D9gVFcoI6MTXP4D4+Ct2p5BKlilvM92T6TRxE6VLl+HChfNUrVoVgK1bt/Ltt98SERFBbGwsBoOBevXq0blzZ4YMGULHjrd8F1a/4w4aPzdbV52+/gE0axbCli25DcfVq1epU79h4ZTyIpQR0MmNA8tYv95+GDKEyLFIwlvoPn0/d7d5iOvnDlOlShXmzp3LggULbMquWrWKqlWrkpCYSFpqKg9+uFV32LCpL3QiuH5N+/P5mXnHJciP5PNHGD9+fJ4y58+fp3aGEYOvf6HqcjXKCOhk6kcfkng1b7faznaz5a406PM6u78exqZNmxgzZgyPPfaYTbmHHnqITZs20aRJE9q8OL9AcQNTbySydu1a/P1t34C9evXSXWYWpzZ+gzBlsHfv3jzl7rzzTjZNvJd6Xf9LvYdeKHB9rkaNmujkoYEv2xwgVNyiSZMmXLlyhXnz5nHq1CmMRiNGo5FTp04xb9484uPjadKkSaHq6Nz/v9Svf1euufxLly4xYcKEPIPG5sflo1spVaoU+/bty/djEILLR7cWShdXo3oCGrl66RyZGek0va8XjVp2IvynmezdtAIhBFJK/EuUZMikb6hauyH/xhwmsHRZKlStlX/BHoiU0jomMGXKFGJjYwGoWbMmnTt35quvvqJjx466vQYDGNNSSIi/YD0PE6bOpHv37tb8rPMwadEe/o05jH+JQMpXvgM//xKa6zD4+HLltilIe3Tp0oWD55LzF3RjlBHIg8yMdIa3zft9L2vxkDH1JnPffjpXvjAYePKVafQc/GauPE8jPSURgKpVq2oaE8h+TF5knYdnnnmGxo0bU79+fQx+BoKqBPHKj/9H9erLSU9PRwiBr68v+/btIzY2lrS0NEw3TMQcjOF/U6bw8PD38zwPWVuGW9Ytr3nvgK+vLy3rlmfXlM7FdjuxMgJ58OvcDxAGA9Jkokz5SviVCCQ9LZV0YxoCqHFXUzr2G8LpI3uJ3PIbKcnmC7pkmXKkp6aQdD0eaTKx9IuxXmEEqjZ5kB7TI/n78yd54oknGDlyJI8++ii1a9cG4MyZM6xatYq5c+dSumo9uk9fjxD5v5H+OvcDAkuWZNGiRfj5B1ChSg3SUm+SYUwD4I76wdRs0AwpJYf+WkdKknn3X6myQQBcuRRLRrrRa86DXoQ7uGPSs3fA2Zs1PMFJpzvpcO2f/aQmmAdSS5SrSlDdXOvO7KIcjebP0Ja6Xqn2SilzbeFUPQGFU9Fz0ytcg5odUCi8nGL3OqBQKAqMzdcB1RNQKLwcZQQUCi9HGQGFwstRRkCh8HKUEVAovBxlBBQKL0cZAYXCy1FGQKHwcpQRUCi8HGUEFAovx102ECUDefutLr5UAuJd3Qgn4Kl6gefqVttWorsYgWO21jR7AkKIPZ6om6fqBZ6tmy3U64BC4eUoI6BQeDnuYgRCXd0AJ+KpunmqXuDZuuXCLfwJKBQK1+EuPQGFQuEiXG4EhBA9hRDHhBAnhRB5x31yQ4QQC4QQcUKIQ9nSKgghwoUQJyx/gyzpQgjxpUXXg0KIlq5red4IIWoJITYLIY4IIQ4LIcZY0ou1bkKIEkKIXUKIAxa9PrCk1xVC7LTo9bMQwt+SHmD5fdKSX8eV7XcKUkqXfQAfIAaoB/gDB4DGrmxTAXToBLQEDmVLmw6Mt3wfD0yzfO8NrAME0B7Y6er256FXdaCl5XsZ4DjQuLjrZmlfact3P2Cnpb1LgYGW9K+B/1q+jwK+tnwfCPzsah0c/j9x8Qm5F1if7fcEYIKr/ykF0KPObUbgGFDd8r065nUQAHOB/9iSc/cP8CvQzZN0A0oC+4B2mBcH+VrSrdclsB641/Ld1yInXN12R35c/TpQA3IE8Y21pBV3qkopLwBY/laxpBdLfS1d4BaYn5rFXjchhI8QIhKIA8Ix90avSykzLCLZ227Vy5KfAFQs2hY7F1cbAVuREzx5uqLY6SuEKA0sB16VUuYVM6zY6CalzJRSNgdqAm2Be2yJWf4WG70KiquNQCyQPWpnTeC8i9riSC4JIaoDWP7GWdKLlb5CCD/MBmCRlHKFJdkjdAOQUl4HIjCPCZQXQmQto8/edqtelvxywNWibalzcbUR2A00sIzM+mMeeFnt4jY5gtXAc5bvz2F+n85KH2wZSW8PJGR1rd0NYQ4ZPB84IqX8LFtWsdZNCFFZCFHe8j0Q6AocATYD/S1it+uVpW9/YJO0DBB4DK4elMA8qnwc83vZO65uTwHavxi4AKRjfmoMw/zOuBE4YflbwSIrgFkWXaOA1q5ufx563Y+523sQiLR8ehd33YBmwH6LXoeASZb0esAu4CSwDAiwpJew/D5pya/nah0c/VErBhUKL8fVrwMKhcLFKCOgUHg5yggoFF6OMgIKhZejjIBC4eUoI6BQeDnKCCgUXo4yAgqFl/P/16S/YawXfBMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "for tick in vticks:\n",
    "    _img = cv2.line(_img, (0,tick), (400,tick), (255,0,0), thickness=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x123e09750>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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(_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "for tick in hticks:\n",
    "    _img = cv2.line(_img, (tick,0), (tick,400), (0,255,0), thickness=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x124192150>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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(_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Booyah!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "        # check this shit, the order is weird in opencv\n",
    "        square = gray[xmin:xmax, ymin:ymax]\n",
    "        cv2.imwrite(f'squares/{i}{j}.png', square)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Incredibly, it works!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'boards/r__qkbnr____pp___ppp__p_p_Nn___p_P____PPPQP____N_B_PPP___b__KBR_.png'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a folder for each label. Save pieces to appropriate folder based on filename."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# labels = 'KQRBNPkqrbnp_'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['WK', 'WQ', 'WR', 'WB', 'WN', 'WP',\n",
    "          'BK', 'BQ', 'BR', 'BB', 'BN', 'BP',\n",
    "          '_']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "squares = Path('squares')\n",
    "\n",
    "for label in labels:\n",
    "    (squares/label).mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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": [
    "show(gray)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evidently I started from the top left, and proceeded left to right, top to bottom."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'r__qkbnr____pp___ppp__p_p_Nn___p_P____PPPQP____N_B_PPP___b__KBR_'"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos = Path(b).stem\n",
    "pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_squares(board, gray, hticks, vticks):\n",
    "    pos = Path(board).stem\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 = gray[xmin:xmax, ymin:ymax]\n",
    "            piece = pos[k]\n",
    "            if piece=='_':\n",
    "                label = piece\n",
    "            else:\n",
    "                color = 'B' if piece.isupper() else 'W'\n",
    "                label = color + piece.upper()\n",
    "            folder = f'squares/{label}'\n",
    "            n = len(os.listdir(folder))\n",
    "            fn = f'{folder}/{n}.png'\n",
    "#             print(fn)\n",
    "            cv2.imwrite(fn, square)\n",
    "            k+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "bp = Path(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'r__qkbnr____pp___ppp__p_p_Nn___p_P____PPPQP____N_B_PPP___b__KBR_'"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bp.stem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_out(folder):\n",
    "    [os.remove(f'{folder}/{f}') for f in os.listdir(folder) if not f.startswith('.')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['WK', 'WQ', 'WR', 'WB', 'WN', 'WP',\n",
    "          'BK', 'BQ', 'BR', 'BB', 'BN', 'BP',\n",
    "          '_']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "for label in labels:\n",
    "    folder = f'squares/{label}'\n",
    "    clean_out(folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "999"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(boards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing board 0\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing board 396\n",
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     ]
    },
    {
     "ename": "error",
     "evalue": "OpenCV(4.2.0) /io/opencv/modules/imgproc/src/color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cvtColor'\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31merror\u001b[0m                                     Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-4420d75b08ec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mgray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcvtColor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCOLOR_BGR2GRAY\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     horiz = np.array([[1,2,1],\n",
      "\u001b[0;31merror\u001b[0m: OpenCV(4.2.0) /io/opencv/modules/imgproc/src/color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cvtColor'\n"
     ]
    }
   ],
   "source": [
    "for i,board in enumerate(boards):\n",
    "    print(f'Processing board {i}')\n",
    "    fn = f'boards/{board}'\n",
    "    img = cv2.imread(fn)\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",
    "    vskel = skeletonize_1d(vlines)\n",
    "    vticks = np.where(vskel>(vskel.max()/2))[0]\n",
    "\n",
    "    hskel = skeletonize_1d(hlines)\n",
    "    hticks = np.where(hskel>(hskel.max()/2))[0]\n",
    "    \n",
    "    if len(vticks)==9 and len(hticks)==9:\n",
    "        save_squares(board, gray, hticks, vticks)\n",
    "    else:\n",
    "        print(f\"Board {board} didn't validate\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from IPython.display import display\n",
    "from scipy.signal import convolve2d\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['WK', 'WQ', 'WR', 'WB', 'WN', 'WP',\n",
    "          'BK', 'BQ', 'BR', 'BB', 'BN', 'BP',\n",
    "          '_']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_out(folder):\n",
    "    [os.remove(f'{folder}/{f}') for f in os.listdir(folder) if not f.startswith('.')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for label in labels:\n",
    "#     folder = f'squares/{label}'\n",
    "#     clean_out(folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "for label in labels:\n",
    "    folder = f'squares/{label}'\n",
    "    os.mkdir(folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def skeletonize_1d(arr):\n",
    "    \"\"\"return skeletonized 1d array (thin to single value, favor to the right)\"\"\"\n",
    "    _arr = arr.copy() # create a copy of array to modify without destroying original\n",
    "    # Go forwards\n",
    "    for i in range(_arr.size-1):\n",
    "        # Will right-shift if they are the same\n",
    "        if arr[i] <= _arr[i+1]:\n",
    "            _arr[i] = 0\n",
    "    \n",
    "    # Go reverse\n",
    "    for i in np.arange(_arr.size-1, 0,-1):\n",
    "        if _arr[i-1] > _arr[i]:\n",
    "            _arr[i] = 0\n",
    "    return _arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_squares(fn, img, hticks, vticks):\n",
    "    pos = Path(fn).stem\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",
    "            piece = pos[k]\n",
    "            if piece=='_':\n",
    "                label = piece\n",
    "            else:\n",
    "                color = 'W' if piece.isupper() else 'B'\n",
    "                label = color + piece.upper()\n",
    "            folder = f'squares/{label}'\n",
    "            n = len(os.listdir(folder))\n",
    "            fn = f'{folder}/{n}.png'\n",
    "            cv2.imwrite(fn, square)\n",
    "            k+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_boards(boards):\n",
    "    for i,fn in enumerate(boards):\n",
    "        print(f'Board {i}')\n",
    "        \n",
    "        path = f'boards/{fn}'\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",
    "        vskel = skeletonize_1d(vlines)\n",
    "        vticks = np.where(vskel>(vskel.max()/2))[0]\n",
    "\n",
    "        hskel = skeletonize_1d(hlines)\n",
    "        hticks = np.where(hskel>(hskel.max()/2))[0]\n",
    "\n",
    "        if len(vticks)==9 and len(hticks)==9:\n",
    "            save_squares(fn, img, hticks, vticks)\n",
    "        else:\n",
    "            print(f\"Board {board} didn't validate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "boards = os.listdir('boards')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Board 0\n",
      "Board 1\n",
      "Board 2\n",
      "Board 3\n",
      "Board 4\n",
      "Board 5\n",
      "Board 6\n",
      "Board 7\n",
      "Board 8\n",
      "Board 9\n",
      "Board 10\n",
      "Board 11\n",
      "Board 12\n",
      "Board 13\n",
      "Board 14\n",
      "Board 15\n",
      "Board 16\n",
      "Board 17\n",
      "Board 18\n",
      "Board 19\n",
      "Board 20\n",
      "Board 21\n",
      "Board 22\n",
      "Board 23\n",
      "Board 24\n",
      "Board 25\n",
      "Board 26\n",
      "Board 27\n",
      "Board 28\n",
      "Board 29\n",
      "Board 30\n",
      "Board 31\n",
      "Board 32\n",
      "Board 33\n",
      "Board 34\n",
      "Board 35\n",
      "Board 36\n",
      "Board 37\n",
      "Board 38\n",
      "Board 39\n",
      "Board 40\n",
      "Board 41\n",
      "Board 42\n",
      "Board 43\n",
      "Board 44\n",
      "Board 45\n",
      "Board 46\n",
      "Board 47\n",
      "Board 48\n",
      "Board 49\n",
      "Board 50\n",
      "Board 51\n",
      "Board 52\n",
      "Board 53\n",
      "Board 54\n",
      "Board 55\n",
      "Board 56\n",
      "Board 57\n",
      "Board 58\n",
      "Board 59\n",
      "Board 60\n",
      "Board 61\n",
      "Board 62\n",
      "Board 63\n",
      "Board 64\n",
      "Board 65\n",
      "Board 66\n",
      "Board 67\n",
      "Board 68\n",
      "Board 69\n",
      "Board 70\n",
      "Board 71\n",
      "Board 72\n",
      "Board 73\n",
      "Board 74\n",
      "Board 75\n",
      "Board 76\n",
      "Board 77\n",
      "Board 78\n",
      "Board 79\n",
      "Board 80\n",
      "Board 81\n",
      "Board 82\n",
      "Board 83\n",
      "Board 84\n",
      "Board 85\n",
      "Board 86\n",
      "Board 87\n",
      "Board 88\n",
      "Board 89\n",
      "Board 90\n",
      "Board 91\n",
      "Board 92\n",
      "Board 93\n",
      "Board 94\n",
      "Board 95\n",
      "Board 96\n",
      "Board 97\n",
      "Board 98\n",
      "Board 99\n"
     ]
    }
   ],
   "source": [
    "convert_boards(boards[:100])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lichess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "lichess = Path('lichess')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Path('lichess')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lichess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'r_b_r_kQQpQ__pQQ__prqn_Q____Q_B_____P_PN__NP___NPNP__N_QR__QK__R'"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "for label in labels:\n",
    "    (lichess/'squares'/label).mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "fen = 'r1b1r1k1/ppp2ppp/2pbqn2/4p1B1/4P1PN/2NP3P/PPP2P2/R2QK2R b KQ - 2 11'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "board = chess.Board(fen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "Board('r1b1r1k1/ppp2ppp/2pbqn2/4p1B1/4P1PN/2NP3P/PPP2P2/R2QK2R b KQ - 2 11')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "board"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "label = fen2label(fen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'r_b_r_k_ppp__ppp__pbqn______p_B_____P_PN__NP___PPPP__P__R__QK__R'"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make squares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_squares(img):\n",
    "    h,w,_ = img.shape\n",
    "    k = 0\n",
    "    for y in range(8):\n",
    "        for x in range(8):\n",
    "            square = img[y*h//8:(y+1)*h//8, x*w//8:(x+1)*w//8]\n",
    "            cv2.imwrite(f'lichess/temp/{k}.png', square)\n",
    "            k+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "img = cv2.imread(f'sample.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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": [
    "show(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_squares(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "learner = load_learner('lichess_finetuned.pkl')\n",
    "temp = Path('lichess/temp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
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       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
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      ],
      "text/plain": [
       "Board('r1bq1rk1/3n1ppp/p3Bn2/1pb3N1/5B2/2N1P3/PP3PPP/R2Q1RK1 w - - 0 1')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items = []\n",
    "for i in range(64):\n",
    "    fn = f'{i}.png'\n",
    "    path = temp/fn\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": "markdown",
   "metadata": {},
   "source": [
    "## Make lichess data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Path('lichess')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lichess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(#3) [Path('lichess/temp'),Path('lichess/squares'),Path('lichess/boards')]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lichess.ls()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'WK': 'K',\n",
       " 'WQ': 'Q',\n",
       " 'WR': 'R',\n",
       " 'WB': 'B',\n",
       " 'WN': 'N',\n",
       " 'WP': 'P',\n",
       " 'BK': 'k',\n",
       " 'BQ': 'q',\n",
       " 'BR': 'r',\n",
       " 'BB': 'b',\n",
       " 'BN': 'n',\n",
       " 'BP': 'p',\n",
       " '_': '_'}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_lib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_squares(img, labels):\n",
    "    h,w,_ = img.shape\n",
    "    k = 0\n",
    "    for y in range(8):\n",
    "        for x in range(8):\n",
    "            square = img[y*h//8:(y+1)*h//8, x*w//8:(x+1)*w//8]\n",
    "            piece = labels[k]\n",
    "            if piece=='_':\n",
    "                label = piece\n",
    "            else:\n",
    "                color = 'W' if piece.isupper() else 'B'\n",
    "                label = color + piece.upper()\n",
    "            folder = f'lichess/squares/{label}'\n",
    "            n = len(os.listdir(folder))\n",
    "            fn = f'{folder}/{n}.png'\n",
    "            cv2.imwrite(fn, square)\n",
    "            k+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "board_dir = lichess/'boards'\n",
    "boards = board_dir.ls()\n",
    "for board in boards:\n",
    "    img = cv2.imread(str(board))\n",
    "    label = board.stem\n",
    "    save_squares(img, label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = Path('chesscom')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "path.mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Path('chesscom')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "for subdir in ['boards', 'squares', 'temp']:\n",
    "    (path/subdir).mkdir()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "squares = path/'squares'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['WK', 'WQ', 'WR', 'WB', 'WN', 'WP', 'BK', 'BQ', 'BR', 'BB', 'BN', 'BP', '_']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "for label in labels:\n",
    "    (squares/label).mkdir()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Upload your images to the boards and generate labels from fens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'_r___b___q_n_kp___b__n_p___pp____p__P__B_B__N__P_PPQ_PP_____R_K_'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fen2label('1r3b2/1q1n1kp1/2b2n1p/3pp3/1p2P2B/1B2N2P/1PPQ1PP1/4R1K1 ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_squares(img, labels, squaredir):\n",
    "    h,w,_ = img.shape\n",
    "    k = 0\n",
    "    for y in range(8):\n",
    "        for x in range(8):\n",
    "            square = img[y*h//8:(y+1)*h//8, x*w//8:(x+1)*w//8]\n",
    "            piece = labels[k]\n",
    "            if piece=='_':\n",
    "                label = piece\n",
    "            else:\n",
    "                color = 'W' if piece.isupper() else 'B'\n",
    "                label = color + piece.upper()\n",
    "            folder = str(squaredir/label)\n",
    "            n = len(os.listdir(folder))\n",
    "            fn = f'{folder}/{n}.png'\n",
    "            cv2.imwrite(fn, square)\n",
    "            k+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(#9) [Path('chesscom/boards/r___k_nrpp___pb____pp_p_q_p__P_p___nP_____NPB_PBPPP____PR__Q_RK_.png'),Path('chesscom/boards/____r_k______nb__PN___p___BQ___pP___pq_________P_____PP____R__K_.png'),Path('chesscom/boards/.ipynb_checkpoints'),Path('chesscom/boards/_r___rk_q____p_p__bNpQp_p_p_P__P__P_P____P__P_____B___P___KR___R.png'),Path('chesscom/boards/r__q_rk_pbn_bppp_p_np____N_p____PPP__B___Q_BPN_______PPPR__R__K_.png'),Path('chesscom/boards/_r___b___q_n_kp___b__n_p___pp____p__P__B_B__N__P_PPQ_PP_____R_K_.png'),Path('chesscom/boards/r____rk_pb____qp_p__pp________p__n_PB__Q_____N__PP___PPPR____RK_.png'),Path('chesscom/boards/_______r___kNppp___r_____p_P____nPq___P___p___QP__b___B_K__R____.png'),Path('chesscom/boards/rnb__rk______ppp_p_qpn__p__p_______N_P____PB_NP_PP__QP_PR___K__R.png')]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boards = (path/'boards').ls()\n",
    "boards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for label in labels:\n",
    "#     subdir = squares/label\n",
    "#     empty(subdir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "for board in boards:\n",
    "    if str(board.name).startswith('.'): continue\n",
    "    img = cv2.imread(str(board))\n",
    "    if img is None: print(board)\n",
    "    label = board.stem\n",
    "    save_squares(img, label, squares)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finetune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "learner = load_learner('lichess_finetuned.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "dblock = DataBlock(blocks = (ImageBlock, CategoryBlock),\n",
    "                  get_items = get_image_files,\n",
    "                  get_y = parent_label,\n",
    "                  splitter = RandomSplitter(),\n",
    "                  item_tfms = Resize(224),\n",
    "                  batch_tfms=aug_transforms(size=224))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(PILImage mode=RGB size=54x54, TensorCategory(3))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsets = dblock.datasets('chessbase/squares')\n",
    "dsets.train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "dls = dblock.dataloaders('lichess/squares')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dls.show_batch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "learner.dls = dls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.014992</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>00:05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
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      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.008071</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>00:05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learner.fine_tune(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "learner.export('chessbase.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combine all training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "comb = Path('combined')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "comb.mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['WK', 'WQ', 'WR', 'WB', 'WN', 'WP', 'BK', 'BQ', 'BR', 'BB', 'BN', 'BP', '_']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "for label in labels:\n",
    "    (comb/label).mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(#13) [Path('combined/WP'),Path('combined/WN'),Path('combined/BP'),Path('combined/WR'),Path('combined/WB'),Path('combined/BB'),Path('combined/BK'),Path('combined/_'),Path('combined/BN'),Path('combined/BQ')...]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb.ls()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from shutil import copyfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = 'chesscom'\n",
    "square_path = Path(f'{ds}/squares')\n",
    "for label in labels:\n",
    "    squares = (square_path/label).ls()\n",
    "    for square in squares:\n",
    "        target_dir = comb/label\n",
    "        n = len(target_dir.ls())\n",
    "        dst = target_dir/f'{n}.png'\n",
    "        copyfile(square, dst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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