numpyReshaping.ipynb

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    "About\n",
    "\n",
    "- Comes from CH 5 Finding Road Markings Using OpenCV of Applied DL and CV for Self-Driving Cars\n",
    "\n",
    "- Skill : practice reshaping array\n",
    "- Columns : X1, Y1, X2, Y2\n",
    "- Rows : \n",
    "        X1_11, Y1_12, X2_13, Y2_14\n",
    "        X1_21, Y1_22, X2_23, Y2_24\n",
    "        ...,  \n",
    "        X1_N1, Y1_N2, X2_N3, Y2_N4"
   ]
  },
  {
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   "id": "fa179646-b462-49a2-b6ac-8335de4e0f2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
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   "id": "0a75f7ea-2aa6-4544-af70-e5ea59f2be10",
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     "text": [
      "[[[ 846  373  942  373]]\n",
      "\n",
      " [[ 823  369  881  369]]\n",
      "\n",
      " [[ 900  364  950  364]]\n",
      "\n",
      " [[ 920  367  971  367]]\n",
      "\n",
      " [[ 945  357 1022  357]]\n",
      "\n",
      " [[ 875  360  942  360]]\n",
      "\n",
      " [[ 935  365  997  365]]\n",
      "\n",
      " [[ 766  373  832  373]]\n",
      "\n",
      " [[ 892  352  935  352]]\n",
      "\n",
      " [[ 823  367  863  367]]\n",
      "\n",
      " [[ 900  344  966  344]]\n",
      "\n",
      " [[ 843  347  887  347]]\n",
      "\n",
      " [[ 883  351  969  348]]\n",
      "\n",
      " [[ 953  353 1001  353]]\n",
      "\n",
      " [[ 917  363  965  362]]\n",
      "\n",
      " [[ 933  369  994  369]]\n",
      "\n",
      " [[ 817  355  878  355]]\n",
      "\n",
      " [[ 847  359  893  359]]\n",
      "\n",
      " [[ 806  339  849  339]]\n",
      "\n",
      " [[ 973  373 1026  373]]\n",
      "\n",
      " [[ 249  365  299  365]]\n",
      "\n",
      " [[ 816  337  864  337]]\n",
      "\n",
      " [[ 874  343  945  343]]\n",
      "\n",
      " [[ 857  353  922  354]]\n",
      "\n",
      " [[1015  368 1066  368]]\n",
      "\n",
      " [[ 801  365  858  366]]\n",
      "\n",
      " [[ 833  344  907  341]]\n",
      "\n",
      " [[ 749  350  806  349]]\n",
      "\n",
      " [[ 796  356  844  356]]\n",
      "\n",
      " [[ 798  343  873  339]]\n",
      "\n",
      " [[ 771  337  830  333]]\n",
      "\n",
      " [[ 805  348  848  348]]\n",
      "\n",
      " [[ 275  358  318  358]]\n",
      "\n",
      " [[ 889  351  962  349]]\n",
      "\n",
      " [[ 864  346  928  347]]\n",
      "\n",
      " [[ 838  333  878  333]]\n",
      "\n",
      " [[ 799  345  850  342]]\n",
      "\n",
      " [[ 947  366 1002  366]]\n",
      "\n",
      " [[ 875  337  925  337]]\n",
      "\n",
      " [[ 757  301  800  310]]\n",
      "\n",
      " [[1040  370 1080  370]]\n",
      "\n",
      " [[ 842  328  882  328]]\n",
      "\n",
      " [[ 808  310  869  324]]\n",
      "\n",
      " [[ 826  325  869  325]]\n",
      "\n",
      " [[ 764  321  809  319]]\n",
      "\n",
      " [[ 810  353  850  353]]\n",
      "\n",
      " [[ 813  374  875  374]]] (47, 1, 4)\n"
     ]
    }
   ],
   "source": [
    "lines = np.array([[[ 846,  373, 942,  373]],\n",
    "\n",
    " [[ 823,  369,  881,  369],],\n",
    "\n",
    " [[ 900,  364,  950,  364],],\n",
    "\n",
    " [[ 920,  367,  971,  367],],\n",
    "\n",
    " [[ 945,  357, 1022,  357],],\n",
    "\n",
    " [[ 875,  360,  942,  360],],\n",
    "\n",
    " [[ 935,  365,  997,  365],],\n",
    "\n",
    " [[ 766,  373,  832,  373],],\n",
    "\n",
    " [[ 892,  352,  935,  352],],\n",
    "\n",
    " [[ 823,  367,  863,  367],],\n",
    "\n",
    " [[ 900,  344,  966,  344],],\n",
    "\n",
    " [[ 843,  347,  887,  347],],\n",
    "\n",
    " [[ 883,  351,  969,  348],],\n",
    "\n",
    " [[ 953,  353, 1001,  353],],\n",
    "\n",
    " [[ 917,  363,  965,  362],],\n",
    "\n",
    " [[ 933,  369,  994,  369],],\n",
    "\n",
    " [[ 817,  355,  878,  355],],\n",
    "\n",
    " [[ 847,  359,  893,  359],],\n",
    "\n",
    " [[ 806,  339,  849,  339],],\n",
    "\n",
    " [[ 973,  373, 1026,  373],],\n",
    "\n",
    " [[ 249,  365,  299,  365],],\n",
    "\n",
    " [[ 816,  337,  864,  337],],\n",
    "\n",
    " [[ 874,  343,  945,  343],],\n",
    "\n",
    " [[ 857,  353,  922,  354],],\n",
    "\n",
    " [[1015,  368, 1066,  368],],\n",
    "\n",
    " [[ 801,  365,  858,  366],],\n",
    "\n",
    " [[ 833,  344,  907,  341],],\n",
    "\n",
    " [[ 749,  350,  806,  349],],\n",
    "\n",
    " [[ 796,  356,  844,  356],],\n",
    "\n",
    " [[ 798,  343,  873,  339],],\n",
    "\n",
    " [[ 771,  337,  830,  333],],\n",
    "\n",
    " [[ 805,  348,  848,  348],],\n",
    "\n",
    " [[ 275,  358,  318,  358],],\n",
    "\n",
    " [[ 889,  351,  962,  349],],\n",
    "\n",
    " [[ 864,  346,  928,  347],],\n",
    "\n",
    " [[ 838,  333,  878,  333],],\n",
    "\n",
    " [[ 799,  345,  850,  342],],\n",
    "\n",
    " [[ 947,  366, 1002,  366],],\n",
    "\n",
    " [[ 875,  337,  925,  337],],\n",
    "\n",
    " [[ 757,  301,  800,  310],],\n",
    "\n",
    " [[1040,  370, 1080,  370],],\n",
    "\n",
    " [[ 842,  328,  882,  328],],\n",
    "\n",
    " [[ 808,  310,  869,  324],],\n",
    "\n",
    " [[ 826,  325,  869,  325],],\n",
    "\n",
    " [[ 764,  321,  809,  319],],\n",
    "\n",
    " [[ 810,  353,  850,  353],],\n",
    "\n",
    " [[ 813,  374,  875 , 374],]])\n",
    "print(lines, np.shape(lines))"
   ]
  },
  {
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   "id": "8be666da-d6ea-4457-ab57-df82cbfc4693",
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  {
   "cell_type": "code",
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   "id": "ba31921e-b28e-4312-8e76-55208107538c",
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     "name": "stdout",
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     "text": [
      "47\n",
      "0\n",
      "0 [846 373 942 373]\n",
      "1\n",
      "0 [823 369 881 369]\n",
      "2\n",
      "0 [900 364 950 364]\n",
      "3\n",
      "0 [920 367 971 367]\n",
      "4\n",
      "0 [ 945  357 1022  357]\n",
      "5\n",
      "0 [875 360 942 360]\n",
      "6\n",
      "0 [935 365 997 365]\n",
      "7\n",
      "0 [766 373 832 373]\n",
      "8\n",
      "0 [892 352 935 352]\n",
      "9\n",
      "0 [823 367 863 367]\n",
      "10\n",
      "0 [900 344 966 344]\n",
      "11\n",
      "0 [843 347 887 347]\n",
      "12\n",
      "0 [883 351 969 348]\n",
      "13\n",
      "0 [ 953  353 1001  353]\n",
      "14\n",
      "0 [917 363 965 362]\n",
      "15\n",
      "0 [933 369 994 369]\n",
      "16\n",
      "0 [817 355 878 355]\n",
      "17\n",
      "0 [847 359 893 359]\n",
      "18\n",
      "0 [806 339 849 339]\n",
      "19\n",
      "0 [ 973  373 1026  373]\n",
      "20\n",
      "0 [249 365 299 365]\n",
      "21\n",
      "0 [816 337 864 337]\n",
      "22\n",
      "0 [874 343 945 343]\n",
      "23\n",
      "0 [857 353 922 354]\n",
      "24\n",
      "0 [1015  368 1066  368]\n",
      "25\n",
      "0 [801 365 858 366]\n",
      "26\n",
      "0 [833 344 907 341]\n",
      "27\n",
      "0 [749 350 806 349]\n",
      "28\n",
      "0 [796 356 844 356]\n",
      "29\n",
      "0 [798 343 873 339]\n",
      "30\n",
      "0 [771 337 830 333]\n",
      "31\n",
      "0 [805 348 848 348]\n",
      "32\n",
      "0 [275 358 318 358]\n",
      "33\n",
      "0 [889 351 962 349]\n",
      "34\n",
      "0 [864 346 928 347]\n",
      "35\n",
      "0 [838 333 878 333]\n",
      "36\n",
      "0 [799 345 850 342]\n",
      "37\n",
      "0 [ 947  366 1002  366]\n",
      "38\n",
      "0 [875 337 925 337]\n",
      "39\n",
      "0 [757 301 800 310]\n",
      "40\n",
      "0 [1040  370 1080  370]\n",
      "41\n",
      "0 [842 328 882 328]\n",
      "42\n",
      "0 [808 310 869 324]\n",
      "43\n",
      "0 [826 325 869 325]\n",
      "44\n",
      "0 [764 321 809 319]\n",
      "45\n",
      "0 [810 353 850 353]\n",
      "46\n",
      "0 [813 374 875 374]\n"
     ]
    }
   ],
   "source": [
    "final = []\n",
    "print(len(lines))\n",
    "# Get each line and it's shape\n",
    "for line in range(len(lines)):\n",
    "    # print(line, np.shape(line), lines[line])\n",
    "    print(line)\n",
    "    a_line = lines[line]\n",
    "    for row in range(len(a_line)):\n",
    "        print(row, a_line[row])\n",
    "        # v_stack = np.v_stack(a_line[row], a_line[row])\n",
    "        # for value in row:\n",
    "        #     print(row[value])\n",
    "            "
   ]
  },
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