Untitled1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([599,649,1173,1491,641,740])\n",
    "y = np.array([950,1000,1500,1798,1000,1096])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2179c8e00f0>"
      ]
     },
     "execution_count": 84,
     "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.scatter(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.2659564e-01],\n",
       "       [6.8706536e-01],\n",
       "       [8.8216669e+02],\n",
       "       [1.2240000e+03]], dtype=float32)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = cv2.fitLine(np.array([x,y]).transpose(), cv2.DIST_L2, 0, 0.01, 0.01)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "vec = a\n",
    "offset = vec[3] - vec[2] / vec[0] * vec[1];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.9455952], dtype=float32)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vec[1] / vec[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([389.8274], dtype=float32)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "offset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 599,  950],\n",
       "       [ 649, 1000],\n",
       "       [1173, 1500],\n",
       "       [1491, 1798],\n",
       "       [ 641, 1000],\n",
       "       [ 740, 1096]])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([x,y]).transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "y2 = (y - 364) / math.cos(math.radians(10.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1224.0"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "offset = 364"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.2077465e-01],\n",
       "       [6.9316941e-01],\n",
       "       [8.8216669e+02],\n",
       "       [8.7464600e+02]], dtype=float32)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = cv2.fitLine(np.array([x,y2]).transpose(), cv2.DIST_L2, 0, 0.01, 0.01)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([26.265808], dtype=float32)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vec = a\n",
    "offset = vec[3] - vec[2] / vec[0] * vec[1]\n",
    "offset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.9617006], dtype=float32)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vec[1] / vec[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "angle = 10.133\n",
    "scale =  math.cos(math.radians(angle))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "offset = 357 \n",
    "y2 = (y - offset) / scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "x2 = x\n",
    "xx = np.sum(x2 * x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "xy = np.sum( x2 * y2)\n",
    "yy = np.sum( y2 * y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 599,  649, 1173, 1491,  641,  740])"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 602.39616777,  653.18842475, 1161.11099454, 1463.83284614,\n",
       "        653.18842475,  750.70955815])"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[7.2117841e-01],\n",
       "        [6.9274938e-01],\n",
       "        [8.8216669e+02],\n",
       "        [8.8073773e+02]], dtype=float32),\n",
       " array([0.96057975], dtype=float32),\n",
       " array([33.346252], dtype=float32))"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = cv2.fitLine(np.array([x2,y2]).transpose(), cv2.DIST_L2, 0, 0.01, 0.01)\n",
    "a, a[1] / a[0], a[3] - a[2] / a[0] * a[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9938173549116758"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yy / xy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "357"
      ]
     },
     "execution_count": 227,
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