label_line.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cdc89115-388a-4716-8a37-4085fe6c3fc2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-10T10:46:09.804303Z",
     "iopub.status.busy": "2022-04-10T10:46:09.804065Z",
     "iopub.status.idle": "2022-04-10T10:46:09.808436Z",
     "shell.execute_reply": "2022-04-10T10:46:09.807666Z",
     "shell.execute_reply.started": "2022-04-10T10:46:09.804279Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from matplotlib.collections import LineCollection\n",
    "\n",
    "from shapely.geometry import Point,LineString"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "25cd999f-d1cc-4732-8f7d-99a0e4711ec8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-10T10:42:27.614034Z",
     "iopub.status.busy": "2022-04-10T10:42:27.613775Z",
     "iopub.status.idle": "2022-04-10T10:42:27.616978Z",
     "shell.execute_reply": "2022-04-10T10:42:27.616332Z",
     "shell.execute_reply.started": "2022-04-10T10:42:27.614007Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# https://stackoverflow.com/questions/45776737/anottating-along-a-matplotlib-curve\n",
    "# https://stackoverflow.com/questions/18780198/how-to-rotate-matplotlib-annotation-to-match-a-line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ca855d72-1c49-4f0a-b723-09d3f08c20d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-10T10:42:28.645296Z",
     "iopub.status.busy": "2022-04-10T10:42:28.644436Z",
     "iopub.status.idle": "2022-04-10T10:42:29.066289Z",
     "shell.execute_reply": "2022-04-10T10:42:29.065712Z",
     "shell.execute_reply.started": "2022-04-10T10:42:28.645189Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "img = cv2.imread(\"BEL17-2-2_1.66x_milieu0001.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f2c8fdf3-5193-46d0-9967-e0793087eaaa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-10T10:42:29.189617Z",
     "iopub.status.busy": "2022-04-10T10:42:29.189257Z",
     "iopub.status.idle": "2022-04-10T10:42:29.356901Z",
     "shell.execute_reply": "2022-04-10T10:42:29.356359Z",
     "shell.execute_reply.started": "2022-04-10T10:42:29.189578Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8,6))\n",
    "\n",
    "ax.set_aspect('equal')\n",
    "\n",
    "x1 = 600\n",
    "y1 = 600\n",
    "x2 = 1200\n",
    "y2 = 1200\n",
    "line, = ax.plot([x1, x2], [y1, y2], c='r')\n",
    "\n",
    "# compute angle in raw data coordinates (no manual transforms)\n",
    "dy = y2 - y1\n",
    "dx = x2 - x1\n",
    "angle = np.rad2deg(np.arctan2(dy, dx))\n",
    "\n",
    "# annotate with transform_rotates_text to align text and line\n",
    "plt.text(x1, y1, 'rotation=%.2f'%angle, ha='left', va='bottom', fontsize=12,\n",
    "         transform_rotates_text=True, rotation=angle, rotation_mode='anchor')\n",
    "\n",
    "line = LineString([(x1,y1), (x2,y2)])\n",
    "ax.plot(line.xy[0], line.xy[1], c='black')\n",
    "\n",
    "left = line.parallel_offset(50, 'left')\n",
    "right0 = line.parallel_offset(50, 'right')\n",
    "right = LineString([right0.boundary.geoms[1], right0.boundary.geoms[0]])\n",
    "\n",
    "ax.plot(left.xy[0], left.xy[1], c='r')\n",
    "ax.plot(right.xy[0], right.xy[1], c='g')\n",
    "ax.text(left.boundary.geoms[0].xy[0][0], left.boundary.geoms[0].xy[1][0], 'left', ha='left', va='bottom', fontsize=12,\n",
    "         transform_rotates_text=True, rotation=angle, rotation_mode='anchor', clip_on=True)\n",
    "ax.text(right.boundary.geoms[0].xy[0][0], right.boundary.geoms[0].xy[1][0], 'right', ha='left', va='bottom', fontsize=12,\n",
    "         transform_rotates_text=True, rotation=angle, rotation_mode='anchor', clip_on=True)\n",
    "\n",
    "c = Point(800,800)\n",
    "a = left.interpolate(line.project(c))\n",
    "b = right.interpolate(line.project(c))\n",
    "ax.plot(a.coords[0], b.coords[0], c='b')\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "ee6b784f-9f38-4eca-8a3b-28350d567b1d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-10T14:40:21.481169Z",
     "iopub.status.busy": "2022-04-10T14:40:21.480803Z",
     "iopub.status.idle": "2022-04-10T14:40:21.619871Z",
     "shell.execute_reply": "2022-04-10T14:40:21.619366Z",
     "shell.execute_reply.started": "2022-04-10T14:40:21.481131Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8,6))\n",
    "\n",
    "ax.set_aspect('equal')\n",
    "\n",
    "x1 = 600\n",
    "y1 = 600\n",
    "x2 = 1200\n",
    "y2 = 1200\n",
    "line, = ax.plot([x1, x2], [y1, y2], c='r')\n",
    "\n",
    "ax.grid()\n",
    "\n",
    "def draw_tick(line, x, y):\n",
    "    line = LineString(line.get_xydata())   \n",
    "    left = line.parallel_offset(20, 'left')\n",
    "    right0 = line.parallel_offset(20, 'right')\n",
    "    right = LineString([right0.boundary.geoms[1], right0.boundary.geoms[0]])   # flip order because right\n",
    "    point = Point(x,y)\n",
    "    a = left.interpolate(line.project(point))\n",
    "    b = right.interpolate(line.project(point))\n",
    "    line  = LineString([a, b])\n",
    "    return line\n",
    "\n",
    "aLine1 = draw_tick(line, 800, 800)\n",
    "#ax.plot(aLine1.xy[0], aLine1.xy[1], c='b')\n",
    "\n",
    "aLine2 = draw_tick(line, 1000, 1000)\n",
    "#ax.plot(aLine2.xy[0], aLine2.xy[1], c='b')\n",
    "\n",
    "#ax.plot(aLine1.coords[0],aLine1.coords[1])\n",
    "\n",
    "a = []\n",
    "a.append(aLine1.coords)\n",
    "a.append(aLine2.coords)\n",
    "\n",
    "lc = LineCollection(a, linewidths=2, colors='b')\n",
    "ax.add_collection(lc)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "9da96ebb-c930-45f3-81af-23294886529c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T21:32:32.315824Z",
     "iopub.status.busy": "2022-04-07T21:32:32.315569Z",
     "iopub.status.idle": "2022-04-07T21:32:32.449993Z",
     "shell.execute_reply": "2022-04-07T21:32:32.449513Z",
     "shell.execute_reply.started": "2022-04-07T21:32:32.315799Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8,6))\n",
    "\n",
    "sc1 = ax.scatter([12,23,34],[2,6,4])\n",
    "sc2 = ax.scatter([19,3,14],[3,8,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "97b51d26-5793-4d91-9c4b-d14659e404f0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T21:41:05.340931Z",
     "iopub.status.busy": "2022-04-07T21:41:05.340649Z",
     "iopub.status.idle": "2022-04-07T21:41:05.344949Z",
     "shell.execute_reply": "2022-04-07T21:41:05.344181Z",
     "shell.execute_reply.started": "2022-04-07T21:41:05.340905Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "a = sc2.get_offsets()\n",
    "b = sc1.get_offsets()\n",
    "c = sc1.get_offsets()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "0023d26c-c695-44df-bf51-e699fdb5111a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T22:11:15.251261Z",
     "iopub.status.busy": "2022-04-07T22:11:15.250807Z",
     "iopub.status.idle": "2022-04-07T22:11:15.255936Z",
     "shell.execute_reply": "2022-04-07T22:11:15.255171Z",
     "shell.execute_reply.started": "2022-04-07T22:11:15.251232Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fbcbec1a1c0>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "1569b6db-0e91-4bbf-b9bf-e048b1581f72",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T22:03:56.238800Z",
     "iopub.status.busy": "2022-04-07T22:03:56.238539Z",
     "iopub.status.idle": "2022-04-07T22:03:56.244623Z",
     "shell.execute_reply": "2022-04-07T22:03:56.244000Z",
     "shell.execute_reply.started": "2022-04-07T22:03:56.238774Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[masked_array(\n",
       "   data=[[12.0, 2.0],\n",
       "         [23.0, 6.0],\n",
       "         [34.0, 4.0]],\n",
       "   mask=[[False, False],\n",
       "         [False, False],\n",
       "         [False, False]],\n",
       "   fill_value=1e+20),\n",
       " masked_array(\n",
       "   data=[[19.0, 3.0],\n",
       "         [3.0, 8.0],\n",
       "         [14.0, 1.0]],\n",
       "   mask=[[False, False],\n",
       "         [False, False],\n",
       "         [False, False]],\n",
       "   fill_value=1e+20)]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = [a.get_offsets() for a in [sc1, sc2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "c7059132-3685-49ca-a037-04da9aac5042",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T22:00:41.499358Z",
     "iopub.status.busy": "2022-04-07T22:00:41.498580Z",
     "iopub.status.idle": "2022-04-07T22:00:41.507014Z",
     "shell.execute_reply": "2022-04-07T22:00:41.506241Z",
     "shell.execute_reply.started": "2022-04-07T22:00:41.499272Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(\n",
       "  data=[[12.,  2.],\n",
       "        [23.,  6.],\n",
       "        [34.,  4.],\n",
       "        [19.,  3.],\n",
       "        [ 3.,  8.],\n",
       "        [14.,  1.]],\n",
       "  mask=False,\n",
       "  fill_value=1e+20)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.concatenate(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cc2c079b-c2fc-4d61-96a7-baa25c56724b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-08T18:23:32.149345Z",
     "iopub.status.busy": "2022-04-08T18:23:32.148656Z",
     "iopub.status.idle": "2022-04-08T18:23:32.304185Z",
     "shell.execute_reply": "2022-04-08T18:23:32.303668Z",
     "shell.execute_reply.started": "2022-04-08T18:23:32.149313Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8,6))\n",
    "sc1 = ax.scatter([12,23,34],[2,6,4])\n",
    "sc2 = ax.scatter([19,3,14],[3,8,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "517ddc33-a0fd-4524-8e22-e4b5395caf89",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-08T18:36:39.640226Z",
     "iopub.status.busy": "2022-04-08T18:36:39.639824Z",
     "iopub.status.idle": "2022-04-08T18:36:39.844639Z",
     "shell.execute_reply": "2022-04-08T18:36:39.844186Z",
     "shell.execute_reply.started": "2022-04-08T18:36:39.640185Z"
    }
   },
   "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": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.collections import LineCollection\n",
    "from matplotlib import colors as mcolors\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "# In order to efficiently plot many lines in a single set of axes,\n",
    "# Matplotlib has the ability to add the lines all at once. Here is a\n",
    "# simple example showing how it is done.\n",
    "\n",
    "x = np.arange(100)\n",
    "# Here are many sets of y to plot vs. x\n",
    "ys = x[:50, np.newaxis] + x[np.newaxis, :]\n",
    "\n",
    "segs = np.zeros((50, 100, 2))\n",
    "segs[:, :, 1] = ys\n",
    "segs[:, :, 0] = x\n",
    "\n",
    "# Mask some values to test masked array support:\n",
    "segs = np.ma.masked_where((segs > 50) & (segs < 60), segs)\n",
    "\n",
    "# We need to set the plot limits.\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_xlim(x.min(), x.max())\n",
    "ax.set_ylim(ys.min(), ys.max())\n",
    "\n",
    "# *colors* is sequence of rgba tuples.\n",
    "# *linestyle* is a string or dash tuple. Legal string values are\n",
    "# solid|dashed|dashdot|dotted.  The dash tuple is (offset, onoffseq) where\n",
    "# onoffseq is an even length tuple of on and off ink in points.  If linestyle\n",
    "# is omitted, 'solid' is used.\n",
    "# See `matplotlib.collections.LineCollection` for more information.\n",
    "colors = [mcolors.to_rgba(c)\n",
    "          for c in plt.rcParams['axes.prop_cycle'].by_key()['color']]\n",
    "\n",
    "line_segments = LineCollection(segs, linewidths=(0.5, 1, 1.5, 2),\n",
    "                               colors=colors, linestyle='solid')\n",
    "ax.add_collection(line_segments)\n",
    "ax.set_title('Line collection with masked arrays')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e9796bfb-9300-4f89-9f8f-76642681920c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-08T18:41:41.372120Z",
     "iopub.status.busy": "2022-04-08T18:41:41.371742Z",
     "iopub.status.idle": "2022-04-08T18:41:41.376520Z",
     "shell.execute_reply": "2022-04-08T18:41:41.376163Z",
     "shell.execute_reply.started": "2022-04-08T18:41:41.372098Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[  0.,   0.],\n",
       "        [  1.,   1.],\n",
       "        [  2.,   2.],\n",
       "        ...,\n",
       "        [ 97.,  97.],\n",
       "        [ 98.,  98.],\n",
       "        [ 99.,  99.]],\n",
       "\n",
       "       [[  0.,   1.],\n",
       "        [  1.,   2.],\n",
       "        [  2.,   3.],\n",
       "        ...,\n",
       "        [ 97.,  98.],\n",
       "        [ 98.,  99.],\n",
       "        [ 99., 100.]],\n",
       "\n",
       "       [[  0.,   2.],\n",
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       "        ...,\n",
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       "        [ 99., 101.]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[  0.,  47.],\n",
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       "        ...,\n",
       "        [ 97., 144.],\n",
       "        [ 98., 145.],\n",
       "        [ 99., 146.]],\n",
       "\n",
       "       [[  0.,  48.],\n",
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       "        [  2.,  50.],\n",
       "        ...,\n",
       "        [ 97., 145.],\n",
       "        [ 98., 146.],\n",
       "        [ 99., 147.]],\n",
       "\n",
       "       [[  0.,  49.],\n",
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       "        [  2.,  51.],\n",
       "        ...,\n",
       "        [ 97., 146.],\n",
       "        [ 98., 147.],\n",
       "        [ 99., 148.]]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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    "segs.data"
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   "execution_count": null,
   "id": "f2842220-bc8a-41f5-a0a5-da0360f6d652",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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