test_resolution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import analyse_usaf_image\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_loc = \"/media/Saturday/Temporary/xi/MCAM_Re-imaging/abberation test/singlet/center_focus_20240403_180921_945/\"\n",
    "file_name = \"cam7_0\" + \".tif\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Searching for targets.............................................done\n",
      "Searching for targets.............................................done\n",
      "Exception: attempt to get argmin of an empty sequence\n",
      "Exception: attempt to get argmin of an empty sequence\n",
      "Exception: attempt to get argmin of an empty sequence\n",
      "Problem: spline failed for data of size 4\n",
      "Exception: (m>k) failed for hidden m: fpcurf0:m=3\n",
      "Problem: spline failed for data of size 4\n",
      "Exception: (m>k) failed for hidden m: fpcurf0:m=3\n",
      "Linear fitting gives the smallest period as 20.458931410634854 pixels\n",
      "Assuming intercept is zero gives 19.94019734650578 +/- 0.14663462124101087\n",
      "Assuming smallest block is group 7, element 6,\n",
      "pixel size is 219.9 +/- 1.6 nm\n",
      "FOV is 450.3x450.3um +/- 0.74%\n",
      "diagonal is 636.9 +/- 4.7 um\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 640x480 with 1 Axes>,\n",
       " {'group': 7,\n",
       "  'element': 6,\n",
       "  'pixel_nm': 219.88836419298664,\n",
       "  'pixel_nm_se': 1.616997386657971,\n",
       "  'field_of_view': array([450.33136987, 450.33136987]),\n",
       "  'field_of_view_x': 450.3313698672366,\n",
       "  'field_of_view_y': 450.3313698672366,\n",
       "  'pixels': (2048, 2048),\n",
       "  'pixels_x': 2048,\n",
       "  'pixels_y': 2048,\n",
       "  'diagonal': 636.8647308283006,\n",
       "  'fractional_standard_error': 0.007353719659485034,\n",
       "  'smallest_period_pixels': 19.94019734650578,\n",
       "  'smallest_period_se': 0.14663462124101087,\n",
       "  'polyfit_coefficients': array([20.45893141, -0.8472414 ])})"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = cv2.imread(file_loc + file_name)\n",
    "analyse_usaf_image.analyse_image(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fab62628320>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(template(8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "No such file: '/home/xi/usaf_analysis/--f=/home/xi/.local/share/jupyter/runtime/kernel-v2-405191EXItfiT9oStc.json'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[8], line 431\u001b[0m\n\u001b[1;32m    429\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    430\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m filename \u001b[38;5;129;01min\u001b[39;00m sys\u001b[38;5;241m.\u001b[39margv[\u001b[38;5;241m1\u001b[39m:]:\n\u001b[0;32m--> 431\u001b[0m         \u001b[43manalyse_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[8], line 384\u001b[0m, in \u001b[0;36manalyse_file\u001b[0;34m(filename, generate_pdf)\u001b[0m\n\u001b[1;32m    382\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21manalyse_file\u001b[39m(filename, generate_pdf\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m    383\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Analyse the image file specified by the given filename\"\"\"\u001b[39;00m\n\u001b[0;32m--> 384\u001b[0m     gray_image \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmean(\u001b[43mimread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m)\u001b[49m, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\u001b[38;5;241m.\u001b[39mastype(np\u001b[38;5;241m.\u001b[39muint8)\n\u001b[1;32m    385\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m PdfPages(filename\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_analysis.pdf\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m pdf:\n\u001b[1;32m    386\u001b[0m         fig, parameters \u001b[38;5;241m=\u001b[39m analyse_image(gray_image, pdf)\n",
      "File \u001b[0;32m~/miniconda3/envs/ImageProcess/lib/python3.12/site-packages/skimage/io/_io.py:53\u001b[0m, in \u001b[0;36mimread\u001b[0;34m(fname, as_gray, plugin, **plugin_args)\u001b[0m\n\u001b[1;32m     50\u001b[0m         plugin \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtifffile\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m file_or_url_context(fname) \u001b[38;5;28;01mas\u001b[39;00m fname:\n\u001b[0;32m---> 53\u001b[0m     img \u001b[38;5;241m=\u001b[39m \u001b[43mcall_plugin\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mimread\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mplugin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mplugin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mplugin_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     55\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(img, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mndim\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m     56\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m img\n",
      "File \u001b[0;32m~/miniconda3/envs/ImageProcess/lib/python3.12/site-packages/skimage/io/manage_plugins.py:205\u001b[0m, in \u001b[0;36mcall_plugin\u001b[0;34m(kind, *args, **kwargs)\u001b[0m\n\u001b[1;32m    202\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m:\n\u001b[1;32m    203\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCould not find the plugin \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mplugin\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m for \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkind\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 205\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/ImageProcess/lib/python3.12/site-packages/skimage/io/_plugins/imageio_plugin.py:11\u001b[0m, in \u001b[0;36mimread\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(imageio_imread)\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mimread\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 11\u001b[0m     out \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(\u001b[43mimageio_imread\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m     12\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m out\u001b[38;5;241m.\u001b[39mflags[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mWRITEABLE\u001b[39m\u001b[38;5;124m'\u001b[39m]:\n\u001b[1;32m     13\u001b[0m         out \u001b[38;5;241m=\u001b[39m out\u001b[38;5;241m.\u001b[39mcopy()\n",
      "File \u001b[0;32m~/miniconda3/envs/ImageProcess/lib/python3.12/site-packages/imageio/v3.py:53\u001b[0m, in \u001b[0;36mimread\u001b[0;34m(uri, index, plugin, extension, format_hint, **kwargs)\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m index \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     51\u001b[0m     call_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mindex\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m index\n\u001b[0;32m---> 53\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mimopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43muri\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mplugin_kwargs\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m img_file:\n\u001b[1;32m     54\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39masarray(img_file\u001b[38;5;241m.\u001b[39mread(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcall_kwargs))\n",
      "File \u001b[0;32m~/miniconda3/envs/ImageProcess/lib/python3.12/site-packages/imageio/core/imopen.py:113\u001b[0m, in \u001b[0;36mimopen\u001b[0;34m(uri, io_mode, plugin, extension, format_hint, legacy_mode, **kwargs)\u001b[0m\n\u001b[1;32m    111\u001b[0m     request\u001b[38;5;241m.\u001b[39mformat_hint \u001b[38;5;241m=\u001b[39m format_hint\n\u001b[1;32m    112\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 113\u001b[0m     request \u001b[38;5;241m=\u001b[39m \u001b[43mRequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43muri\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mio_mode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mformat_hint\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mformat_hint\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextension\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextension\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    115\u001b[0m source \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m<bytes>\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(uri, \u001b[38;5;28mbytes\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m uri\n\u001b[1;32m    117\u001b[0m \u001b[38;5;66;03m# fast-path based on plugin\u001b[39;00m\n\u001b[1;32m    118\u001b[0m \u001b[38;5;66;03m# (except in legacy mode)\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/ImageProcess/lib/python3.12/site-packages/imageio/core/request.py:247\u001b[0m, in \u001b[0;36mRequest.__init__\u001b[0;34m(self, uri, mode, extension, format_hint, **kwargs)\u001b[0m\n\u001b[1;32m    244\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid Request.Mode: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmode\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    246\u001b[0m \u001b[38;5;66;03m# Parse what was given\u001b[39;00m\n\u001b[0;32m--> 247\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parse_uri\u001b[49m\u001b[43m(\u001b[49m\u001b[43muri\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    249\u001b[0m \u001b[38;5;66;03m# Set extension\u001b[39;00m\n\u001b[1;32m    250\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m extension \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/miniconda3/envs/ImageProcess/lib/python3.12/site-packages/imageio/core/request.py:407\u001b[0m, in \u001b[0;36mRequest._parse_uri\u001b[0;34m(self, uri)\u001b[0m\n\u001b[1;32m    404\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_read_request:\n\u001b[1;32m    405\u001b[0m     \u001b[38;5;66;03m# Reading: check that the file exists (but is allowed a dir)\u001b[39;00m\n\u001b[1;32m    406\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(fn):\n\u001b[0;32m--> 407\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo such file: \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m fn)\n\u001b[1;32m    408\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    409\u001b[0m     \u001b[38;5;66;03m# Writing: check that the directory to write to does exist\u001b[39;00m\n\u001b[1;32m    410\u001b[0m     dn \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mdirname(fn)\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: No such file: '/home/xi/usaf_analysis/--f=/home/xi/.local/share/jupyter/runtime/kernel-v2-405191EXItfiT9oStc.json'"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "Analyse a USAF test target image, to determine the image's dimensions.\n",
    "\n",
    "See: https://en.wikipedia.org/wiki/1951_USAF_resolution_test_chart\n",
    "\n",
    "(c) Richard Bowman 2017, released under GNU GPL\n",
    "\n",
    "\n",
    "From Wikipedia, the number of line pairs/mm is 2^(g+(h-1)/6) where g is the\n",
    "\"group number\" and h is the \"element number\".  Each group has 6 elements, \n",
    "numbered from 1 to 6.  My ThorLabs test target goes down to group 7, meaning\n",
    "the finest pitch is 2^(7+(6-1)/6)=2^(47/8)=\n",
    "\n",
    "\"\"\"\n",
    "from __future__ import print_function\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib.patches\n",
    "\n",
    "from skimage import data\n",
    "from skimage.feature import corner_harris, corner_subpix, corner_peaks\n",
    "from skimage.transform import warp, AffineTransform\n",
    "from skimage.draw import ellipse\n",
    "from skimage.io import imread\n",
    "import numpy as np\n",
    "import cv2\n",
    "from sklearn.cluster import MeanShift\n",
    "from contextlib import closing\n",
    "\n",
    "import scipy.ndimage\n",
    "import scipy.interpolate\n",
    "import itertools\n",
    "import os.path\n",
    "import os\n",
    "import sys\n",
    "from skimage.io import imread\n",
    "from matplotlib.backends.backend_pdf import PdfPages\n",
    "\n",
    "#################### Rotate the image so the bars are X/Y aligned #############\n",
    "def find_image_orientation(gray_image, fuzziness = 5):\n",
    "    \"\"\"Determine the angle we need to rotate a USAF target image by.\n",
    "    \n",
    "    This is a two-step \"straightener\", first looking at the gradient directions\n",
    "    in the image, then fine-tuning it to get a more accurate answer.  Rotating\n",
    "    the image by this amount should cause the bars to align with the x/y axes.\n",
    "    It may be beneficial to do this iteratively, i.e. run it, then rotate, then\n",
    "    run it again, then tweak - this should help avoid any issues with linearity\n",
    "    in the calculation of gradient angles.\n",
    "    \n",
    "    fuzziness sets the size of the Gaussian kernel used for gradient estimation\n",
    "    \n",
    "    returns: the angle of the bars, as a floating-point number.\n",
    "    \"\"\"\n",
    "    image = gray_image / gray_image.max()\n",
    "    # Calculate local gradient directions (to find angle)\n",
    "    Gx = scipy.ndimage.gaussian_filter1d(image, axis=0, order=1, sigma=fuzziness)\n",
    "    Gy = scipy.ndimage.gaussian_filter1d(image, axis=1, order=1, sigma=fuzziness)\n",
    "    angles = np.arctan2(Gx, Gy)\n",
    "    weights = Gx**2+Gy**2\n",
    "    \n",
    "    # First, find the histogram and pick the maximum\n",
    "    h, e = np.histogram(angles % (np.pi), bins=100, weights=weights)\n",
    "    #plt.plot((e[1:] + e[:-1])/2, h, 'o-')\n",
    "    rough_angle = ((e[1:] + e[:-1])/2)[np.argmax(h)] #pick the peak\n",
    "    bin_width = np.mean(np.diff(e))\n",
    "    \n",
    "    # Recalculate the histogram about the peak (avoids wrapping issues)\n",
    "    h, e = np.histogram(((angles - rough_angle + np.pi/4) % (np.pi/2)) - np.pi/4,\n",
    "                        bins=50, range=(-0.1, 0.1), weights=weights)\n",
    "    h *= bin_width/np.mean(np.diff(e))\n",
    "    h /= 2\n",
    "    #plt.plot((e[1:] + e[:-1])/2+rough_angle, h)\n",
    "    \n",
    "    # Use spline interpolation to fit the peak and find the rotation angle\n",
    "    spline = scipy.interpolate.UnivariateSpline(e[1:-1], np.diff(h))\n",
    "    root = spline.roots()[np.argmin(spline.roots()**2)] #pick root nearest zero\n",
    "    #plt.plot(e+rough_angle, spline(e))\n",
    "    \n",
    "    fine_angle = root + rough_angle\n",
    "    \n",
    "    #TODO: do we want to rotate the image here and repeat the above analysis?\n",
    "    \n",
    "    return fine_angle\n",
    "\n",
    "\n",
    "################### Find the bars ############################\n",
    "\n",
    "def template(n):\n",
    "    \"\"\"Generate a template image of three horizontal bars, n x n pixels\n",
    "    \n",
    "    NB the bars occupy the central square of the template, with a margin\n",
    "    equal to one bar all around.  There are 3 bars and 2 spaces between,\n",
    "    so bars are m=n/7 wide.\n",
    "    \n",
    "    returns: n x n numpy array, uint8\n",
    "    \"\"\"\n",
    "    n = int(n)\n",
    "    template = np.ones((n, n), dtype=np.uint8)\n",
    "    template *= 255\n",
    "    \n",
    "    for i in range(3):\n",
    "        template[n//7:-n//7,  (1 + 2*i) * n//7:(2 + 2*i) * n//7] = 0\n",
    "    return template\n",
    "\n",
    "def find_elements(image,\n",
    "                  template_fn=template, \n",
    "                  scale_increment=1.04,\n",
    "                  n_scales=100,\n",
    "                  return_all=False):\n",
    "    \"\"\"Use a multi-scale template match to find groups of 3 bars in the image.\n",
    "    \n",
    "    We return a list of tuples, (score, (x,y), size) for each match.\n",
    "    \n",
    "    image: a 2D uint8 numpy array in which to search\n",
    "    template_fn: a function to generate a square uint8 array, which takes one\n",
    "        argument n that specifies the side length of the square.\n",
    "    scale_increment: the factor by which the template size is increased each\n",
    "        iteration.  Should be a floating point number a little bigger than 1.0\n",
    "    n_scales: the number of sizes searched.  The largest size is half the image\n",
    "        size.\n",
    "        \n",
    "    \"\"\"\n",
    "    matches = []\n",
    "    start = np.log(image.shape[0]/2)/np.log(scale_increment) - n_scales\n",
    "    print(\"Searching for targets\", end='')\n",
    "    for nf in np.logspace(start, start + n_scales, base=scale_increment):\n",
    "        if nf < 28:  #There's no point bothering with tiny boxes...\n",
    "            continue\n",
    "        templ = template(nf) #NB n is rounded down from nf\n",
    "        res = cv2.matchTemplate(image,templ,cv2.TM_CCOEFF_NORMED)\n",
    "        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)\n",
    "        matches.append((max_val, max_loc, templ.shape[0]))\n",
    "        print('.', end='')\n",
    "    print(\"done\")\n",
    "    \n",
    "    # Take the matches at different scales and filter out the good ones\n",
    "    scores = np.array([m[0] for m in matches])\n",
    "    threshold_score = (scores.max() + scores.min()) / 2\n",
    "    filtered_matches = [m for m in matches if m[0] > threshold_score]\n",
    "    \n",
    "    # Group overlapping matches together, and pick the best one\n",
    "    def overlap1d(x1, n1, x2, n2):\n",
    "        \"\"\"Return the overlapping length of two 1d regions\n",
    "        \n",
    "        Draw four positions, indicating the edges of the regions (i.e. x1, \n",
    "        c1+n1, x2, x2+n2).  The smallest distance between a starting edge (x1\n",
    "        or x2) and a stopping edge (x+n) gives the overlap.  This will be\n",
    "        one of the four values in the min().  The overlap can't be <0, so if\n",
    "        the minimum is negative, return zero.\n",
    "        \"\"\"\n",
    "        return max(min(x1 + n1 - x2, x2 + n2 - x1, n1, n2), 0)\n",
    "    \n",
    "    unique_matches = []\n",
    "    while len(filtered_matches) > 0:\n",
    "        current_group = []\n",
    "        new_matches = [filtered_matches.pop()]\n",
    "        while len(new_matches) > 0:\n",
    "            current_group += new_matches\n",
    "            new_matches = []\n",
    "            for m1 in filtered_matches:\n",
    "                for m2 in current_group:\n",
    "                    s1, (x1, y1), n1 = m1\n",
    "                    s2, (x2, y2), n2 = m2\n",
    "                    overlap = (overlap1d(x1, n1, x2, n2) * \n",
    "                               overlap1d(y1, n1, y2, n2))\n",
    "                    if overlap > 0.5 * min(n1, n2)**2:\n",
    "                        new_matches.append(m1)\n",
    "                        filtered_matches.remove(m1)\n",
    "                        break\n",
    "        # Now we should have current_group full of overlapping matches.  Pick\n",
    "        # the best one.\n",
    "        best_score_index = np.argmax([m[0] for m in current_group])\n",
    "        unique_matches.append(current_group[best_score_index])\n",
    "    \n",
    "    elements = unique_matches\n",
    "    if return_all:\n",
    "        return elements, matches\n",
    "    else:\n",
    "        return elements\n",
    "        \n",
    "\n",
    "def plot_matches(image, elements, elements_T=[]):\n",
    "    \"\"\"Plot the image, then highlight the groups.\"\"\"\n",
    "    f, ax = plt.subplots(1,1)\n",
    "    if len(image.shape)==2:\n",
    "        ax.imshow(image, cmap='gray')\n",
    "    elif len(image.shape)==3 and image.shape[2]==3 or image.shape[2]==4:\n",
    "        ax.imshow(image)\n",
    "    else:\n",
    "        raise ValueError(\"The image must be 2D or 3D\")\n",
    "    for score, (x, y), n in elements:\n",
    "        ax.add_patch(matplotlib.patches.Rectangle((x,y), n, n, \n",
    "                                                  fc='none', ec='red'))\n",
    "    for score, (y, x), n in elements_T:\n",
    "        ax.add_patch(matplotlib.patches.Rectangle((x,y), n, n, \n",
    "                                                  fc='none', ec='blue'))\n",
    "    return f\n",
    "        \n",
    "\n",
    "def find_peak_position(y, **kwargs):\n",
    "    \"\"\"Use spline interpolation to find the peak position of a curve.\n",
    "    \n",
    "    We differentiate the curve numerically (no smoothing) then use a spline to\n",
    "    find the root closest to the middle.  Additional keyword arguments are \n",
    "    passed to `scipy.interpolate.UnivariateSpline`.\n",
    "    \n",
    "    returns: the floating-point x value (i.e. index) of the peak.\n",
    "    \"\"\"\n",
    "    n = len(y)\n",
    "    try:\n",
    "        spline = scipy.interpolate.UnivariateSpline(np.arange(n-1)+0.5, \n",
    "                                                    np.diff(y), **kwargs)\n",
    "    except Exception as e:\n",
    "        print(\"Problem: spline failed for data of size {}\".format(n))\n",
    "        raise e\n",
    "    return spline.roots()[np.argmin((spline.roots()-n/2)**2)] #pick centre root\n",
    "\n",
    "# Now we've got a list of good elements in the image, with approximate sizes.\n",
    "# We can extract these for further analysis.\n",
    "def analyse_elements(image, elements, plot=False):\n",
    "    \"\"\"Calculate the precise period of each group of bars, from autocorrelation\n",
    "    \n",
    "    returns: a list of tuples, (p1, p2) for each element, where p1 and p2 are\n",
    "    the distances between the two outer bars and the central bar.\n",
    "    \n",
    "    It also correlates each row of the image with the average row, which allows\n",
    "    us to correct the values for tilt.\n",
    "    \"\"\"\n",
    "    if plot: f, axes = plt.subplots(2,len(elements))\n",
    "    else: f, axes = None, [[None]*len(elements)]*2\n",
    "    analysis = []\n",
    "    for (score, (x, y), n), ax0, ax1 in zip(elements, axes[0], axes[1]):\n",
    "        try:\n",
    "            gray_roi = image[y:y+n, x:x+n]\n",
    "            marginal = np.mean(gray_roi[n*3//14:-n*3//14, :], axis=0) \n",
    "                                    # average over the bars, ignoring the ends\n",
    "            target = marginal[np.newaxis, n//14:-n//14].astype(np.uint8)\n",
    "                            # target is a 1px-high image with 3 bars in it\n",
    "            # We correlate the thin target with each row of the image, to\n",
    "            # recover the tilt of the image.\n",
    "            slant = cv2.matchTemplate(gray_roi[n*3//14:-n*3//14, :],\n",
    "                                      target.astype(np.uint8), cv2.TM_CCOEFF_NORMED)\n",
    "            # threshold and centre of mass for each row\n",
    "            slant -= slant.min(axis=1)[:,np.newaxis]\n",
    "            slant /= slant.max(axis=1)[:,np.newaxis]\n",
    "            slant -= 0.8\n",
    "            slant[slant < 0] = 0\n",
    "            shifts = np.sum(slant * np.arange(slant.shape[1]), axis=1)/np.sum(slant, axis=1)\n",
    "            gradient, offset = np.polyfit(np.arange(len(shifts)), shifts, 1)\n",
    "            tilt = np.arctan(gradient) # fit the peak in each row to find the angle\n",
    "\n",
    "            centre = marginal[n*5//14:n*9//14] #the central bar\n",
    "            ccor = np.convolve(marginal, centre[::-1] - np.mean(centre), mode='valid')\n",
    "                            # NB the central peak will always be at ccor[n*5//14]\n",
    "                            # Outer peaks should be that +/- n*2//7\n",
    "            if plot: ax1.plot(np.abs(np.arange(len(ccor)) - n*5//14), ccor)\n",
    "            maxcor = ccor[n*5//14]\n",
    "            period_1 = n*5//14 - find_peak_position(ccor[:n//7])\n",
    "            period_1 *= np.cos(tilt)\n",
    "            period_2 = find_peak_position(ccor[4*n//7:]) + 4*n//7 - n*5//14\n",
    "            period_2 *= np.cos(tilt)\n",
    "            if plot:\n",
    "                for x in [period_1, period_2]:\n",
    "                    ax1.plot(np.abs([x, x]), [0, maxcor])\n",
    "                ax0.imshow(gray_roi, cmap=\"cubehelix\")\n",
    "                ax0.axis(\"off\")\n",
    "                ys = np.arange(len(shifts)) + 3*n//14\n",
    "                cx = (n-1.)/2\n",
    "                ax0.plot(shifts - np.mean(shifts) + cx, ys, 'r+') # plot fitted X values\n",
    "                for x in [-period_1, 0, period_2]:\n",
    "                    ax0.plot(x + (ys - np.mean(ys)) * np.tan(tilt) + cx, ys - x * np.tan(tilt), 'b-')\n",
    "                \n",
    "            analysis.append((period_1, period_2))\n",
    "        except Exception as e:\n",
    "            print(\"Exception: {}\".format(e))\n",
    "    if plot:\n",
    "        return f, analysis\n",
    "    else:\n",
    "        return analysis\n",
    "\n",
    "def fit_periods(periods, gray_image, g=7, h=6, plot=True):\n",
    "    \"\"\"Assume the smallest element is group g, element h, and analyse.\n",
    "    \n",
    "    We return a dictionary with relevant parameters of the image.  This\n",
    "    fits the measured periods of elements in the image to the known\n",
    "    periods of the USAF pattern, assuming that the smallest period is\n",
    "    group <g> element <h>.  If plot is set to True, the periods are\n",
    "    plotted on a graph along with the line of best fit used to calculate\n",
    "    the magnification.\n",
    "    \"\"\"\n",
    "    minp = np.max(gray_image.shape)\n",
    "    for p in periods:\n",
    "        p.sort()\n",
    "        minp = min(minp, min(p))\n",
    "        \n",
    "    xs = []\n",
    "    for p in periods:\n",
    "        # The sizes ought to be quantised in the sixth root of 2\n",
    "        xs.append(2**(np.round(np.log(p/minp)/np.log(2)*6)/6))\n",
    "   # x = 2**(np.arange(len(p1))/6)\n",
    "    if plot:\n",
    "        f, ax = plt.subplots(1,1)\n",
    "        for x, p in zip(xs, periods):\n",
    "            ax.plot(x, p, 'o')\n",
    "    #assume the X and Y magnifications are equal...\n",
    "    m = np.polyfit(np.concatenate(xs), np.concatenate(periods), 1)\n",
    "    if plot:\n",
    "        ax.plot(x, m[0]*x + m[1], '-')\n",
    "    print(\"Linear fitting gives the smallest period as {} pixels\".format(m[0]))\n",
    "    rs = np.concatenate(periods)/np.concatenate(xs)\n",
    "    period_gradient = np.mean(rs)\n",
    "    period_gradient_se = np.std(rs)/np.sqrt(len(rs) - 1)\n",
    "    if plot:\n",
    "        ax.plot(x, period_gradient*x, '-')\n",
    "    print(\"Assuming intercept is zero gives {} +/- {}\".format(rs.mean(), \n",
    "                                              rs.std()/np.sqrt(len(rs)-1)))\n",
    "    print(\"Assuming smallest block is group {}, element {},\".format(g,h))\n",
    "    line_pairs_mm = 2**(g + (h - 1)/6.0)\n",
    "    period_um = 1000 / line_pairs_mm\n",
    "    pixel_nm = 1000 * period_um / period_gradient\n",
    "    pixel_nm_se = pixel_nm / period_gradient * period_gradient_se\n",
    "    print(\"pixel size is {:.1f} +/- {:.1f} nm\".format(pixel_nm, pixel_nm_se))\n",
    "    FoV = np.array(gray_image.T.shape) * pixel_nm / 1000.0\n",
    "    print(\"FOV is {:.1f}x{:.1f}um +/- {:.2f}%\".format(FoV[0], FoV[1], pixel_nm_se/pixel_nm*100))\n",
    "    diagonal = np.sqrt(np.sum(FoV**2))\n",
    "    print(\"diagonal is {:.1f} +/- {:.1f} um\".format(diagonal, \n",
    "                                            diagonal * pixel_nm_se/pixel_nm))\n",
    "    parameters = {\n",
    "            'group':g,\n",
    "            'element':h,\n",
    "            'pixel_nm':pixel_nm,\n",
    "            'pixel_nm_se':pixel_nm_se,\n",
    "            'field_of_view':FoV,\n",
    "            'field_of_view_x':FoV[0],\n",
    "            'field_of_view_y':FoV[1],\n",
    "            'pixels':gray_image.T.shape,\n",
    "            'pixels_x':gray_image.T.shape[0],\n",
    "            'pixels_y':gray_image.T.shape[1],\n",
    "            'diagonal':diagonal,\n",
    "            'fractional_standard_error':pixel_nm_se/pixel_nm,\n",
    "            'smallest_period_pixels':period_gradient,\n",
    "            'smallest_period_se':period_gradient_se,\n",
    "            'polyfit_coefficients':m,\n",
    "            }\n",
    "    if plot:\n",
    "        return f, parameters\n",
    "    else:\n",
    "        return parameters\n",
    "            \n",
    "def analyse_image(gray_image, pdf=None):\n",
    "    \"\"\"Find USAF groups in the image and fit them to determine magnification.\n",
    "    \n",
    "    This is the top-level function that you should call to analyse an image.\n",
    "    The image should be a 2D numpy array.  If the optional \"pdf\" argument is\n",
    "    supplied, several graphs will be plotted into the given PdfPages object.\n",
    "    \n",
    "    returns: fig, parameters\n",
    "    \n",
    "    fig is a matplotlib figure object plotting to the image with each\n",
    "    element highlighted in a box.\n",
    "    parameters is a dictionary summarising the fitted values, as returned\n",
    "    by fit_periods.\"\"\"\n",
    "    elementsx, matchesx = find_elements(gray_image, return_all=True)\n",
    "    elementsy, matchesy = find_elements(gray_image.T, return_all=True)\n",
    "    fig = plot_matches(gray_image, elementsx, elementsy)\n",
    "    fax, analysisx = analyse_elements(gray_image, elementsx, plot=True)\n",
    "    fay, analysisy = analyse_elements(gray_image.T, elementsy, plot=True)\n",
    "    \n",
    "    #Now generate four lists, of first/second periods in X and Y\n",
    "    periods = [[a[i] for a in analysis] \n",
    "                for i in range(2) for analysis in (analysisx, analysisy)]\n",
    "    \n",
    "    ffit, parameters = fit_periods(periods, gray_image)\n",
    "    \n",
    "    if pdf is not None:\n",
    "        for f in [fig, fax, fay, ffit]:\n",
    "            pdf.savefig(f)\n",
    "    for f in [fax, fay, ffit]:\n",
    "        plt.close(f)\n",
    "    return fig, parameters\n",
    "    \n",
    "def analyse_file(filename, generate_pdf=True):\n",
    "    \"\"\"Analyse the image file specified by the given filename\"\"\"\n",
    "    gray_image = np.mean(imread(filename), axis=2).astype(np.uint8)\n",
    "    with PdfPages(filename+\"_analysis.pdf\") as pdf:\n",
    "        fig, parameters = analyse_image(gray_image, pdf)\n",
    "        with open(filename+\"_analysis.txt\",'w') as text:\n",
    "            text.write(\"Assuming the smallest element is number {element} from group {group}.\\n\".format(**parameters))\n",
    "            text.write(\"That means one pixel is {pixel_nm} +/- {pixel_nm_se} nm.\\n\".format(**parameters))\n",
    "            text.write(\"The field of view is {field_of_view} um.\\n\".format(**parameters))\n",
    "            text.write(\"\\n\")\n",
    "            \n",
    "            text.write(\"pixel_nm: {}\\n\".format(parameters['pixel_nm']))\n",
    "            text.write(\"pixel_nm_se: {}\\n\".format(parameters['pixel_nm_se']))\n",
    "        fig.suptitle(filename)\n",
    "        return fig, parameters\n",
    "\n",
    "def analyse_folders(datasets):\n",
    "    \"\"\"Analyse a folder hierarchy containing a number of calibration images.\n",
    "    \n",
    "    Given a folder that contains a number of other folders (one per microscope usually),\n",
    "    find all the USAF images (<datasets>/*/usaf_*.jpg) and analyse them.  It also generates\n",
    "    a summary file in CSV format, and a PDF with all the images and the detected elements.\n",
    "    \"\"\"\n",
    "    files = []\n",
    "    for dir in [os.path.join(datasets, d) for d in os.listdir(datasets)]: # if d.startswith('6led')]:\n",
    "        files += [os.path.join(dir,f) for f in os.listdir(dir) if f.startswith(\"usaf_\") and f.endswith(\".jpg\")]\n",
    "    summary_data_columns = ['group', 'element', 'pixel_nm', 'pixel_nm_se', 'diagonal', \n",
    "                            'fractional_standard_error', 'smallest_period_pixels', 'smallest_period_se', 'pixels_x', 'pixels_y', 'field_of_view_x', 'field_of_view_y']\n",
    "    with PdfPages(\"usaf_calibration.pdf\") as pdf, \\\n",
    "        open(\"usaf_calibration_summary.csv\", \"w\") as summary_text:\n",
    "        summary_text.write(\"filename, \" + \", \".join(summary_data_columns) + \"\\n\")\n",
    "        for filename in files:\n",
    "            print(\"\\nAnalysing file {}\".format(filename))\n",
    "            fig, parameters = analyse_file(filename)\n",
    "            pdf.savefig(fig)\n",
    "            summary_text.write(\", \".join([filename] + [str(parameters[c]) for c in summary_data_columns]) + \"\\n\")\n",
    "    \n",
    "if __name__ == '__main__':\n",
    "    if len(sys.argv) == 1:\n",
    "        print(\"Usage: {} <file_or_folder> [<file2> ...]\".format(sys.argv[0]))\n",
    "        print(\"If a file is specified, we produce <file>_analysis.pdf and <file>_analysis.txt\")\n",
    "        print(\"If a folder is specified, we produce usaf_calibration.pdf and usaf_calibration_summary.csv\")\n",
    "        print(\"as well as the single-file analysis for <folder>/*/usaf_*.jpg\")\n",
    "        print(\"Multiple files may be specified, using wildcards if your OS supports it - e.g. myfolder/calib*.jpg\")\n",
    "        exit(-1)\n",
    "    if len(sys.argv) == 2 and os.path.isdir(sys.argv[1]):\n",
    "        analyse_folders(sys.argv[1])\n",
    "    else:\n",
    "        for filename in sys.argv[1:]:\n",
    "            analyse_file(filename)"
   ]
  }
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