{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "unlimited-butler",
   "metadata": {},
   "source": [
    "# Demonstration zu Image Stitching mit OpenCV\n",
    "\n",
    "Hier präsentieren wir drei verschiedene Methoden zum Image Stitching mit OpenCV in Python. Als Beispiel dienen zwei Scans einer Donaukarte um Wien:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "chubby-cornwall",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": "[encoded data, 441284 characters]\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/jpeg": {
       "width": 800
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/jpeg": "[encoded data, 626012 characters]\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/jpeg": {
       "width": 800
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, Image\n",
    "import cv2 as cv\n",
    "import numpy as np\n",
    "\n",
    "display(Image(filename='imgs/wien_low1.jpg', width=800), Image(filename='imgs/wien_low2.jpg', width=800))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sudden-memorabilia",
   "metadata": {},
   "source": [
    "## 1 Verwendung des fertigen OpenCV Tools\n",
    "Zunächst verwenden wir die Stitcher-Klasse (von https://docs.opencv.org/master/d5/d48/samples_2python_2stitching_8py-example.html) im Scan Modus:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "executive-documentation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": "[encoded data, 1784952 characters]\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/jpeg": {
       "width": 2000
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stitcher = cv.Stitcher_create(mode=cv.Stitcher_SCANS)\n",
    "img_names = ['wien_low1.jpg', 'wien_low2.jpg']\n",
    "imgs = [cv.imread(f'imgs/{name}') for name in img_names]\n",
    "status, pano = stitcher.stitch(imgs)\n",
    " \n",
    "if status != cv.Stitcher_OK:\n",
    "    print(\"Can't stitch images, error code = %d\" % status)\n",
    "    sys.exit(-1)\n",
    "\n",
    "cv.imwrite('imgs_out/wien_low.jpg', pano)\n",
    "display(Image(filename='imgs_out/wien_low.jpg', width=2000))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "representative-branch",
   "metadata": {},
   "source": [
    "## 2 Anpassung des \"stitching_detailed.py\" Beispiels\n",
    "Der Code von https://github.com/opencv/opencv/blob/master/samples/python/stitching_detailed.py macht es möglich, mehr Einstellungen vorzunehmen, siehe auch https://docs.opencv.org/master/d8/d19/tutorial_stitcher.html für weitere Beispiele. Die hier eingestellten Optionen sind für das Stitching von gescannten Bildern optimiert."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "finished-hierarchy",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n"
     ]
    }
   ],
   "source": [
    "from stitch_demo import *\n",
    "\n",
    "!python stitch_demo.py imgs/wien_low1.jpg imgs/wien_low2.jpg --work_megapix 0.6 --features orb --matcher affine --estimator affine --match_conf 0.3 --conf_thresh 0.3 --ba affine --ba_refine_mask xxxxx --wave_correct no --warp affine --output imgs_out/wien_low2.jpg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "auburn-opening",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": "[encoded data, 1754208 characters]\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/jpeg": {
       "width": 2000
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Image(filename='imgs_out/wien_low2.jpg', width=2000))"
   ]
  },
  {
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    }
   },
   "cell_type": "markdown",
   "id": "hollywood-mother",
   "metadata": {},
   "source": [
    "## 3 Eigene Verwendung der OpenCV Python API\n",
    "Referenz: Matthew Brown and David G Lowe. [Automatic panoramic image stitching using invariant features](http://matthewalunbrown.com/papers/ijcv2007.pdf). International journal of computer vision, 74(1):59–73, 2007. Die OpenCV Bibliothek beinhaltet bereits implementierte Algorithmen für jeden Baustein im Diagramm, in scan.py wurden diese mit für Scans optimierten Einstellungen zusammengefügt.\n",
    "![StitchingPipeline.jpg](attachment:781fa54e-b0aa-40e9-be56-cb196c644449.jpg \"stitching pipeline\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "soviet-manufacturer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": "[encoded data, 1852560 characters]\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/jpeg": {
       "width": 2000
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scan import *\n",
    "\n",
    "result = full_stitching_pipeline(imgs, img_names)\n",
    "out_name = 'imgs_out/wien_low3.jpg'\n",
    "cv.imwrite(out_name, result)\n",
    "display(Image(filename=out_name, width=2000))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "later-adelaide",
   "metadata": {},
   "source": [
    "## 4 Illustration zu Identifizierung von Merkmalen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fatty-landscape",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": "[encoded data, 724384 characters]\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "orb = cv.ORB.create()\n",
    "kp1, des1 = orb.detectAndCompute(imgs[0], None)\n",
    "kp2, des2 = orb.detectAndCompute(imgs[1], None)\n",
    "feat1 = cv.detail.computeImageFeatures2(orb, imgs[0])\n",
    "feat2 = cv.detail.computeImageFeatures2(orb, imgs[1])\n",
    "afm = cv.detail_AffineBestOf2NearestMatcher.create()\n",
    "matches_info = afm.apply(feat1, feat2)\n",
    "matches = matches_info.getMatches()\n",
    "matches = sorted(matches, key=lambda x: x.distance)\n",
    "img = cv.drawMatches(imgs[0], kp1, imgs[1], kp2, matches[:20], None)\n",
    "crop = img[0:1347, 1500:2600]\n",
    "cv.imwrite('imgs_out/matches2.jpg', crop)\n",
    "display(Image(filename='imgs_out/matches2.jpg'))"
   ]
  }
 ],
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