created framework for excersice 02_01

2434ef13 · lhuxohl · 8679b8c0 · 2434ef13 · 2434ef13
Commit 2434ef13 authored 4 years ago by lhuxohl
--- a/.gitignore
+++ b/.gitignore
 *.ipynb_checkpoints
+bildverarbeitung/
\ No newline at end of file
--- a/02_image_enhancement/01_resize.ipynb
+++ b/02_image_enhancement/01_resize.ipynb
@@ -8,6 +8,67 @@
    "\n",
    "Schreiben Sie eine Funktion, die eine beliebige Skalierung von Bildern durchführt. Implementieren Sie hierzu das Verfahren aus der Vorlesung und verwenden Sie nicht die Skalierfunktion aus OpenCV."
   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x648 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "import cv2 as cv\n",
+    "import numpy as np\n",
+    "import os\n",
+    "\n",
+    "def scale(img, height, width):\n",
+    "    # Hinweis: Der Typ der Bilder wird hier geändert um Overflows zu vermeiden (siehe Beispiele)\n",
+    "    img = img.astype(np.float)\n",
+    "    result = np.zeros((height,width,3), np.float)\n",
+    "    \n",
+    "    # TODO: skalieren Sie das Bild und speichern Sie das Ergebnis in result\n",
+    "    \n",
+    "    return result.astype(np.uint8)\n",
+    "\n",
+    "\n",
+    "img_directory = '../resources'\n",
+    "file_uniansicht = os.path.join(img_directory, 'Uniansicht.jpg')\n",
+    "file_windrad = os.path.join(img_directory, 'Windrad.jpg')\n",
+    "\n",
+    "img_uniansicht = cv.imread(file_uniansicht)\n",
+    "img_windrad = cv.imread(file_windrad)\n",
+    "\n",
+    "fig, axs = plt.subplots(2, 2, figsize = (9, 9))\n",
+    "fig.tight_layout(h_pad=4)\n",
+    "axs[0, 0].imshow(cv.cvtColor(scale(img_uniansicht, 454, 454), cv.COLOR_BGR2RGB))\n",
+    "axs[0, 1].imshow(cv.cvtColor(scale(img_windrad, 437, 437), cv.COLOR_BGR2RGB))\n",
+    "axs[1, 0].imshow(cv.cvtColor(scale(img_uniansicht, 1200, 900), cv.COLOR_BGR2RGB))\n",
+    "axs[1, 1].imshow(cv.cvtColor(scale(img_windrad, 300, 200), cv.COLOR_BGR2RGB))\n",
+    "_ = axs[1, 1].set_title('')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
  }
 ],
 "metadata": {

 %% Cell type:markdown id: tags:

 # Aufgabe

 Schreiben Sie eine Funktion, die eine beliebige Skalierung von Bildern durchführt. Implementieren Sie hierzu das Verfahren aus der Vorlesung und verwenden Sie nicht die Skalierfunktion aus OpenCV.
+
+%% Cell type:code id: tags:
+
+``` python
+%matplotlib inline
+from matplotlib import pyplot as plt
+
+import cv2 as cv
+import numpy as np
+import os
+
+def scale(img, height, width):
+    # Hinweis: Der Typ der Bilder wird hier geändert um Overflows zu vermeiden (siehe Beispiele)
+    img = img.astype(np.float)
+    result = np.zeros((height,width,3), np.float)
+
+    # TODO: skalieren Sie das Bild und speichern Sie das Ergebnis in result
+
+    return result.astype(np.uint8)
+
+
+img_directory = '../resources'
+file_uniansicht = os.path.join(img_directory, 'Uniansicht.jpg')
+file_windrad = os.path.join(img_directory, 'Windrad.jpg')
+
+img_uniansicht = cv.imread(file_uniansicht)
+img_windrad = cv.imread(file_windrad)
+
+fig, axs = plt.subplots(2, 2, figsize = (9, 9))
+fig.tight_layout(h_pad=4)
+axs[0, 0].imshow(cv.cvtColor(scale(img_uniansicht, 454, 454), cv.COLOR_BGR2RGB))
+axs[0, 1].imshow(cv.cvtColor(scale(img_windrad, 437, 437), cv.COLOR_BGR2RGB))
+axs[1, 0].imshow(cv.cvtColor(scale(img_uniansicht, 1200, 900), cv.COLOR_BGR2RGB))
+axs[1, 1].imshow(cv.cvtColor(scale(img_windrad, 300, 200), cv.COLOR_BGR2RGB))
+_ = axs[1, 1].set_title('')
+```
+
+%% Output
+
+
+
+%% Cell type:code id: tags:
+
+``` python
+```