test_cases/measure_aspect_ratio_of_regions.ipynb

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    "def measure_aspect_ratio_of_regions(label_image):\n",
    "    \"\"\"\n",
    "    Takes a label image and returns a pandas dataframe\n",
    "    with measurements for aspect_ratio of the objects\n",
    "    \"\"\"\n",
    "    import skimage\n",
    "    import pandas as pd\n",
    "    stats = pd.DataFrame(skimage.measure.regionprops_table(label_image, properties=('minor_axis_length', 'major_axis_length')))\n",
    "    stats['aspect_ratio'] = stats['major_axis_length'] / stats['minor_axis_length']\n",
    "    return stats"
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    "def check(candidate):\n",
    "    import numpy as np\n",
    "\n",
    "    label_image = np.asarray([\n",
    "        [0,1,1,0,0,3,3],\n",
    "        [0,1,1,0,0,3,3],\n",
    "        [0,2,2,2,2,3,3],\n",
    "        [0,2,2,2,2,3,3],\n",
    "        [0,4,4,4,0,3,3],\n",
    "        [0,4,4,4,0,3,3],\n",
    "        [0,4,4,4,0,0,0],\n",
    "    ])\n",
    "    \n",
    "    result = candidate(label_image) \n",
    "    print(result['aspect_ratio'])\n",
    "    assert np.allclose(result['aspect_ratio'], [1,2,3,1], atol=0.5)\n"
   ]
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  {
   "cell_type": "code",
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     "name": "stdout",
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     "text": [
      "0    1.000000\n",
      "1    2.236068\n",
      "2    3.415650\n",
      "3    1.000000\n",
      "Name: aspect_ratio, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "check(measure_aspect_ratio_of_regions)"
   ]
  },
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   "cell_type": "code",
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   "id": "d9670160-e3b3-43cf-a1c8-b30f812fae64",
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