test_cases/remove_noise_edge_preserving.ipynb

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    "def remove_noise_edge_preserving(image, radius:int=1):\n",
    "    \"\"\"\n",
    "    Applies an edge-preserving noise-removal filter to an image.\n",
    "    \"\"\"\n",
    "    from scipy.ndimage import median_filter\n",
    "    filtered_image = median_filter(image, size=radius * 2 + 1)\n",
    "    return filtered_image"
   ]
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    "def check(candidate):\n",
    "    import numpy as np\n",
    "\n",
    "    image = np.asarray([\n",
    "        [1,1,2,2,2],\n",
    "        [1,2,2,2,2],\n",
    "        [1,1,2,2,2],\n",
    "        [1,1,1,2,2],\n",
    "        [1,1,2,2,2],\n",
    "    ])\n",
    "\n",
    "    reference = np.asarray([\n",
    "        [1,1,2,2,2],\n",
    "        [1,1,2,2,2],\n",
    "        [1,1,2,2,2],\n",
    "        [1,1,2,2,2],\n",
    "        [1,1,2,2,2],\n",
    "    ])\n",
    "    \n",
    "    assert np.array_equal(candidate(image), reference)\n",
    "\n",
    "    assert candidate(image, radius=5).mean() == 2\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e4789ed1-dc01-46a6-80b2-dd98d8d7b875",
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      "[[1 1 2 2 2]\n",
      " [1 1 2 2 2]\n",
      " [1 1 2 2 2]\n",
      " [1 1 2 2 2]\n",
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   "source": [
    "check(remove_noise_edge_preserving)"
   ]
  },
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   "id": "b7881349-f4e7-4451-80d7-fde8031fdb1c",
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   "source": []
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