test_cases/label_binary_image_and_count_labels.ipynb

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   "outputs": [],
   "source": [
    "def label_binary_image_and_count_labels(binary_image):\n",
    "    \"\"\"\n",
    "    Consumes as input a binary image, applies connected component labeling to it, \n",
    "    counts the labeled objects and returns their count as single number.\n",
    "    \"\"\"\n",
    "    import skimage\n",
    "    import numpy as np\n",
    "    label_image = skimage.measure.label(binary_image)\n",
    "\n",
    "    return len(np.unique(label_image)) - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e2c6981f-99ff-4b38-b150-fe03dfdac762",
   "metadata": {},
   "outputs": [],
   "source": [
    "def check(candidate):\n",
    "    import numpy as np\n",
    "    \n",
    "    assert candidate(np.asarray([\n",
    "        [0,0,0,0,0],\n",
    "        [0,1,0,0,0],\n",
    "        [0,0,0,0,0],\n",
    "        [1,0,0,0,0],\n",
    "        [0,0,0,1,0],\n",
    "    ])) == 3\n",
    "\n",
    "    assert candidate(np.asarray([\n",
    "        [0,0,0,0,0],\n",
    "        [0,1,0,0,0],\n",
    "        [0,0,0,0,0],\n",
    "        [0,0,0,0,0],\n",
    "        [0,0,0,0,0],\n",
    "    ])) == 1\n",
    "\n",
    "    assert candidate(np.asarray([\n",
    "        [0,0,0,0,0],\n",
    "        [0,0,0,0,0],\n",
    "        [0,0,0,0,0],\n",
    "        [0,0,0,0,0],\n",
    "        [0,0,0,0,0],\n",
    "    ])) == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e4789ed1-dc01-46a6-80b2-dd98d8d7b875",
   "metadata": {},
   "outputs": [],
   "source": [
    "check(label_binary_image_and_count_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b38939da-37b8-4572-8227-b35354f3d60d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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