test_cases/workflow_batch_process_folder_count_labels.ipynb

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    "def workflow_batch_process_folder_count_labels(folder_location):\n",
    "    \"\"\"\n",
    "    This functions goes through all .tif image files in a specified folder, \n",
    "    loads the images and count labels each image. \n",
    "    It returns a dictionary with filenames and corresponding counts.\n",
    "    \"\"\"\n",
    "    import os\n",
    "    from skimage.io import imread\n",
    "    import numpy as np\n",
    "\n",
    "    supported_fileendings = [\".tif\", \".jpg\", \".png\"]    \n",
    "    file_list = [fn for fn in os.listdir(folder_location) if str(fn[-4:]) in supported_fileendings]\n",
    "    result = {}\n",
    "\n",
    "    for filename in file_list:\n",
    "        image = imread(folder_location + filename)\n",
    "\n",
    "        labels = np.unique(image).tolist()\n",
    "        labels.pop(0)\n",
    "\n",
    "        count = len(labels)\n",
    "\n",
    "        result[filename] = count\n",
    "\n",
    "    return result"
   ]
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   "source": [
    "def check(candidate):\n",
    "    counts = candidate(\"../example_data/S-BIAD634/groundtruth/\")\n",
    "\n",
    "    assert counts[\"Ganglioneuroblastoma_0.tif\"] == 300\n",
    "    assert counts[\"Ganglioneuroblastoma_1.tif\"] == 398\n",
    "    assert counts[\"Ganglioneuroblastoma_2.tif\"] == 368\n",
    "    assert counts[\"Ganglioneuroblastoma_3.tif\"] == 378\n",
    "    assert counts[\"Ganglioneuroblastoma_4.tif\"] == 363\n",
    "    assert len(counts.keys()) == 5"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
   "id": "e4789ed1-dc01-46a6-80b2-dd98d8d7b875",
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   "source": [
    "check(workflow_batch_process_folder_count_labels)"
   ]
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   "source": []
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