{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":52279,"databundleVersionId":5822112,"sourceType":"competition"}],"dockerImageVersionId":30664,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# HuBMAP - Hacking the Human Vasculature. EDA\n![vascular-man.jpg](attachment:f0296c61-2c03-4dbf-867f-81402d135b5a.jpg)\n\n## 1. Data overview\n**Overview:**\n- **Goal:** The objective is to develop a model that can segment microvascular structures in 2D PAS-stained histology images obtained from healthy human kidney tissue slides.\n- **Importance:** Automating the segmentation process aids researchers in understanding the arrangement of blood vessels in tissues. This is vital for studying cell interaction and organization.\n- **Context:** The competition aligns with HuBMAP's goal to map healthy cells in the human body. Automating segmentation fills knowledge gaps and contributes to the Vasculature Common Coordinate Framework (VCCF) and Human Reference Atlas (HRA) development.\n- **Research Focus:** HuBMAP researchers use advanced technologies to study cell connections. Machine learning insights from microvasculature segmentation enhance our understanding of vessel distribution and cell relationships, ultimately impacting human health 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"}}},{"cell_type":"code","source":"# let's import all dependencies \nimport os\nimport json\nfrom PIL import Image\nfrom collections import Counter\n\nimport numpy as np\nimport pandas as pd\nimport plotly.express as px\nimport plotly.graph_objects as go\nimport tifffile as tiff\nimport matplotlib.pyplot as plt","metadata":{"execution":{"iopub.status.busy":"2024-03-18T14:09:36.948923Z","iopub.execute_input":"2024-03-18T14:09:36.949408Z","iopub.status.idle":"2024-03-18T14:09:39.319363Z","shell.execute_reply.started":"2024-03-18T14:09:36.949373Z","shell.execute_reply":"2024-03-18T14:09:39.317717Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class CFG:\n    img_path_template: str = \"/kaggle/input/hubmap-hacking-the-human-vasculature/train/{}.tif\"","metadata":{"execution":{"iopub.status.busy":"2024-03-18T14:09:39.330815Z","iopub.execute_input":"2024-03-18T14:09:39.331257Z","iopub.status.idle":"2024-03-18T14:09:39.337978Z","shell.execute_reply.started":"2024-03-18T14:09:39.331216Z","shell.execute_reply":"2024-03-18T14:09:39.336102Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def get_cartesian_coords(coords, img_height):\n    coords_array = np.array(coords).squeeze()\n    xs = coords_array[:, 0]\n    ys = -coords_array[:, 1] + img_height\n    \n    return xs, ys","metadata":{"execution":{"iopub.status.busy":"2024-03-18T14:09:39.339571Z","iopub.execute_input":"2024-03-18T14:09:39.340338Z","iopub.status.idle":"2024-03-18T14:09:39.354975Z","shell.execute_reply.started":"2024-03-18T14:09:39.340308Z","shell.execute_reply":"2024-03-18T14:09:39.354020Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def plot_annotated_image(image_dict, scale_factor: float = 1.0) -> None:\n   \n    img_path = CFG.img_path_template.format(image_dict[\"id\"])\n    array = tiff.imread(img_path)\n    img_example = Image.fromarray(array)\n    annotations = image_dict[\"annotations\"]\n \n    fig = go.Figure()\n\n    img_width, img_height = img_example.size\n  \n    fig.add_trace(\n        go.Scatter(\n            x=[0, img_width],\n            y=[0, img_height],\n            mode=\"markers\",\n            marker_opacity=0\n        )\n    )\n\n\n    fig.update_xaxes(\n        visible=False,\n        range=[0, img_width]\n    )\n\n    fig.update_yaxes(\n        visible=False,\n        range=[0, img_height],\n        # the scaleanchor attribute ensures that the aspect ratio stays constant\n        scaleanchor=\"x\"\n    )\n\n    fig.add_layout_image(\n        x=0,\n        sizex=img_width,\n        y=img_height,\n        sizey=img_height,\n        xref=\"x\", yref=\"y\",\n        opacity=1.0,\n        layer=\"below\",\n        sizing=\"stretch\",\n        source=img_example\n    )\n    \n    for annotation in annotations:\n        name = annotation[\"type\"]\n        xs, ys = get_cartesian_coords(annotation[\"coordinates\"], img_height)\n        fig.add_trace(go.Scatter(\n            x=xs, y=ys, fill=\"toself\",\n            name=name,\n            hovertemplate=\"%{name}\",\n            mode='lines'\n        ))\n\n    fig.update_layout(\n        width=img_width * scale_factor,\n        height=img_height * scale_factor,\n        margin={\"l\": 0, \"r\": 0, \"t\": 0, \"b\": 0},\n        showlegend=False\n    )\n    \n    fig.show(config={'doubleClick': 'reset'})","metadata":{"execution":{"iopub.status.busy":"2024-03-18T14:09:39.356109Z","iopub.execute_input":"2024-03-18T14:09:39.356506Z","iopub.status.idle":"2024-03-18T14:09:39.370833Z","shell.execute_reply.started":"2024-03-18T14:09:39.356463Z","shell.execute_reply":"2024-03-18T14:09:39.369424Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"In this competition, our aim is to identify microvasculature structures (blood vessels) in human kidney histology slides.\n\n**Data Overview:**\nThe competition dataset consists of tiles extracted from 5 Whole Slide Images (WSI) split into 2 datasets.\nDataset 1 contains tiles with expert-reviewed annotations, while Dataset 2 comprises tiles from the same WSIs with sparse, unreviewed annotations.\nAll test set tiles are from Dataset 1.\nThe training set includes Dataset 2 tiles from the public test WSI but not from the private test WSI.\nAdditionally, Dataset 3 consists of tiles from 9 additional WSIs without annotations, providing opportunities for semi- or self-supervised learning approaches.","metadata":{}},{"cell_type":"markdown","source":"**The dataset includes:**\n\ntrain/test folders: Containing TIFF images of tiles, each sized 512x512 pixels.\npolygons.jsonl: Polygonal segmentation masks in JSONL format, provided for Dataset 1 and Dataset 2. Each line contains JSON annotations for a single image.\nwsi_meta.csv: Metadata for the Whole Slide Images from which the tiles were extracted.\ntile_meta.csv: Metadata for each image.","metadata":{}},{"cell_type":"markdown","source":"### Whole Slide Images metadata\n**Description:**\n\nwsi_meta.csv: Metadata for the Whole Slide Images (WSI) containing the extracted tiles.\nsource_wsi: Identifier for the WSI.\nDemographic information: Includes age, sex, race, height, weight, and BMI of the tissue donor.","metadata":{}},{"cell_type":"code","source":"#Let's load the wsi_meta.csv file.\nwsi_meta_df = pd.read_csv(\"/kaggle/input/hubmap-hacking-the-human-vasculature/wsi_meta.csv\")\nwsi_meta_df","metadata":{"execution":{"iopub.status.busy":"2024-03-18T14:09:39.372301Z","iopub.execute_input":"2024-03-18T14:09:39.373396Z","iopub.status.idle":"2024-03-18T14:09:39.418697Z","shell.execute_reply.started":"2024-03-18T14:09:39.373364Z","shell.execute_reply":"2024-03-18T14:09:39.417751Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Out of the five mentioned, only four WSI are present in the available data.","metadata":{}},{"cell_type":"code","source":"#Let's output the pie distribution for the sex and race values.\nsex_counts = wsi_meta_df['sex'].value_counts()\n\nfig = go.Figure(data=[go.Pie(labels=sex_counts.index, values=sex_counts.values, textinfo='label+percent')])\n\nfig.update_traces(textposition='inside', textfont_size=14)\n\nfig.update_layout(\n    title_text=\"Distribution of Sex\",\n    title_x=0.5,\n    title_y=0.95,\n    title_xanchor='center',\n    title_yanchor='top',\n    legend_title_text='Aspect:'\n)\n\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2024-03-18T14:09:39.420069Z","iopub.execute_input":"2024-03-18T14:09:39.420900Z","iopub.status.idle":"2024-03-18T14:09:39.842385Z","shell.execute_reply.started":"2024-03-18T14:09:39.420868Z","shell.execute_reply":"2024-03-18T14:09:39.841202Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"race_counts = wsi_meta_df['race'].value_counts()\n\n# Create pie chart\nfig = go.Figure(data=[go.Pie(labels=race_counts.index, values=race_counts.values, textinfo='label+percent')])\n\n# Update layout\nfig.update_layout(\n    title_text=\"Distribution of Race\",\n    title_x=0.5,\n    title_y=0.95,\n    title_xanchor='center',\n    title_yanchor='top',\n    legend_title_text='Race:'\n)\n\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2024-03-18T14:09:39.844289Z","iopub.execute_input":"2024-03-18T14:09:39.845071Z","iopub.status.idle":"2024-03-18T14:09:39.874372Z","shell.execute_reply.started":"2024-03-18T14:09:39.845032Z","shell.execute_reply":"2024-03-18T14:09:39.873232Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#let's look at the distribution of other WSI meta values.\nfig = px.violin(wsi_meta_df, x='race', y='age', box=True, color='sex', points=\"all\")\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2024-03-18T14:09:39.879951Z","iopub.execute_input":"2024-03-18T14:09:39.880808Z","iopub.status.idle":"2024-03-18T14:09:41.962980Z","shell.execute_reply.started":"2024-03-18T14:09:39.880767Z","shell.execute_reply":"2024-03-18T14:09:41.961169Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig = px.scatter(wsi_meta_df, x='height', y='weight', color='sex')\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2024-03-18T14:09:41.964685Z","iopub.execute_input":"2024-03-18T14:09:41.965073Z","iopub.status.idle":"2024-03-18T14:09:42.112054Z","shell.execute_reply.started":"2024-03-18T14:09:41.965040Z","shell.execute_reply":"2024-03-18T14:09:42.110764Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Now, let's load the tile_meta.csv file and see what it looks like.\n\ntile_meta_df = pd.read_csv(\"/kaggle/input/hubmap-hacking-the-human-vasculature/tile_meta.csv\")\ntile_meta_df.head()","metadata":{"execution":{"iopub.status.busy":"2024-03-18T14:09:42.113755Z","iopub.execute_input":"2024-03-18T14:09:42.114891Z","iopub.status.idle":"2024-03-18T14:09:42.153188Z","shell.execute_reply.started":"2024-03-18T14:09:42.114857Z","shell.execute_reply":"2024-03-18T14:09:42.152157Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tile_meta_df.describe()","metadata":{"execution":{"iopub.status.busy":"2024-03-18T14:09:42.154727Z","iopub.execute_input":"2024-03-18T14:09:42.156046Z","iopub.status.idle":"2024-03-18T14:09:42.185375Z","shell.execute_reply.started":"2024-03-18T14:09:42.155996Z","shell.execute_reply":"2024-03-18T14:09:42.184060Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Let's see how many WSI are there:\n\nprint(\"Unique  WSIs --\", list(np.unique(tile_meta_df.source_wsi)))\nprint(\"Number of unique WSIs --\", len(np.unique(tile_meta_df.source_wsi)))","metadata":{"execution":{"iopub.status.busy":"2024-03-18T14:09:42.187213Z","iopub.execute_input":"2024-03-18T14:09:42.187926Z","iopub.status.idle":"2024-03-18T14:09:42.196610Z","shell.execute_reply.started":"2024-03-18T14:09:42.187879Z","shell.execute_reply":"2024-03-18T14:09:42.195439Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The output above shows that the 5th WSI is missing.","metadata":{}},{"cell_type":"code","source":"# let's check the number of existing datasets \nprint(\"Unique datasets --\", list(np.unique(tile_meta_df.dataset)))\nprint(\"Number of unique datasets --\", len(np.unique(tile_meta_df.dataset)))","metadata":{"execution":{"iopub.status.busy":"2024-03-18T14:09:42.198177Z","iopub.execute_input":"2024-03-18T14:09:42.198680Z","iopub.status.idle":"2024-03-18T14:09:42.206815Z","shell.execute_reply.started":"2024-03-18T14:09:42.198648Z","shell.execute_reply":"2024-03-18T14:09:42.205870Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Let's calculate source WSI counts\nswsi_count = Counter(tile_meta_df['source_wsi'])\n\nfig = go.Figure(data=[go.Bar(x=list(swsi_count.values()), y=list(swsi_count.keys()), orientation='h')])\n\nfig.update_layout(\n    title_text=\"Source WSI Composition\",\n    title_x=0.5,\n    title_y=0.95,\n    title_xanchor='center',\n    title_yanchor='top',\n    xaxis_title=\"Frequency\",\n    yaxis_title=\"Source WSI\"\n)\n\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2024-03-18T14:09:42.208062Z","iopub.execute_input":"2024-03-18T14:09:42.209408Z","iopub.status.idle":"2024-03-18T14:09:42.242771Z","shell.execute_reply.started":"2024-03-18T14:09:42.209376Z","shell.execute_reply":"2024-03-18T14:09:42.241660Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The polygons.jsonl file contains polygonal segmentation masks in JSONL format for Dataset 1 and Dataset 2. Each line provides JSON annotations for a single image (about 600 pieces), including:\n\n- id: Identifies the corresponding image in the train/ directory.\n- annotations: A list of mask annotations with the following attributes:\n  - type: Identifies the type of structure annotated, such as blood_vessel, glomerulus, or unsure.\n  - coordinates: Defines the segmentation mask with a list of polygon coordinates.\n\nThe goal of this competition is to predict masks for blood vessels, while excluding annotations for glomerulus structures.","metadata":{}},{"cell_type":"code","source":"#Let's load annotations\nwith open('/kaggle/input/hubmap-hacking-the-human-vasculature/polygons.jsonl', 'r') as json_file:\n    json_list = list(json_file)\n    \ntiles_dicts = []\nfor json_str in json_list:\n    tiles_dicts.append(json.loads(json_str))\n    \nprint(\"Count of annotated tiles --\", len(tiles_dicts))","metadata":{"execution":{"iopub.status.busy":"2024-03-18T14:09:42.244285Z","iopub.execute_input":"2024-03-18T14:09:42.244637Z","iopub.status.idle":"2024-03-18T14:09:48.460241Z","shell.execute_reply.started":"2024-03-18T14:09:42.244607Z","shell.execute_reply":"2024-03-18T14:09:48.458976Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"markdown","source":"Based on the output displayed above, it can be observed that the number of annotated tiles is more than four times lesser than the total number of tiles. Specifically, out of 7033 tiles, only 1633 have been annotated.\n\nNow, let's move directly to the visualization part. For instance, let's consider a tile with the ID 9b9349a10d8d. This tile has an index of 1000 in the polygons.jsonl list.","metadata":{}},{"cell_type":"code","source":"tiles_dicts[1000][\"id\"]","metadata":{"execution":{"iopub.status.busy":"2024-03-18T14:09:48.461988Z","iopub.execute_input":"2024-03-18T14:09:48.462416Z","iopub.status.idle":"2024-03-18T14:09:48.471049Z","shell.execute_reply.started":"2024-03-18T14:09:48.462373Z","shell.execute_reply":"2024-03-18T14:09:48.469805Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tiles_dicts[1000][\"id\"]","metadata":{"execution":{"iopub.status.busy":"2024-03-18T14:09:48.472787Z","iopub.execute_input":"2024-03-18T14:09:48.473268Z","iopub.status.idle":"2024-03-18T14:09:48.484969Z","shell.execute_reply.started":"2024-03-18T14:09:48.473208Z","shell.execute_reply":"2024-03-18T14:09:48.483500Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_annotated_image(tiles_dicts[1000])","metadata":{"execution":{"iopub.status.busy":"2024-03-18T14:09:48.487286Z","iopub.execute_input":"2024-03-18T14:09:48.488071Z","iopub.status.idle":"2024-03-18T14:09:48.727443Z","shell.execute_reply.started":"2024-03-18T14:09:48.488027Z","shell.execute_reply":"2024-03-18T14:09:48.726286Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#let's try another tile with id 0870e4f9d580 and index of 50.\ntiles_dicts[50][\"id\"]","metadata":{"execution":{"iopub.status.busy":"2024-03-18T14:09:48.728916Z","iopub.execute_input":"2024-03-18T14:09:48.730035Z","iopub.status.idle":"2024-03-18T14:09:48.738628Z","shell.execute_reply.started":"2024-03-18T14:09:48.729995Z","shell.execute_reply":"2024-03-18T14:09:48.737100Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tile_meta_df[tile_meta_df.id == \"0870e4f9d580\"]","metadata":{"execution":{"iopub.status.busy":"2024-03-18T14:09:48.740767Z","iopub.execute_input":"2024-03-18T14:09:48.741273Z","iopub.status.idle":"2024-03-18T14:09:48.760835Z","shell.execute_reply.started":"2024-03-18T14:09:48.741236Z","shell.execute_reply":"2024-03-18T14:09:48.759496Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_annotated_image(tiles_dicts[50])","metadata":{"execution":{"iopub.status.busy":"2024-03-18T14:09:48.762559Z","iopub.execute_input":"2024-03-18T14:09:48.762980Z","iopub.status.idle":"2024-03-18T14:09:48.950777Z","shell.execute_reply.started":"2024-03-18T14:09:48.762945Z","shell.execute_reply":"2024-03-18T14:09:48.949823Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# another tile with id 0033bbc76b6b. In the polygons.jsonl list, this tile has an index of 2.\ntiles_dicts[2][\"id\"]\ntile_meta_df[tile_meta_df.id == \"0033bbc76b6b\"]","metadata":{"execution":{"iopub.status.busy":"2024-03-18T14:09:48.951915Z","iopub.execute_input":"2024-03-18T14:09:48.952304Z","iopub.status.idle":"2024-03-18T14:09:48.969527Z","shell.execute_reply.started":"2024-03-18T14:09:48.952272Z","shell.execute_reply":"2024-03-18T14:09:48.967679Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_annotated_image(tiles_dicts[2])","metadata":{"execution":{"iopub.status.busy":"2024-03-18T14:09:48.971950Z","iopub.execute_input":"2024-03-18T14:09:48.972458Z","iopub.status.idle":"2024-03-18T14:09:49.139694Z","shell.execute_reply.started":"2024-03-18T14:09:48.972421Z","shell.execute_reply":"2024-03-18T14:09:49.137898Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#another tile with id 4ca084aec87b. In the polygons.jsonl list, this tile has an index of 500.\ntiles_dicts[500][\"id\"]\n\ntile_meta_df[tile_meta_df.id == \"4ca084aec87b\"]","metadata":{"execution":{"iopub.status.busy":"2024-03-18T14:09:49.141608Z","iopub.execute_input":"2024-03-18T14:09:49.142017Z","iopub.status.idle":"2024-03-18T14:09:49.157444Z","shell.execute_reply.started":"2024-03-18T14:09:49.141981Z","shell.execute_reply":"2024-03-18T14:09:49.155680Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_annotated_image(tiles_dicts[500])","metadata":{"execution":{"iopub.status.busy":"2024-03-18T14:09:49.159388Z","iopub.execute_input":"2024-03-18T14:09:49.159778Z","iopub.status.idle":"2024-03-18T14:09:49.346946Z","shell.execute_reply.started":"2024-03-18T14:09:49.159740Z","shell.execute_reply":"2024-03-18T14:09:49.345945Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class COCODataset:\n    def __init__(self, images_dirpath: str, annotations_filepath: str, length: int = 1633):\n        self.train_size = None\n        self.val_size = None\n        self.length = length\n        self.classes = None\n        self.labels_counter = None\n        self.normalize = None\n        \n        self.images_dirpath = images_dirpath\n        self.annotations_filepath = annotations_filepath\n        self.dataset_dirpath = os.path.join(os.getcwd(), \"dataset\")\n        self.train_dirpath =  os.path.join(self.dataset_dirpath, \"train\")\n        self.val_dirpath =  os.path.join(self.dataset_dirpath, \"val\")\n        self.config_path = os.path.join(self.dataset_dirpath, \"coco.yaml\")\n\n        self.samples = self.parse_jsonl(annotations_filepath)\n        self.classes_dict = {\n            \"blood_vessel\": 0,\n            \"glomerulus\": 1,\n            \"unsure\": 2,\n        }\n\n    def __prepare_dirs(self) -> None:\n        if not os.path.exists(self.dataset_dirpath):\n            os.makedirs(os.path.join(self.train_dirpath, \"images\"), exist_ok=True)\n            os.makedirs(os.path.join(self.train_dirpath, \"labels\"), exist_ok=True)\n            os.makedirs(os.path.join(self.val_dirpath, \"images\"), exist_ok=True)\n            os.makedirs(os.path.join(self.val_dirpath, \"labels\"), exist_ok=True)\n        else:\n            raise RuntimeError(\"Dataset already exists!\")\n\n    def __define_splitratio(self) -> None:\n        self.train_size = round(self.length * self.train_size)\n        self.val_size = self.length - self.train_size\n        assert self.train_size + self.val_size == self.length\n\n    def parse_jsonl(self, path: str) -> list[dict, ...]:\n        with open(path, 'r') as json_file:\n            jsonl_samples = [\n                json.loads(line)\n                for line in tqdm(\n                    json_file, desc=\"Processing polygons\", total=self.length\n                )\n            ]\n        return jsonl_samples\n\n    def __define_paths(self, i: int) -> dict:\n        data_path = self.val_dirpath\n        if i < self.train_size:\n            data_path = self.train_dirpath\n        return {\n            \"images\": os.path.join(data_path, \"images\"),\n            \"labels\": os.path.join(data_path, \"labels\")\n        }\n\n    @staticmethod\n    def __get_label_path(paths_dict: dict, identifier: str) -> str:\n        return os.path.join(\n            paths_dict[\"labels\"],\n            f\"{identifier}.txt\"\n        )\n\n    @staticmethod\n    def __get_image_path(paths_dict: dict, identifier: str) -> str:\n        return os.path.join(\n            paths_dict[\"images\"],\n            f\"{identifier}.tif\"\n        )\n\n    def __copy_image(self, dst_path: str, identifier: str) -> str:\n        shutil.copyfile(\n            os.path.join(self.images_dirpath, f\"{identifier}.tif\"),\n            dst_path\n        )\n\n    def __copy_label(self, annotations: list, dst_path: str) -> None:\n        with open(dst_path, \"w\") as file:\n            for annotation in annotations:\n                coordinates = annotation[\"coordinates\"][0]\n                label = self.classes_dict[annotation[\"type\"]]\n                if label in self.classes:\n                    if coordinates:\n                        if self.normalize:\n                            coordinates = np.array(coordinates) / 512.0\n                        coordinates = \" \".join(map(str, chain(*coordinates)))\n                        file.write(f\"{label} {coordinates}\\n\")\n                        self.labels_counter += 1\n\n    def __splitfolders(self):\n        for i, line in tqdm(\n                enumerate(self.samples),\n                desc=\"Dataset creation\", total=self.length\n        ):\n            self.labels_counter = 0\n            identifier = line[\"id\"]\n            annotations = line[\"annotations\"]\n            paths_dict = self.__define_paths(i)\n\n            dst_image_path = self.__get_image_path(paths_dict, identifier)\n            dst_label_path = self.__get_label_path(paths_dict, identifier)\n\n            self.__copy_image(dst_image_path, identifier)\n            self.__copy_label(annotations, dst_label_path)\n\n            if self.labels_counter == 0:\n                os.remove(dst_image_path)\n                os.remove(dst_label_path)\n\n    def __count_dataset(self) -> dict:\n        train_images = len(os.listdir(os.path.join(self.train_dirpath, \"images\")))\n        train_labels = len(os.listdir(os.path.join(self.train_dirpath, \"labels\")))\n        val_images = len(os.listdir(os.path.join(self.val_dirpath, \"images\")))\n        val_labels = len(os.listdir(os.path.join(self.val_dirpath, \"labels\")))\n        return {\n            \"train_images\": train_images,\n            \"train_labels\": train_labels,\n            \"val_images\": val_images,\n            \"val_labels\": val_labels\n        }\n\n    @staticmethod\n    def __check_sanity(count_dict: dict) -> None:\n        assert count_dict[\"train_images\"] == count_dict[\"train_labels\"]\n        assert count_dict[\"val_images\"] == count_dict[\"val_labels\"]\n\n    def __finalizing(self, count_dict: dict) -> None:\n        assert os.path.exists(self.dataset_dirpath)\n\n        example_structure = [\n            \"dataset\",\n            \"train\", \"labels\", \"images\",\n            \"val\", \"labels\", \"images\"\n        ]\n\n        dir_bone = (\n            dirname.split(\"/\")[-1]\n            for dirname, _, filenames in os.walk(self.dataset_dirpath)\n            if dirname.split(\"/\")[-1] in example_structure\n        )\n\n        try:\n            print(\"\\n~ HuBMAP Dataset Structure ~\\n\")\n            print(\n            f\"\"\"\n          ├── {next(dir_bone)}\n          │   │\n          │   ├── {next(dir_bone)}\n          │   │   └── {next(dir_bone)}\n          │   │   └── {next(dir_bone)}\n          │   │\n          │   ├── {next(dir_bone)}\n          │   │   └── {next(dir_bone)}\n          │   │   └── {next(dir_bone)}\n            \"\"\"\n            )\n        except StopIteration as e:\n            print(e)\n        else:\n            print(Fore.GREEN + \"-> Success\")\n            print(Fore.GREEN + f\"Train dataset: {count_dict['train_images']}\\nVal dataset: {count_dict['val_images']}\")\n\n    def get_config(self) ->dict:\n        names = [\"blood_vessel\", \"glomerulus\", \"unsure\"]\n        return {\n            \"train\": str(self.train_dirpath),\n            \"val\": str(self.val_dirpath),\n            \"names\": [names[i] for i in self.classes]\n        }\n\n    @staticmethod\n    def display_config(config: dict) -> None:\n        print(Fore.BLACK + \"\\n~ HuBMAP Config Structure ~\\n\")\n        print(\n        f\"\"\"\n      │   │\n      │   ├── train\n      │   │   └── {config['train']}/images\n      │   │\n      │   │\n      │   ├── val\n      │   │   └── {config['val']}/images\n      │   │\n      │   │\n      │   ├── names\n      │   │   └── {' '.join(config['names'])}\n        \"\"\"\n        )\n        print(Fore.GREEN + \"-> Success\")\n        print(Fore.GREEN + f\"Number of classes: {len(config['names'])}\"\n                           f\"\\nClasses: {' '.join(config['names'])}\" \n              )\n\n    def write_config(self, config: dict) -> None:\n        with open(self.config_path, mode=\"w\") as f:\n            yaml.safe_dump(stream=f, data=config)\n\n    def __call__(self, train_size: float,\n                 classes: list[int, ...],\n                 make_config: bool = True,\n                 normalize: bool = True\n                ) -> None:\n        \n        self.train_size = train_size\n        self.classes = classes\n        self.normalize = normalize\n        \n        self.__define_splitratio()\n        self.__prepare_dirs()\n        self.__splitfolders()\n        count_dict = self.__count_dataset()\n        self.__check_sanity(count_dict)\n        self.__finalizing(count_dict)\n        \n        if make_config:\n            config = self.get_config()\n            self.write_config(config)\n            self.display_config(config)","metadata":{"execution":{"iopub.status.busy":"2024-03-18T14:09:49.353351Z","iopub.execute_input":"2024-03-18T14:09:49.353951Z","iopub.status.idle":"2024-03-18T14:09:49.402228Z","shell.execute_reply.started":"2024-03-18T14:09:49.353906Z","shell.execute_reply":"2024-03-18T14:09:49.401275Z"},"trusted":true},"execution_count":null,"outputs":[]}]}