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"cell_type": "code", "execution_count": null, "metadata": { "id": "5N9x9BEWbPco" }, "outputs": [], "source": [ "!pip install -q roboflow datasets datasets[vision] python-dotenv matplotlib seaborn" ] }, { "cell_type": "markdown", "source": [ "## Configuration" ], "metadata": { "id": "GeIJ75hOFZfb" } }, { "cell_type": "code", "source": [ "import os\n", "import sys\n", "from dataclasses import dataclass\n", "from typing import List\n", "\n", "@dataclass\n", "class Config:\n", " workspace: str\n", " project: str\n", " version: int\n", " project_location: str\n", " formats: List[str]\n", " api_key: str\n", "\n", "def is_colab():\n", " return 'google.colab' in sys.modules\n", "\n", "def get_api_key():\n", " api_key = os.getenv('ROBOFLOW_API_KEY')\n", " if not api_key:\n", " if is_colab():\n", " from google.colab import userdata\n", " api_key = userdata.get('ROBOFLOW_API_KEY')\n", " if not api_key:\n", " api_key = input(\"Please enter your Roboflow API key: \")\n", " return api_key\n", "\n", "def load_config() -> Config:\n", " api_key = get_api_key()\n", " return Config(\n", " workspace=\"roboflow-100\",\n", " project=\"bccd-ouzjz\",\n", " version=2,\n", " project_location=\"./datasets\",\n", " formats=[\"paligemma\", \"florence2-od\"],\n", " api_key=api_key\n", " )\n", "\n", "# Usage\n", "config = load_config()\n", "print(\"Configuration loaded successfully.\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qQ1INWeXbRDv", "outputId": "642037b3-c5f6-4709-adc5-b96ca9e917bf" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Configuration loaded successfully.\n" ] } ] }, { "cell_type": "markdown", "source": [ "## Reset Button - remove datasets directory" ], "metadata": { "id": "hsQrH5jmFQVj" } }, { "cell_type": "code", "source": [ "!rm -rf datasets" ], "metadata": { "id": "Jn3YStb8nsrA" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## Prepare Datasets" ], "metadata": { "id": "C6_FRi5GG8fA" } }, { "cell_type": "code", "source": [ "import os\n", "import json\n", "from roboflow import Roboflow\n", "from datasets import Dataset, Features, Image, Value, DatasetDict\n", "import pandas as pd\n", "\n", "class DatasetHandler:\n", " def __init__(self, config: Config):\n", " self.config = config\n", " self.rf = Roboflow(api_key=config.api_key)\n", " self.project = self.rf.workspace(config.workspace).project(config.project)\n", " self.version = self.project.version(config.version)\n", " self.datasets = {}\n", "\n", " def download_datasets(self):\n", " for format in self.config.formats:\n", " try:\n", " self.version.download(model_format=format,\n", " location=f\"{self.config.project_location}/{format}/\",\n", " overwrite=True)\n", " print(f\"Successfully downloaded {format} dataset\")\n", " except Exception as e:\n", " print(f\"Failed to download {format} dataset: {str(e)}\")\n", "\n", " def prepare_datasets(self):\n", " features = Features({\n", " 'image_id': Value('string'),\n", " 'image': Image(),\n", " 'annotations': {\n", " 'image': Value('string'),\n", " 'prefix': Value('string'),\n", " 'suffix': Value('string')\n", " }\n", " })\n", "\n", " for format in self.config.formats:\n", " try:\n", " dataset_dict = DatasetDict()\n", " format_dir = f\"{self.config.project_location}/{format}\"\n", "\n", " if format == \"paligemma\":\n", " splits = ['train', 'test', 'valid']\n", " for split in splits:\n", " file_path = os.path.join(format_dir, 'dataset', f'_annotations.{split}.jsonl')\n", " if os.path.exists(file_path):\n", " data = self._load_annotations(file_path, os.path.join(format_dir, 'dataset'))\n", " dataset_dict[split] = Dataset.from_pandas(pd.DataFrame(data), features=features)\n", "\n", " elif format == \"florence2-od\":\n", " splits = ['train', 'test', 'valid']\n", " for split in splits:\n", " file_path = os.path.join(format_dir, split, 'annotations.jsonl')\n", " if os.path.exists(file_path):\n", " data = self._load_annotations(file_path, os.path.join(format_dir, split))\n", " dataset_dict[split] = Dataset.from_pandas(pd.DataFrame(data), features=features)\n", "\n", " if dataset_dict:\n", " self.datasets[format] = dataset_dict\n", " print(f\"Successfully prepared {format} dataset\")\n", " else:\n", " print(f\"No valid annotation files found for {format} dataset\")\n", "\n", " except Exception as e:\n", " print(f\"Error preparing {format} dataset: {str(e)}\")\n", "\n", " def _load_annotations(self, file_path, image_dir):\n", " with open(file_path, 'r') as f:\n", " return [\n", " {\n", " 'image_id': ann['image'],\n", " 'image': os.path.join(image_dir, ann['image']),\n", " 'annotations': ann\n", " }\n", " for line in f\n", " for ann in [json.loads(line)]\n", " ]\n", "\n", " def get_dataset(self, format: str) -> DatasetDict:\n", " return self.datasets.get(format)\n", "\n", "# Usage\n", "handler = DatasetHandler(config)\n", "handler.download_datasets()\n", "handler.prepare_datasets()\n", "print(\"Datasets prepared successfully.\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "d17JNaT-cqWU", "outputId": "a2478ad8-ca47-46d0-809f-f3e607bc9de9" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "loading Roboflow workspace...\n", "loading Roboflow project...\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "Downloading Dataset Version Zip in ./datasets/paligemma/ to paligemma:: 100%|██████████| 7541/7541 [00:01<00:00, 7425.05it/s]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "Extracting Dataset Version Zip to ./datasets/paligemma/ in paligemma:: 100%|██████████| 370/370 [00:00<00:00, 1288.93it/s]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Successfully downloaded paligemma dataset\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "Downloading Dataset Version Zip in ./datasets/florence2-od/ to florence2-od:: 100%|██████████| 7538/7538 [00:00<00:00, 15493.73it/s]" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\n", "Extracting Dataset Version Zip to ./datasets/florence2-od/ in florence2-od:: 100%|██████████| 372/372 [00:00<00:00, 1625.58it/s]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Successfully downloaded florence2-od dataset\n", "Successfully prepared paligemma dataset\n", "Successfully prepared florence2-od dataset\n", "Datasets prepared successfully.\n" ] } ] }, { "cell_type": "markdown", "source": [ "## Analyze Datasets" ], "metadata": { "id": "nLm5Lhz3G3CL" } }, { "cell_type": "code", "source": [ "# print shape of Florence2 dataset\n", "florence2_ds = handler.get_dataset(\"florence2-od\")\n", "print(florence2_ds)\n", "\n", "# print shape of Paligemma dataset\n", "paligemma_ds = handler.get_dataset(\"paligemma\")\n", "print(paligemma_ds)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-NWPXaoLtdpj", "outputId": "4d8e8d60-53a0-44c8-ee9c-18d41c1dbd0a" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "DatasetDict({\n", " train: Dataset({\n", " features: ['image_id', 'image', 'annotations'],\n", " num_rows: 255\n", " })\n", " test: Dataset({\n", " features: ['image_id', 'image', 'annotations'],\n", " num_rows: 36\n", " })\n", " valid: Dataset({\n", " features: ['image_id', 'image', 'annotations'],\n", " num_rows: 73\n", " })\n", "})\n", "DatasetDict({\n", " train: Dataset({\n", " features: ['image_id', 'image', 'annotations'],\n", " num_rows: 255\n", " })\n", " test: Dataset({\n", " features: ['image_id', 'image', 'annotations'],\n", " num_rows: 36\n", " })\n", " valid: Dataset({\n", " features: ['image_id', 'image', 'annotations'],\n", " num_rows: 73\n", " })\n", "})\n" ] } ] }, { "cell_type": "code", "source": [ "from IPython.display import display\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "import pandas as pd\n", "from IPython.display import display\n", "\n", "def count_labels(annotation):\n", " # Count the occurrences of 'RBC', 'WBC', and 'Platelets' in the annotation suffix\n", " labels = ['RBC', 'WBC', 'Platelets']\n", " count = sum(annotation['suffix'].count(label) for label in labels)\n", " return count\n", "\n", "def display_first_five_records(handler, format):\n", " # Load the dataset for the specified format\n", " dataset = handler.get_dataset(format)\n", "\n", " if 'test' in dataset:\n", " # Convert the test split to a pandas DataFrame\n", " df_test = dataset['test'].to_pandas()\n", "\n", " # Drop the 'image' column since it's in bytes format\n", " df_test = df_test.drop(columns=['image'])\n", "\n", " # Add the annotation count column using the count_labels function\n", " df_test['annotation_count'] = df_test['annotations'].apply(count_labels)\n", "\n", " # Sort by the 'image_id' column\n", " df_test = df_test.sort_values(by='image_id')\n", "\n", " # Reorder the columns to: image_id, annotation_count, annotations\n", " df_test = df_test[['image_id', 'annotation_count', 'annotations']]\n", "\n", " # Get the first five records\n", " first_five_records = df_test.head(5)\n", "\n", " # Adjust the max column width to avoid truncating content\n", " pd.set_option('display.max_colwidth', 500) # Adjust this value as needed for wrapping\n", "\n", " # Display the DataFrame\n", " display(first_five_records)\n", "\n", " # Optionally reset the max_colwidth to its default after display\n", " pd.reset_option('display.max_colwidth')\n", " else:\n", " print(f\"No test dataset available for {format}\")\n", "\n", "def analyze_datasets(handler):\n", " formats = [f for f in handler.config.formats if handler.get_dataset(f)]\n", "\n", " # Prepare to plot side by side\n", " fig, axes = plt.subplots(len(formats), 3, figsize=(15, 6 * len(formats)))\n", "\n", " for idx, format in enumerate(formats):\n", " dataset = handler.get_dataset(format)\n", " if not dataset:\n", " print(f\"\\nNo data available for {format} dataset\")\n", " continue\n", "\n", " print(f\"\\nAnalysis for {format} dataset:\")\n", " for split_idx, (split, ds) in enumerate(dataset.items()):\n", " print(f\" {split} split: {len(ds)} examples\")\n", " df = ds.to_pandas()\n", "\n", " # Apply the count_labels function to count the number of annotations in each row\n", " annotation_counts = df['annotations'].apply(count_labels)\n", "\n", " # Plot side by side\n", " ax = axes[idx, split_idx] if len(formats) > 1 else axes[split_idx]\n", " sns.histplot(annotation_counts, kde=True, ax=ax)\n", " ax.set_title(f'Annotation Distribution - {format} {split} split')\n", " ax.set_xlabel('Number of Annotations')\n", " ax.set_ylabel('Frequency')\n", "\n", " plt.tight_layout()\n", " plt.show()\n" ], "metadata": { "id": "BDKfQdQhcya2" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Usage\n", "analyze_datasets(handler)\n", "print(\"Analysis complete.\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "INb4ah6_29af", "outputId": "ba0fa374-0006-421a-a8a0-984c5ea2d492" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "Analysis for paligemma dataset:\n", " train split: 255 examples\n", " test split: 36 examples\n", " valid split: 73 examples\n", "\n", "Analysis for florence2-od dataset:\n", " train split: 255 examples\n", " test split: 36 examples\n", " valid split: 73 examples\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Analysis complete.\n" ] } ] }, { "cell_type": "code", "source": [ "# Example usage for Florence2 dataset\n", "display_first_five_records(handler, 'florence2-od')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 553 }, "id": "9C8z0tEn3EnZ", "outputId": "ad178df6-00f2-4c6a-d125-8c94cb13e46f" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ " image_id \\\n", "12 BloodImage_00038_jpg.rf.1b0ce1635e11b3b49302de527c86bb02.jpg \n", "16 BloodImage_00044_jpg.rf.1c44102fcdf64fd178f1f16bb988d5cf.jpg \n", "34 BloodImage_00062_jpg.rf.fbed5373cd2e0e732092ed5c7b28aa19.jpg \n", "10 BloodImage_00090_jpg.rf.7e3d419774b20ef93d4ec6c4be8f64df.jpg \n", "5 BloodImage_00099_jpg.rf.0a65e56401cdd71253e7bc04917c3558.jpg \n", "\n", " annotation_count \\\n", "12 28 \n", "16 20 \n", "34 13 \n", "10 15 \n", "5 18 \n", "\n", " annotations \n", "12 {'image': 'BloodImage_00038_jpg.rf.1b0ce1635e11b3b49302de527c86bb02.jpg', 'prefix': '', 'suffix': 'WBCRBCRBCRBCRBCRBCRBCRBCRBCRBCRBC', 'suffix': 'WBCWBCRBCRBCRBCRBCRBCRBCRBCRBCRBC', 'suffix': 'RBCRBCRBCRBCRBCRBCRBCRBCRBCRBCRBC... \n", "10 {'image': 'BloodImage_00090_jpg.rf.7e3d419774b20ef93d4ec6c4be8f64df.jpg', 'prefix': '', 'suffix': 'RBCRBCRBCRBCRBCRBCRBCRBCRBCRBCRBC', 'suffix': 'RBCRBCRBCRBCRBCRBCRBCRBCRBCRBCRBC\n", "
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image_idannotation_countannotations
12BloodImage_00038_jpg.rf.1b0ce1635e11b3b49302de527c86bb02.jpg28{'image': 'BloodImage_00038_jpg.rf.1b0ce1635e11b3b49302de527c86bb02.jpg', 'prefix': '<OD>', 'suffix': 'WBC<loc_648><loc_633><loc_833><loc_890>RBC<loc_242><loc_810><loc_398><loc_948>RBC<loc_150><loc_831><loc_303><loc_992>RBC<loc_17><loc_544><loc_142><loc_750>RBC<loc_30><loc_650><loc_155><loc_856>RBC<loc_22><loc_160><loc_147><loc_367>RBC<loc_34><loc_296><loc_191><loc_506>RBC<loc_95><loc_404><loc_219><loc_615>RBC<loc_145><loc_467><loc_269><loc_677>RBC<loc_183><loc_485><loc_306><loc_696>RBC<loc_...
16BloodImage_00044_jpg.rf.1c44102fcdf64fd178f1f16bb988d5cf.jpg20{'image': 'BloodImage_00044_jpg.rf.1c44102fcdf64fd178f1f16bb988d5cf.jpg', 'prefix': '<OD>', 'suffix': 'WBC<loc_306><loc_535><loc_539><loc_856>WBC<loc_2><loc_83><loc_189><loc_321>RBC<loc_106><loc_773><loc_327><loc_985>RBC<loc_108><loc_600><loc_241><loc_798>RBC<loc_639><loc_113><loc_808><loc_335>RBC<loc_77><loc_296><loc_245><loc_519>RBC<loc_323><loc_123><loc_492><loc_346>RBC<loc_266><loc_302><loc_434><loc_525>RBC<loc_470><loc_48><loc_639><loc_271>RBC<loc_786><loc_350><loc_955><loc_573>RBC<loc_...
34BloodImage_00062_jpg.rf.fbed5373cd2e0e732092ed5c7b28aa19.jpg13{'image': 'BloodImage_00062_jpg.rf.fbed5373cd2e0e732092ed5c7b28aa19.jpg', 'prefix': '<OD>', 'suffix': 'RBC<loc_219><loc_335><loc_413><loc_527>RBC<loc_563><loc_590><loc_705><loc_779>RBC<loc_670><loc_629><loc_873><loc_871>RBC<loc_814><loc_481><loc_983><loc_683>RBC<loc_613><loc_319><loc_825><loc_544>RBC<loc_638><loc_117><loc_784><loc_321>RBC<loc_581><loc_2><loc_728><loc_204>RBC<loc_853><loc_100><loc_999><loc_304>RBC<loc_270><loc_2><loc_447><loc_194>RBC<loc_95><loc_2><loc_250><loc_150>RBC<loc_2>...
10BloodImage_00090_jpg.rf.7e3d419774b20ef93d4ec6c4be8f64df.jpg15{'image': 'BloodImage_00090_jpg.rf.7e3d419774b20ef93d4ec6c4be8f64df.jpg', 'prefix': '<OD>', 'suffix': 'RBC<loc_359><loc_298><loc_559><loc_506>RBC<loc_370><loc_2><loc_544><loc_175>RBC<loc_209><loc_163><loc_383><loc_338>RBC<loc_125><loc_110><loc_270><loc_283>RBC<loc_773><loc_2><loc_923><loc_165>RBC<loc_166><loc_423><loc_311><loc_598>RBC<loc_638><loc_798><loc_783><loc_973>RBC<loc_720><loc_779><loc_866><loc_954>RBC<loc_363><loc_785><loc_508><loc_960>RBC<loc_736><loc_167><loc_892><loc_394>RBC<loc...
5BloodImage_00099_jpg.rf.0a65e56401cdd71253e7bc04917c3558.jpg18{'image': 'BloodImage_00099_jpg.rf.0a65e56401cdd71253e7bc04917c3558.jpg', 'prefix': '<OD>', 'suffix': 'RBC<loc_595><loc_398><loc_742><loc_598>RBC<loc_225><loc_313><loc_398><loc_519>RBC<loc_180><loc_446><loc_353><loc_692>RBC<loc_641><loc_508><loc_814><loc_754>RBC<loc_794><loc_267><loc_939><loc_498>RBC<loc_677><loc_25><loc_834><loc_252>RBC<loc_842><loc_748><loc_999><loc_975>RBC<loc_852><loc_548><loc_999><loc_744>RBC<loc_81><loc_688><loc_230><loc_883>RBC<loc_455><loc_46><loc_638><loc_233>RBC<lo...
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\n", " \n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "summary": "{\n \"name\": \"display_first_five_records(handler, 'florence2-od')\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"image_id\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"BloodImage_00044_jpg.rf.1c44102fcdf64fd178f1f16bb988d5cf.jpg\",\n \"BloodImage_00099_jpg.rf.0a65e56401cdd71253e7bc04917c3558.jpg\",\n \"BloodImage_00062_jpg.rf.fbed5373cd2e0e732092ed5c7b28aa19.jpg\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"annotation_count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5,\n \"min\": 13,\n \"max\": 28,\n \"num_unique_values\": 5,\n \"samples\": [\n 20,\n 18,\n 13\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"annotations\",\n \"properties\": {\n \"dtype\": \"object\",\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "# Example usage for paligemma dataset\n", "display_first_five_records(handler, 'paligemma')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 466 }, "id": "lXwyBQe31mWS", "outputId": "16c890dd-cddb-443a-9732-48409623be86" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ " image_id \\\n", "2 BloodImage_00038_jpg.rf.1b0ce1635e11b3b49302de527c86bb02.jpg \n", "4 BloodImage_00044_jpg.rf.1c44102fcdf64fd178f1f16bb988d5cf.jpg \n", "29 BloodImage_00062_jpg.rf.fbed5373cd2e0e732092ed5c7b28aa19.jpg \n", "17 BloodImage_00090_jpg.rf.7e3d419774b20ef93d4ec6c4be8f64df.jpg \n", "5 BloodImage_00099_jpg.rf.0a65e56401cdd71253e7bc04917c3558.jpg \n", "\n", " annotation_count \\\n", "2 28 \n", "4 20 \n", "29 13 \n", "17 15 \n", "5 18 \n", "\n", " annotations \n", "2 {'image': 'BloodImage_00038_jpg.rf.1b0ce1635e11b3b49302de527c86bb02.jpg', 'prefix': 'detect Platelets ; RBC ; WBC', 'suffix': ' WBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; WBC ; WBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; \n", "
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image_idannotation_countannotations
2BloodImage_00038_jpg.rf.1b0ce1635e11b3b49302de527c86bb02.jpg28{'image': 'BloodImage_00038_jpg.rf.1b0ce1635e11b3b49302de527c86bb02.jpg', 'prefix': 'detect Platelets ; RBC ; WBC', 'suffix': '<loc0648><loc0664><loc0910><loc0852> WBC ; <loc0829><loc0248><loc0970><loc0408> RBC ; <loc0851><loc0153><loc1015><loc0310> RBC ; <loc0556><loc0017><loc0768><loc0145> RBC ; <loc0665><loc0030><loc0876><loc0158> RBC ; <loc0164><loc0022><loc0375><loc0150> RBC ; <loc0302><loc0035><loc0518><loc0195> RBC ; <loc0413><loc0097><loc0629><loc0224> RBC ; <loc0477><loc0148><loc069...
4BloodImage_00044_jpg.rf.1c44102fcdf64fd178f1f16bb988d5cf.jpg20{'image': 'BloodImage_00044_jpg.rf.1c44102fcdf64fd178f1f16bb988d5cf.jpg', 'prefix': 'detect Platelets ; RBC ; WBC', 'suffix': '<loc0548><loc0313><loc0876><loc0552> WBC ; <loc0085><loc0001><loc0328><loc0193> WBC ; <loc0791><loc0108><loc1009><loc0334> RBC ; <loc0614><loc0110><loc0817><loc0246> RBC ; <loc0115><loc0654><loc0343><loc0827> RBC ; <loc0302><loc0078><loc0531><loc0251> RBC ; <loc0125><loc0331><loc0354><loc0504> RBC ; <loc0309><loc0272><loc0537><loc0444> RBC ; <loc0049><loc0481><loc027...
29BloodImage_00062_jpg.rf.fbed5373cd2e0e732092ed5c7b28aa19.jpg13{'image': 'BloodImage_00062_jpg.rf.fbed5373cd2e0e732092ed5c7b28aa19.jpg', 'prefix': 'detect Platelets ; RBC ; WBC', 'suffix': '<loc0343><loc0224><loc0539><loc0422> RBC ; <loc0603><loc0576><loc0797><loc0721> RBC ; <loc0644><loc0686><loc0891><loc0894> RBC ; <loc0492><loc0833><loc0699><loc1006> RBC ; <loc0326><loc0627><loc0556><loc0844> RBC ; <loc0119><loc0652><loc0328><loc0803> RBC ; <loc0002><loc0595><loc0209><loc0745> RBC ; <loc0102><loc0873><loc0311><loc1023> RBC ; <loc0002><loc0276><loc019...
17BloodImage_00090_jpg.rf.7e3d419774b20ef93d4ec6c4be8f64df.jpg15{'image': 'BloodImage_00090_jpg.rf.7e3d419774b20ef93d4ec6c4be8f64df.jpg', 'prefix': 'detect Platelets ; RBC ; WBC', 'suffix': '<loc0305><loc0368><loc0518><loc0572> RBC ; <loc0002><loc0379><loc0179><loc0556> RBC ; <loc0166><loc0214><loc0345><loc0392> RBC ; <loc0113><loc0128><loc0290><loc0276> RBC ; <loc0002><loc0792><loc0168><loc0945> RBC ; <loc0433><loc0169><loc0612><loc0318> RBC ; <loc0817><loc0652><loc0996><loc0801> RBC ; <loc0797><loc0737><loc0977><loc0886> RBC ; <loc0804><loc0371><loc098...
5BloodImage_00099_jpg.rf.0a65e56401cdd71253e7bc04917c3558.jpg18{'image': 'BloodImage_00099_jpg.rf.0a65e56401cdd71253e7bc04917c3558.jpg', 'prefix': 'detect Platelets ; RBC ; WBC', 'suffix': '<loc0407><loc0609><loc0612><loc0760> RBC ; <loc0320><loc0230><loc0531><loc0408> RBC ; <loc0456><loc0184><loc0708><loc0361> RBC ; <loc0520><loc0656><loc0772><loc0833> RBC ; <loc0273><loc0812><loc0509><loc0961> RBC ; <loc0025><loc0692><loc0258><loc0854> RBC ; <loc0765><loc0862><loc0998><loc1023> RBC ; <loc0561><loc0872><loc0761><loc1023> RBC ; <loc0704><loc0083><loc090...
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\n", " \n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "summary": "{\n \"name\": \"display_first_five_records(handler, 'paligemma')\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"image_id\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"BloodImage_00044_jpg.rf.1c44102fcdf64fd178f1f16bb988d5cf.jpg\",\n \"BloodImage_00099_jpg.rf.0a65e56401cdd71253e7bc04917c3558.jpg\",\n \"BloodImage_00062_jpg.rf.fbed5373cd2e0e732092ed5c7b28aa19.jpg\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"annotation_count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5,\n \"min\": 13,\n \"max\": 28,\n \"num_unique_values\": 5,\n \"samples\": [\n 20,\n 18,\n 13\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"annotations\",\n \"properties\": {\n \"dtype\": \"object\",\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "import pandas as pd\n", "import json\n", "from IPython.display import display, Markdown\n", "\n", "def find_and_print_annotation(handler, image_id):\n", " formats = ['paligemma', 'florence2-od']\n", " for format in formats:\n", " dataset = handler.get_dataset(format)\n", " if dataset:\n", " # Convert the dataset to a DataFrame\n", " df = dataset['test'].to_pandas()\n", "\n", " # Drop the 'image' column since it's in bytes format\n", " df = df.drop(columns=['image'])\n", "\n", " # Find the record with the specified image_id\n", " record = df[df['image_id'] == image_id]\n", "\n", " if not record.empty:\n", " print(f\"\\nRecord found in {format}:\")\n", " annotation = record.iloc[0]['annotations']\n", " # Pretty print the annotation output\n", " pretty_annotation = json.dumps(annotation, indent=4)\n", " display(Markdown(f\"```json\\n{pretty_annotation}\\n```\"))\n", " else:\n", " print(f\"No record found with image_id {image_id} in {format} dataset.\")\n", " else:\n", " print(f\"{format} dataset is not available.\")" ], "metadata": { "id": "u2qXUVcq1o8I" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Example usage: Replace with the actual image_id you want to search for\n", "image_id_to_find = 'BloodImage_00038_jpg.rf.1b0ce1635e11b3b49302de527c86bb02.jpg'\n", "find_and_print_annotation(handler, image_id_to_find)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 280 }, "id": "jFpRETW26Qej", "outputId": "62e00503-eb09-4efe-86fe-259ee1d7fd5c" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "Record found in paligemma:\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/markdown": "```json\n{\n \"image\": \"BloodImage_00038_jpg.rf.1b0ce1635e11b3b49302de527c86bb02.jpg\",\n \"prefix\": \"detect Platelets ; RBC ; WBC\",\n \"suffix\": \" WBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; Platelets ; Platelets ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC ; RBC\"\n}\n```" }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "Record found in florence2-od:\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/markdown": "```json\n{\n \"image\": \"BloodImage_00038_jpg.rf.1b0ce1635e11b3b49302de527c86bb02.jpg\",\n \"prefix\": \"\",\n \"suffix\": \"WBCRBCRBCRBCRBCRBCRBCRBCRBCRBCRBCRBCRBCPlateletsPlateletsRBCRBCRBCRBCRBCRBCRBCRBCRBCRBCRBCRBCRBC\"\n}\n```" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "## Push to Hugging Face Hub" ], "metadata": { "id": "8AolyYXpGUDa" } }, { "cell_type": "code", "source": [ "# Push to Hugging Face Hub\n", "florence2_ds.push_to_hub(\"dwb2023/roboflow100-bccd-florence2-temp\")\n", "paligemma_ds.push_to_hub(\"dwb2023/roboflow-bccd-paligemma-temp\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 628, "referenced_widgets": [ "b7d9d1b97cad46c4a0df412c113b74d5", "3d0b4b05a69c472e96b5fe7043609308", "ea00c9db59ff4895ac8db92023c9b26b", "8bc5ff2f94c34434b1eee0f845db1356", "b948e3574f404d5095eabda1c3304761", "573ff7a397b34f998d0ebc4981b34500", "c547067e597f4fbe937dd84e0dded1a8", "248160263a4d47a7ae41bec2308198fd", "ed070cb0ada24b96aaae30f152a0ec50", "8f1f94230d9d43b09444dbcbc853590e", "0c42aa3312ef45118da948dbe77234a0", "5b8294234a9e4860bdd36a60059e2ec9", "0c7a6d87cc914cf1b4d0375307583835", "9b23a0e5ff1f4567b03bfba94ffcd8b2", "b7ce107b6a06492c810852f74334893c", "f404bb49ccd0405ba4a8b196ce7e37d3", "52ae204f576b47049a633a78e9e3e27d", "c6726d8c8cd043929702e4d10de9feab", "7d55495d56ba43bbabab8a20d52bad3a", "3795842fb12c41d89a740db594f1b2f5", "c17401a2de0e49aa980779ed3e8149b3", "458860e60efd41d68fed9b8aec3877ec", "ca72336321d74605b1d73f564def0e15", "c175878b5f9b4baf81132ceaab8442cb", "6b59e8dde21b424f8bcec7334fc3b1be", "961196d4a85f4c9ab027d3fd3648f998", "808adc16931549d2a23373944bc38e13", "b0928c9a81614529b4cffb335362cab2", "a68b3e13ce604781b486c77c01394294", "7989c45921e442bba47d4abcb73f8cbc", "f323e9da0e5a451ebf238b14381a4b5e", "497990a6015a43b5b32f00d11742ebc4", "d26d157a7b054f7fa94988f163c6550b", "33af17450cbb497b917d0c8d8670591c", "2af1486246674420b6ab3b15d5b309b3", "ace46c67fb0b4139886aa7b4fe7c90f1", "1204f816cce44d50929e4e803c44459f", "171667aeb9784d91a1900e998679f9b9", "97d43931e0f843d283c9a50def4b0a1a", "335ac70d7b6f496e969835d582d87dbd", "c23076b13c7341bfae16b048a5c8daf1", "0781e0ffbfc4457496e734d7cc1f8d22", "292dafa8bb204f1f9bd7aaf8f4f1a8f3", "871a152194c949d3a2fce78fa16e27a1", "cd40d9688c0c427f82a5a1284609a209", "631e1a04dda44c44be7676f9a6233fa6", "bc9f4eec16c049809372082b39bbf977", "96af616794844fa38e8d41c8d6af244f", "af4c5389141b49e7aa9407a6344fec62", "54f39aecf8d14fe791bdd9efce6585c5", "49776ef645a640abb4ed3c7231d19702", "f51c831d1d314b6590e15948a5900163", 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