Datasets:
File size: 15,172 Bytes
034deef |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 |
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0fsKoLM5sjn_"
},
"outputs": [],
"source": [
"# Colab Setup Cell\n",
"# Install necessary packages\n",
"!pip -q install -U datasets==3.1.0 pillow pyarrow matplotlib tqdm huggingface_hub\n",
"\n",
"from pathlib import Path\n",
"from datasets import load_dataset, Image\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.patches as patches\n",
"from PIL import Image as PILImage\n",
"from tqdm.auto import tqdm\n",
"from huggingface_hub import hf_hub_url, notebook_login, hf_hub_download # Use hf_hub_download\n",
"import os # Necessary for creating folders\n",
"\n",
"# --- HUGGING FACE AUTHENTICATION (Keep this) ---\n",
"print(\"--- Authenticating with Hugging Face ---\")\n",
"notebook_login()\n",
"\n",
"# --- Configuration ---\n",
"DATASET_ID = \"c-i-ber/Nova\"\n",
"SPLIT_NAME = \"train\"\n",
"PARQUET_FILE_URL = f\"hf://datasets/{DATASET_ID}/data/nova-v1.parquet\"\n",
"\n",
"# --- LOCAL FOLDER CONSTANT ---\n",
"LOCAL_IMAGE_DIR = \"images\" # The folder where images will be saved\n",
"\n",
"MAX_BOXES_TO_DRAW = 5\n",
"\n",
"# --- Visualization Style Constants (Unchanged) ---\n",
"BOX_COLOR = 'cyan'\n",
"TEXT_COLOR = 'black'\n",
"BOX_LABEL_BG_COLOR = 'white'\n",
"TITLE_FONT_SIZE = 12\n",
"METADATA_SECTION_TITLE_SIZE = 12\n",
"METADATA_TEXT_SIZE = 10"
]
},
{
"cell_type": "code",
"source": [
"# --- HUGGING FACE AUTHENTICATION FIX ---\n",
"from huggingface_hub import notebook_login\n",
"import os\n",
"\n",
"# 1. Log in to Hugging Face\n",
"print(\"--- Authenticating with Hugging Face ---\")\n",
"# This command will open an interactive prompt where you paste your token.\n",
"# You can get a token from: https://huggingface.co/settings/tokens\n",
"notebook_login()"
],
"metadata": {
"id": "yRAVEjw-28yU"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def load_and_prepare_dataset(parquet_url: str) -> \"datasets.Dataset\":\n",
" \"\"\"\n",
" Loads the dataset structure (metadata) directly from the remote Parquet file.\n",
" \"\"\"\n",
" print(f\"Loading metadata structure from: {parquet_url}\")\n",
" try:\n",
" ds = load_dataset(\"parquet\", data_files=parquet_url, split=SPLIT_NAME)\n",
" except Exception as e:\n",
" print(f\"\\n🚨 Fatal Error: Could not load Parquet file from URL. Details: {e}\")\n",
" return None\n",
"\n",
" print(f\"\\nDataset loaded successfully. Total examples: {len(ds)}\")\n",
" if \"bboxes\" in ds.column_names:\n",
" print(\"✅ SUCCESS: All metadata columns loaded correctly.\")\n",
" else:\n",
" print(\"🚨 CRITICAL ERROR: 'bboxes' column is missing.\")\n",
"\n",
" return ds\n",
"\n",
"\n",
"def cache_all_images(dataset: \"datasets.Dataset\", dataset_id: str):\n",
" \"\"\"\n",
" Downloads all images from the HF Hub to the local folder and saves them as JPEGs.\n",
" \"\"\"\n",
" Path(LOCAL_IMAGE_DIR).mkdir(exist_ok=True)\n",
" print(f\"\\n--- Downloading and Caching All Images to: {LOCAL_IMAGE_DIR}/ ---\")\n",
"\n",
" for example in tqdm(dataset, desc=\"Downloading and saving images\", unit=\"img\"):\n",
" try:\n",
" # 1. Get the image's remote path and local target path\n",
" image_url_path = example['image_path']\n",
"\n",
" # The filename is the part after the last slash\n",
" filename = Path(image_url_path).name\n",
" local_save_path = Path(LOCAL_IMAGE_DIR) / filename\n",
"\n",
" # Skip if the file already exists locally\n",
" if local_save_path.exists():\n",
" continue\n",
"\n",
" # 2. Download the file's absolute path from HF cache\n",
" # This uses the correct function and full internal path\n",
" repo_file_path = image_url_path\n",
" cached_file_path = hf_hub_download(\n",
" repo_id=dataset_id,\n",
" filename=repo_file_path,\n",
" repo_type=\"dataset\"\n",
" )\n",
"\n",
" # 3. Open the downloaded file from the cache and save it to the local folder\n",
" img = PILImage.open(cached_file_path)\n",
" img.save(local_save_path)\n",
"\n",
" except Exception as e:\n",
" print(f\"\\nWarning: Could not process image {example.get('filename')}. Error: {e}\")\n",
"\n",
" print(\"Caching complete. Demo examples will load instantly from the local folder.\")\n",
"\n",
"\n",
"def load_local_image(filename: str) -> PILImage.Image:\n",
" \"\"\"Loads an image from the local directory using its simple filename.\"\"\"\n",
" local_path = Path(LOCAL_IMAGE_DIR) / filename\n",
" return PILImage.open(local_path)\n",
"\n",
"\n",
"# --- Example Selector Functions (Unchanged) ---\n",
"# ... (has_boxes, get_example_by_filename, find_first_example_with_boxes are here) ...\n",
"\n",
"def has_boxes(example: dict) -> bool:\n",
" bboxes = example.get(\"bboxes\", None)\n",
" return isinstance(bboxes, list) and len(bboxes) > 0\n",
"\n",
"def get_example_by_filename(dataset: \"datasets.Dataset\", filename: str) -> dict:\n",
" \"\"\"Retrieves an example from the dataset by its 'filename'.\"\"\"\n",
" for i, ex in tqdm(\n",
" enumerate(dataset),\n",
" total=len(dataset),\n",
" desc=f\"Searching for '{filename}'\",\n",
" unit=\"ex\",\n",
" leave=False\n",
" ):\n",
" if ex.get(\"filename\") == filename:\n",
" return dataset[i]\n",
" raise ValueError(f\"Example with filename '{filename}' not found.\")\n",
"\n",
"def find_first_example_with_boxes(dataset: \"datasets.Dataset\") -> dict:\n",
" \"\"\"Finds the first example in the dataset that contains bounding boxes.\"\"\"\n",
" print(\"Searching for first example with bounding boxes...\")\n",
" for ex in tqdm(dataset, desc=\"Finding example with boxes\", unit=\"ex\"):\n",
" if has_boxes(ex):\n",
" return ex\n",
"\n",
" print(\"Warning: No example with bounding boxes found. Displaying index 0 instead.\")\n",
" return dataset[0]"
],
"metadata": {
"id": "5u2Ezk2YFh3o"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Execute the loading\n",
"ds = load_and_prepare_dataset(PARQUET_FILE_URL)\n",
"\n",
"if ds is None:\n",
" raise RuntimeError(\"Dataset loading failed. Cannot proceed with demo.\")\n",
"\n",
"# --- STEP 2: CACHE ALL IMAGES ---\n",
"cache_all_images(ds, DATASET_ID)"
],
"metadata": {
"id": "hPB6_pzXIBup"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def display_nova_example(dataset: \"datasets.Dataset\", selector: str | int = None):\n",
" # --- Selection Logic (Unchanged) ---\n",
" example = None\n",
" try:\n",
" if isinstance(selector, int):\n",
" if 0 <= selector < len(dataset):\n",
" example = dataset[selector]\n",
" else:\n",
" raise IndexError(f\"Index {selector} out of bounds for dataset of size {len(dataset)}.\")\n",
" elif isinstance(selector, str):\n",
" example = get_example_by_filename(dataset, selector)\n",
" elif selector is None:\n",
" example = find_first_example_with_boxes(dataset)\n",
" else:\n",
" raise TypeError(f\"Selector must be an integer index or a string filename, got {type(selector)}.\")\n",
"\n",
" if example is None: return\n",
"\n",
" filename = example.get('filename', 'N/A')\n",
" total_boxes = len(example.get('bboxes', []))\n",
" print(f\"\\n--- Displaying File: {filename} | Total Boxes: {total_boxes} ---\")\n",
"\n",
" except (ValueError, IndexError, TypeError) as e:\n",
" print(f\"Error selecting example: {e}\")\n",
" return\n",
"\n",
" # --- IMAGE LOADING (FAST, LOCAL) ---\n",
" try:\n",
" # Load the image using its filename from the local folder\n",
" img = load_local_image(filename)\n",
"\n",
" except Exception as e:\n",
" print(f\"\\n🛑 Fatal Error: Image for file {filename} could not be loaded from local path. Error: {e}\")\n",
" return\n",
"\n",
" # --- Visualization Setup (Plotting - Unchanged) ---\n",
" fig, (ax_img, ax_meta) = plt.subplots(1, 2, figsize=(14, 7), gridspec_kw={'width_ratios': [1, 1]})\n",
"\n",
" # Left Subplot: Image and Bounding Boxes\n",
" ax_img.imshow(img)\n",
" bboxes = example.get(\"bboxes\", [])\n",
"\n",
" img_title = f\"Image: {filename} ({total_boxes} Boxes)\"\n",
" ax_img.set_title(img_title, fontsize=TITLE_FONT_SIZE, fontweight='bold')\n",
"\n",
" # Draw Bounding Boxes\n",
" for i, b in enumerate(bboxes[:MAX_BOXES_TO_DRAW]):\n",
" x, y, w, h = b[\"x\"], b[\"y\"], b[\"width\"], b[\"height\"]\n",
" source = b.get(\"source\", \"N/A\")\n",
"\n",
" rect = patches.Rectangle((x, y), w, h,\n",
" linewidth=2,\n",
" edgecolor=BOX_COLOR,\n",
" linestyle='-',\n",
" fill=False)\n",
" ax_img.add_patch(rect)\n",
"\n",
" ax_img.text(x, y - 5, f\"Source: {source}\",\n",
" fontsize=8,\n",
" color=TEXT_COLOR,\n",
" bbox=dict(facecolor=BOX_LABEL_BG_COLOR, alpha=0.8, edgecolor='none', pad=2))\n",
"\n",
" ax_img.axis(\"off\")\n",
"\n",
" # Right Subplot: Structured Metadata\n",
" ax_meta.set_axis_off()\n",
" ax_meta.set_xlim(0, 1)\n",
" ax_meta.set_ylim(0, 1)\n",
"\n",
" y_pos = 0.95\n",
" x_pos = 0.05\n",
" line_height = 0.08\n",
"\n",
" ax_meta.text(x_pos, y_pos, \"Metadata Details\", fontsize=METADATA_SECTION_TITLE_SIZE + 2,\n",
" fontweight='bold', transform=ax_meta.transAxes)\n",
" y_pos -= line_height * 1.5\n",
"\n",
" # Caption\n",
" caption = example.get(\"caption_text\", \"N/A (No caption available)\")\n",
" ax_meta.text(x_pos, y_pos, \"Caption:\", fontsize=METADATA_SECTION_TITLE_SIZE,\n",
" fontweight='bold', transform=ax_meta.transAxes)\n",
" y_pos -= line_height\n",
"\n",
" wrapped_caption = \"\\n\".join([caption[i:i+60] for i in range(0, len(caption), 60)])\n",
" ax_meta.text(x_pos, y_pos, wrapped_caption, fontsize=METADATA_TEXT_SIZE, transform=ax_meta.transAxes,\n",
" verticalalignment='top')\n",
" y_pos -= line_height * (wrapped_caption.count('\\n') + 2)\n",
"\n",
" # Clinical and Publication Details\n",
" meta = example.get(\"meta\", {})\n",
" ax_meta.text(x_pos, y_pos, \"Clinical & Publication:\", fontsize=METADATA_SECTION_TITLE_SIZE,\n",
" fontweight='bold', transform=ax_meta.transAxes)\n",
" y_pos -= line_height\n",
"\n",
" details = [\n",
" (\"Final Diagnosis:\", meta.get('final_diagnosis', 'N/A')),\n",
" (\"Title:\", meta.get('title', 'N/A')),\n",
" (\"Publication Date:\", meta.get('publication_date', 'N/A')),\n",
" (\"Link:\", meta.get('link', 'N/A'))\n",
" ]\n",
"\n",
" for label, value in details:\n",
" ax_meta.text(x_pos, y_pos, f\"{label:<18} {value}\", fontsize=METADATA_TEXT_SIZE, transform=ax_meta.transAxes)\n",
" y_pos -= line_height\n",
"\n",
" plt.tight_layout()\n",
" plt.show()"
],
"metadata": {
"id": "-QzYNu9dFtG9"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# --- Demo Examples (Instant after initial caching) ---\n",
"\n",
"# 1. Default Example: Find the first entry that has bounding boxes\n",
"print(\"--- DEMO 1: Displaying first example with boxes ---\")\n",
"display_nova_example(ds)\n",
"\n",
"# 2. Select by Integer Index\n",
"print(\"\\n--- DEMO 2: Select by Index (95) ---\")\n",
"display_nova_example(ds, selector=543)\n",
"\n",
"# 3. Select by String Filename\n",
"print(\"\\n--- DEMO 3: Select by Filename (Logical ID) ---\")\n",
"display_nova_example(ds, selector=ds[0]['filename'])"
],
"metadata": {
"id": "6O1rffbKwOxV"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import os\n"
],
"metadata": {
"id": "zZ8r9w7YFa6Y"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "mPsz_1vZLIFM"
},
"execution_count": null,
"outputs": []
}
]
} |