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AURAD_dataset
File size: 4,315 Bytes
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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "from glob import glob\n",
    "from tqdm import tqdm\n",
    "from PIL import Image\n",
    "\n",
    "def remove_black_border(image):\n",
    "    \"\"\"去除X光片黑边\"\"\"\n",
    "    gray = np.array(image.convert(\"L\"))  # 转为灰度\n",
    "    mask = gray > 10  # 过滤黑色背景，避免完全黑的像素影响裁剪\n",
    "    coords = np.argwhere(mask)\n",
    "    if coords.shape[0] == 0:\n",
    "        return image  # 如果没有找到非黑区域，返回原图\n",
    "    y0, x0 = coords.min(axis=0)\n",
    "    y1, x1 = coords.max(axis=0) + 1\n",
    "    return image.crop((x0, y0, x1, y1))\n",
    "\n",
    "def process_images(input_dir, output_dir, target_size=(512, 512)):\n",
    "    \"\"\"批量处理X光片：去黑边 + 调整大小\"\"\"\n",
    "    os.makedirs(output_dir, exist_ok=True)\n",
    "    image_paths = glob(os.path.join(input_dir, \"**/*.png\"), recursive=True)\n",
    "\n",
    "    for img_path in tqdm(image_paths, desc=\"Processing images\"):\n",
    "        try:\n",
    "            img = Image.open(img_path).convert(\"RGB\")\n",
    "        except Exception as e:\n",
    "            print(f\"Error loading {img_path}: {e}\")\n",
    "            continue\n",
    "        \n",
    "        cropped = remove_black_border(img)\n",
    "        resized = cropped.resize(target_size, Image.BILINEAR)\n",
    "\n",
    "        # 生成输出路径\n",
    "        rel_path = os.path.relpath(img_path, input_dir)\n",
    "        save_path = os.path.join(output_dir, rel_path)\n",
    "        os.makedirs(os.path.dirname(save_path), exist_ok=True)\n",
    "        \n",
    "        resized.save(save_path)\n",
    "\n",
    "# 使用示例\n",
    "input_dir = \"/data16T/chestx-ray/text2layout-single/\"\n",
    "output_dir = \"/data16T/chestx-ray/text2layout-single-processed/\"\n",
    "process_images(input_dir, output_dir)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed and saved: /data16T/chestx-ray/text2layout-ptx/0.0.07.109556.72.6.9.7.18989625890.5633519597565.8.png\n"
     ]
    }
   ],
   "source": [
    "from PIL import Image\n",
    "import numpy as np\n",
    "\n",
    "def remove_black_border(image):\n",
    "    \"\"\"去除X光片黑边\"\"\"\n",
    "    gray = np.array(image.convert(\"L\"))  # 转换为灰度图\n",
    "    mask = gray > 10  # 过滤接近黑色的像素，避免裁剪全黑区域\n",
    "    coords = np.argwhere(mask)\n",
    "    \n",
    "    if coords.shape[0] == 0:\n",
    "        return image  # 如果没有有效区域，返回原图\n",
    "    \n",
    "    y0, x0 = coords.min(axis=0)\n",
    "    y1, x1 = coords.max(axis=0) + 1\n",
    "    return image.crop((x0, y0, x1, y1))\n",
    "\n",
    "def process_xray(image_path, output_path, target_size=(512, 512)):\n",
    "    \"\"\"对单张X光片去黑边 + 调整大小\"\"\"\n",
    "    img = Image.open(image_path).convert(\"RGB\")\n",
    "    cropped = remove_black_border(img)\n",
    "    resized = cropped.resize(target_size, Image.BILINEAR)\n",
    "    resized.save(output_path)\n",
    "    print(f\"Processed and saved: {output_path}\")\n",
    "\n",
    "# 示例：对 `image.png` 进行处理并保存\n",
    "input_image = \"/data16T/chestx-ray/text2layout-ptx/train/0.0.07.109556.72.6.9.7.18989625890.5633519597565.8/0.0.07.109556.72.6.9.7.18989625890.5633519597565.8.png\"\n",
    "output_image = \"/data16T/chestx-ray/text2layout-ptx/0.0.07.109556.72.6.9.7.18989625890.5633519597565.8.png\"\n",
    "process_xray(input_image, output_image)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "biomedclip",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.15"
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 },
 "nbformat": 4,
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