tattoo-bg-removal.ipynb

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   "source": [
    "import os\n",
    "import cv2\n",
    "import subprocess\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import FileLink, display\n",
    "\n",
    "INPUT_DIR = \"/kaggle/input/tattoos\"\n",
    "image_paths = os.listdir(INPUT_DIR)\n",
    "image_paths = [os.path.join(INPUT_DIR, path) for path in image_paths]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ae0c3a08",
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    "def download_file(folder=\"/kaggle/working\"):\n",
    "    command = f\"zip out.zip {folder} -r\"\n",
    "    subprocess.run(command, shell=True)\n",
    "\n",
    "\n",
    "def clear_dir(folder=\"/kaggle/working\"):\n",
    "    for the_file in os.listdir(folder):\n",
    "        file_path = os.path.join(folder, the_file)\n",
    "        try:\n",
    "            if os.path.isfile(file_path):\n",
    "                os.unlink(file_path)\n",
    "            elif os.path.isdir(file_path):\n",
    "                clear_dir(file_path)\n",
    "                os.rmdir(file_path)\n",
    "        except Exception as e:\n",
    "            print(e)\n",
    "\n",
    "\n",
    "def visualize(*imgs):\n",
    "    for i in range(len(imgs)):\n",
    "        plt.subplot(2, 2, i + 1)\n",
    "        plt.imshow(imgs[i], 'gray')\n",
    "\n",
    "\n",
    "def get_contour_mask(image, threshed):\n",
    "    contours, H = cv2.findContours(image=threshed, mode=cv2.RETR_EXTERNAL, method=cv2.CHAIN_APPROX_SIMPLE)\n",
    "    contours = sorted(contours, key=lambda x: cv2.contourArea(x), reverse=True)\n",
    "    rects = [cv2.boundingRect(contour) for contour in contours[:5]]\n",
    "    mask = np.zeros(image.shape[:2], np.uint8)\n",
    "    for x, y, w, h in rects:\n",
    "        mask[x:x+w, y:y+h] = 255\n",
    "    return mask\n",
    "\n",
    "\n",
    "def remove_background(image_paths):\n",
    "    for path in image_paths:\n",
    "        src = cv2.imread(path, 1)\n",
    "        tmp = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "        mean = round(cv2.mean(tmp)[0])\n",
    "        algo = cv2.THRESH_BINARY if mean < 127 else cv2.THRESH_BINARY_INV\n",
    "        _, thr = cv2.threshold(tmp, mean, 255, algo)\n",
    "        \n",
    "        mask = get_contour_mask(src, thr)\n",
    "        cv2.bitwise_and(thr, thr, mask=mask)\n",
    "        final = cv2.merge([*cv2.split(src), thr], 4)\n",
    "\n",
    "        cv2.imwrite(f\"output{len(os.listdir())}.png\", final)\n",
    "\n",
    "\n",
    "# clear_dir()\n",
    "# download_file()\n",
    "# remove_background(image_paths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "593600bc",
   "metadata": {
    "execution": {
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    "tags": []
   },
   "outputs": [],
   "source": [
    "remove_background(image_paths)"
   ]
  },
  {
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   "id": "533d878b",
   "metadata": {
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