{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "from lang_sam import LangSAM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def save_mask_as_image(mask, output_path):\n",
    "    # Create a blank image with the same dimensions as the mask\n",
    "    width, height = mask.shape[1], mask.shape[0]\n",
    "    image = Image.new(\"L\", (width, height))\n",
    "\n",
    "    # Set the pixel values based on the mask\n",
    "    for y in range(height):\n",
    "        for x in range(width):\n",
    "            pixel_value = 255 if mask[y, x] == 1 else 0\n",
    "            image.putpixel((x, y), pixel_value)\n",
    "\n",
    "    # Save the image as a PNG\n",
    "    image.save(output_path)\n",
    "\n",
    "\n",
    "\n",
    "model = LangSAM()\n",
    "TEST_DIR = '/Users/ludovicaschaerf/Desktop/Data/VA_textiles_masks/'\n",
    "OUT_DIR = '/Users/ludovicaschaerf/Desktop/Data/VA_textiles/'\n",
    "image_pil = Image.open(TEST_DIR + \"O25495.jpg\").convert(\"RGB\")\n",
    "text_prompt = \"textile\"\n",
    "masks, boxes, phrases, logits = model.predict(image_pil, text_prompt)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "save_mask_as_image(masks[0], OUT_DIR + \"O25495.jpg\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "art-reco_x86",
   "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": "3.8.16"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}