{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "import pathlib\n",
    "import numpy as np\n",
    "from tqdm.notebook import tqdm\n",
    "import napari\n",
    "import dask.array as da\n",
    "from shapely.affinity import translate, scale\n",
    "from coda_visual.datasets.raw_hamamatsu_file import RawHamamatsuFile\n",
    "from coda_visual.analyses.tissue_area_segmentation import TissueAreaSegmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "RAW_IMAGE_DIR = pathlib.Path().resolve().parent.parent / \"sample_dataset\" / \"20x files\"\n",
    "raw_image_list = sorted(list(RAW_IMAGE_DIR.glob(\"*.ndpi\")), key=lambda x: x.name)\n",
    "TEST_OUTLINE_FILE = RAW_IMAGE_DIR / \"tissue_area.h5\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "LOW_RES_LEVEL = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "largest_layer_width = 0.\n",
    "largest_layer_height = 0.\n",
    "for fp in raw_image_list:\n",
    "    hamamatsu_file = RawHamamatsuFile(fp)\n",
    "    level_width, level_height = hamamatsu_file.openslide_slide.level_dimensions[LOW_RES_LEVEL]\n",
    "    if level_width>largest_layer_width:\n",
    "        largest_layer_width = level_width\n",
    "    if level_height>largest_layer_height:\n",
    "        largest_layer_height = level_height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "12ae86eb12eb4aca8080b9f27b1eec51",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading images and outlines...:   0%|          | 0/150 [00:00<?, ?step/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "progress_bar = tqdm(total=len(raw_image_list), desc=\"Loading images and outlines...\", unit=\"step\")\n",
    "layer_images = []\n",
    "outlines = []\n",
    "for irfp, raw_image_fp in enumerate(raw_image_list):\n",
    "    hamamatsu_file = RawHamamatsuFile(raw_image_fp)\n",
    "    level_width, level_height = hamamatsu_file.openslide_slide.level_dimensions[LOW_RES_LEVEL]\n",
    "    layer_image = hamamatsu_file.get_dask_image_level(LOW_RES_LEVEL, pad_to=(largest_layer_width, largest_layer_height))\n",
    "    layer_images.append(layer_image)\n",
    "    segmentation = TissueAreaSegmentation(hamamatsu_file, TEST_OUTLINE_FILE)\n",
    "    segmentation.read_from_file()\n",
    "    for full_res_polygon in segmentation.polygons:\n",
    "        scale_factor = 1./hamamatsu_file.openslide_slide.level_downsamples[LOW_RES_LEVEL]\n",
    "        lower_res_polygon = scale(full_res_polygon, xfact=scale_factor, yfact=scale_factor, origin=(0, 0))\n",
    "        left_pad = (largest_layer_width - level_width) // 2\n",
    "        top_pad = (largest_layer_height - level_height) // 2\n",
    "        translated_polygon = translate(lower_res_polygon, xoff=left_pad, yoff=top_pad)\n",
    "        xy_arr = np.array(translated_polygon.exterior.xy)[:, :-1]\n",
    "        z_arr = irfp*np.ones((xy_arr.shape[1]))\n",
    "        xyz_arr = np.vstack((xy_arr, z_arr))\n",
    "        coords = xyz_arr.T[:, [2,1,0]]\n",
    "        outlines.append(coords)\n",
    "    progress_bar.update(1)\n",
    "layer_images = da.stack(layer_images, axis=0)\n",
    "progress_bar.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "viewer = napari.Viewer()\n",
    "image = viewer.add_image(\n",
    "    layer_images,\n",
    "    name=\"dataset\",\n",
    "    contrast_limits=[0, 255],\n",
    "    rgb=True,\n",
    ")\n",
    "shape = viewer.add_shapes(\n",
    "    outlines,\n",
    "    name=\"Tissue Outlines\",\n",
    "    shape_type=\"polygon\",\n",
    "    edge_color=\"red\",\n",
    "    face_color=\"transparent\",\n",
    "    edge_width=3,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "coda-visual",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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