notebook/1.2.Image_QC_report.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image Quality Control report\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we display all the channels in all images in each sample and provide compare before and after preprocessing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "import os\n",
    "import numpy as np\n",
    "import glob\n",
    "import tifffile\n",
    "import gc\n",
    "import scipy.ndimage as ndimage\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "from davinci.vectra_tool import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "tiff_image_path = '../data/tif/*.tif'\n",
    "output_dir = '../report/'\n",
    "project_name = 'demo'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique sample name ['Sample1' 'Sample2']\n"
     ]
    }
   ],
   "source": [
    "tiff_images = sorted(glob.glob(tiff_image_path))\n",
    "channel_name = ['chan1', 'chan2', 'chan3', 'chan4', 'chan5', 'chan6', 'chan7']   # for real data: [x.description.split('<Name>')[1].split(' ')[0].split('<')[0].split('+')[0] for x in  tifffile.TiffFile(tiff_images[0]).pages[:7] ]\n",
    "sample_ids = (np.array([x.split('/')[-1].split(' ')[0] for i,x in enumerate(tiff_images)]))\n",
    "unique_sample = np.unique(sample_ids)\n",
    "print('Unique sample name',unique_sample)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before clean report. No threshold and Max cap values needed here. This can be done with raw data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i,sample in enumerate(tqdm(unique_sample)):\n",
    "    paths = [x for x in tiff_images if sample in x]\n",
    "    id_ = [list(tiff_images).index(x) for x in paths]\n",
    "    one_report(img_paths=paths,save_dir=output_dir,show=False,project_name=project_name,channel_name=channel_name)\n",
    "    gc.collect()\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before and after Clean report. Need threshold and max cap values from previous notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "thod_df = pd.read_csv('../parameter/threshold.csv',header=None)\n",
    "cap_df = pd.read_csv('../parameter/max_cap.csv',header=0,index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i,sample in enumerate(tqdm(unique_sample)):\n",
    "    paths = [x for x in tiff_images if sample in x]\n",
    "    id_ = [list(tiff_images).index(x) for x in paths]\n",
    "    one_report_clean(img_paths=paths,sample_name=sample,id_=id_,save_dir=output_dir,show=False,project_name=project_name,thod_df=thod_df,cap_df=cap_df,channel_name=channel_name)\n",
    "    gc.collect()\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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