4_Analysis.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ENGdLb-vBpcb"
      },
      "source": [
        "# Import packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nw3ipHHaCS6Q"
      },
      "outputs": [],
      "source": [
        "from matplotlib import pyplot as plt\n",
        "from pathlib import Path\n",
        "from PIL import Image\n",
        "from skimage.metrics import structural_similarity as ssim\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, jaccard_score\n",
        "from tqdm.notebook import tqdm\n",
        "import numpy as np\n",
        "import pandas as pd\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "sets=['B0', 'B1', 'deeplabv3p_resnet50', 'unet', 'unet_3plus', 'LDA', 'RF', 'SVC', 'FRF', 'MLP', 'DT-SegNet']\n",
        "root_path = Path('Output')"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Convert pseudo_color_prediction to numpy array"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "for current_set in sets + ['groundtruth']:\n",
        "    data_dir = root_path / current_set\n",
        "    output_root = data_dir\n",
        "    for img_path in list(data_dir.glob('*.png')):\n",
        "        print(f'Processing {str(img_path)}')\n",
        "        img = Image.open(img_path)\n",
        "        output = np.array(img)\n",
        "        output[output>=1]=1\n",
        "        data = np.array(output)\n",
        "        new_data = data.astype('uint8')\n",
        "        print(data.shape, new_data.shape, np.unique(new_data))\n",
        "        #plt.imshow(new_data, interpolation='nearest', cmap='Greys')\n",
        "        # white background\n",
        "        #plt.show()\n",
        "        np.save(output_root / f'{img_path.stem}.npy', new_data)\n",
        "        Image.fromarray(\n",
        "            new_data*255).save(output_root / f'{img_path.stem}.png')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7u6xiDGUPvx_"
      },
      "outputs": [],
      "source": [
        "Path('Analysis').mkdir(exist_ok=True, parents=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3cGRZ0hECkHx"
      },
      "source": [
        "## Calculate metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NVj5lqWJ0qwW"
      },
      "outputs": [],
      "source": [
        "sub_arr = []\n",
        "file_arr = []\n",
        "accuracy_score_arr = []\n",
        "precision_score_arr = []\n",
        "recall_score_arr = []\n",
        "ssim_arr = []\n",
        "f1_score_arr = []\n",
        "jaccard_score_arr = []"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "for sub in sets:\n",
        "    path = Path(f'Output/{sub}')\n",
        "    files = list(path.glob('*.npy'))\n",
        "    for file in files:\n",
        "        gt = f'Output/groundtruth/{file.stem}.npy'\n",
        "        gt_data=np.load(gt)\n",
        "        data=np.load(file)\n",
        "        sub_arr.append(sub)\n",
        "        file_arr.append(file.stem)\n",
        "        accuracy_score_arr.append(\n",
        "            accuracy_score(gt_data.flatten(), data.flatten())*100\n",
        "        )\n",
        "        precision_score_arr.append(\n",
        "            precision_score(gt_data.flatten(), data.flatten())*100\n",
        "        )\n",
        "        recall_score_arr.append(\n",
        "            recall_score(gt_data.flatten(), data.flatten())*100\n",
        "        )\n",
        "        f1_score_arr.append(\n",
        "            f1_score(gt_data.flatten(), data.flatten())*100\n",
        "        )\n",
        "        jaccard_score_arr.append(\n",
        "            jaccard_score(gt_data.flatten(), data.flatten())*100\n",
        "        )\n",
        "        ssim_arr.append(\n",
        "            ssim(gt_data, data, data_range=1)*100\n",
        "        )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Fp82F7HNQZD-",
        "outputId": "b6767aa5-df39-43b8-ff65-aeec6df7b21e"
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      "outputs": [],
      "source": [
        "df = pd.DataFrame({\n",
        "    'sub': sub_arr,\n",
        "    'file': file_arr,\n",
        "    'accuracy_score': accuracy_score_arr,\n",
        "    'precision_score': precision_score_arr,\n",
        "    'recall_score': recall_score_arr,\n",
        "    'jaccard_score': jaccard_score_arr,\n",
        "    'structural_similarity': ssim_arr,\n",
        "    'f1_score': f1_score_arr,\n",
        "    })\n",
        "df\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "sdxjTu_BZSaK",
        "outputId": "ae9abf9d-1af6-40d4-de70-f5a5d0202f3b"
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      "outputs": [],
      "source": [
        "# group by software\n",
        "df.groupby('sub').mean(numeric_only=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "df.groupby('sub').std()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5qjJYDcBoRVP"
      },
      "source": [
        "## Calculate precipitate scales distribution"
      ]
    },
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      "source": [
        "# precipitate scale in ROI\n",
        "for cset in ['train', 'val', 'test']:\n",
        "    path = Path('/root/autodl-tmp/DT_SegNet/Dataset/segmentation_labels/') / cset\n",
        "    big_root = Path('/root/autodl-tmp/DT_SegNet/Dataset/segmentation_labels')\n",
        "    files = list(path.glob('*.png'))\n",
        "\n",
        "    bg_cnt_arr = []\n",
        "    ppt_cnt_arr = []\n",
        "    ratio_arr = []\n",
        "    for file in tqdm(files):\n",
        "        img = Image.open(file)\n",
        "        np_data = np.asarray(img)\n",
        "        big_name = file.stem.split('_')[0]\n",
        "        big_path = big_root / f'{big_name}.png'\n",
        "        \n",
        "        bigimg = Image.open(big_path)\n",
        "        bignp_data = np.asarray(bigimg)\n",
        "\n",
        "        bigarea = bignp_data.shape[0] * bignp_data.shape[1]\n",
        "\n",
        "        bg_cnt, ppt_cnt = np.count_nonzero(np_data == 0), np.count_nonzero(np_data == 1)\n",
        "\n",
        "        assert np_data.size == np.count_nonzero(np_data == 0) + np.count_nonzero(np_data == 1)\n",
        "        #assert bg_cnt > ppt_cnt\n",
        "        ratio_arr.append(100*ppt_cnt/bigarea)\n",
        "\n",
        "        bg_cnt_arr.append(bg_cnt)\n",
        "        ppt_cnt_arr.append(ppt_cnt)\n",
        "\n",
        "    np.savetxt(f'Analysis/{cset}_hist.txt', ratio_arr)\n",
        "\n",
        "    ppt_ratio = np.array(ppt_cnt_arr).sum() / (np.array(bg_cnt_arr).sum()+np.array(ppt_cnt_arr).sum())\n",
        "\n",
        "    print(cset, ppt_ratio)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sFlsFQAl0cgc"
      },
      "source": [
        "## Generate visual inspection"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "VB650OZm0nS0",
        "outputId": "8f26b3b0-8cc3-4c97-91f5-4786a6ad1359"
      },
      "outputs": [],
      "source": [
        "Path('/root/autodl-tmp/DT_SegNet/Analysis/Error').mkdir(exist_ok=True, parents=True)\n",
        "\n",
        "for isuffix in suffix:\n",
        "    for iid in iids:\n",
        "        gt_filename = f'/root/autodl-tmp/DT_SegNet/Output/{iid}_groundtruth.npy'\n",
        "        filename = f'/root/autodl-tmp/DT_SegNet/Output/{iid}_{isuffix}.npy'\n",
        "        output_filename = f'/root/autodl-tmp/DT_SegNet/Analysis/Error/{iid}_{isuffix}_error'\n",
        "        print(filename)\n",
        "        data = np.load(filename)\n",
        "        gt_data = np.load(gt_filename)\n",
        "\n",
        "        data = data.astype('uint8')\n",
        "        gt_data = gt_data.astype('uint8')\n",
        "        error = np.bitwise_xor(data, gt_data)\n",
        "        error_cls = np.bitwise_and(error, gt_data)\n",
        "        error = error + error_cls\n",
        "        plt.imshow(error, interpolation='nearest', cmap='Greys')\n",
        "        # white is background\n",
        "        plt.show()\n",
        "        Image.fromarray(error*127).save(f'{output_filename}.png')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oa06yPVL14WZ"
      },
      "source": [
        "## Generate red error overlay"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X9i1499j14Lz"
      },
      "outputs": [],
      "source": [
        "error_path = Path('/root/autodl-tmp/DT_SegNet/Analysis/Error')\n",
        "files = list(error_path.glob('*.png'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5bRpVddx2kZc"
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      "outputs": [],
      "source": [
        "def add_overlay(np_data: np.ndarray, gt_data: np.ndarray, colorset: dict):\n",
        "    output = np_data.copy()\n",
        "    for y in range(np_data.shape[0]):\n",
        "        for x in range(np_data.shape[1]):\n",
        "            pt = gt_data[y,x]\n",
        "            if pt in colorset:\n",
        "                clr = colorset[pt]\n",
        "                ca=output[y,x,0]\n",
        "                cb=output[y,x,1]\n",
        "                cc=output[y,x,2]\n",
        "                fa=min(clr[0]+ca, 255)\n",
        "                fb=min(clr[1]+cb, 255)\n",
        "                fc=min(clr[2]+cc, 255)\n",
        "                ma=fa-ca\n",
        "                mb=fb-cb\n",
        "                mc=fc-cc\n",
        "                output[y,x,0]+=ma\n",
        "                output[y,x,1]+=mb\n",
        "                output[y,x,2]+=mc\n",
        "    return output\n"
      ]
    },
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      "source": [
        "Path('/root/autodl-tmp/DT_SegNet/Analysis/Error_Overlay').mkdir(exist_ok=True, parents=True)\n",
        "for filename in tqdm(files):\n",
        "    data = Image.open(filename)\n",
        "    print(filename)\n",
        "    np_data = np.array(data)\n",
        "    oid=filename.stem.split('_')[0]\n",
        "    opath=f'/root/autodl-tmp/DT_SegNet/Dataset/test/{oid}.png'\n",
        "    o_data = Image.open(opath)\n",
        "    np_odata = np.array(o_data)\n",
        "    overlay_error = add_overlay(np_odata, np_data, {\n",
        "        127: (255,0,0),\n",
        "        254: (0,0,255),\n",
        "    })\n",
        "    Image.fromarray(overlay_error, mode='RGB').save(\n",
        "        f'/root/autodl-tmp/DT_SegNet/Analysis/Error_Overlay/{filename.stem}.png')\n",
        "    plt.imshow(overlay_error, interpolation='nearest', cmap='Greys')\n",
        "    # white is background\n",
        "    plt.show()\n"
      ]
    }
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