hikarijuku.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled1.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "BGK-0aviby9H",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8e04f546-abe6-4d27-ed20-42c132078d45"
      },
      "source": [
        "import sys\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "# HTTP library\n",
        "import requests\n",
        "\n",
        "# Stream of in-memory bytes\n",
        "from io import BytesIO\n",
        "# Pillow library\n",
        "from PIL import Image\n",
        "# OpenCV library\n",
        "import cv2\n",
        "\n",
        "from skimage.segmentation import active_contour\n",
        "\n",
        "print (cv2.__version__)\n",
        "# tested Python 2 or 3 + OpenCV v 3.2.0"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3.4.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eg_bt1Tqb6g-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "76bc3875-e054-4518-809c-7df1aa4a087e"
      },
      "source": [
        "SSBD_BASE_URL = \"http://ssbd.qbic.riken.jp/image\"\n",
        "\n",
        "url = SSBD_BASE_URL\n",
        "if requests.session().get(url).status_code == 200:\n",
        "    print(\"Successfully connected to SSBD\")\n",
        "else:\n",
        "    print(\"Failed to connect to SSBD\")\n",
        "print(url)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Successfully connected to SSBD\n",
            "http://ssbd.qbic.riken.jp/image\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "egKQsKrhb-5j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "outputId": "830a6862-42ba-4060-d0e4-acb66880dce4"
      },
      "source": [
        "# Aoki-san's image T=0\n",
        "IMAGE_ID = 33724\n",
        "Z = 0\n",
        "T = 0\n",
        "\n",
        "TMP_URL = \"{base}/webgateway/render_image/{image_id}/{z}/{t}\"\n",
        "qs = {'base': SSBD_BASE_URL, 'image_id': IMAGE_ID, 'z': Z, 't': T}\n",
        "\n",
        "url = TMP_URL.format(**qs)\n",
        "\n",
        "r = requests.session().get(url)\n",
        "if r.status_code != 200:\n",
        "    print(\"access error (1)\")\n",
        "    sys.exit()\n",
        "\n",
        "# PIL (RGB) -> OpenCV (Grayscale)\n",
        "cv_img1 = cv2.cvtColor(np.array(Image.open(BytesIO(r.content))), cv2.COLOR_RGB2GRAY)\n",
        "plt.imshow(cv_img1, 'gray')\n",
        "\n",
        "print(url)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "http://ssbd.qbic.riken.jp/image/webgateway/render_image/33724/0/0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MqnZ6VfbWeHKLstQqM61WXZEi5nLMl3K3pfvF/0vBSyvQxKT7KFgoDt0VaT6/NAvQrNVv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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4G5ALgsGcPoW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "outputId": "c4b6f423-0756-4e34-d64e-d8417502e64b"
      },
      "source": [
        "# Aoki-san's image T=150\n",
        "IMAGE_ID = 33724\n",
        "Z = 0\n",
        "T = 150\n",
        "\n",
        "TMP_URL = \"{base}/webgateway/render_image/{image_id}/{z}/{t}\"\n",
        "qs = {'base': SSBD_BASE_URL, 'image_id': IMAGE_ID, 'z': Z, 't': T}\n",
        "\n",
        "url = TMP_URL.format(**qs)\n",
        "\n",
        "r = requests.session().get(url)\n",
        "if r.status_code != 200:\n",
        "    print(\"access error (1)\")\n",
        "    sys.exit()\n",
        "\n",
        "# PIL (RGB) -> OpenCV (Grayscale)\n",
        "cv_img1 = cv2.cvtColor(np.array(Image.open(BytesIO(r.content))), cv2.COLOR_RGB2GRAY)\n",
        "plt.imshow(cv_img1, 'gray')\n",
        "\n",
        "print(url)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "http://ssbd.qbic.riken.jp/image/webgateway/render_image/33724/0/150\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bHD9+fNW+VVCNX/1uJXoui6CUa94/Hydzjd9xo035OlafQ0sWAS655BKgykqi\nY5qUXaHBUvhRO6y0/YxBO6ZR9C5Cm6f9LkvTfCUiXDYIesJMlf3tt9/mxIkTq9QPVgcaaE5e4bN+\nEUHXlPYyE345o47RHl4an0Ol7D6ssTS+F5p58J9dNN6tIy/9RKzVDsJN5c57l9v4pQRBT5ipsp89\ne5ZXXnllJdXOvn37Vt7T3LvufgqP1bGnT58eet//X3reRJt5an+XVSivduFUDECu7Jo9kK9BFolU\nO5+N0DGyFJr2Ta+rU8yvNzNN+0xiNU3yG5y2/NjFNQiCIWaelurNN9/kueeeA4b3sNbiGCngyZPL\nAXnKm60EfvmdrW5Bf5tkA3V3w6YFDFJ0jcv1mEcC6nzvUS/dxf28ul8QU4qs8z4An84rmJxZ+T+6\nUvQ27+WEsgdBT4jOHgQ9YeZ547ds2bLidMun05RrTqbUq6++ClSOuaYtmyfJz1W3+CQ35WSCy4Em\nM3779u1A5bDL0Wdqyopbd47PHFq3iWCw+JRMdb/9+CS5DyIHXRAEI5nLjjAi3zVDU23eAdVmCeo4\nS1zrjtVj7jS87LLLhl5TWKyU3e8OApUVoHN8ZtE8h54ChvTZpew+n3h+Hf/ZJtnKetrAj/VEG6fb\nOG1Xl/lGlLLPCP2Gfa75krXqrbouNiQNZQ+CnjBzZU8pNS5BrRuHt1H4cfCLWvSYh/Dqf6m0ptg0\nVvdj+hzVU6GvWuQiNYfVil7ne+gqP/1GwX/2tZ4ya7Ks6upSWlBVl1W2yZ+j34S3+iYhlD0IesLM\nlb1OmevG0E3nT6P2frztVRuqRBR6TXdVjb9Ly2J9PZuU3S9pFXXZSJvoQ1CNb482Fk8XKaFyy60u\ngUkTOtZbhE3H+jp1kX4slD0IesJcxuyl/70yjbIA8mMmuXv7ZasqK7+uH5OXkkbC8OKWOiXXY568\norQjZ4nSjiFiIyt6F36JujLG+c3k3nL9FkbVrfQ7UpyGn2UpfYe+fl2ERYeyB0FPiM4eBD1h5qve\n9KfnnlFmSldmq8wxmUel9eYKgPHHyoTT+5pCgyqvvcx4ledXuEG9KdnG+dPnvPE+OCWnKfx5FHXt\nnjvo6kJefaBMXoYfrjVNvfmgHH9MmPFBEIxkrg66kuNp1IaFbbK3lK5Vh19/LkWG4dBZWJ0/To66\nfDrtzJkzQLUhZZvQXV//pvX5kzglNyptVK5O2Zucnt5KyqdjvfpLcXVMaUtlOWXrvrO8jvpfv60u\nLbhQ9iDoCXMNl23K0e5fbwqgabN9cdtgnXxqTMtrNZ3m7966bm4N+Gk5X/86y6ZU1yb6MEYf5dMo\n4X8LbcoY1Za5H0fBV1oUpcUHRj4AAAVZSURBVMArZRrW8zxDsH5HKkfWX/678XXxY/8ufFWh7EHQ\nE+a6xDWnbRhiV8szvffULziAatztd6ORwuvcfNmq35nVB+vkoZI+7LNNwEzdGHQjBte0XXTSdMwk\n+DJyX82ll14KwIEDBwA4dOgQAAcPHgSqxVP67UCVIfmRRx4ZKl9WZP6b878Jv0gmvPFBEIxk5so+\n7jjV373z595j6Y8p3QU1Z+rzuvv38/P9Pux+x5YSdYsd2swNN7EevPHjLNEdJ9GIL78pcUed5Vaq\nU119ZcHl35mU/AMf+AAAR44cAaqxusb0uSLLGtizZw8Ad999N1Alb8ktQ/+78XsfNrXXKKsmlD0I\nesLCjtlFk6d9klQ9G3Fsu1GZxnppirIbVbaPktN4HODo0aMAHD58GIDdu3cDVUyGn3/PX9NvW2nN\ndE5uKdb9Pv1eiJMQyh4EPSE6exD0hIUx44WfeqjLsgmrTbU22WWDjUObaba630RTUI3/zeVh09rM\nU0E1Qo60UvYhlaOwah0jx1xpcZR3MKqMMOODIBjJXMNlc+oUvCnwpG5Z4TR3v/VKV+Gz68EqmiTP\n+yiHXRMXXHDByv8Ki1Wgjc9mpECZ3Ommablnn30WgJdeemno2Da/6S4IZQ+CnmCzvJOb2cvAD4Hv\nz+yi03Ep66eusL7qu57qCuunvu9JKe0uvTHTzg5gZg+klK6b6UUnZD3VFdZXfddTXWH91bdEmPFB\n0BOiswdBT5hHZ791DteclPVUV1hf9V1PdYX1V99VzHzMHgTBfAgzPgh6QnT2IOgJM+vsZvYJM3vM\nzJ40s5tndd22mNlBM7vXzB41s0fM7POD13ea2V+b2RODx0vmXVdhZpvN7Dtm9o3B8yNmdt+gjf/M\nzM4ZVcasMLMdZvY1M/tHMztmZh9e1LY1s98c/Ab+wcz+p5mdt8ht25aZdHYz2wz8N+BfAlcBv2Rm\nV83i2mNwFvitlNJVwIeAXxvU8WbgnpTSUeCewfNF4fPAsez5F4EvpZTeB7wGfG4utSpzC/BXKaWf\nBK5hud4L17Zmth/4DeC6lNLVwGbgMyx227Yj35Jprf6ADwN3Zc+/AHxhFteeos53Aj8PPAbsG7y2\nD3hs3nUb1OUAyx3kZ4FvAMZyhNeWUpvPua7bgeMMHMLZ6wvXtsB+4DlgJ8trR74B/MKitu04f7My\n49WA4sTgtYXEzA4D1wL3AXtSSi8M3joF7JlTtTx/DPw2oNQmu4DTKSWtl1ykNj4CvAz86WDY8WUz\nu5AFbNuU0kngD4FngReAM8CDLG7btiYcdA4zuwj4S+A/pJRez99Ly7f1uc9VmtmngJdSSg/Ouy4t\n2QL8FPAnKaVrWV4fMWSyL1DbXgJcz/IN6nLgQuATc61UR8yqs58EDmbPDwxeWyjMbCvLHf2rKaU7\nBi+/aGb7Bu/vA16aV/0yPgr8opk9A9zOsil/C7DDzLRseZHa+ARwIqV03+D511ju/IvYtj8HHE8p\nvZxSege4g+X2XtS2bc2sOvv9wNGBR/Mclh0eX5/RtVthy4ukvwIcSyn9UfbW14EbB//fyPJYfq6k\nlL6QUjqQUjrMclv+bUrpl4F7gRsGhy1EXQFSSqeA58zsJwYvfRx4lAVsW5bN9w+Z2QWD34TqupBt\nOxYzdHx8EngceAr4z/N2VhTq9zMsm5HfAx4e/H2S5bHwPcATwN8AO+ddV1fvjwHfGPx/BfD/gCeB\nvwDOnXf9snr+M+CBQfv+L+CSRW1b4L8A/wj8A/A/gHMXuW3b/kW4bBD0hHDQBUFPiM4eBD0hOnsQ\n9ITo7EHQE6KzB0FPiM4eBD0hOnsQ9IT/D0Y4LVkuUMvsAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rnDxU-uecj-0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "outputId": "7ea169c1-f137-4f2e-8191-752edb47d305"
      },
      "source": [
        "TMP_URL = \"{base}/webclient/imgData/{image_id}/\"\n",
        "qs = {'base': SSBD_BASE_URL, 'image_id': IMAGE_ID}\n",
        "url = TMP_URL.format(**qs)\n",
        "\n",
        "r = requests.session().get(url)\n",
        "if r.status_code != 200:\n",
        "    print(\"access error\")\n",
        "    sys.exit()\n",
        "\n",
        "metadata = r.json()\n",
        "\n",
        "print (url)\n",
        "print (\"ProjectName:\", metadata['meta']['projectName'])\n",
        "print (\"Dataset:\", metadata['meta']['datasetName'])\n",
        "print (\"X:\", metadata['size']['width'])\n",
        "print (\"Y:\", metadata['size']['height'])\n",
        "print (\"Z:\", metadata['size']['z'])\n",
        "print (\"T:\", metadata['size']['t'])\n",
        "print (\"C:\", metadata['size']['c'])\n",
        "print (\"PixelsType: \", metadata['meta']['pixelsType'])\n",
        "print (\"PixwlRange: \", metadata['pixel_range'][1])\n",
        "\n",
        "print (\"\\n\", metadata)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "http://ssbd.qbic.riken.jp/image/webclient/imgData/33724/\n",
            "ProjectName: 17-Aoki-MolDyn\n",
            "Dataset: sample1\n",
            "X: 100\n",
            "Y: 100\n",
            "Z: 1\n",
            "T: 391\n",
            "C: 1\n",
            "PixelsType:  uint16\n",
            "PixwlRange:  65535\n",
            "\n",
            " {'init_zoom': 0, 'tiles': False, 'perms': {'canAnnotate': False, 'canEdit': False, 'canDelete': False, 'canLink': False}, 'rdefs': {'defaultT': 0, 'model': 'greyscale', 'invertAxis': False, 'projection': 'normal', 'defaultZ': 0}, 'pixel_range': [0, 65535], 'interpolate': True, 'channels': [{'coefficient': 1.0, 'active': True, 'window': {'max': 670.0, 'end': 670.0, 'start': 0.0, 'min': 0.0}, 'family': 'linear', 'color': '808080', 'inverted': False, 'reverseIntensity': False, 'emissionWave': None, 'label': 'CFP'}], 'meta': {'projectName': '17-Aoki-MolDyn', 'imageId': 33724, 'imageAuthor': 'public data', 'imageTimestamp': 1342683835.0, 'pixelsType': 'uint16', 'datasetId': 208, 'projectDescription': 'See details in Aoki et al. (2013) Molecular Cell, 52(4): 529-540', 'datasetName': 'sample1', 'wellSampleId': '', 'projectId': 6, 'imageDescription': 'See details in Aoki et al. (2013) Molecular Cell, 52(4): 529-540', 'wellId': '', 'imageName': 'BG-CFP.stk', 'datasetDescription': 'See details in Aoki et al. (2013) Molecular Cell, 52(4): 529-540'}, 'id': 33724, 'pixel_size': {'y': 1.0, 'x': 1.0, 'z': None}, 'size': {'width': 100, 'c': 1, 'z': 1, 't': 391, 'height': 100}, 'split_channel': {'c': {'width': 206, 'gridy': 1, 'border': 2, 'gridx': 2, 'height': 104}, 'g': {'width': 104, 'gridy': 1, 'border': 2, 'gridx': 1, 'height': 104}}}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rOfK3DHicx8Y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        },
        "outputId": "5532e8eb-98dc-461f-b59e-51ae67e8999d"
      },
      "source": [
        "# Filterd\n",
        "cv_img2 = cv2.GaussianBlur(cv_img1, (3,3), 0)\n",
        "#cv_img2 = cv2.medianBlur(cv_img1, 3)\n",
        "\n",
        "maxPixel = int(metadata['pixel_range'][1])\n",
        "threshold, cv_img3 = cv2.threshold(cv_img2, 0, maxPixel, cv2.THRESH_BINARY | cv2.THRESH_OTSU)\n",
        "print (\"maxPixel\", maxPixel, \"threshold\", threshold)\n",
        "#thresh = 60\n",
        "#ret, cv_img3 = cv2.threshold(cv_img2, thresh, maxPixel, cv2.THRESH_BINARY)\n",
        "\n",
        "titles = ['Input image', \"Gaussian Filtering\", \"Otsu's Binarization\"]\n",
        "images = [cv_img1, cv_img2, cv_img3]\n",
        "\n",
        "plt.figure(figsize=(10,3))\n",
        "for i in range(0, 3):\n",
        "    plt.subplot(1, 3, i+1)\n",
        "    plt.imshow(images[i], 'gray')\n",
        "    plt.title(titles[i])\n",
        "    plt.xticks([]), plt.yticks([])\n",
        "\n",
        "plt.savefig('result1.tiff')"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "maxPixel 65535 threshold 47.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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eT6lUctemE57lOesP0VGRtuMbZnRIyf/QGmPcs19aWnIj765fvw7g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\nH0IIIYRkhv8PzXVE6veghVwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x216 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "al0GW0CydWaR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "outputId": "aaa93551-9a0f-43b5-f57c-1a724dada32e"
      },
      "source": [
        "# Filterd\n",
        "cv_img2 = cv2.GaussianBlur(cv_img1, (3,3), 0)\n",
        "#cv_img2 = cv2.medianBlur(cv_img1, 3)\n",
        "\n",
        "maxPixel = int(metadata['pixel_range'][1])\n",
        "threshold, cv_img3 = cv2.threshold(cv_img2, 0, maxPixel, cv2.THRESH_BINARY | cv2.THRESH_OTSU)\n",
        "print (\"maxPixel\", maxPixel, \"threshold\", threshold)\n",
        "#thresh = 60\n",
        "#ret, cv_img3 = cv2.threshold(cv_img2, thresh, maxPixel, cv2.THRESH_BINARY)\n",
        "\n",
        "retval, labels, stats, centroids = cv2.connectedComponentsWithStats(cv_img3)\n",
        "print(\"number of labels:\", retval)\n",
        "print(labels.shape, labels.dtype)\n",
        "\n",
        "fig, ax = plt.subplots(facecolor=\"w\")\n",
        "ax.imshow(labels)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "maxPixel 65535 threshold 47.0\n",
            "number of labels: 11\n",
            "(100, 100) int32\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qQmNjI/r7+5GZmYmWlpYkTjdXQ0MDduzYgXv37uHOnTswmUwpeW59Ph/Onj0Lt9uNu3fv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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XT-Yhcrgen0L",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        },
        "outputId": "475559f7-f488-4d42-c8ea-7a099bb0d130"
      },
      "source": [
        "print(stats.shape, stats.dtype)\n",
        "for i, row in enumerate(stats):\n",
        "  print(f\"label {i}\")\n",
        "  print(f\"  size: ({row[cv2.CC_STAT_WIDTH]} px, {row[cv2.CC_STAT_HEIGHT]} px)\")\n",
        "  print(f\"  area: {row[cv2.CC_STAT_AREA]}\")"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(11, 5) int32\n",
            "label 0\n",
            "  size: (100 px, 100 px)\n",
            "  area: 9393\n",
            "label 1\n",
            "  size: (10 px, 9 px)\n",
            "  area: 75\n",
            "label 2\n",
            "  size: (10 px, 9 px)\n",
            "  area: 71\n",
            "label 3\n",
            "  size: (9 px, 10 px)\n",
            "  area: 64\n",
            "label 4\n",
            "  size: (8 px, 8 px)\n",
            "  area: 50\n",
            "label 5\n",
            "  size: (8 px, 9 px)\n",
            "  area: 60\n",
            "label 6\n",
            "  size: (8 px, 8 px)\n",
            "  area: 54\n",
            "label 7\n",
            "  size: (9 px, 10 px)\n",
            "  area: 72\n",
            "label 8\n",
            "  size: (8 px, 10 px)\n",
            "  area: 58\n",
            "label 9\n",
            "  size: (10 px, 10 px)\n",
            "  area: 55\n",
            "label 10\n",
            "  size: (10 px, 6 px)\n",
            "  area: 48\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "USMQcoJ1gbL2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "e8851f05-f2ad-43b7-bd32-0fbda0d7a46a"
      },
      "source": [
        "print(centroids.shape, centroids.dtype)\n",
        "for i, row in enumerate(centroids):\n",
        "    print(f\"label {i} topleft: ({row[0]}, {row[1]})\")"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(11, 2) float64\n",
            "label 0 topleft: (49.88555307143618, 48.744490578090065)\n",
            "label 1 topleft: (27.48, 32.16)\n",
            "label 2 topleft: (58.53521126760563, 34.943661971830984)\n",
            "label 3 topleft: (26.0625, 52.6875)\n",
            "label 4 topleft: (63.36, 53.44)\n",
            "label 5 topleft: (9.683333333333334, 58.166666666666664)\n",
            "label 6 topleft: (48.388888888888886, 61.611111111111114)\n",
            "label 7 topleft: (90.38888888888889, 71.375)\n",
            "label 8 topleft: (3.189655172413793, 82.25862068965517)\n",
            "label 9 topleft: (25.727272727272727, 87.72727272727273)\n",
            "label 10 topleft: (84.79166666666667, 96.95833333333333)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "smSQAwZQwF5Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "9be321f0-4de0-4c45-b9ba-137b14b3b8e8"
      },
      "source": [
        "height, width = cv_img1.shape[:2]\n",
        "total_intensity = np.zeros(retval)\n",
        "\n",
        "for y in range(0, height):\n",
        "  for x in range(0, width):\n",
        "    if labels[y, x] > 0:\n",
        "#      print(f\"intensity {cv_img1[y, x]}\")\n",
        "      total_intensity[labels[y, x]] = total_intensity[labels[y, x]] + cv_img1[y, x]\n",
        "\n",
        "for i in range(0, retval):\n",
        "  print(f\"label {i+1} total_intensity: {total_intensity[i]}\")"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "label 1 total_intensity: 0.0\n",
            "label 2 total_intensity: 8547.0\n",
            "label 3 total_intensity: 9137.0\n",
            "label 4 total_intensity: 5468.0\n",
            "label 5 total_intensity: 3565.0\n",
            "label 6 total_intensity: 6058.0\n",
            "label 7 total_intensity: 3895.0\n",
            "label 8 total_intensity: 6063.0\n",
            "label 9 total_intensity: 5194.0\n",
            "label 10 total_intensity: 3144.0\n",
            "label 11 total_intensity: 4845.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gf7D0AHle9eq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}