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   "source": [
    "# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.\n",
    "# SPDX-License-Identifier: MIT-0\n",
    "\n",
    "# Install missing deps from the `TensorFlow 2.11.0 Python 3.9` instance\n",
    "!pip install --upgrade pip\n",
    "!pip install scikit-image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d5fa325-f877-4b1b-89ef-b380ad47c7ec",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# REQUIRED: Set pathname which contains the `BBBC005_v1` dataset used for training the model\n",
    "IMG_PATH = \"/root/BBBC005_v1_images/\"\n",
    "\n",
    "# Specify the S3 bucket used for the workshop\n",
    "BUCKET=\"YOUR_BUCKET\"\n",
    "\n",
    "# Sample image to process\n",
    "SAMPLE_IMG=\"SIMCEPImages_A16_C66_F1_s23_w1.TIF\"\n",
    "\n",
    "# Other test images to try\n",
    "# SIMCEPImages_A16_C66_F1_s22_w1.TIF\n",
    "# SIMCEPImages_A16_C66_F1_s22_w2.TIF\n",
    "# SIMCEPImages_A16_C66_F1_s23_w1.TIF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b42f9c5e-8ee2-4cf3-ad10-251dbd0925d9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import random\n",
    "import time\n",
    "import boto3\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from skimage.io import imread, imshow\n",
    "from skimage.transform import resize\n",
    "\n",
    "# Don't Show Warning Messages\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e121f48-acca-479b-8835-86075c4288e4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Create a new S3 session\n",
    "session = boto3.Session()\n",
    "s3 = session.resource('s3')\n",
    "bucket = s3.Bucket(BUCKET)\n",
    "\n",
    "print(\"Uploading \", IMG_PATH + SAMPLE_IMG)\n",
    "\n",
    "# Upload the specified filename to our S3 bucket, triggering an S3 Event notification for the PUT event\n",
    "bucket.upload_file(Filename=IMG_PATH + SAMPLE_IMG , Key='input/{}'.format(SAMPLE_IMG))\n",
    "\n",
    "print(\"Upload Complete\")\n",
    "\n",
    "print(\"Polling changes from Lambda invocation ...\")\n",
    "\n",
    "# Confirm the Lambda function is complete, displaying our mask\n",
    "while True:\n",
    "\n",
    "    try:\n",
    "        bucket.download_file('output/{}'.format(SAMPLE_IMG), '/tmp/{}'.format(SAMPLE_IMG))\n",
    "        print(\"File processed\")\n",
    "        break\n",
    "        \n",
    "    except:\n",
    "        print(\"Still processing ...\")\n",
    "        time.sleep(5)\n",
    "\n",
    "# Read our processed image and display\n",
    "image = imread('/tmp/{}'.format(SAMPLE_IMG))\n",
    "\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.title(\"Cell segmentation mask\")\n",
    "plt.imshow(image)"
   ]
  },
  {
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   "id": "a0626360-4c19-4a49-8e7a-9b29e2e83fa0",
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   "source": []
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