image_processing_walk-through.ipynb

{
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  "metadata": {
    "colab": {
      "name": "image_processing_walk-through.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/github/tjdahlke/satellite_imagery_analysis/blob/master/image_processing_walk-through.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "E5UDxTpcy2X5",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Hyperspectral image analysis walk-through"
      ]
    },
    {
      "metadata": {
        "id": "hprTVO1aAGGz",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "---\n",
        "## Hyperspectral what??\n",
        "Planet (the company) has a good deal of **analytical** imagery available to access through it's API. Analytical means that it isn't just plain RGB images (like the pictures your cell phone takes), but is hyperspectral instead. **Hyperspectral** imagery includes capture of frequency bands that cant be seen with the naked eye, like the NIR band (Near InfraRed). In this notebook I'll walkthrough all steps from identifying an area of interest (AOI), to loading the image into a numpy array (easy place to start other analysis), to plotting the different frequency bands for interpretation. \n",
        "\n",
        "Make sure you have:\n",
        "\n",
        "*   Python 3\n",
        "*   A Planet API key (if you dont have an account/API key, sign up [here](https://www.planet.com/explorer/?signup=535muk17m5))\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "IXrVZs-97HS6",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Setup your API key"
      ]
    },
    {
      "metadata": {
        "id": "1nRuWjek7J-J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2b20f8ec-2d09-469b-f692-2da3eb9387ef"
      },
      "cell_type": "code",
      "source": [
        "# Set this with your own Planet API key. \n",
        "PLANET_API_KEY='yourPlanetAPIKeyHere'\n",
        "!echo $PLANET_API_KEY"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "yourPlanetAPIKeyHere\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "P0Ro5VRHjpUl",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Setup environment\n"
      ]
    },
    {
      "metadata": {
        "id": "YmQ7oDr8AKU2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "03e42ef6-e308-47f7-dc2d-b46853da0a0a"
      },
      "cell_type": "code",
      "source": [
        "!pip install tqdm\n",
        "!mkdir -p output\n",
        "!ls"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages\n",
            "datalab  output\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "SYhucG79AONh",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Choose an Area of Interest (AOI)\n",
        "This is the fun part. Select your own AOI [here](http://geojson.io/#map=2/20.0/0.0) and then copy/paste the .json info into this notebook. Of course you can use the example below which is of a small farm in Brentwood CA.\n",
        "\n",
        "**NOTE**: If you have a free Planet account, then your API key will only let you download imagery from California at this time"
      ]
    },
    {
      "metadata": {
        "id": "bc3Ug9A3y4kO",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Some farm in Brentwood area. AOI size about the same as a well pad\n",
        "geojson_geometry={\n",
        "        \"type\": \"Polygon\",\n",
        "        \"coordinates\": [\n",
        "          [\n",
        "            [\n",
        "              -121.67504310607909,\n",
        "              37.89233101674256\n",
        "            ],\n",
        "            [\n",
        "              -121.67195320129395,\n",
        "              37.89233101674256\n",
        "            ],\n",
        "            [\n",
        "              -121.67195320129395,\n",
        "              37.89443076208804\n",
        "            ],\n",
        "            [\n",
        "              -121.67504310607909,\n",
        "              37.89443076208804\n",
        "            ],\n",
        "            [\n",
        "              -121.67504310607909,\n",
        "              37.89233101674256\n",
        "            ]\n",
        "          ]\n",
        "        ]\n",
        "      }\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "5b8Z2-4rbZoP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Create Filters\n",
        "\n",
        "Now let's set up some **filters** to further constrain our Data API search. Here we choose to select imagery within a certain date range, and with little cloud cover to interfere. "
      ]
    },
    {
      "metadata": {
        "id": "SWmiLjm7bU4X",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Get images that overlap with our AOI \n",
        "geometry_filter = {\n",
        "  \"type\": \"GeometryFilter\",\n",
        "  \"field_name\": \"geometry\",\n",
        "  \"config\": geojson_geometry\n",
        "}\n",
        "\n",
        "# Get images acquired within a date range\n",
        "date_range_filter = {\n",
        "  \"type\": \"DateRangeFilter\",\n",
        "  \"field_name\": \"acquired\",\n",
        "  \"config\": {\n",
        "    \"gte\": \"2011-08-31T00:00:00.000Z\",\n",
        "    \"lte\": \"2018-02-01T00:00:00.000Z\"\n",
        "  }\n",
        "}\n",
        "\n",
        "# Only get images which have <10% cloud coverage\n",
        "cloud_cover_filter = {\n",
        "  \"type\": \"RangeFilter\",\n",
        "  \"field_name\": \"cloud_cover\",\n",
        "  \"config\": {\n",
        "    \"lte\": 0.1\n",
        "  }\n",
        "}\n",
        "\n",
        "\n",
        "# Combine our geo, date, cloud filters\n",
        "combined_filter = {\n",
        "  \"type\": \"AndFilter\",\n",
        "  \"config\": [geometry_filter,cloud_cover_filter,date_range_filter]\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "p-Q5KdgnblHX",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Searching and Clipping the data\n",
        "Now we send a post request via the API to retrieve the results of all images that fall within our filter criteria.\n"
      ]
    },
    {
      "metadata": {
        "id": "lS42NmThbupW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1550
        },
        "outputId": "ccef807c-cd93-4a11-9d83-29e1b0a14ea7"
      },
      "cell_type": "code",
      "source": [
        "import os\n",
        "import json\n",
        "import requests\n",
        "from requests.auth import HTTPBasicAuth\n",
        "\n",
        "# API request object\n",
        "search_request = {\n",
        "    \"interval\": \"day\",\n",
        "    \"item_types\": [\"PSScene4Band\"],\n",
        "    \"filter\": combined_filter\n",
        "}\n",
        "\n",
        "# fire off the POST request\n",
        "search_result = \\\n",
        "  requests.post(\n",
        "    'https://api.planet.com/data/v1/quick-search',\n",
        "    auth=HTTPBasicAuth(PLANET_API_KEY, ''),\n",
        "    json=search_request)\n",
        "\n",
        "print(json.dumps(search_result.json(), indent=2)[0:3500][:])"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{\n",
            "  \"_links\": {\n",
            "    \"_first\": \"https://api.planet.com/data/v1/searches/93dff5cde2df420b9ae77543d20c7692/results?_page=eyJxdWVyeV9wYXJhbXMiOiB7fSwgInNvcnRfcHJldiI6IGZhbHNlLCAicGFnZV9zaXplIjogMjUwLCAic29ydF9ieSI6ICJwdWJsaXNoZWQiLCAic29ydF9zdGFydCI6IG51bGwsICJzb3J0X2xhc3RfaWQiOiBudWxsLCAic29ydF9kZXNjIjogdHJ1ZX0%3D\",\n",
            "    \"_next\": \"https://api.planet.com/data/v1/searches/93dff5cde2df420b9ae77543d20c7692/results?_page=eyJxdWVyeV9wYXJhbXMiOiB7fSwgInNvcnRfcHJldiI6IGZhbHNlLCAicGFnZV9zaXplIjogMjUwLCAic29ydF9ieSI6ICJwdWJsaXNoZWQiLCAic29ydF9zdGFydCI6ICIyMDE2LTA5LTE0VDE5OjAyOjI3LjAwMDAwMFoiLCAic29ydF9sYXN0X2lkIjogIjIwMTYwODMxXzE4MDIzN18wZTBlIiwgInNvcnRfZGVzYyI6IHRydWV9\",\n",
            "    \"_self\": \"https://api.planet.com/data/v1/searches/93dff5cde2df420b9ae77543d20c7692/results?_page=eyJxdWVyeV9wYXJhbXMiOiB7fSwgInNvcnRfcHJldiI6IGZhbHNlLCAicGFnZV9zaXplIjogMjUwLCAic29ydF9ieSI6ICJwdWJsaXNoZWQiLCAic29ydF9zdGFydCI6IG51bGwsICJzb3J0X2xhc3RfaWQiOiBudWxsLCAic29ydF9kZXNjIjogdHJ1ZX0%3D\"\n",
            "  },\n",
            "  \"features\": [\n",
            "    {\n",
            "      \"_links\": {\n",
            "        \"_self\": \"https://api.planet.com/data/v1/item-types/PSScene4Band/items/20180128_181615_1040\",\n",
            "        \"assets\": \"https://api.planet.com/data/v1/item-types/PSScene4Band/items/20180128_181615_1040/assets/\",\n",
            "        \"thumbnail\": \"https://api.planet.com/data/v1/item-types/PSScene4Band/items/20180128_181615_1040/thumb\"\n",
            "      },\n",
            "      \"_permissions\": [\n",
            "        \"assets.analytic:download\",\n",
            "        \"assets.analytic_xml:download\",\n",
            "        \"assets.analytic_sr:download\",\n",
            "        \"assets.udm:download\"\n",
            "      ],\n",
            "      \"geometry\": {\n",
            "        \"coordinates\": [\n",
            "          [\n",
            "            [\n",
            "              -121.88470619059756,\n",
            "              37.960533515013196\n",
            "            ],\n",
            "            [\n",
            "              -121.60127084812474,\n",
            "              37.91132961686346\n",
            "            ],\n",
            "            [\n",
            "              -121.62147706778691,\n",
            "              37.83827004518404\n",
            "            ],\n",
            "            [\n",
            "              -121.904977905578,\n",
            "              37.88764086646912\n",
            "            ],\n",
            "            [\n",
            "              -121.8890671115725,\n",
            "              37.94668989110159\n",
            "            ],\n",
            "            [\n",
            "              -121.88844377847953,\n",
            "              37.94658127889372\n",
            "            ],\n",
            "            [\n",
            "              -121.88470619059756,\n",
            "              37.960533515013196\n",
            "            ]\n",
            "          ]\n",
            "        ],\n",
            "        \"type\": \"Polygon\"\n",
            "      },\n",
            "      \"id\": \"20180128_181615_1040\",\n",
            "      \"properties\": {\n",
            "        \"acquired\": \"2018-01-28T18:16:15.72552Z\",\n",
            "        \"anomalous_pixels\": 0,\n",
            "        \"cloud_cover\": 0,\n",
            "        \"columns\": 8890,\n",
            "        \"epsg_code\": 32610,\n",
            "        \"ground_control\": true,\n",
            "        \"gsd\": 3.9,\n",
            "        \"instrument\": \"PS2\",\n",
            "        \"item_type\": \"PSScene4Band\",\n",
            "        \"origin_x\": 596286,\n",
            "        \"origin_y\": 4202025,\n",
            "        \"pixel_resolution\": 3,\n",
            "        \"provider\": \"planetscope\",\n",
            "        \"published\": \"2018-01-28T22:06:23Z\",\n",
            "        \"quality_category\": \"standard\",\n",
            "        \"rows\": 4420,\n",
            "        \"satellite_id\": \"1040\",\n",
            "        \"strip_id\": \"1119100\",\n",
            "        \"sun_azimuth\": 146.7,\n",
            "        \"sun_elevation\": 27,\n",
            "        \"updated\": \"2018-01-29T07:39:07Z\",\n",
            "        \"usable_data\": 0,\n",
            "        \"view_angle\": 0.8\n",
            "      },\n",
            "      \"type\": \"Feature\"\n",
            "    },\n",
            "    {\n",
            "      \"_links\": {\n",
            "        \"_self\": \"https://api.planet.com/data/v1/item-types/PSScene4Band/items/20180121_181515_1035\",\n",
            "        \"assets\": \"https://api.planet.com/data/v1/item-types/PSScene4Band/items/20180121_181515_1035/assets/\",\n",
            "        \"thumbnail\": \"https://api.planet.com/data/v1/item-types/PSScene4Band/items/20180121_181515_1035/thumb\"\n",
            "      },\n",
            "      \"_permiss\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "b0tItA4db4bl",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Our search returns metadata for all of the images within our AOI that match our date range and cloud coverage filters. It looks like there are multiple images here; let's extract a list of just the image IDs:"
      ]
    },
    {
      "metadata": {
        "id": "yvBkY29Wb6TW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "dc1af971-43b5-44a5-a2a6-888f35043f12"
      },
      "cell_type": "code",
      "source": [
        "# extract image IDs only\n",
        "image_ids = [feature['id'] for feature in search_result.json()['features']]\n",
        "item_typeList = [feature['properties']['item_type'] for feature in search_result.json()['features']]\n",
        "\n",
        "print(image_ids)"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['20180128_181615_1040', '20180121_181515_1035', '20180120_181551_103b', '20171228_181501_1042', '20171220_181435_0f34', '20171220_181434_0f34', '20171217_181354_0f22', '20171216_181428_103b', '20171216_181427_103b', '20171216_181343_0f43', '20171214_181613_0e14', '20171213_181347_1009', '20171212_181402_0f31', '20171210_181223_1015', '20171206_181402_1011', '20171205_181251_1025', '20171204_185251_0f32', '20171129_181333_0f42', '20171129_181219_0e26', '20171129_181218_0e26', '20171128_185400_0f36', '20171127_181245_1030', '20171119_181240_1013', '20171117_181248_102c', '20171106_181308_0f25', '20171101_181255_102e', '20171101_181254_102e', '20171029_181144_0f15', '20171029_181022_0f42', '20170824_190605_1054', '20171028_185724_1052', '20171028_185723_1052', '20171028_181239_0f34', '20171027_185829_104c', '20170925_190249_101c', '20170908_190429_0f3b', '20171025_181143_0f3f', '20171025_181142_0f3f', '20171025_185832_1053', '20171025_185831_1053', '20170906_190408_0f2b', '20171023_181239_0f4e', '20171023_181238_0f4e', '20171021_181058_0f35', '20170908_180808_1038', '20170908_180807_1038', '20171018_171924_0c54', '20171018_181324_1031', '20171014_180927_1027', '20171012_181156_0f35', '20171006_190128_104f', '20171004_180940_1007', '20171001_180915_1006', '20171001_180914_1006', '20170908_181116_1034', '20170927_181052_101b', '20170926_180958_1027', '20170926_180957_1027', '20170925_181004_102d', '20170910_181106_1023', '20170910_181105_1023', '20170923_181347_0e0f', '20170923_180908_0f31', '20170922_181044_1004', '20170922_190235_0f21', '20170818_190638_1053', '20170818_190637_1053', '20170802_190928_1020', '20170802_190927_1020', '20170909_180825_101b', '20170909_180824_101b', '20170909_180913_0f43', '20170901_180919_1010', '20170901_180921_1008', '20170901_180921_1010', '20170830_190549_0f54', '20170829_180858_103e', '20170828_180812_101f', '20170827_180928_0f12', '20170823_180834_0f18', '20170822_181351_0e1f', '20170822_180918_103d', '20170821_180933_1040', '20170820_180648_1039', '20170819_180643_1029', '20170818_180757_1022', '20170815_185125_0c38', '20170814_180738_100c', '20170813_180859_0f31', '20170813_180900_0f31', '20170812_180941_1004', '20170802_180628_100a', '20170808_180855_0f43', '20170807_180742_1038', '20170806_180736_1007', '20170731_180752_0f18', '20170731_180529_0f1b', '20170730_180746_0f17', '20170729_180847_1044', '20170729_180846_1044', '20170729_181150_0e2f', '20170721_180632_103d', '20170728_180706_1030', '20170728_180826_1039', '20170728_180825_1039', '20170727_180739_1002', '20170725_180823_0f10', '20170725_180704_0f34', '20170725_180703_0f34', '20170722_180610_100c', '20170722_180609_100c', '20170722_181129_0e30', '20170720_181220_0e0f', '20170720_180659_1006', '20170720_180658_1006', '20170720_180659_102e', '20170715_180738_1027', '20170714_180815_1007', '20170714_180456_1028', '20170714_180728_1030', '20170713_180631_1001', '20170713_180630_1001', '20170707_180705_0f17', '20170709_180708_100e', '20170706_181047_0e0f', '20170704_180757_102f', '20170703_180657_0f22', '20170702_180747_0f10', '20170702_180746_0f10', '20170702_180543_0f34', '20170702_181233_0e20', '20170701_180635_101e', '20170701_180602_1031', '20170630_180712_1018', '20170630_180629_1030', '20170630_180628_1030', '20170630_180436_1028', '20170629_180537_0f35', '20170629_180626_101f', '20170629_180625_101f', '20170628_180631_0f51', '20170628_180630_0f51', '20170625_180613_102d', '20170625_181129_0e0e', '20170624_180542_0f18', '20170623_180608_103a', '20170622_180624_1038', '20170622_180623_1038', '20170619_180604_0f42', '20170619_180625_0f12', '20170615_180541_0f35', '20170613_180508_1044', '20170610_180544_0f18', '20170601_180432_0f35', '20170529_180458_1007', '20170520_180430_1036', '20170520_180431_1036', '20170521_180359_1013', '20170510_180351_1008', '20170521_180400_1004', '20170517_180451_103b', '20170515_181129_0e0f', '20170515_180426_1005', '20170513_181055_0e0d', '20170507_180400_102e', '20170504_180313_102f', '20170427_180307_1019', '20170428_180926_0e30', '20170423_180322_102e', '20170420_181005_0e16', '20151213_224824_0c73', '20170415_180233_1025', '20161104_180541_0e16', '20170404_180925_0e30', '20170402_180908_0e0f', '20170402_180907_0e0f', '20170312_180822_0e0e', '20170303_180814_0e26', '20170223_180850_0e20', '20170121_180628_0e30', '20170116_180722_0e30', '20161109_180553_0e16', '20161105_180509_0e1f', '20161105_180508_0e1f', '20161129_180545_0e16', '20161124_180553_0e16', '20161122_180514_0e30', '20161114_180558_0e16', '20160809_180208_0e0d', '20151231_223424_0c73', '20160720_142327_0c82', '20160720_142328_0c82', '20160717_153447_1_0c65', '20160714_164506_0c43', '20160714_164505_0c43', '20161017_180331_0e19', '20160825_165903_1_0c24', '20160816_203146_0c24', '20160816_203145_0c24', '20160819_192108_1_0c24', '20160819_192107_0c24', '20161012_180400_0e30', '20160815_180239_0e30', '20160816_203214_0c82', '20160809_180224_0e0f', '20160809_180246_0e16', '20160803_233804_1_0c74', '20161001_180338_0e14', '20161001_180337_0e14', '20160930_180402_0e0f', '20160828_154740_0c24', '20160914_155725_1_0c24', '20160831_143842_0c79', '20160831_180237_0e0e']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "TgZIeqPX6roB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Since this wont be the last time we'll try to retrieve an image from Planet, lets first build a class to organzie the tools we're going to be using a lot. We'll make this class and add a few functions that we'll use next."
      ]
    },
    {
      "metadata": {
        "id": "pHnGYODT5Pbz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import time\n",
        "from tqdm import tqdm\n",
        "import zipfile\n",
        "\n",
        "# Define a new class and functions for image retrieval\n",
        "class image_retrieval(object):\n",
        "    def __init__(self, api_key):\n",
        "        self.api_key=api_key\n",
        "        return\n",
        "\n",
        "    def check_assets(self,item_type,scene_id):\n",
        "        # Check to see what assets exist for this item\n",
        "        id0_url = 'https://api.planet.com/data/v1/item-types/{}/items/{}/assets'.format(item_type, scene_id)\n",
        "        # Returns JSON metadata for assets in this ID. Learn more: planet.com/docs/reference/data-api/items-assets/#asset\n",
        "        result = \\\n",
        "          requests.get(\n",
        "            id0_url,\n",
        "            auth=HTTPBasicAuth(self.api_key, '')\n",
        "          )\n",
        "        # List of asset types available for this particular satellite image\n",
        "        return result.json().keys()\n",
        "      \n",
        "      \n",
        "    def request_clip(self,item_type,scene_id,asset_type):\n",
        "        # Construct clip API payload\n",
        "        clip_payload = {\n",
        "            'aoi': geojson_geometry,\n",
        "            'targets': [\n",
        "              {\n",
        "                'item_id': scene_id,\n",
        "                'item_type': item_type,\n",
        "                'asset_type': asset_type\n",
        "              }\n",
        "            ]\n",
        "        }\n",
        "        # Request clip of scene (This will take some time to complete)\n",
        "        request = requests.post('https://api.planet.com/compute/ops/clips/v1', auth=(self.api_key, ''), json=clip_payload)\n",
        "        print(request.json())\n",
        "        clip_url = request.json()['_links']['_self']\n",
        "        # Poll API to monitor clip status. Once finished, download and upzip the scene\n",
        "        clip_succeeded = False\n",
        "        while not clip_succeeded:\n",
        "          # Poll API\n",
        "            check_state_request = requests.get(clip_url, auth=(PLANET_API_KEY, ''))\n",
        "            # If clipping process succeeded , we are done\n",
        "            if check_state_request.json()['state'] == 'succeeded':\n",
        "                clip_download_url = check_state_request.json()['_links']['results'][0]\n",
        "                clip_succeeded = True\n",
        "                print(\"Clip of scene succeeded and is ready to download\") \n",
        "            # Still activating. Wait 2 second and check again.\n",
        "            else:\n",
        "                print(\"...Still waiting for clipping to complete...\")\n",
        "                time.sleep(2)\n",
        "        return clip_download_url"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2PEbtM_57Bq4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Since we just want a single image, and this is only a demonstration, for our purposes here we can arbitrarily select an image in that list. Let's do that, and get the asset list available for that image using the function we just wrote:"
      ]
    },
    {
      "metadata": {
        "id": "UcEzRN1v7Hl5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ac4cb279-cbf5-4e67-ec7f-91b816176a94"
      },
      "cell_type": "code",
      "source": [
        "# Pick an item (say the first one)\n",
        "ii=50\n",
        "scene_id = image_ids[ii]\n",
        "item_type=item_typeList[ii]\n",
        "\n",
        "# Initialize image retrieval object\n",
        "imgRetObj=image_retrieval(PLANET_API_KEY)\n",
        "jsonList=imgRetObj.check_assets(item_type,scene_id)\n",
        "print(jsonList)"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['analytic', 'analytic_sr', 'analytic_xml', 'udm'])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "N0YPKnijcC4q",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Clip and Download the chosen asset\n",
        "We're interested in looking at the **analytic_sr** (surface reflectance) asset, which has all four frequency bands in it, and is adjusted for atmospheric conditions (read more about this [here](https://assets.planet.com/marketing/PDF/Planet_Surface_Reflectance_Technical_White_Paper.pdf)). We'll clip the AOI out of the asset from the seleced image ID, then \"activate\" it."
      ]
    },
    {
      "metadata": {
        "id": "bZLlhfyF9b_D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        },
        "outputId": "176c168e-ca2b-41e9-8bab-bb9a667d2d67"
      },
      "cell_type": "code",
      "source": [
        "asset_type = 'analytic_sr'\n",
        "clip_download_url=imgRetObj.request_clip(item_type,scene_id,asset_type)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-c8bbe7789503>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0masset_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'analytic_sr'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mclip_download_url\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimgRetObj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest_clip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem_type\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mscene_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0masset_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'imgRetObj' is not defined"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "RRuAJfvlcMOk",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Last, we download the .zip file and extract the .tif image. "
      ]
    },
    {
      "metadata": {
        "id": "E5TM5cSR_rT1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "a2fec22f-34a0-4039-96ff-565e868b49ec"
      },
      "cell_type": "code",
      "source": [
        "def download_image(clip_download_url,local_folder):\n",
        "    # Download clip\n",
        "    response = requests.get(clip_download_url, stream=True)\n",
        "    zipname=local_folder + scene_id + '.zip'\n",
        "    with open(zipname, \"wb\") as handle:\n",
        "        for data in tqdm(response.iter_content()):\n",
        "            handle.write(data)\n",
        "    # Extract the image file\n",
        "    ziped_item = zipfile.ZipFile(zipname,'r')\n",
        "    imagename=ziped_item.namelist()[0]\n",
        "    outputpath=os.getcwd()+'/'+local_folder\n",
        "    zf = zipfile.ZipFile(zipname)\n",
        "    zf.extract(imagename,outputpath)    \n",
        "    # Delete zip file\n",
        "    os.remove(local_folder + scene_id + '.zip')\n",
        "    print('\\nDownloaded clips located in: %s' %(outputpath))\n",
        "    return\n",
        "  \n",
        " \n",
        "download_image(clip_download_url,'output/')"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "66634it [00:00, 101583.84it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Downloaded clips located in: /content/output/\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "iPjb4wzU9gaS",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "---\n",
        "## Plotting\n",
        "From here, we can load the .tif image and split it into the respective frequency band components. According to the Planet sensor documentation, these bands are:\n",
        "\n",
        "\n",
        "*   Blue: **455 - 515** nm\n",
        "*   Green: **500 - 590** nm\n",
        "*   Red: **590 - 670** nm\n",
        "*   NIR: **780 - 860** nm\n",
        "\n",
        "Then we can plot the results! \n"
      ]
    },
    {
      "metadata": {
        "id": "ctnh-GBzzKlY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1066
        },
        "outputId": "19afc7b8-9120-47f4-d8d4-5dc03d27ba2f"
      },
      "cell_type": "code",
      "source": [
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Reading the downloaded image into an array\n",
        "imagefile=outputpath+imagename\n",
        "print(\"Reading in file %s\" % (imagefile))\n",
        "image = cv2.imread(imagefile, -1)\n",
        "red=image[:,:,0]\n",
        "green=image[:,:,1]\n",
        "blue=image[:,:,2]\n",
        "nir=image[:,:,3]\n",
        "\n",
        "# Plotting the different frequency bands in the array\n",
        "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize=(16, 16))\n",
        "\n",
        "im = ax1.imshow(red, extent=[0, 200, 0, 200], cmap='Reds')\n",
        "fig.colorbar(im, ax=ax1, shrink=0.8)\n",
        "ax1.set_title(\"Band 1 (Red)\")\n",
        "ax1.grid(False)\n",
        "\n",
        "im2 = ax2.imshow(green, extent=[0, 200, 0, 200], cmap='Greens')\n",
        "fig.colorbar(im2, ax=ax2, shrink=0.8)\n",
        "ax2.set_title(\"Band 2 (Green)\")\n",
        "ax2.grid(False)\n",
        "\n",
        "im3 = ax3.imshow(blue, extent=[0, 200, 0, 200], cmap='Blues')\n",
        "fig.colorbar(im3, ax=ax3, shrink=0.8)\n",
        "ax3.set_title(\"Band 3 (Blue)\")\n",
        "ax3.grid(False)\n",
        "\n",
        "im4 = ax4.imshow(nir, extent=[0, 200, 0, 200], cmap='hot')\n",
        "fig.colorbar(im4, ax=ax4, shrink=0.8)\n",
        "ax4.set_title(\"Band 4 (Near Infared)\")\n",
        "ax4.grid(False)\n",
        "\n",
        "fig.tight_layout()\n"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading in file /content/output/20171006_190128_104f_3B_AnalyticMS_SR_clip.tif\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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i6dKlaG9vx+7du6u5KSIiIiLYsWMHTj311MHXzz77LFavXg3gcAjuiy++iKlT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\nJT9cmTZtmrss4XXXXTfsDomIiMj/qqebkkrQfYyIiEj1lPV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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f6a963097f0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "4b507ed2AktJ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Ok, great. But what does a \"normal\" image look like?\n",
        "Sometimes its hard to tell what we're looking at when we analyze the bands of a hyperspectral image. So let's combine the red, green, and blue bands back into a tpyical RGB image."
      ]
    },
    {
      "metadata": {
        "id": "AEFV6t8SBAY5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "outputId": "a43985ae-8a14-4050-99e2-231e91b6bae4"
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# Stack and normalize the RGB values to 256\n",
        "rgb=np.dstack((red,green,blue))\n",
        "rgb=255.99*rgb/np.amax(rgb)\n",
        "rgb_uint8 = (rgb) .astype(np.uint8)\n",
        "\n",
        "# Plot the figure\n",
        "plt.imshow(rgb_uint8,interpolation='nearest')\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Sy7Nd1nhH06Q2cyl8uqw/39xnMdPI1UwbZ2E//circME206R1f67yVI7SqSnR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\nF3WukGUGAoFAIImY8AOBQGAgiAk/EAgEBoIN4fC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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f6a96375278>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}