BingImageSearch/BingImageSearch.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gathering Images for Image Recogntion with Bing Image Search API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Install/Import necessary packages\n",
    "The Bing Image Search has its own Python SDK, what is very handy. But if you want you can also make use of the classic HTTP request methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install azure-cognitiveservices-search-imagesearch\n",
    "from azure.cognitiveservices.search.imagesearch import ImageSearchClient\n",
    "from msrest.authentication import CognitiveServicesCredentials\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Configure subscription key + endpoint\n",
    "Subscription Key + Endpoint can be found in your Azure Portal after you create a Cognitive Service resource. You don't need to look for a specific Bing Search Cognitive Services. Since a while Bing Search and many more cognitive services have been implemented in 1 resource. Which means 1 endpoint and 1 key to use them all.\n",
    "\n",
    "I also added the search term here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "subscription_key = \"[YourKeyHere]\"\n",
    "subscription_endpoint = \"[YourEndpointHere]\"\n",
    "search_term = \"apples\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Configure Client\n",
    "So in this step we create 2 new objects. One is the 'credentials' which contains the subscription key that we just configured. And the other is the client that we will use to make the call. Last mentioned also needs the endpoint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "credentials = CognitiveServicesCredentials(subscription_key)\n",
    "client = ImageSearchClient(endpoint=subscription_endpoint, credentials=credentials)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "### Step 4: Search and gather the results\n",
    "Searching for items is very easy. Just by making use of the client object and the search function that has a bunch of variables that you can configure (like color, size, ...) In this case we only configure what we are search for and how many results we want to get back."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Search for images\n",
    "image_results = client.images.search(query=search_term,count=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5: Convert to dataframe\n",
    "The result we receive from the API is a list of objects. This might we handy if we are in an c# application or so. But in this case we want to work with clear data. So with below functionality we convert the data to a dataframe.\n",
    "You will notice we get quite some information back. Some information is still stored in an object like ''_type'', but in this case we don't need this data. So we will leave it like this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert to dataframe\n",
    "df = pd.DataFrame([x.as_dict() for x in image_results.value])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
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       "\n",
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       "1  https://www.bing.com/images/search?view=detail...   1200  \n",
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      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6: Generate new filenames\n",
    "Since we get data back from many different websites, there is a chance that some filenames might be totally the same.\n",
    "Plus some files might not have an extension within the URI, because of routing or image generation.\n",
    "To fix this we add a function that guesses the MimeType and based on that looks up the extension that belongs to it.\n",
    "This in combination with a GUID that is generated we are sure we have a unique filename."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mimetypes\n",
    "import uuid\n",
    "\n",
    "def getFileName(contentUrl):\n",
    "    mt = mimetypes.guess_type(contentUrl)\n",
    "    if mt[0] != None :\n",
    "        ext = mimetypes.guess_extension(mt[0])\n",
    "        return str(uuid.uuid1()) + ext\n",
    "    else:\n",
    "        return \"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By applying above funtion to each row we can add an extra column to the dataframe that contains the newly generated fileName.\n",
    "NOTE: you might have noticed that the function can return an emtpy string. This is only the case when it can't figure out the MimeType. Those results we filter out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['fileName'] = df.apply(lambda x: getFileName(x.content_url), axis=1)\n",
    "df = df[df['fileName'] != \"\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
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       "      <td>https://www.tasteofhome.com/wp-content/uploads...</td>\n",
       "      <td>2018-08-15T00:12:00.0000000Z</td>\n",
       "      <td>jpeg</td>\n",
       "      <td>1200</td>\n",
       "      <td>https://www.tasteofhome.com/collection/new-typ...</td>\n",
       "      <td>https://www.tasteofhome.com/collection/new-typ...</td>\n",
       "      <td>EC1EBFE716FD1033C0E0983931FEC881E8A1177D</td>\n",
       "      <td>ccid_7jSg14s4*mid_EC1EBFE716FD1033C0E0983931FE...</td>\n",
       "      <td>{}</td>\n",
       "      <td>15 New Types of Apples You Should Be Buying | ...</td>\n",
       "      <td>{'_type': 'ImageObject', 'width': 474, 'height...</td>\n",
       "      <td>https://tse2.mm.bing.net/th?id=OIP.7jSg14s46gp...</td>\n",
       "      <td>https://www.bing.com/images/search?view=detail...</td>\n",
       "      <td>1200</td>\n",
       "      <td>11c6e60a-3197-11ea-87d5-000d3aaa7d6e.jpe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ImageObject</td>\n",
       "      <td>7E0F24</td>\n",
       "      <td>4462721 B</td>\n",
       "      <td>http://www.michiganapples.com/portals/0/MAC%20...</td>\n",
       "      <td>2019-11-15T14:57:00.0000000Z</td>\n",
       "      <td>jpeg</td>\n",
       "      <td>3738</td>\n",
       "      <td>www.michiganapples.com/About/Varieties</td>\n",
       "      <td>http://www.michiganapples.com/About/Varieties</td>\n",
       "      <td>753A53E3C6A184B4C55A074E275F120D20D4914C</td>\n",
       "      <td>ccid_3X+TRdGR*mid_753A53E3C6A184B4C55A074E275F...</td>\n",
       "      <td>{}</td>\n",
       "      <td>Michigan Apple Varieties | Michigan Apple Comm...</td>\n",
       "      <td>{'_type': 'ImageObject', 'width': 474, 'height...</td>\n",
       "      <td>https://tse2.mm.bing.net/th?id=OIP.3X-TRdGReOQ...</td>\n",
       "      <td>https://www.bing.com/images/search?view=detail...</td>\n",
       "      <td>3738</td>\n",
       "      <td>11c6e8e4-3197-11ea-87d5-000d3aaa7d6e.jpe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ImageObject</td>\n",
       "      <td>4F1E0F</td>\n",
       "      <td>419131 B</td>\n",
       "      <td>http://www.flinchbaughsorchard.com/wp-content/...</td>\n",
       "      <td>2019-11-01T04:51:00.0000000Z</td>\n",
       "      <td>jpeg</td>\n",
       "      <td>1691</td>\n",
       "      <td>www.flinchbaughsorchard.com/apple-varieties</td>\n",
       "      <td>http://www.flinchbaughsorchard.com/apple-varie...</td>\n",
       "      <td>7480E3C7DDE4776D891D003001872D954786935C</td>\n",
       "      <td>ccid_GBWUKwPQ*mid_7480E3C7DDE4776D891D00300187...</td>\n",
       "      <td>{}</td>\n",
       "      <td>Apple Varieties | Flinchbaugh's Orchard &amp; Farm...</td>\n",
       "      <td>{'_type': 'ImageObject', 'width': 474, 'height...</td>\n",
       "      <td>https://tse1.mm.bing.net/th?id=OIP.GBWUKwPQyNo...</td>\n",
       "      <td>https://www.bing.com/images/search?view=detail...</td>\n",
       "      <td>1800</td>\n",
       "      <td>11c6ebaa-3197-11ea-87d5-000d3aaa7d6e.jpe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         _type accent_color content_size  \\\n",
       "0  ImageObject       9D2E39     224643 B   \n",
       "1  ImageObject       4F300B     161831 B   \n",
       "2  ImageObject       C47807     172706 B   \n",
       "3  ImageObject       7E0F24    4462721 B   \n",
       "4  ImageObject       4F1E0F     419131 B   \n",
       "\n",
       "                                         content_url  \\\n",
       "0  https://upload.wikimedia.org/wikipedia/commons...   \n",
       "1  https://upload.wikimedia.org/wikipedia/commons...   \n",
       "2  https://www.tasteofhome.com/wp-content/uploads...   \n",
       "3  http://www.michiganapples.com/portals/0/MAC%20...   \n",
       "4  http://www.flinchbaughsorchard.com/wp-content/...   \n",
       "\n",
       "                 date_published encoding_format  height  \\\n",
       "0  2019-09-14T23:40:00.0000000Z            jpeg    1200   \n",
       "1  2019-12-21T20:48:00.0000000Z            jpeg    1083   \n",
       "2  2018-08-15T00:12:00.0000000Z            jpeg    1200   \n",
       "3  2019-11-15T14:57:00.0000000Z            jpeg    3738   \n",
       "4  2019-11-01T04:51:00.0000000Z            jpeg    1691   \n",
       "\n",
       "                               host_page_display_url  \\\n",
       "0                https://en.wikipedia.org/wiki/Apple   \n",
       "1           https://en.wikipedia.org/wiki/Honeycrisp   \n",
       "2  https://www.tasteofhome.com/collection/new-typ...   \n",
       "3             www.michiganapples.com/About/Varieties   \n",
       "4        www.flinchbaughsorchard.com/apple-varieties   \n",
       "\n",
       "                                       host_page_url  \\\n",
       "0                https://en.wikipedia.org/wiki/Apple   \n",
       "1           https://en.wikipedia.org/wiki/Honeycrisp   \n",
       "2  https://www.tasteofhome.com/collection/new-typ...   \n",
       "3      http://www.michiganapples.com/About/Varieties   \n",
       "4  http://www.flinchbaughsorchard.com/apple-varie...   \n",
       "\n",
       "                                   image_id  \\\n",
       "0  6AEAF1C3894ED7D563916634549ED00175C970D3   \n",
       "1  4531DA369849BECBA2980492B1F021A9EF15BB99   \n",
       "2  EC1EBFE716FD1033C0E0983931FEC881E8A1177D   \n",
       "3  753A53E3C6A184B4C55A074E275F120D20D4914C   \n",
       "4  7480E3C7DDE4776D891D003001872D954786935C   \n",
       "\n",
       "                                image_insights_token insights_metadata  \\\n",
       "0  ccid_pfp3ysAm*mid_6AEAF1C3894ED7D563916634549E...                {}   \n",
       "1  ccid_wPWDNJmR*mid_4531DA369849BECBA2980492B1F0...                {}   \n",
       "2  ccid_7jSg14s4*mid_EC1EBFE716FD1033C0E0983931FE...                {}   \n",
       "3  ccid_3X+TRdGR*mid_753A53E3C6A184B4C55A074E275F...                {}   \n",
       "4  ccid_GBWUKwPQ*mid_7480E3C7DDE4776D891D00300187...                {}   \n",
       "\n",
       "                                                name  \\\n",
       "0                                  Apple - Wikipedia   \n",
       "1                             Honeycrisp - Wikipedia   \n",
       "2  15 New Types of Apples You Should Be Buying | ...   \n",
       "3  Michigan Apple Varieties | Michigan Apple Comm...   \n",
       "4  Apple Varieties | Flinchbaugh's Orchard & Farm...   \n",
       "\n",
       "                                           thumbnail  \\\n",
       "0  {'_type': 'ImageObject', 'width': 474, 'height...   \n",
       "1  {'_type': 'ImageObject', 'width': 474, 'height...   \n",
       "2  {'_type': 'ImageObject', 'width': 474, 'height...   \n",
       "3  {'_type': 'ImageObject', 'width': 474, 'height...   \n",
       "4  {'_type': 'ImageObject', 'width': 474, 'height...   \n",
       "\n",
       "                                       thumbnail_url  \\\n",
       "0  https://tse3.mm.bing.net/th?id=OIP.pfp3ysAmXA6...   \n",
       "1  https://tse1.mm.bing.net/th?id=OIP.wPWDNJmRmNP...   \n",
       "2  https://tse2.mm.bing.net/th?id=OIP.7jSg14s46gp...   \n",
       "3  https://tse2.mm.bing.net/th?id=OIP.3X-TRdGReOQ...   \n",
       "4  https://tse1.mm.bing.net/th?id=OIP.GBWUKwPQyNo...   \n",
       "\n",
       "                                      web_search_url  width  \\\n",
       "0  https://www.bing.com/images/search?view=detail...   1200   \n",
       "1  https://www.bing.com/images/search?view=detail...   1200   \n",
       "2  https://www.bing.com/images/search?view=detail...   1200   \n",
       "3  https://www.bing.com/images/search?view=detail...   3738   \n",
       "4  https://www.bing.com/images/search?view=detail...   1800   \n",
       "\n",
       "                                   fileName  \n",
       "0  11c6ddea-3197-11ea-87d5-000d3aaa7d6e.jpe  \n",
       "1  11c6e2ea-3197-11ea-87d5-000d3aaa7d6e.jpe  \n",
       "2  11c6e60a-3197-11ea-87d5-000d3aaa7d6e.jpe  \n",
       "3  11c6e8e4-3197-11ea-87d5-000d3aaa7d6e.jpe  \n",
       "4  11c6ebaa-3197-11ea-87d5-000d3aaa7d6e.jpe  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7: Create folder to save the images\n",
    "This is a folder on your local or cloud environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: wget in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (3.2)\n",
      "Directory  image_gathering/apples  already exists. Folder will be cleared\n"
     ]
    }
   ],
   "source": [
    "dirName = \"image_gathering\"\n",
    "os.mkdir(dirName)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8: Download images\n",
    "First we create a function that tries to download the file to the new destination with the new filename.\n",
    "Bing search might be outdated, so there are possibilities that certain downloads fails (Ex. NotFound, NotAuthenticated, ...)\n",
    "Therefore the function returns True or False depending if download was succesful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "import wget\n",
    "def downloadFile(dirName, contentUrl, fileName):\n",
    "    try:\n",
    "        wget.download(contentUrl, dirName + \"/\" + fileName)\n",
    "        return True\n",
    "    except:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By applying this function do every row, every result will be downloaded and we add an extra column that keeps track if it was successfull or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['fileDownloaded'] = df.apply(lambda x: downloadFile(dirName, x.content_url, x.fileName), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## THE END\n",
    "Its not that hard and quite fast to gather your own data based on results from Bing Search. Depending on what you want to realize you will need to run this script multiple times if you want images for different search terms. Or you can convert this script into a function that accepts an array of search strings.\n",
    "\n",
    "### Extra - Move data to datastore\n",
    "In case you are using Azure Machine Learning Studio, its a good idea to move your images to a datastore. That way other data scientists can also make use of it. Below code uploads the folder of images to the same folder on your datastore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import azureml.core\n",
    "from azureml.core import Workspace, Datastore\n",
    "\n",
    "ws = Workspace.from_config()\n",
    "\n",
    "datastore_name = 'workspaceblobstore'\n",
    "datastore = Datastore.get(ws, datastore_name)\n",
    "datastore.upload(dirName, dirName)"
   ]
  }
 ],
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