CV0101_labs_Module4_Training_Custom_Classifiers_with_Watson.ipynb

{"cells":[{"cell_type":"markdown","metadata":{},"source":["<a href=\"https://cognitiveclass.ai/\">\n","    <img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/CV0101/Logo/SNLogo.png\" width=\"200\" align=\"center\">\n","</a>\n"]},{"cell_type":"markdown","metadata":{},"source":["<h1>Lab - Training Custom Classifiers with IBM Watson Visual Recognition in Python</h1>\n","\n","<h2>Introduction</h2>\n","\n","## Objectives\n","\n","After completing this lab you will be able to:\n","\n","-   How to operate the Watson Visual Recognition API and OpenCV using the Python Programming Language\n","-   Understand the advantage of using the Watson Visual Recognition API over the Graphic User Interface on the Browser\n","-   How to automate the training, and testing of your Visual Recognition model.\n","\n","<p>In this lab you will be training a Visual Recognition model that classify different kinds of dogs by running python code.</p>\n"]},{"cell_type":"markdown","metadata":{},"source":["<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n","<font size=\"3\"><strong>Click on the links to go to the following sections:</strong></font>\n","<br>\n","<h2>Table of Contents</h2>\n","<ol>\n","    <li><a href=\"#ref1\">IBM Watson Package</a></li>\n","    <li><a href=\"#ref2\">Setting the API key for IBM Watson Visual Recognition</a></li>\n","    <li><a href=\"#ref3\">Training the Classifier</a></li>\n","    <li><a href=\"#ref4\">Testing the Classifier</a></li>\n","    <li><a href=\"#ref5\">Exercises</a></li>\n","</ol>    \n","</div>\n"]},{"cell_type":"markdown","metadata":{},"source":["<a id=\"ref1\"></a>\n","\n","<h2>IBM Watson Package</h2>\n","In order to run this lab we need to import the following package.\n","<ul>\n","    <li>IBM Watson: which allows access to the Watson Visual Recognition API</li>\n","</ul>\n","The code below will install IBM Watson. \n","\n","To run, click on the code cell below and press \"shift + enter\".\n","\n","<b>NOTE - The Watson Developer Cloud Package has been deprecated and has been replaced by the IBM Watson Package </b>\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["!pip install --upgrade ibm-watson"]},{"cell_type":"markdown","metadata":{},"source":["<h3>Goal of this lab:</h3>\n"]},{"cell_type":"markdown","metadata":{},"source":["<p>In this lab, we will be creating a completely new image classifier using training images. We will train a custom classifier to identify between three different dog breeds (Golden Retriever, Beagle and Husky).</p>\n"]},{"cell_type":"markdown","metadata":{},"source":["<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/CV0101/Images/dog-breed.png\" width=\"480\"/>\n"]},{"cell_type":"markdown","metadata":{},"source":["<a id=\"ref2\"></a>\n","\n","<h2>Setting the API key for IBM Watson Visual Recognition</h2>\n","\n","<p>In order for you to use the IBM Watson Visual Recognition API, you will need the API key of the Visual Recognition instance that you have created in the previous sections.</p>\n","\n","<p>Log into your IBM Cloud Account with the following link.</p> <a href=\"https://cocl.us/CV0101EN_IBM_Cloud_Login\">https://cloud.ibm.com</a>\n","<ol>\n","    <li>Click on <b>Services</b></li>\n","    <li>Under Services, click on your Watson Visual Recognition Instance</li>\n","    <li>Copy the <b>API Key</b> and past it in the code cell below</li>\n","    <img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/CV0101/Images/API_Key.png\" width=\"680\">\n","    <li>Then press \"ctrl + enter\" to run the code cell.</li>\n","</ol>\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Paste your API key for IBM Watson Visual Recognition below:\n","my_apikey = 'Paste_your_API_key'"]},{"cell_type":"markdown","metadata":{},"source":["<h4>Initialize Watson Visual Recognition</h4>\n","Lets create your own Watson Visual Recognition instance, it will allow you to make calls to the Watson Visual Recognition API.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from ibm_watson import VisualRecognitionV3\n","from ibm_cloud_sdk_core.authenticators import IAMAuthenticator\n","authenticator = IAMAuthenticator(my_apikey)\n","\n","visrec = VisualRecognitionV3('2018-03-19', \n","                             authenticator=authenticator)\n"]},{"cell_type":"markdown","metadata":{},"source":["<p>We are going to train an Image Recognition model to classify different types of dog. The dataset that we are going to use are the zip files that we use below</p>\n"]},{"cell_type":"markdown","metadata":{},"source":["<ul>\n","    <li>beagle.zip</li>\n","    <li>husky.zip</li>\n","    <li>golden-retriever.zip</li>\n","</ul>\n"]},{"cell_type":"markdown","metadata":{},"source":["<a id=\"ref3\"></a>\n","\n","<h2>Training Classifier</h2>\n"]},{"cell_type":"markdown","metadata":{},"source":["<h4>Download the differerent breed of dog images as zip files</h4>\n","<p>We will use the <b>urlretrieve</b> method from the <b>urllib.request</b> library to download the dataset above.</p> \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import urllib.request\n","\n","# Downloading Beagle dataset\n","urllib.request.urlretrieve(\"http://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/CV0101/Dataset/Beagle.zip\", \n","                           \"beagle.zip\")\n","\n","# Downloading Husky dataset\n","urllib.request.urlretrieve(\"http://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/CV0101/Dataset/Husky.zip\", \n","                           \"husky.zip\")\n","\n","# Downloading Golden Retriever dataset\n","urllib.request.urlretrieve(\"http://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/CV0101/Dataset/GoldenRetriever.zip\", \n","                           \"goldenretriever.zip\") #note that we should remove any hyphens from the zip file name"]},{"cell_type":"markdown","metadata":{},"source":["<p>Lets train our Visual Recognition model to recognize the three breeds of dogs using the <b>create_classifier</b> method from the Watson Image Recognition API.</p>\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import json\n","with open('beagle.zip', 'rb') as beagle, \\\n","     open('goldenretriever.zip', 'rb') as gretriever, \\\n","     open('husky.zip', 'rb') as husky:\n","        response = visrec.create_classifier(name=\"dogbreedclassifier\",\n","                                        positive_examples={'beagle': beagle, \\\n","                                                           'goldenretriever': gretriever, \\\n","                                                           'husky': husky})\n","print(json.dumps(response.get_result(), indent=2))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["#lets grab the classifier id\n","classifier_id = response.get_result()[\"classifier_id\"]\n","classifier_id"]},{"cell_type":"markdown","metadata":{},"source":["<div style=\"background-color: #fcf2f2\">\n","    <h2>Note!</h2> \n","    <p>If you receive the following error.</p>\n","    <img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/CV0101/Images/Error-Code.png\" width=\"1080\">\n","    <p>It means that you have more than 1 Visual Recognition Instances running on your lite plan, and the lite plan only allows for no more than 2 Visual Recognition instances. So you might want to delete one of your custom classifier in your Watson Visual Recognition Instance.</p>\n","        <p>Log into your IBM Cloud Account with the following link.</p> <p><a href=\"https://cocl.us/CV0101EN_IBM_Cloud_Login\">https://cloud.ibm.com</a></p>\n","    <ol>\n","        <li>Click on <b>Services</b></li>\n","        <li>Under Services, click on your Watson Visual Recognition Instance</li>\n","        <li>Then click on Create a Custom Model</li>\n","        <li>Then delete one of your Custom Visual Recognition Model</li>\n","    </ol>\n","    <img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/CV0101/Images/Delete-Instance.png\" width=\"680\">\n","</div>\n"," \n"]},{"cell_type":"markdown","metadata":{},"source":["<h4>Is the model still training?</h4>\n","Depending on the number of images, it may take <b>a Several Minutes </b> for Watson to build a custom classifier. Please wait tell you get <b> Good to Go<b> \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["Status = visrec.get_classifier(classifier_id=classifier_id, verbose=True).get_result()['status']\n","if Status=='training': \n","    print (\"Please, Wait to complete training.......\")\n","else:\n","    print(\"Good to go \")"]},{"cell_type":"markdown","metadata":{},"source":["<h4>List all (custom) classifiers</h4>\n"]},{"cell_type":"markdown","metadata":{},"source":["<h4>If the status is still training, please rerun the above cell and wait until you see ready</h4> \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["visrec.list_classifiers(verbose=True).get_result()"]},{"cell_type":"markdown","metadata":{},"source":["<a id=\"ref4\"></a>\n","\n","<h2>Testing Classifier</h2>\n","<p>Let's test the classifier, the function <b>getdf_visrec</b> below uses the method <b>classify</b> from Watson Visual Recognition API to upload the image to the classifier and give us a result in JSON(JavaScript Object Notation) format. Then we use the method <b>json_normalize</b> from the \"Pandas\" library in Python to turn the result into a table because it is more human readable.</p>\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from pandas.io.json import json_normalize\n","\n","def getdf_visrec(url, classifier_ids, apikey = my_apikey):\n","    \n","    json_result = visrec.classify(url=url,\n","                              threshold='0.6',\n","                              classifier_ids=classifier_id).get_result()\n","    \n","    json_classes = json_result['images'][0]['classifiers'][0]['classes']\n","    \n","    df = json_normalize(json_classes).sort_values('score', ascending=False).reset_index(drop=True)\n","    \n","    return df"]},{"cell_type":"markdown","metadata":{},"source":["<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/CV0101/Dataset/GoldenRetriever1_stacked.jpg\">\n","<p>Let's test our Visual Recognition model on this picture of Golden Retriever</p>\n"]},{"cell_type":"markdown","metadata":{},"source":["<p>Please wait for your Custom Model to finish training before you upload your test image to your Custom Classifier, <b>you might get an error if your model is still training and you run the function below.</p>\n","<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/CV0101/Images/Training.png\" width=\"680\">\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["getdf_visrec(url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/CV0101/Dataset/GoldenRetriever1_stacked.jpg',\n","            classifier_ids=classifier_id)"]},{"cell_type":"markdown","metadata":{},"source":["<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/CV0101/Dataset/cat-2083492_960_720.jpg\">\n","<p>Lets test our Visual Recognition model on this picture of cat</p>\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["getdf_visrec(url = 'http://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/CV0101/Dataset/cat-2083492_960_720.jpg',\n","            classifier_ids=classifier_id)"]},{"cell_type":"markdown","metadata":{},"source":["<p>Our model will mis-classify the cat in the picture because our custom visual recognition model is only trained for recognizing different breeds of dogs.</p>\n"]},{"cell_type":"markdown","metadata":{},"source":["<h4>Delete all classifiers</h4>\n","<p>If you want to delete you classifiers, lets get the classifier id tht you want to delete. The method <b>list_classifiers</b> from Watson Visual Recognition API list all the classifier in your IBM Cloud account.</p>\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import json\n","\n","classifiers = visrec.list_classifiers(verbose=True).get_result()['classifiers']\n","print(json.dumps(classifiers, indent=2))"]},{"cell_type":"markdown","metadata":{},"source":["Just paste your classifier id and it will be deleted\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["mycid = '' #the classifier id you want to delete\n","visrec.delete_classifier(classifier_id = mycid)"]},{"cell_type":"markdown","metadata":{},"source":["<a id=\"ref5\"></a>\n","\n","<h2>Exercises</h2>\n","<p>For the following exercises you are going to train a Custom Visual Recognition Classifier to recognize fast food items, in particular it will be able to classify food items into <b>Burger</b>, <b>Fries</b> or <b>Coke</b>.<p>\n"]},{"cell_type":"markdown","metadata":{},"source":["<h3>Question 1: Optional</h3>\n","<p>After training your custom classifier, you might want to delete all the custom classifiers from your account, write a piece of code to use the *list_classifier* and *delete_classifier* method to delete all the custom classifiers in your account.</p>\n","\n","<p><b>Note!</b> This question is optional, if there is a classifier that you have trained and do not want to delete, skip this question.</p>\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","metadata":{},"source":["Double-click <font color=\"red\"><b><u>here</b></u></font> for the solution.\n","\n","<!-- The answer is below:\n","\n","# The code below will delete all classifiers in your account\n","for i in visrec.list_classifiers().get_result()['classifiers']:\n","    print('Deleting ...' + i['classifier_id'])\n","    visrec.delete_classifier(classifier_id=i['classifier_id'])\n","-->\n"]},{"cell_type":"markdown","metadata":{},"source":["<h3>Question 2</h3>\n","<p>The link to the data set for Burger, Fries and Coke is given below</p> \n","    \n","<p>We will use the <b>urlretrieve</b> method from the <b>urllib.request</b> library to download the dataset below.</p> \n","\n","<ul>\n","    <li>https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/CV0101/Dataset/Burger.zip</li>\n","    <li>https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/CV0101/Dataset/Fries.zip</li>\n","    <li>https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/CV0101/Dataset/Pizza.zip</li>\n","</ul>\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n","\n"]},{"cell_type":"markdown","metadata":{},"source":["Double-click <font color=\"red\"><b><u>here</b></u></font> for the solution.\n","\n","<!-- The answer is below:\n","import urllib.request\n","\n","#Downloading Burger dataset\n","urllib.request.urlretrieve(\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/CV0101/Dataset/Burger.zip\", \n","                           \"burger.zip\")\n","\n","#Downloading Fries dataset\n","urllib.request.urlretrieve(\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/CV0101/Dataset/Fries.zip\", \n","                           \"fries.zip\")\n","\n","#Downloading Pizza dataset\n","urllib.request.urlretrieve(\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/CV0101/Dataset/Pizza.zip\", \n","                           \"pizza.zip\")\n","-->\n"]},{"cell_type":"markdown","metadata":{},"source":["<h3>Question 3.1</h3>\n","<p>Now we have the dataset, use the <b>create_classifier</b> method to create your fast food classifier.</p>\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","metadata":{},"source":["Double-click <font color=\"red\"><b><u>here</b></u></font> for the solution.\n","\n","<!-- The answer is below:\n","my_apikey = '<paste your api key here>'\n","\n","from ibm_watson import VisualRecognitionV3\n","\n","visrec_fast_food = VisualRecognitionV3(version = '2019-01-01', \n","                             iam_apikey = my_apikey)\n","\n","import json\n","with open('burger.zip', 'rb') as burger, \\\n","     open('fries.zip', 'rb') as fries, \\\n","     open('pizza.zip', 'rb') as pizza:\n","    new_response = visrec_fast_food.create_classifier(name=\"fastfoodclassifier\",\n","                                        positive_examples={'burger': burger, \\\n","                                                           'fries': fries, \\\n","                                                           'pizza': pizza})\n","\n","    print(json.dumps(new_response.get_result(), indent=2))\n","-->\n"]},{"cell_type":"markdown","metadata":{},"source":["<h3>Question 3.2</h3>\n","<p>Since we will need the classifier_id, grab the classifier_id from the response of create_classifier and store it into a variable.</p>\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n","#lets grab the classifier id"]},{"cell_type":"markdown","metadata":{},"source":["Double-click <font color=\"red\"><b><u>here</b></u></font> for the solution.\n","\n","<!-- The answer is below:\n","#lets grab the classifier id\n","fast_food_classifier_id = new_response.get_result()['classifier_id']\n","-->\n"]},{"cell_type":"markdown","metadata":{},"source":["<h3>Question 4</h3>\n","<p>Get a url of a picture of fast food and use the <b>getdf_visrec</b> function to classify the picture. <b>Before that, please make sure that your model is trained and ready </b>\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to check if your model is ready to go \n"]},{"cell_type":"markdown","metadata":{},"source":["Double-click <font color=\"red\"><b><u>here</b></u></font> for the solution.\n","\n","<!-- The answer is below:\n","gStatus = visrec.get_classifier(classifier_id=fast_food_classifier_id, verbose=True).get_result()['status']\n","if Status=='training': \n","    print (\"Please, Wait to complete training.......\")\n","else:\n","    print(\"Good to go \")\n","-->\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","metadata":{},"source":["Double-click <font color=\"red\"><b><u>here</b></u></font> for the solution.\n","\n","<!-- The answer is below:\n","getdf_visrec(url = 'fast_food_image_url',\n","            classifier_ids=fast_food_classifier_id)\n","-->\n"]},{"cell_type":"markdown","metadata":{},"source":["<h1>Thank you for completing this notebook</h1>\n","You can read more about Watson Visual Recognition APIs from the following link.\n","<a href=\"https://cloud.ibm.com/apidocs/visual-recognition?code=python\">https://cloud.ibm.com/apidocs/visual-recognition</a>\n","\n","<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n","<h2>Get IBM Watson Studio free of charge!</h2>\n","    <p><a href=\"https://cloud.ibm.com/catalog/services/watson-studio\"><img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/CV0101/Logo/BottomAd.png\" width=\"750\" align=\"center\"></a></p>\n","</div>\n"]},{"cell_type":"markdown","metadata":{},"source":["## Author\n","\n","<a href=\"https://www.linkedin.com/in/yi-leng-yao-84451275/\" target=\"_blank\" >Yi Yao</a>\n","\n","## Other Contributors\n","\n","<a href=\"https://www.linkedin.com/in/nayefaboutayoun/\" target=\"_blank\">Nayef Abou Tayoun</a> \n","\n","## Change Log\n","\n","| Date (YYYY-MM-DD) | Version | Changed By | Change Description                 |\n","| ----------------- | ------- | ---------- | ---------------------------------- |\n","| 2020-08-27        | 2.0     | Anamika    | Moved lab to course repo in GitLab |\n","\n","<hr>\n","\n","## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.6"}},"nbformat":4,"nbformat_minor":4}