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-#+title: IBM Watson Visual Recognition
-#+date: 2020-09-01
-#+description: Exploring and visualizing data with Python.
-#+filetags: :dev:
-
-* What is IBM Watson?
-If you've never heard of [[https://www.ibm.com/watson][Watson]], this
-service is a suite of enterprise-ready AI services, applications, and
-tooling provided by IBM. Watson contains quite a few useful tools for
-data scientists and students, including the subject of this post today:
-visual recognition.
-
-If you'd like to view the official documentation for the Visual
-Recognition API, visit the
-[[https://cloud.ibm.com/apidocs/visual-recognition/visual-recognition-v3?code=python][API
-Docs]].
-
-* Prerequisites
-To be able to use Watson Visual Recognition, you'll need the following:
-
-1. Create a free account on
-   [[https://www.ibm.com/cloud/watson-studio][IBM Watson Studio]].
-2. Add the [[https://www.ibm.com/cloud/watson-visual-recognition][Watson
-   Visual Recognition]] service to your IBM Watson account.
-3. Get your API key and URL. To do this, first go to the
-   [[https://dataplatform.cloud.ibm.com/home2?context=cpdaas][profile
-   dashboard]] for your IBM account and click on the Watson Visual
-   Recognition service you created. This will be listed in the section
-   titled *Your services*. Then click the *Credentials** tab and open the
-   *Auto-generated credentials** dropdown. Copy your API key and URL so
-   that you can use them in the Python script later.
-4. *[Optional]** While not required, you can also create the Jupyter
-   Notebook for this project right inside
-   [[https://www.ibm.com/cloud/watson-studio][Watson Studio]]. Watson
-   Studio will save your notebooks inside an organized project and allow
-   you to use their other integrated products, such as storage
-   containers, AI models, documentation, external sharing, etc.
-
-* Calling the IBM Watson Visual Recognition API
-Okay, now let's get started.
-
-To begin, we need to install the proper Python package for IBM Watson.
-
-#+begin_src sh
-pip install --upgrade --user "ibm-watson>=4.5.0"
-#+end_src
-
-Next, we need to specify the API key, version, and URL given to us when
-we created the Watson Visual Recognition service.
-
-#+begin_src python
-apikey = "<your-apikey>"
-version = "2018-03-19"
-url = "<your-url>"
-#+end_src
-
-Now, let's import the necessary libraries and authenticate our service.
-
-#+begin_src python
-import json
-from ibm_watson import VisualRecognitionV3
-from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
-
-authenticator = IAMAuthenticator(apikey)
-visual_recognition = VisualRecognitionV3(
-  version=version,
-  authenticator=authenticator
-)
-
-visual_recognition.set_service_url(url)
-#+end_src
-
-*[Optional]* If you'd like to tell the API not to use any data to
-improve their products, set the following header.
-
-#+begin_src python
-visual_recognition.set_default_headers({'x-watson-learning-opt-out': "true"})
-#+end_src
-
-Now we have our API all set and ready to go. For this example, I'm going
-to include a =dict= of photos to load as we test out the API.
-
-#+begin_src python
-data = [
-  {
-    "title": "Grizzly Bear",
-    "url": "https://example.com/photos/image1.jpg"
-  },
-  {
-    "title": "Nature Lake",
-    "url": "https://example.com/photos/image2.jpg"
-  },
-  {
-    "title": "Welcome Sign",
-    "url": "https://example.com/photos/image3.jpg"
-  },
-  {
-    "title": "Honey Badger",
-    "url": "https://example.com/photos/image4.jpg"
-  },
-  {
-    "title": "Grand Canyon Lizard",
-    "url": "https://example.com/photos/image5.jpg"
-  },
-  {
-    "title": "Castle",
-    "url": "https://example.com/photos/image6.jpg"
-  }
-]
-#+end_src
-
-Now that we've set up our libraries and have the photos ready, let's
-create a loop to call the API for each image. The code below shows a
-loop that calls the URL of each image and sends it to the API,
-requesting results with at least 60% confidence. The results are output
-to the console with dotted lines separating each section.
-
-In the case of an API error, the codes and explanations are output to
-the console.
-
-#+begin_src python
-from ibm_watson import ApiException
-
-for x in range(len(data)):
-try:
-   url = data[x]["url"]
-   images_filename = data[x]["title"]
-   classes = visual_recognition.classify(
-       url=url,
-       images_filename=images_filename,
-       threshold='0.6',
-       owners=["IBM"]).get_result()
-   print("-----------------------------------------------")
-   print("Image Title: ", data[x]["title"], "\n")
-   print("Image URL: ", data[x]["url"], "\n")
-   classification_results = classes["images"][0]["classifiers"][0]["classes"]
-   for result in classification_results:
-       print(result["class"], "(", result["score"], ")")
-   print("-----------------------------------------------")
-except ApiException as ex:
-   print("Method failed with status code " + str(ex.code) + ": " + ex.message)
-#+end_src
-
-* The Results
-Here we can see the full result set of our function above. If you view
-each of the URLs that we sent to the API, you'll be able to see that it
-was remarkably accurate. To be fair, these are clear high-resolution,
-clear photos shot with a professional camera. In reality, you will most
-likely be processing images that are lower quality and may have a lot of
-noise in the photo.
-
-However, we can clearly see the benefit of being able to call this API
-instead of attempting to write our own image recognition function. Each
-of the classifications returned was a fair description of the image.
-
-If you wanted to restrict the results to those that are at least 90%
-confident or greater, you would simply adjust the =threshold= in the
-=visual_recognition.classify()= function.
-
-When your program runs, it should show the output below for each photo
-you provide.
-
-#+begin_src txt
-----------------------------------------------------------------
-Image Title:  Grizzly Bear
-Image URL: https://example.com/photos/image1.jpg
-
-brown bear ( 0.944 )
-bear ( 1 )
-carnivore ( 1 )
-mammal ( 1 )
-animal ( 1 )
-Alaskan brown bear ( 0.759 )
-greenishness color ( 0.975 )
-----------------------------------------------------------------
-#+end_src
-
-* Discussion
-Now, this was a very minimal implementation of the API. We simply
-supplied some images and looked to see how accurate the results were.
-However, you could implement this type of API into many machine learning
-(ML) models.
-
-For example, you could be working for a company that scans their
-warehouses or inventory using drones. Would you want to pay employees to
-sit there and watch drone footage all day in order to identify or count
-things in the video? Probably not. Instead, you could use a
-classification system similar to this one in order to train your machine
-learning model to correctly identify items that the drones show through
-video. More specifically, you could have your machine learning model
-watch a drone fly over a field of sheep in order to count how many sheep
-are living in that field.
-
-There are many ways to implement machine learning functionality, but
-hopefully this post helped inspire some deeper thought about the tools
-that can help propel us further into the future of machine learning and
-AI.