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diff --git a/blog/2020-09-01-visual-recognition.org b/blog/2020-09-01-visual-recognition.org deleted file mode 100644 index 9e4f739..0000000 --- a/blog/2020-09-01-visual-recognition.org +++ /dev/null @@ -1,189 +0,0 @@ -#+date: 2020-09-01 -#+title: IBM Watson Visual Recognition - -* 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. |