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diff --git a/content/blog/2020-09-01-visual-recognition.md b/content/blog/2020-09-01-visual-recognition.md index 8329a5a..8d71286 100644 --- a/content/blog/2020-09-01-visual-recognition.md +++ b/content/blog/2020-09-01-visual-recognition.md @@ -7,42 +7,41 @@ draft = false # What is IBM Watson? -If you\'ve never heard of [Watson](https://www.ibm.com/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 [API +If you've never heard of [Watson](https://www.ibm.com/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 [API Docs](https://cloud.ibm.com/apidocs/visual-recognition/visual-recognition-v3?code=python). # Prerequisites -To be able to use Watson Visual Recognition, you\'ll need the following: - -1. Create a free account on [IBM Watson - Studio](https://www.ibm.com/cloud/watson-studio). -2. Add the [Watson Visual - Recognition](https://www.ibm.com/cloud/watson-visual-recognition) - service to your IBM Watson account. -3. Get your API key and URL. To do this, first go to the [profile - dashboard](https://dataplatform.cloud.ibm.com/home2?context=cpdaas) - 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 [Watson - Studio](https://www.ibm.com/cloud/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. +To be able to use Watson Visual Recognition, you'll need the following: + +1. Create a free account on [IBM Watson + Studio](https://www.ibm.com/cloud/watson-studio). +2. Add the [Watson Visual + Recognition](https://www.ibm.com/cloud/watson-visual-recognition) service to + your IBM Watson account. +3. Get your API key and URL. To do this, first go to the [profile + dashboard](https://dataplatform.cloud.ibm.com/home2?context=cpdaas) 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 [Watson + Studio](https://www.ibm.com/cloud/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. +Okay, now let's get started. To begin, we need to install the proper Python package for IBM Watson. @@ -50,8 +49,8 @@ To begin, we need to install the proper Python package for IBM Watson. pip install --upgrade --user "ibm-watson>=4.5.0" ``` -Next, we need to specify the API key, version, and URL given to us when -we created the Watson Visual Recognition service. +Next, we need to specify the API key, version, and URL given to us when we +created the Watson Visual Recognition service. ``` python apikey = "<your-apikey>" @@ -59,7 +58,7 @@ version = "2018-03-19" url = "<your-url>" ``` -Now, let\'s import the necessary libraries and authenticate our service. +Now, let's import the necessary libraries and authenticate our service. ``` python import json @@ -75,16 +74,15 @@ visual_recognition = VisualRecognitionV3( visual_recognition.set_service_url(url) ``` -**\[Optional\]** If you\'d like to tell the API not to use any data to -improve their products, set the following header. +**[Optional]** If you'd like to tell the API not to use any data to improve +their products, set the following header. ``` python visual_recognition.set_default_headers({'x-watson-learning-opt-out': "true"}) ``` -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. +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. ``` python data = [ @@ -115,14 +113,14 @@ data = [ ] ``` -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. +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. +In the case of an API error, the codes and explanations are output to the +console. ``` python from ibm_watson import ApiException @@ -149,23 +147,22 @@ except ApiException as ex: # 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. +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. +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. +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. +When your program runs, it should show the output below for each photo you +provide. ``` txt ---------------------------------------------------------------- @@ -184,22 +181,19 @@ greenishness color ( 0.975 ) # 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. +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. |