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diff --git a/content/blog/2020-09-01-visual-recognition.md b/content/blog/2020-09-01-visual-recognition.md deleted file mode 100644 index 912aabf..0000000 --- a/content/blog/2020-09-01-visual-recognition.md +++ /dev/null @@ -1,204 +0,0 @@ -+++ -date = 2020-09-01 -title = "IBM Watson Visual Recognition" -description = "Exploring and visualizing data with Python." -+++ - -# 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 -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. - -# 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. - -```sh -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. - -```python -apikey = "<your-apikey>" -version = "2018-03-19" -url = "<your-url>" -``` - -Now, let's import the necessary libraries and authenticate our service. - -```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) -``` - -**[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. - -```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" - } -] -``` - -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. - -```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) -``` - -# 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. - -```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 ) ----------------------------------------------------------------- -``` - -# 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. |