conversion from Zola to Weblorg

1 files changed, 0 insertions, 199 deletions

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 d143d52..0000000
--- a/content/blog/2020-09-01-visual-recognition.md
+++ /dev/null
@@ -1,199 +0,0 @@
-+++
-date = 2020-09-01
-title = "IBM Watson Visual Recognition"
-description = ""
-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
-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.