Have you heard about text-to-image models like DALL-E 2, Stable Diffusion and MidJourney? These are AI algorithms that take in text (the “prompt”) that describes what kind of picture you want as input and as output the algorithm creates that picture, based on billions of images.
An example could be “an astronaut on a bicycle on the moon by Van Gogh”. And this would be one of the results:
{“prompt”: {“software”: “imaginairy”, “prompts”: [[1, “an astronaut on a bicycle on the moon in the style of Van Gogh”]], “prompt_strength”: 7.5, “init_image”: “None”, “init_image_strength”: 0.6, “seed”: 938321671, “steps”: 40, “height”: 512, “width”: 512, “upscale”: false, “fix_faces”: false, “sampler_type”: “plms”}}
I got access to DALL-E 2 in July this year. DALL-E 2 is a closed source algorithm made by OpenAI. You can sign up to request access to DALL-E 2. Once you get access you can use it for free for a limited of runs. After that you have to pay to use it more.
Having markers of videos and photos taken during my bike ride is cool and all, but how about having a track of the bike ride itself? All my bike rides are registered on Strava, the cycling and running app. Strava has an API for developers, but it requires connecting via OAuth 2.0 and knowledge of the API. I decided to go an easier route: because I’m Strava Premium member, I can download the GPX track of any ride, including my own.
These .gpx track files are of the same XML structure as we saw embedded in video files in my last blogpost. I can just open the file and use almost the same Python code to read the locations.
When I was finished last week with creating my video location map in Python, I thought “shame I can’t plot photo locations”. That’s because my Fuji X-T30 camera doesn’t store GPS info. When I bought the camera I assumed every modern camera had GPS tagging, so I didn’t even checked that feature. Too bad. But I also made some photo’s during my vacations with my humble iPhone 8. And it does have GPS tags. So let’s plot some photo locations.
Recently I got the request to add specific data quality metadata with csv datasets that my client delivers to customers. It was very simple. Just counts, min, max and -in case of integers – sums per attribute. Not a difficult task. After a short talk with architects we decided to build this with Python and pandas. Because my efforts were required in another project at that time, my fellow DIKW entrepeneur Wyas build most of it. It worked out well. It ran through a couple of GBs in minutes.
I’m keeping tech dossiers in Evernote on open source products I want to keep track of. And I decided to put them on my blog. My previous ones were on Kubernetes and Elasticsearch. This one is on the Python data management library pandas. A short description – in English Read more
Data engineering isn’t always creating serverless APIs and ingressing terrabyte a minute streams with do-hickeys on Kubernetes. Sometimes people just want their Excel sheet in the data lake. Is that big data? Not even close. It’s very small. But for some people it’s a first step in a data driven world.
But does Hadoop read Excel? Not to my knowledge. But NiFi, that wonderful open source data flow software has an Excel processor. It can even help you to work the data a little. But some Excel sheets simply need too much reworking. And that’s simply too big a job for NiFi. I’ve used Python and the pandas library to create a csv file that Hadoop can handle.
