monitoring my gas counter with a webcam

During my certification I have learned about DocumentAI and I am eager to put it into practical use. So, here is my project: I will install a webcam to watch my gas counter and take a picture every day. Will use DocumentAI to find out its read and store it in a table.

Here is the prototype:

Webcam monitoring my gas counter

OK, first we have the physical setup. I use an old Ubuntu laptop (will be a Raspberry Pi soon) with a very new and good webcam: 

On Ubuntu, I issue the command to grab the picture from the webcam:

$ fswebcam -r 4000x3000 -d /dev/video0 test2.jpg
--- Opening /dev/video0...
Trying source module v4l2...
/dev/video0 opened.
No input was specified, using the first.
Adjusting resolution from 4000x3000 to 2304x1296.
--- Capturing frame...
Captured frame in 0.00 seconds.
--- Processing captured image...
Writing JPEG image to 'test2.jpg'.

When I upload it to DocumentAI, it does recognize the counter's number, in this case 9153.486 m³

Now, this deserves to be automated for daily uploads and a statistic. First thing to do is

sudo apt install python3-pip

Then, follow https://cloud.google.com/document-ai/docs/process-documents-client-libraries. In the end, my python script docai.py looks like this:

from google.api_core.client_options import ClientOptions
from google.cloud import documentai

# TODO(developer): Uncomment these variables before running the sample.
project_id = 'XXXXXXXXX'
location = 'us' # Format is 'us' or 'eu'
processor_id = 'x1x1x1x1x1' #  Create processor before running sample
file_path = '/home/thorsten/test.jpg'
mime_type = 'image/jpeg' # Refer to https://cloud.google.com/document-ai/docs/file-types for supported file types


def quickstart(
    project_id: str, location: str, processor_id: str, file_path: str, mime_type: str
):
    # You must set the api_endpoint if you use a location other than 'us', e.g.:
    opts = ClientOptions(api_endpoint=f"{location}-documentai.googleapis.com")

    client = documentai.DocumentProcessorServiceClient(client_options=opts)

    # The full resource name of the processor, e.g.:
    # projects/project_id/locations/location/processor/processor_id
    name = client.processor_path(project_id, location, processor_id)

    # Read the file into memory
    with open(file_path, "rb") as image:
        image_content = image.read()

    # Load Binary Data into Document AI RawDocument Object
    raw_document = documentai.RawDocument(content=image_content, mime_type=mime_type)

    # Configure the process request
    request = documentai.ProcessRequest(name=name, raw_document=raw_document)

    result = client.process_document(request=request)

    # For a full list of Document object attributes, please reference this page:
    # https://cloud.google.com/python/docs/reference/documentai/latest/google.cloud.documentai_v1.types.Document
    document = result.document

    # Read the text recognition output from the processor
    print("The document contains the following text:")
    print(document.text)

quickstart(project_id,location,processor_id,file_path,mime_type)

as you can see, it expects a file /home/thorsten/test.jpg and OCRs it.

If I run it, it outputs all text it can read from the image:

CE
TRENAMIEN
koom
schroder
mi
TUN
BK-G4
C4 2002
(
SEA
18777
o SWH-AG
U9165012 m³
Elster Handel GmbH Mainz
way
Bel GASGERUCH:
Durchschn
Tindavon unten maches
stadtwerke
heldelberg.
SUNITS WAS A
KAZANHADINE
Manchma
2022-10-05 20:01 (CEST)

Using the grep utility, I can limit the output to all lines that contain m³:

$ python3 docai.py | grep m³
U9165012 m³