FWIW: I'm not the author of the content presented here (which is an outline from Edmond Lau's book). I've just copy-pasted it from somewhere over the Internet, but I cannot remember what exactly the original source is. I was also not able to find the author's name, so I cannot give him/her the proper credits.
| #!/usr/bin/env python3 | |
| # | |
| # Example Script | |
| # Get a daily update of what you are interested in. | |
| # What was detected in the last 24hours | |
| # | |
| # For detail on the fields for each message type please check https://docs.binaryedge.io/modules/ | |
| from pybinaryedge import BinaryEdge | |
| import math |
curl -u admin:admin -H "X-Requested-By: ambari" -X DELETE \
http://ambari:8080/api/v1/clusters/analytics/services/STORM/components/NIMBUS
curl -u admin:admin -H "X-Requested-By: ambari" -X POST \
http://ambari:8080/api/v1/clusters/analytics/services/STORM/components/NIMBUS
| # Example Dockerfile | |
| FROM hello-world |
Just a quickie test in Python 3 (using Requests) to see if Google Cloud Vision can be used to effectively OCR a scanned data table and preserve its structure, in the way that products such as ABBYY FineReader can OCR an image and provide Excel-ready output.
The short answer: No. While Cloud Vision provides bounding polygon coordinates in its output, it doesn't provide it at the word or region level, which would be needed to then calculate the data delimiters.
On the other hand, the OCR quality is pretty good, if you just need to identify text anywhere in an image, without regards to its physical coordinates. I've included two examples:
####### 1. A low-resolution photo of road signs
#Some discussions on logging from docker: Using logstash Using Papertrail
A lot of this boils down to whether you want a single or multi-process (systemd, supervisord etc.) container...
| # to generate your dhparam.pem file, run in the terminal | |
| openssl dhparam -out /etc/nginx/ssl/dhparam.pem 2048 |
| global | |
| log 127.0.0.1 local2 | |
| chroot /var/lib/haproxy | |
| pidfile /var/run/haproxy.pid | |
| maxconn 4000 | |
| user haproxy | |
| group haproxy | |
| daemon | |
| stats socket /var/lib/haproxy/stats | |
| tune.bufsize 128000 |
| require 'open-uri' | |
| require 'zlib' | |
| require 'yajl' | |
| # References | |
| # - https://developers.google.com/bigquery/preparing-data-for-bigquery#dataformats | |
| # - https://developers.google.com/bigquery/docs/data#nested | |
| # | |
| def type(t) |