npinto

import cv2
import cv2.cv as cv


def detect(img, cascade_fn='haarcascades/haarcascade_frontalface_alt.xml',
           scaleFactor=1.3, minNeighbors=4, minSize=(20, 20),
           flags=cv.CV_HAAR_SCALE_IMAGE):

    cascade = cv2.CascadeClassifier(cascade_fn)
    rects = cascade.detectMultiScale(img, scaleFactor=scaleFactor,

willurd

Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.

Discussion on reddit.

Python 2.x

$ python -m SimpleHTTPServer 8000

denji

Moved to git repository: https://github.com/denji/nginx-tuning

NGINX Tuning For Best Performance

For this configuration you can use web server you like, i decided, because i work mostly with it to use nginx.

Generally, properly configured nginx can handle up to 400K to 500K requests per second (clustered), most what i saw is 50K to 80K (non-clustered) requests per second and 30% CPU load, course, this was 2 x Intel Xeon with HyperThreading enabled, but it can work without problem on slower machines.

You must understand that this config is used in testing environment and not in production so you will need to find a way to implement most of those features best possible for your servers.

kaushikgopal

#!/usr/bin/env ruby

# This pre-commit hook will prevent any commit to forbidden branches
# (by default, "staging" and "production").
# Put this file in your local repo, in the .git/hooks folder
# and make sure it is executable.
# The name of the file *must* be "pre-commit" for Git to pick it up.

def current_branch()
  branches = `git branch --no-color`.split(/\n/)

dannguyen

Using Python 3 + Google Cloud Vision API's OCR to extract text from photos and scanned documents

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

mgoodness

AWS Service annotations

• service.beta.kubernetes.io/aws-load-balancer-access-log-emit-interval (in minutes)
• service.beta.kubernetes.io/aws-load-balancer-access-log-enabled (true|false)
• service.beta.kubernetes.io/aws-load-balancer-access-log-s3-bucket-name
• service.beta.kubernetes.io/aws-load-balancer-access-log-s3-bucket-prefix
• service.beta.kubernetes.io/aws-load-balancer-additional-resource-tags (comma-separated list of key=value)
• service.beta.kubernetes.io/aws-load-balancer-backend-protocol (http|https|ssl|tcp)
• service.beta.kubernetes.io/aws-load-balancer-connection-draining-enabled (true|false)

lukebussey

Direct copy of pre-encoded file:

$ ffmpeg -i filename.mp4 -codec: copy -start_number 0 -hls_time 10 -hls_list_size 0 -f hls filename.m3u8

DrSleep

## http://cvlibs.net/datasets/kitti/eval_semantics.php
## https://omnomnom.vision.rwth-aachen.de/data/rwth_kitti_semantics_dataset.zip

### DATASET FOR SEMANTIC SEGMENTATION

mikepfeiffer

sudo amazon-linux-extras install epel -y
sudo yum install stress -y

JakeSteam

# Overall config
dist: xenial
language: android

# Android version config
android:
  components:
    - build-tools-28.0.3
    - android-28