hrshovon

This is a note on how to cross compile opencv for pretty much any ARM device(HardFP supported in this case) and deploy. Native
compiling in ARM devices can be painfully slow and they seem to hang often during build(mine got stuck at 43%). So if you happen
to have a desktop/laptop/server running ubuntu or similar linux distro, u can build opencv in fractionth of the time taken for 
native compiling without any issues.
Building opencv3 with TBB and NEON and VFP support can boost opencv performance. Thanks to Adrian at pyimagesearch for pointing 
that out.
Both my PC and target machine aka orange pi zero are running ubuntu 16.04 with python2.7 and python 3.5.
Let us use the term "build machine" for your PC where you are building opencv and "target machine" for the ARM single board computer.

1.Run the following commands in both machines(I think installing these in target machine only would do) to install the necessary libraries etc.(mine worked with them,so they should be enough 

rednafi

import json
import os
import sys
from datetime import timedelta

import httpx
import redis
from dotenv import load_dotenv
from fastapi import FastAPI
from asgiref.sync import sync_to_async

anchetaWern

Have you ever wanted to get a specific data from another website but there's no API available for it? That's where Web Scraping comes in, if the data is not made available by the website we can just scrape it from the website itself.

But before we dive in let us first define what web scraping is. According to Wikipedia:

{% blockquote %} Web scraping (web harvesting or web data extraction) is a computer software technique of extracting information from websites. Usually, such software programs simulate human exploration of the World Wide Web by either implementing low-level Hypertext Transfer Protocol (HTTP), or embedding a fully-fledged web browser, such as Internet Explorer or Mozilla Firefox. {% endblockquote %}

eamartin

bistaumanga

# -*- coding: utf-8 -*-
"""
Created on Sat May  3 10:21:21 2014

@author: umb
"""

import numpy as np
    
class GMM:

zacstewart

import os
import numpy
from pandas import DataFrame
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.cross_validation import KFold
from sklearn.metrics import confusion_matrix, f1_score

NEWLINE = '\n'

paduvi

import torch
import torch.nn as nn
import torch.nn.init as init

dropout_prob = 0.5


class FlatCnnLayer(nn.Module):
    def __init__(self, embedding_size, sequence_length, filter_sizes=[3, 4, 5], out_channels=128):
        super(FlatCnnLayer, self).__init__()

chetanppatil

#!/bin/bash

# Download Postman
cd /tmp || exit
echo "Downloading Postman..."
wget -q https://dl.pstmn.io/download/latest/linux?arch=64 -O postman.tar.gz

# Extract and install Postman to /opt
echo "Extracting and installing to /opt..."
sudo tar -xzf postman.tar.gz -C /opt/

iHassan

 
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rluts

from channels.auth import AuthMiddlewareStack
from rest_framework.authtoken.models import Token
from django.contrib.auth.models import AnonymousUser
from django.db import close_old_connections


class TokenAuthMiddleware:
    """
    Token authorization middleware for Django Channels 2
    """