Red-Eyed

#!/usr/bin/env python3


from pathlib import Path
import os
import sys
from subprocess import Popen, PIPE, getoutput
from shlex import split
from contextlib import redirect_stdout
import argparse

Red-Eyed

#!/usr/bin/env python3

from argparse import ArgumentParser
from functools import partial
from shutil import copy
from subprocess import Popen, PIPE
from shlex import split
import os
import re
import atexit

Red-Eyed

from pathlib import Path
import logging
import sys

def set_logger(error_file: Path, info_file: Path):
    error_file = Path(error_file).expanduser().resolve()
    error_file.parent.mkdir(exist_ok=True, parents=True)

    info_file = Path(info_file).expanduser().resolve()
    info_file.parent.mkdir(exist_ok=True, parents=True)

Red-Eyed

Usefull commands

Get current ativity: dumpsys activity activities | grep mResumedActivity

Uninstall app example: pm uninstall -k --user 0 com.aura.oobe.samsung.gl

Restore removed app example: pm install-existing com.aura.oobe.samsung.gl

Red-Eyed

[includeIf "gitdir:~/Projects/work/"]        
    path = .gitconfig_job                    
                                             
[includeIf "gitdir:~/Projects/personal/"]    
    path = .gitconfig_personal               
                                             
[filter "lfs"]                               
    clean = git-lfs clean -- %f              
    smudge = git-lfs smudge -- %f            
    process = git-lfs filter-process         

Red-Eyed

type ~/.ssh/id_rsa.pub | ssh USER@HOST "mkdir -p ~/.ssh && LANG=C sed 's/[\d128-\d255]//g' >> ~/.ssh/authorized_keys && chmod 0600 ~/.ssh/authorized_keys"

deepak-karkala

# FLASK Webapp for Image Segmentation Model

import os, sys, io
sys.path.append(".")
import webapp

from flask import Flask
import flask
import numpy as np
import pandas as pd

gatopeich

from dataclasses import dataclass, fields as datafields
from ujson import dumps, loads
# Note: ujson seamlessly serializes dataclasses, unlike stdlib's json

@dataclass
class Point:
    x: float
    y: float

# Shallow dataclass can be rebuilt from dict/json:

mjdietzx

from keras import layers


def residual_block(y, nb_channels, _strides=(1, 1), _project_shortcut=False):
    shortcut = y

    # down-sampling is performed with a stride of 2
    y = layers.Conv2D(nb_channels, kernel_size=(3, 3), strides=_strides, padding='same')(y)
    y = layers.BatchNormalization()(y)
    y = layers.LeakyReLU()(y)

iago-suarez

Setting Up OpenCL for OpenCV on Android, the full story

The tutorial Use OpenCL in Android camera preview based CV application show us how we can use the Transparent API to dramatically increase the performance of some expensive operations.

The first step in order to be able to execute the tutorial example is to re-compile opencv with the correct flags:

# Export your NDK, it will be looked for OpenCV to compile your project. In my case
export ANDROID_NDK=~/graffter/libs/android-ndk-r10d/

# Download the necessary code