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# 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

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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

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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:

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$ ffmpeg -i input.mp4 -vcodec h264 -acodec mp2 output.mp4

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//eos3
flatpak remote-add --user eos-apps --no-gpg-verify https://ostree.endlessm.com/ostree/eos-apps
flatpak install --user eos-apps com.google.Chrome
git clone git@github.com:endlessm/eos-google-chrome-app.git
cd eos-google-chrome-app
chmod +x eos-google-chrome-app
./eos-google-chrome-app

//master
flatpak remote-add --user --no-gpg-verify chrome http://blog.kukuh.syafaat.id/google-chrome-flatpak-repo-testing/repo

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#include <stdio.h>	
#include <string.h>

#define MAX_DATA_LEN 1024

static unsigned char hex_string[MAX_DATA_LEN];
 
static inline char convert_to_printable(char c)
{
    return c < 32 || c > 126 ? '.' : c;