ankitshekhawat

# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

ankitshekhawat

from scipy.ndimage.morphology import binary_erosion
from keras.utils import to_categorical
from fast_slic.avx2 import SlicAvx2


def clip_to_superpixel(image, z, num_segments=100, erosion=12, threshold=0.5, iou_thresh=0.5):
    fast_slic = SlicAvx2(num_components=num_segments, compactness=10, quantize_level=1)
    segments = fast_slic.iterate(image)
    onehot = to_categorical(segments,  num_classes=num_segments, dtype='uint8')
    threshold = (np.max(z)+np.min(z))*threshold

ankitshekhawat

import kmc2
from sklearn.cluster import MiniBatchKMeans
from skimge.color import lab2rgb, rgb2lab

def get_colors(array, clusters=3):
    labs = rgb2lab([array]).squeeze()
    seeding = kmc2.kmc2(labs, clusters)
    kmeans = MiniBatchKMeans(n_clusters = clusters, init=seeding)
    kmeans.fit(np.squeeze(labs))
    LABELS = kmeans.labels_

ankitshekhawat

#PIL method
from PIL import Image

def letterbox(image, size=299, bg=(0,0,0)):
    (w,h) = image.size
    ratio = size / max([w,h])
    image = image.resize((int(ratio*w),int(ratio*h)))
    background = Image.new('RGB', (size, size), bg)
    background.paste(image, [int((size-s)/2) for s in image.size])
    return background

ankitshekhawat

//
//  GIF2MP4.swift
//
//  Created by PowHu Yang on 2017/1/24.
//  Copyright © 2017 PowHu Yang. All rights reserved.
//

ankitshekhawat

#python3

def pil2datauri(img):
    #converts PIL image to datauri
    data = BytesIO()
    img.save(data, "JPEG")
    data64 = base64.b64encode(data.getvalue())
    return u'data:img/jpeg;base64,'+data64.decode('utf-8')

ankitshekhawat

TensorFlow Serving in 10 minutes!

TensorFlow SERVING is Googles' recommended way to deploy TensorFlow models. Without proper computer engineering background, it can be quite intimidating, even for people who feel comfortable with TensorFlow itself. Few things that I've found particularly hard were:

• Tutorial examples have C++ code (which I don't know)
• Tutorials have Kubernetes, gRPG, Bezel (some of which I saw for the first time)
• It needs to be compiled. That process takes forever!

After all, it worked just fine. Here I present an easiest possible way to deploy your models with TensorFlow Serving. You will have your self-built model running inside TF-Serving by the end of this tutorial. It will be scalable, and you will be able to query it via REST.

ankitshekhawat

Verifying that "ankitshekhawat.id" is my Blockstack ID. https://onename.com/ankitshekhawat

ankitshekhawat

// ==UserScript==
// @name         Moonraider
// @namespace    http://moonraft.com/
// @version      0.2
// @description  Chanege the mail logo to one you want
// @author       You
// @match        https://mail.google.com/mail/*
// @grant        none
// ==/UserScript==

ankitshekhawat

from subprocess import check_output
import re

def scan_networks():
	airport = '/System/Library/PrivateFrameworks/Apple80211.framework/Versions/A/Resources/airport -s'
	rc  = check_output(airport, shell=True).split('\n')[1:-1]

	networks =[]
	for i in rc:
		i =  i.lstrip().rstrip()