sreekanthgoud

sudo apt-get update && sudo apt-get install -y gconf-service libasound2 libatk1.0-0 libc6 libcairo2 libcups2 libdbus-1-3 libexpat1 libfontconfig1 libgcc1 libgconf-2-4 libgdk-pixbuf2.0-0 libglib2.0-0 libgtk-3-0 libnspr4 libpango-1.0-0 libpangocairo-1.0-0 libstdc++6 libx11-6 libx11-xcb1 libxcb1 libxcomposite1 libxcursor1 libxdamage1 libxext6 libxfixes3 libxi6 libxrandr2 libxrender1 libxss1 libxtst6 ca-certificates fonts-liberation libnss3 lsb-release xdg-utils wget ca-certificates
#https://stackoverflow.com/questions/58134793/error-while-loading-shared-libraries-libnss3-so-while-running-gtlab-ci-job-to

sreekanthgoud

docker pull mongo
docker run --name my_mongo -p 27017:27017 -d mongo

sreekanthgoud

function get_otp_timer() {
        const now = new Date();
        const hours = ("0" + now.getHours()).slice(-2);
        const minutes = ("0" + now.getMinutes()).slice(-2);
        const seconds = ("0" + now.getSeconds()).slice(-2);
        const milliseconds = ("0" + now.getMilliseconds()).slice(-3);
        return hours + minutes + seconds + milliseconds;
    }

sreekanthgoud

exit($stmt->debugDumpParams());

sreekanthgoud

// Wrap the original window.alert function...
const windowAlert = window.alert;

window.alert = function(message) {
  console.log(`window.alert called with message: ${message}`);
  return windowAlert(message);
};
alert('FOO');
// Console Output:
// window.alert called with message: FOO

sreekanthgoud

import numpy as np, random
from PIL import Image

dX, dY = 512, 512
xArray = np.linspace(0.0, 1.0, dX).reshape((1, dX, 1))
yArray = np.linspace(0.0, 1.0, dY).reshape((dY, 1, 1))

def randColor():
    return np.array([random.random(), random.random(), random.random()]).reshape((1, 1, 3))
def getX(): return xArray

sreekanthgoud

import random
import uuid

from PIL import Image, ImageDraw    


def generate_random_image(width=128, height=128):
    rand_pixels = [random.randint(0, 255) for _ in range(width * height * 3)]
    rand_pixels_as_bytes = bytes(rand_pixels)
    text_and_filename = str(uuid.uuid4())

sreekanthgoud

import numpy, Image

for n in xrange(10):
    a = numpy.random.rand(30,30,3) * 255
    im_out = Image.fromarray(a.astype('uint8')).convert('RGBA')
    im_out.save('out%000d.jpg' % n)

sreekanthgoud

import numpy as np
import os
import numpy
from tensorflow.python.keras.layers import Dense
from tensorflow.python.keras import optimizers
from keras import regularizers
from keras.regularizers import l2
from keras.layers import Dropout
from keras.applications.mobilenet import MobileNet
from keras.layers import GlobalAveragePooling2D, Dense, Dropout, Flatten, BatchNormalization

sreekanthgoud

db.productsTrousers.find().forEach(function(obj){ 
   db.products.insert(obj)
});