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import falcon

class HelloResource:
    def on_get(self, req, resp):
        resp.status = falcon.HTTP_200
        resp.body = 'Hello World!'

api = falcon.API()
api.add_route('/', HelloResource())

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import os
import falcon
from keras.models import load_model
from .predict import PredictResource

api = application = falcon.API()


def load_trained_model():
    global model

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import base64
import json
import falcon
import numpy as np
from io import BytesIO
from PIL import Image, ImageOps


def convert_image(image):
    img = Image.open(image).convert('L')

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import os
import cv2
import time
import numpy as np
import tensorflow as tf

from utils import FPS
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util

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Spark on Cloud Foundry

This document describes one means of running a simple Apache Spark cluster on Cloud Foundry. It makes heavy use of Cloud Foundry's container networking features.

You can see an example running at http://spark-ui-proxy.184.73.108.92.xip.io.

Deploy BOSH-Lite on AWS

This cluster was deployed using BOSH-Lite on AWS. Note, this Director cannot be targetted with the new BOSH CLI (see cloudfoundry-attic/bosh-lite#424), but you can use the "old" Ruby CLI just fine. You can use the new CLI for local workflows like manifest interpolation, and then the "old" CLI for remote workflows like deploying and SSH.

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import numpy as np

np.random.seed(1337)
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import RMSprop

NB_CLASSES = 10

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        grabFrame: function() {
            var self = this;

            this.canvasCtx.drawImage(this.video, 0, 0, this.canvas.width, this.canvas.height);
            this.canvas.toBlob(function(imageData) {
                var postImageReq = new XMLHttpRequest();
                postImageReq.open("POST", "/prediction", true);
                postImageReq.responseType = "json";

                postImageReq.onload = function(event) {

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def detect_faces(image):
    faceCascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
    img = Image.open(StringIO(image))
    img_cv2 = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2RGBA)
    gray = cv2.cvtColor(img_cv2, cv2.COLOR_RGBA2GRAY)

    faces = faceCascade.detectMultiScale(
        gray,
        scaleFactor=1.1,
        minNeighbors=5,

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logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
ch = logging.StreamHandler()
ch.setLevel(logging.DEBUG)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
ch.setFormatter(formatter)
logger.addHandler(ch)