fourier-ondemand/ondemand/service.py

# -*- coding: utf8 -*-
from __future__ import print_function
from tornado.ioloop import IOLoop
from tornado.web import Application
from fourier.api.client import Client, ConnectionError
from fourier.boxconfig import parse_config
from fourier.dejavu.recognize import FilePerSecondRecognizer
from datetime import datetime, timedelta
from fourier.dejavu import Dejavu
from Queue import Queue, Empty
from firebase_admin import credentials
from firebase_admin import db as fbdb
from binascii import hexlify
from base64 import b64decode
import firebase_admin
import requests
import dateutil
import sqlite3
import time
import sys
import os

AUDIOS_PATH = '/tmp'

config = parse_config()
queue = Queue()
client = Client(config['device_id'],
                config['apiSecret'])
cloud_base_url = 'https://storage.googleapis.com/{}'\
                .format(config['bucket'])
base_path = config.get("basepath", "/var/fourier")
fb_credentials = credentials.Certificate('/etc/Fourier-key.json')
firebase_admin.initialize_app(fb_credentials, config['firebase'])
dejavu = Dejavu({"database_type":"mem"})
device_id = config['device_id']
device_path = os.path.join(base_path, device_id)
recognizer = FilePerSecondRecognizer

db_path = os.path.join(device_path, 'files.db')
db = sqlite3.connect(db_path)
cloud_cache = {}

def feed_queue():
    """ Search for pending scheduled work in
    server and add them to a memory queue. """
    try:
        response = client.get_schedule_pending()
        for item in response['items']:
            queue.put(item)

        if queue.qsize() > 0:
            loop.add_callback(process_queue)
        else:
            loop.add_timeout(time.time() + 30, feed_queue)

    except ConnectionError as ex:
        print('cannot feed: {}, retryig later'.format(ex),
              file=sys.stderr)
        loop.add_timeout(time.time() + 15, feed_queue)

    except Exception as ex:
        """ Errores desconocidos """
        print(ex, file=sys.stderr)
        loop.add_timeout(time.time() + 60, feed_queue)

def process_queue():
    """ Try to the next item in a queue and start
    processing it accordingly. If success, repeat
    the function or go to feed if no more items. """
    try:
        item = queue.get(False)
        process_segment(item)
        loop.add_callback(process_queue)
    except Empty:
        loop.add_callback(feed_queue)
    except Exception as ex:
        print(str(ex), file=sys.stderr)
        loop.add_callback(process_queue)

def process_segment(item):
    """ Procesa una hora de audio """

    station = item['station']
    date = dateutil.parser.parse(item['date'])

    # 1. obtener el audio desde firebase
    #    y calcular su fingerprint.
    filename, md5hash = cloud_download(ad_key=item['ad'])
    if not filename:
        print('ad file missing', file=sys.stderr)
        return
    
    try:
        dejavu.fingerprint_file(filename)
    except Exception as ex:
        print('cannot fingerprint: {}'.format(ex))

    # 2. Read the list of files from local database
    results = []
    for path, name, ts in iterate_audios(date, station):
        print(path)
        for match in dejavu.recognize(recognizer, path, 5):
            try:
                results.append({
                    'confidence': match['confidence'],
                    'timestamp': ts,
                    'offset': match['offset']
                })
                print("{} {}".format(ts, match['confidence']))
            except KeyError as ex:
                print(ex, file=sys.stderr)

            ts += match['length'] / 1000
    
    try:
        response = client.put_schedule_results(
            item['schedule'],
            item['id'],
            None, # TODO: send results again
            found=find_repetitions(results),
        )

        print("api response: {}".format(response))
    except ConnectionError as ex:
        print(ex, file=sys.stderr)
    except UserWarning as warn:
        print(str(warn), file=sys.stderr)

def find_repetitions(results):
    found_counter = 0
    found_index = None
    segments_needed = 2
    seconds_needed = 9
    threshold = 20
    expect_space = False
    found = []

    for index, result in enumerate(results):
        if not expect_space:
            if result['confidence'] > threshold:
                found_counter += 1
                if found_index is None:
                    found_index = index
            else:
                found_counter = 0
                found_index = None
        else:
            if result['confidence'] <= threshold:
                expect_space = False

        if found_counter >= segments_needed:
            """ TODO: It will be neccessary to improve this
            further, so it can recognize more than one
            audio in the same segment of 1 hour. Also the
            seconds transcurred is important; a time
            difference is needed """
            found.append(results[found_index]['timestamp'])
            found_counter = 0
            expect_space = True

    return found

def iterate_audios(dt, station):
    """ Given a datetime object and an station,
    iterate a list of files that are between
    the the date and itself plus 5 minutes;
    station must match too """
    from_time = time.mktime(dt.timetuple())
    to_time = from_time + 3599

    print('from {} to {}'.format(from_time, to_time))

    cursor = db.cursor()
    cursor.execute((
        'select "filename", "timestamp" '
        'from "file" '
        'where "timestamp" between ? and ? '
            'and "station" = ? '
        'order by "timestamp" asc'
        ),   
        (from_time, to_time, station, ),
    )
    files = [file for file in cursor]
    cursor.close()

    for mp3 in files:
        mp3path, ts = mp3
        mp3name = os.path.basename(mp3path)
        yield (mp3path, mp3name, ts)

def cloud_download(ad_key=None):
    """ Given an ad key, the file is downloaded to
    the system temporal folder to be processed """

    if ad_key in cloud_cache:
        """ If this file has already been downloaded,
        will not be downloaded again, instead will
        be taken from cloud_cache dictionary """
        filename, md5hash = cloud_cache[ad_key]
        if os.path.isfile(filename):
            return filename, md5hash

    ad = fbdb.reference('ads/{}'.format(ad_key)).get()
    filename = os.path.basename(ad['path'])
    out_file = os.path.join(AUDIOS_PATH, filename)
    url = '{}/{}'.format(cloud_base_url, ad['path'])
    response = requests.get(url)
    
    if response.status_code == 200:
        hashes = response.headers['x-goog-hash']
        hashes = hashes.split(',')
        hashes = [h.split('=', 1) for h in hashes]
        hashes = {h[0].strip(): hexlify(b64decode(h[1])) for h in hashes}
        md5sum = hashes['md5']

        with open(out_file, "wb") as fp:
            fp.write(response.content)
            tp = (out_file, md5sum)
            cloud_cache[ad_key] = tp
            return tp

app = Application()
loop = IOLoop.current()
loop.add_callback(feed_queue)

if __name__ == '__main__':
    loop.start()