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#!/usr/bin/env python3

import glob
from collections import defaultdict
import shutil
import os

def create_dir(dirname):
    if not os.path.isdir(dirname):
        os.mkdir(dirname)

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import numpy as np
import matplotlib.pyplot as plt
from scipy.spatial import Voronoi

def voronoi_finite_polygons_2d(vor, radius=None):
    """
    Reconstruct infinite voronoi regions in a 2D diagram to finite
    regions.

    Parameters

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# coords => List of tuple => [(x1, y1), ....(xn, yn)]
def voronoi(coords, width=800, height=800, use_max=False):
    coord_matrix = np.array(coords)
    X, Y = zip(*coords)
    if use_max:
        w, h = max(X) + 2, max(Y) + 2
    else:
        w, h = width, height
    print("Computing voronoi diagram of size :: {}, {}".format(w, h))

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First Create Noise Profile

This is done using ffmpeg:

ffmpeg -i source.mp3 -vn -ss 00:00:18 -t 00:00:20 noisesample.wav

Create Noise Profile for Sox

sox noisesample.wav -n noiseprof noise_profile_file

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ffmpeg -i source_video -i source_audio -c:v copy -map 0:v:0 -map 1:a:0 output_video

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Suppose there are 2 branches: master and feature

Squash Commits in Feature

• git checkout feature
• git rebase feature -i

This will open editor in interactive mode. DIfferent options like pick and squash are available

Rebase Feature into master without squash

First use rebase command standing in the feature branch.

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First get list of all the layers (after activation).

from keras.models import Model
layer_outputs = [layer.output for layer in model.layers]

Predict image and get corresponding activation in each layer

activation_model = Model(inputs=model.input, outputs=layer_outputs)

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{0: 'tench, Tinca tinca',
 1: 'goldfish, Carassius auratus',
 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',
 3: 'tiger shark, Galeocerdo cuvieri',
 4: 'hammerhead, hammerhead shark',
 5: 'electric ray, crampfish, numbfish, torpedo',
 6: 'stingray',
 7: 'cock',
 8: 'hen',
 9: 'ostrich, Struthio camelus',

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from rasa_nlu.featurizers import Featurizer

import tensorflow_hub as hub
import tensorflow as tf


class UniversalSentenceEncoderFeaturizer(Featurizer):
    """Appends a universal sentence encoding to the message's text_features."""

    # URL of the TensorFlow Hub Module