tldrafael

# 
# Generic example
#
from flask import Flask, json, request, Response
import helpers

app = Flask(__name__)

@app.route('/predict', methods = ['POST'])
def api_predict():

tldrafael

## summary.csv and input_postconnect.csv are dummy files of example.

# Filter examples
## print entire row
awk -F';' '{ if( ($3=="") && (($5~"human")||($5~"machine"))) { print } }' summary.csv
awk -F';' '{ if( ($3~"human") && ($5!~"human") && ($5!="")) { print } }' summary.csv

## print specific column $1
awk -F';' '{ if( ($3~"human") && ($5!~"human") && ($5!="")) { print $1 } }' summary.csv

tldrafael

# get mp3 stdout output, pipe it to sox to remove beginning and end silence, and force specific rate sample and monochannel
gtts-cli "Hello World" | sox -t mp3 - -r 8000 -c 1  out.wav silence 1 0.1 1% reverse silence 1 0.1 1% reverse

tldrafael

# Install nvidia-drivers
add-apt-repository -y ppa:graphics-drivers/ppa && \
apt-get update && \
apt-get install -y nvidia-415 && \
# Install the latest docker-ce
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | apt-key add - && \
apt-key fingerprint 0EBFCD88 && \
add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable" && \
apt-get update && \
apt-get install -y docker-ce && \

tldrafael

import cv2
import numpy as np
from keras.applications import ResNet50
from keras.applications import imagenet_utils
from keras.preprocessing.image import img_to_array


model = ResNet50(weights='imagenet')
camera = cv2.VideoCapture(0)
# If possible control the FPS to constrain the labels appearances

tldrafael

# Code snippet to train lightgbm models quickly, using the number of iterations based on CV

import lightgbm as lgb
lgb_dftrain = lgb.Dataset(features_train, target_train)

params = {'boosting_type': 'gbdt',
          'objective': 'binary',
          'enable_bundle': True,
          'max_conflict_rate': 0,
          'max_depth': 20,

tldrafael

import matplotlib.pyplot as plt

fig, axs = plt.subplots(4, figsize=(12, 12))

for i in range(4):
    axs[i].imshow(sample_masks[:, :, i])
    axs[i].axis('off')

tldrafael

# Easy to go tools
  - http://www.robots.ox.ac.uk/~vgg/software/via/via-1.0.6.html
  - https://github.com/virajmavani/semi-auto-image-annotation-tool
  - https://labelbox.com/ -> Free until 2500 images per year.

# More references
  - https://github.com/jsbroks/awesome-dataset-tools
  - https://www.datasetlist.com/tools/

tldrafael

import argparse

def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument('-e', type=str, help='Environment variable', required=True, dest='var1',
                        choices=['staging', 'production'])
    parser.add_argument('--hypertuning', help='Run hypertuning process', action='store_true', dest='var2')
    return parser.parse_args()

tldrafael

import rlcompleter
import pdb
pdb.Pdb.complete = rlcompleter.Completer(locals()).complete
pdb.set_trace()

# Oneline to insert into code
import rlcompleter; import pdb; pdb.Pdb.complete = rlcompleter.Completer(locals()).complete; pdb.set_trace()