ZackAkil

from flask import Flask, send_from_directory

app = Flask(__name__)

@app.route('/')
def index():
    return 'Hello, World!'

@app.route('/<path:path>')
def serve_files(path):

ZackAkil

function onOpen() {
  var ui = SpreadsheetApp.getUi();

  ui.createMenu("🔮 Predictions for cell")
    .addItem('🌸 Iris', 'predict_on_row')
    .addToUi();
}

ZackAkil

ZackAkil

#!/usr/bin/env python3
# rpi_ws281x library strandtest example
# Author: Tony DiCola (tony@tonydicola.com)
#
# Direct port of the Arduino NeoPixel library strandtest example.  Showcases
# various animations on a strip of NeoPixels.
 
import time
from rpi_ws281x import *
import argparse

ZackAkil

# given a folder of videos, generate thumbnails for each video and store them in a new folder structure

# !pip install opencv-python
# !sudo apt-get install ffmpeg libsm6 libxext6  -y

import cv2
import os
from PIL import Image

ZackAkil

from google.cloud import storage

folder_location = 'YOUR GCS FOLDER LOCATION' # e.g automl-ui-dataset/x-ray-dataset

parts = folder_location.split('/')
bucket_name = parts[0]
prefix = '/'.join(parts[1:]) if len(parts) > 1 else ''

storage_client = storage.Client()
blobs = storage_client.list_blobs(bucket_name, prefix=prefix)

ZackAkil

from google.cloud import videointelligence
import time

video_client = videointelligence.VideoIntelligenceServiceClient()

OUTPUT_BUCKET = 'gs://YOUR OUTPUT BUCKET NAME/'

features = [
    videointelligence.Feature.OBJECT_TRACKING

ZackAkil

from google.cloud import storage

folder_location = 'automl-ui-dataset/x-ray-dataset'

parts = folder_location.split('/')
prefix = '/'.join(parts[1:]) if len(parts) > 1 else ''

storage_client = storage.Client()
blobs = storage_client.list_blobs(parts[0], prefix=prefix)

ZackAkil

import io
import numpy as np

import imageio
import cv2

from google.cloud import storage
from google.cloud import videointelligence_v1p3beta1 as videointelligence

storage_client = storage.Client()

ZackAkil

from google.cloud import videointelligence_v1p3beta1 as videointelligence

client = videointelligence.VideoIntelligenceServiceClient()

OUTPUT_BUCKET = 'gs://YOUR OUTPUT BUCKET NAME/'

# configure person detection
config = videointelligence.types.PersonDetectionConfig(
    include_bounding_boxes=True, # include bounding box round whole body