mitkrieg

import cv2
import numpy as np 

#path to classifiers
path = '/Users/mitchellkrieger/opt/anaconda3/envs/learn-env/share/opencv4/haarcascades/'

#get image classifiers
face_cascade = cv2.CascadeClassifier(path +'haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier(path +'haarcascade_eye.xml')

mitkrieg

#read image
img = cv2.imread('people.jpg')

#convert to gray
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

#detect faces
faces = face_cascade.detectMultiScale(gray, 1.3, 5)

#for each face

mitkrieg

import cv2
import numpy as np 

path = '/Users/YOUR/PATH/HERE/share/opencv4/haarcascades/'

#get facial classifiers
face_cascade = cv2.CascadeClassifier(path +'haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier(path +'haarcascade_eye.xml')

#read images

mitkrieg

for (x,y,w,h) in faces:
    #retangle for testing purposes
    #img = cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)

    #coordinates of face region
    face_w = w
    face_h = h
    face_x1 = x
    face_x2 = face_x1 + face_w
    face_y1 = y

mitkrieg

import cv2
import numpy as np 

path = '/Users/YOUR/PATH/HERE/share/opencv4/haarcascades/'

#get facial classifiers
face_cascade = cv2.CascadeClassifier(path +'haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier(path +'haarcascade_eye.xml')

#read images

mitkrieg

import pandas as pd

# read data from NY times
df = pd.read_csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us.csv")

# engineer new cases
df['new_cases'] = df.cases - df.cases.shift().fillna(0)

# create pandas time series
df.date = pd.to_datetime(df.date)

mitkrieg

from fbprophet import Prophet

# instantiate the model and fit the timeseries
prophet = Prophet()
prophet.fit(ts)

# create a future data frame 
future = prophet.make_future_dataframe(periods=25)
forecast = prophet.predict(future)

mitkrieg

from fbprophet.plot import add_changepoints_to_plot
a = add_changepoints_to_plot(fig.gca(),m,forcast)
fig

mitkrieg

# instantiate the model and fit the timeseries
prophet = Prophet(weekly_seasonality=False, changepoint_range=1,changepoint_prior_scale=0.75)
prophet.fit(ts)

# create a future data frame 
future = prophet.make_future_dataframe(periods=25)
forecast = prophet.predict(future)

# display the most critical output columns from the forecast
forecast[['ds','yhat','yhat_lower','yhat_upper']].head()

mitkrieg

import dash
import dash_bootstrap_components as dbc

#Instantiates the Dash app and identify the server
app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
server = app.server