In [12]:

    

from sklearn.externals import joblib
import numpy as np
import sqlite3
from enum import Enum
from skimage import data

clf = joblib.load('classifier.pkl') 
conn = sqlite3.connect('sessions.db')
c = conn.cursor()
class Participant(Enum):
     none = 0
     adult = 1
     child = 2
     pet = 3
rows = [r for r in c.execute("SELECT * FROM readings WHERE subject_type='none'")]
image_paths = ['image_data/{}'.format(r[5]) for r in rows]
X = [np.array(data.imread(p)).flatten() for p in image_paths]
y = [Participant[r[2]].value for r in rows]

clf.predict(X[0].reshape(1, -1))


    

    
Out[12]:





array([0])

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