def main():
dbms = mydatabase.MyDatabase(mydatabase.SQLITE,
dbname='mydb.sqlite',
log=log)
# dbms.create_db_tables()
dbms.print_all_data(mydatabase.USERS)
def main():
dbms = mydatabase.MyDatabase(mydatabase.SQLITE, dbname='mydb.sqlite')
# Create Tables
dbms.create_db_tables()
# dbms.insert_single_data()
dbms.print_all_data(mydatabase.USERS)
dbms.print_all_data(mydatabase.ADDRESSES)
dbms.sample_query() # simple query
dbms.sample_delete() # delete data
dbms.sample_insert() # insert data
dbms.sample_update() # update data
from PIL import Image
import face_recognition
import glob
from database import mydatabase
dbms = mydatabase.MyDatabase(mydatabase.SQLITE, dbname='mydb.sqlite')
# dbms.create_db_tables()
def findFace(filenames):
# Load the jpg file into a numpy array
all_data = []
for file in filenames:
data = []
image = face_recognition.load_image_file(file)
face_locations = face_recognition.face_locations(image)
# print("I found {} face(s) in this photograph.".format(len(face_locations)))
if (len(face_locations) > 0):
data = [
"'" + file + "'", "'" + file + "'",
str(len(face_locations))
]
data_string = ",".join(data)
all_data.append(data_string)
# for face_location in face_locations:
# top, right, bottom, left = face_location
# print("A face is located at pixel location Top: {}, Left: {}, Bottom: {}, Right: {}".format(top, left, bottom, right))
dbms.insertmany_sqlite3("imagelist", "imagename,imagepath,faceCount",
all_data)
print("--------- completed")
import numpy as np
import sklearn
import pickle
from face_recognition import face_locations
from PIL import Image, ImageDraw, ImageFont
from tqdm import tqdm
import cv2
import pandas as pd
# we are only going to use 4 attributes
COLS = ['Asian', 'White', 'Black']
N_UPSCLAE = 1
# ------- Ethnicity Prediction
from database import mydatabase
dbms = mydatabase.MyDatabase(
mydatabase.SQLITE,
dbname=
'/Users/divyachandana/Documents/NJIT/work/summertasks/may25-may30/Park_face/mydb.sqlite'
)
# images_path = '/Users/divyachandana/Documents/NJIT/work/summertasks/jun1-jun5/atlanta'
# images_path = '/Users/divyachandana/Documents/NJIT/work/summertasks/jun1-jun5/nyc'
def main():
with open('face_model.pkl', 'rb') as f:
clf, labels = pickle.load(f, encoding="latin1")
# db_table = 'face_attributes_atlanta'
db_table = 'face_attributes_nyc'
# files = glob.glob(r'/Users/divyachandana/Documents/NJIT/work/summertasks/jun1-jun5/atlanta/*.jpg')
import numpy
from PIL import Image
model = gluoncv.model_zoo.get_model('psp_resnet101_ade', pretrained=True, ctx=mx.cpu(0))
ctx = mx.cpu(0)
import numpy
from PIL import Image
import csv
import glob
from datetime import datetime
from timeit import default_timer as timer
# ------- gluon ----
from database import mydatabase
dbms = mydatabase.MyDatabase(mydatabase.SQLITE, dbname='/Users/divyachandana/Documents/NJIT/work/summertasks/june15-june20/semantic-segmentation-pixel/semanticdb.sqlite')
def main():
start = timer()
print('Processing Start time: %.1f' % (start))
print("current time", datetime.now())
gauth = GoogleAuth()
gauth.LocalWebserverAuth()
drive = GoogleDrive(gauth)
# Auto-iterate through all files that matches this query
file_list = drive.ListFile({'q': "'root' in parents"}).GetList()
for file in file_list:
# print('title: {}, id: {}'.format(file1['title'], file1['id']))