def create_color(name):
"""Create color"""
color = Color(name=name)
db.session.add(color)
db.session.commit()
return color
def add_color_data(hex_code, color_name):
"""Assume hex_code is a 7-character string that's a hex code.
Add it to the database."""
color = Color(hex_code=hex_code.rstrip().lower(),
color_name=color_name.rstrip().lower())
db.session.add(color)
try:
db.session.commit()
except (Exception, exc.SQLAlchemyError, exc.InvalidRequestError, exc.IntegrityError) as e:
print(hex_code + '\n' + str(e))
def load_colors():
"""Load colors/hex from css3 dict into database."""
print "Color"
for key, value in css3_hex_to_names.items():
color_hex, color_name = key, value
color = Color(color_hex=color_hex, color_name=color_name)
db.session.add(color)
db.session.commit()
def load_color():
"Load colors from colors into database"
print "Colors"
for row in open("seed_data/colors"):
color_name = row.rstrip()
color_name = Color(color=color_name)
db.session.add(color_name)
db.session.commit()
def load_colors(color_filename):
"""Load colors from seed_data/generic_colors into database."""
# Delete all rows in table, so if we need to run this a second time,
# we won't be trying to add duplicate users
Color.query.delete()
#Read generic_colors file and insert data
for row in open(color_filename):
row = row.rstrip()
color_id, color = row.split("|")
colors = Color(color=color)
# We need to add to the session or it won't ever be stored
db.session.add(colors)
# Once we're done, we should commit our work
db.session.commit()
#finished the function
print("Colors inserted")
import cv2 as cv
import dlib
import numpy as np
from gender_clasification import gender_faces
from logo_detector import run_template_detector
from model import Color, Box, Point, Face, GenderedFace
from utils import create_output_dir, load_and_sort_dir, write_boxes
DETECTION_MODEL = "models/face_detection/res10_300x300_ssd_iter_140000.caffemodel"
DETECTION_MODEL_CONFIG = "models/face_detection/deploy.prototxt.txt"
RECOGNITION_MODEL = 'models/face_recognition/dlib_face_recognition_resnet_model_v1.dat'
SHAPE_MODEL = 'models/face_recognition/shape_predictor_5_face_landmarks.dat'
DETECTION_NETWORK_INPUT_SIZE = 300, 300
# Value from https://towardsdatascience.com/face-detection-models-which-to-use-and-why-d263e82c302c
BLOB_MEAN_SUBTRACTION: Color = Color(r=123, b=104, g=117)
"""
Challenge track robust to cuts
"""
@dataclass
class TrackedFace(GenderedFace):
id_: int = None
tracker: dlib.correlation_tracker = None
staleness: int = 0
class FaceTracker:
def __init__(
self,
