import numpy as np
from our_profile import Profile
from sys import exit
from dlib_models import download_model, download_predictor
download_model()
download_predictor()
from dlib_models import load_dlib_models
# this loads the dlib models into memory. You should only import the models *after* loading them.
# This does lazy-loading: it doesn't do anything if the models are already loaded.
load_dlib_models()
from dlib_models import models
face_detect = models["face detect"]
face_rec_model = models["face rec"]
shape_predictor = models["shape predict"]
def match(descriptor, database, threshold):
"""
Takes a descriptor and tries to find a match to a person.
Parameters
----------
descriptor: [np.array()]
A descriptor for an image.
database: Dictionary
{String name : Profile profile}
def main():
namereturn=''
descreturn=np.array([])
download_model()
download_predictor()
# take the picture
pic = take_picture()
# first, we load the models that dlib has to detect faces.
load_dlib_models()
face_detect = models["face detect"]
face_rec_model = models["face rec"]
shape_predictor = models["shape predict"]
# detects the face through corners
detections = list(face_detect(pic))
#print(detections) # list of shape n for n faces
fig, ax = plt.subplots()
ax.imshow(pic)
database = {}
with open("database.pkl", mode="rb") as opened_file:
database = pickle.load(opened_file)
#print("DATABASE")
#print(database)
print("Number of faces detected: {}".format(len(detections)))
for k, d in enumerate(detections):
#Get the landmarks/parts for the face in box d.
shape = shape_predictor(pic, d)
# Draw the face landmarks on the screen.
for i in range(68):
ax.plot(shape.part(i).x, shape.part(i).y, '+', color="blue")
import matplotlib.patches as patches
for faces in detections:
# Create a Rectangle patch
rect = patches.Rectangle((faces.left(), faces.bottom()), faces.width(), -faces.height(), linewidth=1, edgecolor='g',
facecolor='none')
# Add the patch to the Axes
ax.add_patch(rect)
names = {}
database["Unknown Counter"]=0
unknown_counter = database["Unknown Counter"]
for face in detections:
# let's take a look as to what the descriptor is!!
shape = shape_predictor(pic, face)
descriptor = np.array(face_rec_model.compute_face_descriptor(pic, shape))
#print(descriptor)
# compares descriptor to database through img_in_database
cutoff = .4
#print("DATABASE")
#print(database)
name = match.img_in_database(descriptor, database, cutoff)
if name == "not found":
name = "Unknown" + str(unknown_counter)
database["Unknown Counter"] += 1
# plots name underneath square
ax.text(face.left()+(0.25*faces.width()), face.bottom()+(0.2*faces.height()), name, bbox=dict(facecolor='green', alpha=0.5))
# adds to names dictionary
names[name] = descriptor
descreturn = descriptor
print(descreturn)
#plt.show()
return names, name, descreturn
from dlib_models import download_model, download_predictor from dlib_models import load_dlib_models from dlib_models import models download_model() download_predictor() # this loads the dlib models into memory. You should only import the models *after* loading them. # This does lazy-loading: it doesn't do anything if the models are already loaded. load_dlib_models() face_detect = models["face detect"] face_rec_model = models["face rec"] shape_predictor = models["shape predict"] face_rec_model.download_predictor() face_rec_model.download_predictor()
def start_skill():
welcome_message = 'Hello there, would you like me to take your photo?'
download_model()
download_predictor()
load_dlib_models()
return question(welcome_message)