class InvaderDetectorWithFace:
def __init__(self):
self.face_finder = FaceFinder()
self.recognizer = FaceRecognizer()
self.recognizer.load('trainer.yml')
self.security_trigger = Trigger(20, lambda similarity: similarity > 80)
def on_enabled_start(self):
pass
def on_disabled_update(self):
pass
def detect(self, frame):
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = self.face_finder.find(gray)
best_face = None
best_confidence = 1000
for coordinates in faces:
region_of_interest = get_region_of_interest(gray, coordinates)
id_, conf = self.recognizer.predict(region_of_interest)
if conf < best_confidence:
best_face = region_of_interest
best_confidence = conf
print('{}, {}, {}'.format(datetime.now(), id_, conf))
# save_region_of_interest(gray, coordinates)
self.highlight_face(frame, coordinates)
if best_face is not None:
if self.security_trigger.update(best_confidence):
print('Face not match!')
return True
return False
def highlight_face(self, image, coordinates, color=(0, 0, 255)):
x, y, w, h = coordinates
x_end = x + w
y_end = y + h
stroke = 2
cv2.rectangle(image, (x, y), (x_end, y_end), color, stroke)
import cv2
import os
from face_finder import FaceFinder, get_region_of_interest
from face_recognizer import FaceRecognizer
from image_saver import ImageSaver
test_dir = os.path.join('dataset', 'test')
face_finder = FaceFinder()
recognizer = FaceRecognizer()
recognizer.load('trainer.yml')
for file in os.listdir(test_dir):
if file.endswith('png') or file.endswith('jpg'):
filepath = os.path.join(test_dir, file)
image_array = cv2.imread(filepath)
image_array = cv2.cvtColor(image_array, cv2.COLOR_BGR2GRAY)
faces = face_finder.find(image_array)
for coordinates in faces:
region_of_interest = get_region_of_interest(
image_array, coordinates)
predicted_label, confidence = recognizer.predict(
region_of_interest)
print('{}: {}, {}'.format(file, predicted_label, confidence))