def __init__(self, filename='config.csv', video=0):
self.mask = None
self.detected = False
self.right = True
self.forward = False
self.rest = False
self.frame = np.zeros([600, 600, 3], dtype=np.uint8).fill(255)
self.distance = 0
self.vs = cv2.VideoCapture(video)
self.mask_detector = MaskDetector(filename)
class DlibProcessor(BaseProcessor):
def __init__(self):
super().__init__()
self.recognizer = Recognizer()
self.detector = MaskDetector()
def process_detected_faces(self, image, locations):
self.process_faces(image, locations)
def get_face_locations(self, image):
return face_recognition.face_locations(image)
def process_faces(self, image, locations):
mask_per_person = self.detector.detect_and_predict_mask(image, locations)
face_encodings_on_frame = face_recognition.face_encodings(image, locations)
face_landmarks_list = face_recognition.face_landmarks(image, locations)
number_of_faces = len(face_landmarks_list)
number_of_face_encoded = len(face_encodings_on_frame)
print("I found {} face(s) and encoded {} faces in this photograph.".format(number_of_faces,
number_of_face_encoded))
if len(mask_per_person) != len(locations):
return
for index in range(0, len(locations)):
location = locations[index]
in_mask = self.detector.is_mask(mask_per_person[index])
print("In mask", in_mask)
if in_mask:
color = (0, 255, 0)
else:
color = (0, 0, 255)
if self.config.drawLandmarks:
face_landmarks = face_landmarks_list[index]
self.draw_landmarks(face_landmarks, image)
if self.config.enableFaceRecognition:
print("location dlib ", location)
face_encoding = face_encodings_on_frame[index]
person_name = self.recognizer.recognize_person(face_encoding)
print("Recognized : ", person_name)
else:
person_name = None
image = self.draw_face_rectangle(image, location, self.get_rectangle_title(in_mask, person_name), color)
class CustomModelProcessor(BaseProcessor):
def __init__(self):
super().__init__()
self.recognizer = Recognizer()
self.detector = MaskDetector()
self.face_detector = FaceDetector()
self.dlibProcessor = DlibProcessor()
def get_face_locations(self, image):
return self.face_detector.detect_faces(image)
def process_detected_faces(self, image, faces):
if faces is None or len(faces) == 0:
return
detected_faces, locations = [], []
for x, y in faces:
detected_faces.append(x)
locations.append(y)
mask_per_person = self.detector.predict_mask(detected_faces)
if len(mask_per_person) < len(faces):
return
all_without_mask = self.is_all_without_masks(mask_per_person)
if all_without_mask:
self.dlibProcessor.process_single_image(image)
else:
self.process_with_masks(faces, image, locations, mask_per_person)
def process_with_masks(self, faces, image, locations, mask_per_person):
for index in range(0, len(faces)):
location = locations[index]
(mask, withoutMask) = mask_per_person[index]
# determine the class label and color we'll use to draw
# the bounding box and text
in_mask = mask > withoutMask
label = "Mask" if in_mask else "No Mask"
if label == "Mask":
color = (0, 255, 0)
else:
color = (0, 0, 255)
label = "{}: {:.2f}%".format(label, max(mask, withoutMask) * 100)
image = self.draw_face_rectangle(image, location, label, color)
def is_all_without_masks(self, mask_per_persons):
if mask_per_persons is None or len(mask_per_persons) == 0:
return True
for index in range(0, len(mask_per_persons)):
(mask, without_mask) = mask_per_persons[index]
if mask > without_mask:
return False
return True
def __init__(self):
super().__init__()
self.recognizer = Recognizer()
self.detector = MaskDetector()
def __init__(self):
super().__init__()
self.recognizer = Recognizer()
self.detector = MaskDetector()
self.face_detector = FaceDetector()
self.dlibProcessor = DlibProcessor()
class BallDetector(object):
def __init__(self, filename='config.csv', video=0):
self.mask = None
self.detected = False
self.right = True
self.forward = False
self.rest = False
self.frame = np.zeros([600, 600, 3], dtype=np.uint8).fill(255)
self.distance = 0
self.vs = cv2.VideoCapture(video)
self.mask_detector = MaskDetector(filename)
def getFrame(self):
_, img = self.vs.read()
self.frame = img
self.frame = imutils.resize(self.frame, width=600)
self.view = self.frame.copy()
return self.frame
def detectBall(self):
self.mask = self.mask_detector.getMask(self.getFrame())
cnts = cv2.findContours(self.mask.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
radius = 0
self.rest = False
self.detected = False
if len(cnts) > 0:
self.detected = True
c = max(cnts, key=cv2.contourArea)
((x, y), radius) = cv2.minEnclosingCircle(c)
cv2.circle(self.view, (int(x), int(y)), int(radius), (0, 255, 255),
2)
distance = 0
if (radius > MIN_RADIUS) and (radius < MAX_RADIUS):
# Values range
distance = map_values(radius, MIN_RADIUS, MAX_RADIUS,
MIN_DISTANCE, MAX_DISTANCE)
distance = abs(distance - 100)
print(distance)
self.rest = True
self.forward = False
if distance > 20:
self.rest = False
if (x >= 0) and (x < 100):
self.right = False
elif (x >= 100) and (x < 500):
self.forward = True
else:
self.right = True
def getValue(self):
self.detectBall()
cv2.imshow("Frame", self.view)
cv2.waitKey(1)
return self.detected, self.forward, self.right, self.rest
def close(self):
self.vs.release()
import cv2
from detector import MaskDetector
from detector import ColorDetertor
detect_mask = MaskDetector(filename='config.csv')
detect_color = ColorDetertor(filename='config.csv')
image = cv2.imread('../images/sample.jpg')
mask = detect_mask.getMask(image)
color = detect_color.detect(image)
print('Color Status: {}'.format(color))
cv2.imshow('Mask', mask)
cv2.waitKey(0)