def run(self,
detector='detector_ssd_mobilenet_v2',
recognizer=FaceRecognizerResnet.NAME,
image='./samples/redhot/redhot_1.jpg',
visualize=True):
self.set_detector(detector) # Inicializar modelo de deteccion
self.set_recognizer(recognizer) # Inicializar modelo de reconocimiento
npimg = cv2.imread(image, cv2.IMREAD_COLOR) # Leer imagen
if npimg is None: # Verificar lectura de imagen
sys.exit(-1)
t = time.time()
faces = self.detector.detect(npimg) # Correr detector de faces
#print("Detecta {} faces en {} segundos".format(len(faces), round(time.time()-t,2)))
if recognizer:
t = time.time()
faces = self.run_recognizer(
npimg, faces, recognizer) # Correr Reconocimiento de rostros
# print("Reconoce {} faces en {} segundos".format(len(faces), round(time.time()-t, 2)))
img = draw_bboxs(np.copy(npimg), faces) # Dibujar los bounding boxes
if visualize:
f = 2
cv2.imshow('DeepFace',
cv2.resize(img, (img.shape[1] * f,
img.shape[0] * f))) # Mostrar imagen
cv2.waitKey(0)
return faces
def run(self, detector='detector_ssd_mobilenet_v2', recognizer=FaceRecognizerResnet.NAME, image='./samples/blackpink/blackpink1.jpg',
visualize=False):
self.set_detector(detector)
self.set_recognizer(recognizer)
if image is None:
return []
elif isinstance(image, str):
logger.debug('read image, path=%s' % image)
npimg = cv2.imread(image, cv2.IMREAD_COLOR)
elif isinstance(image, np.ndarray):
npimg = image
else:
logger.error('Argument image should be str or ndarray. image=%s' % str(image))
sys.exit(-1)
if npimg is None:
logger.error('image can not be read, path=%s' % image)
sys.exit(-1)
logger.debug('run face detection+ %dx%d' % (npimg.shape[1], npimg.shape[0]))
faces = self.detector.detect(npimg)
logger.debug('run face detection- %s' % len(faces))
if recognizer:
faces = self.run_recognizer(npimg, faces, recognizer)
img = draw_bboxs(np.copy(npimg), faces)
cv2.imwrite('result.jpg', img)
if visualize and visualize not in ['false', 'False']:
cv2.imshow('DeepFace', img)
cv2.waitKey(0)
return faces
def show_with_face(npimg, faces, visualize=False):
if visualize:
img = draw_bboxs(np.copy(npimg), faces)
cv2.imshow('DeepFace', img)
cv2.waitKey(0)