def recognize_video(detector,
embedder: Embedder,
recognizer: Recognizer,
detector_params='default',
source=0):
# инициализация видеопотока
print('Starting video stream...')
vs = VideoStream(src=source).start()
if not is_detector(detector):
raise TypeError('Incorrect type of detector')
# разогрев камеры
time.sleep(0.5)
# запуск оценщика пропускной способности FPS
fps = FPS().start()
# цикл по фреймам из видео
while True:
frame = vs.read()
if detector_params == 'default':
faces_roi, boxes = detector.calc_image(frame, return_mode='both')
elif type(detector) == DetectorSSD:
confidence = detector_params[0]
faces_roi, boxes = detector.calc_image(frame,
confidence=confidence,
return_mode='both')
elif type(detector) == DetectorVJ or type(detector) == DetectorLBP:
[scale_factor, min_neighbors] = detector_params
faces_roi, boxes = detector.calc_image(frame,
scale_factor=scale_factor,
min_neighbors=min_neighbors,
return_mode='both')
elif type(detector) == DetectorHOG or type(detector) == DetectorMMOD:
upsampling_times = detector_params[0]
faces_roi, boxes = detector.calc_image(
frame, upsampling_times=upsampling_times, return_mode='both')
for i in range(len(faces_roi)):
embeddings = embedder.calc_face(faces_roi[i])
name = recognizer.recognize(embeddings)
start_x, start_y, end_x, end_y = boxes[i]
text = '{}'.format(name)
y = start_y - 10 if start_y - 10 > 10 else start_y + 10
cv2.rectangle(frame, (start_x, start_y), (end_x, end_y),
(0, 0, 255), 2)
cv2.putText(frame, text, (start_x, y), cv2.FONT_HERSHEY_SIMPLEX,
0.45, (0, 0, 255), 2)
# обновление счетчика FPS
fps.update()
# показ выходного фрейма
cv2.imshow('Frame', frame)
key = cv2.waitKey(1) & 0xFF
# завершение при нажатии 'q'
if key == ord("q"):
break
fps.stop()
print('Elasped time: {:.2f}'.format(fps.elapsed()))
print('Approx. FPS: {:.2f}'.format(fps.fps()))
cv2.destroyAllWindows()
vs.stop()
def recognize_image(detector,
embedder: Embedder,
recognizer: Recognizer,
image,
detector_params='default',
showing=True,
saving_path=''):
image_t = type(image)
if image_t is str:
image = image.replace('/', os.path.sep)
image = cv2.imread(image)
elif image_t is list:
image = np.array(image)
if not is_detector(detector):
raise TypeError('Incorrect type of detector')
# вызов детектора с заданными параметрами
if detector_params == 'default':
faces_roi, boxes = detector.calc_image(image, return_mode='both')
elif type(detector) == DetectorSSD:
confidence = detector_params[0]
faces_roi, boxes = detector.calc_image(image,
confidence=confidence,
return_mode='both')
elif type(detector) == DetectorVJ or type(detector) == DetectorLBP:
[scale_factor, min_neighbors] = detector_params
faces_roi, boxes = detector.calc_image(image,
scale_factor=scale_factor,
min_neighbors=min_neighbors,
return_mode='both')
elif type(detector) == DetectorHOG or type(detector) == DetectorMMOD:
upsampling_times = detector_params[0]
faces_roi, boxes = detector.calc_image(
image, upsampling_times=upsampling_times, return_mode='both')
# для каждого обнаруженного лица
for i in range(len(faces_roi)):
# получение признаков
embeddings = embedder.calc_face(faces_roi[i])
# получение класса
name = recognizer.recognize(embeddings)
start_x, start_y, end_x, end_y = boxes[i]
# рисование ограничительной рамки лица, написание имени
text = '{}'.format(name)
y = start_y - 10 if start_y - 10 > 10 else start_y + 10
cv2.rectangle(image, (start_x, start_y), (end_x, end_y), (255, 0, 0),
4)
cv2.putText(image, text, (start_x, y), cv2.FONT_HERSHEY_SIMPLEX, 1.5,
(255, 0, 0), 4)
if saving_path != '':
cv2.imwrite(saving_path, image)
# если нужно вывести изоражение на экран
if showing:
cv2.imshow('Image', image)
cv2.waitKey(0)
def make_dataset(images_path='dataset'):
detector = DetectorSSD()
embedder = Embedder()
images_path = images_path.replace('/', os.path.sep)
ind_image_paths = list(paths.list_images(images_path))
# списки для лиц и имен, взаимосоответствующие по индексу
known_ems = []
known_names = []
for (i, image_path) in enumerate(ind_image_paths):
print(i + 1)
# получение имя из пути к файлу
name = image_path.split(os.path.sep)[-2]
image = cv2.imread(image_path)
# ОБЫЧНОЕ
temp_img = image.copy()
face = detector.calc_image(temp_img)[0]
embeddings = embedder.calc_face(face)
known_ems.append(embeddings.flatten())
known_names.append(name)
# ТЕМНЕЕ 4
for i in range(1, 5):
temp_img = image.copy()
face = detector.calc_image(ie.darken(temp_img, i / 10))
if len(face) > 0:
embeddings = embedder.calc_face(face[0])
known_ems.append(embeddings.flatten())
known_names.append(name)
# СВЕТЛЕЕ 4
for i in range(1, 5):
temp_img = image.copy()
face = detector.calc_image(ie.brighten(temp_img, i / 10))
if len(face) > 0:
embeddings = embedder.calc_face(face[0])
known_ems.append(embeddings.flatten())
known_names.append(name)
# ДОЖДЬ 3
temp_img = image.copy()
face = detector.calc_image(ie.add_rain(temp_img, rain_type='drizzle'))
if len(face) > 0:
embeddings = embedder.calc_face(face[0])
known_ems.append(embeddings.flatten())
known_names.append(name)
temp_img = image.copy()
face = detector.calc_image(ie.add_rain(temp_img, rain_type='heavy'))
if len(face) > 0:
embeddings = embedder.calc_face(face[0])
known_ems.append(embeddings.flatten())
known_names.append(name)
temp_img = image.copy()
face = detector.calc_image(
ie.add_rain(temp_img, rain_type='torrential'))
if len(face) > 0:
embeddings = embedder.calc_face(face[0])
known_ems.append(embeddings.flatten())
known_names.append(name)
# ТУМАН 4
for i in range(1, 5):
temp_img = image.copy()
face = detector.calc_image(ie.add_fog(temp_img, i / 10))
if len(face) > 0:
embeddings = embedder.calc_face(face[0])
known_ems.append(embeddings.flatten())
known_names.append(name)
embeddings = {'embeddings': known_ems, 'names': known_names}
f = open('weather_ems/ems_weather_4.pickle', 'wb')
f.write(pickle.dumps(embeddings))
f.close()
print(len(known_ems), len(known_names))
return known_ems, known_names