def __init__(self):
self.logger = LoggerFactory()
self.imagesProvider = PeopleImagesProvider()
self.recognizersProvider = FaceRecognizerProvider()
self.openCvRecognizer = OpenCvFaceRecognizerForTesting()
self.azureRecognizer = AzureFaceRecognizer()
self.faceDetector = FaceDetectorsManager()
self.neuralNetworkToUse = 40
class OpenCvFaceRecognizerForTesting():
def __init__(self):
self.logger = LoggerFactory()
self.faceDetectorManager = FaceDetectorsManager()
self.imageConverter = ImageConverter()
def recognize_with_single_recognizer_with_haar(self, face_recognizer,
image_path):
detection_start = time.time()
image = cv2.imread(image_path)
detected_faces = self.faceDetectorManager.get_face_by_haar(image)
detection_end = time.time()
detection_time = detection_end - detection_start
start_time = time.time()
self.logger.info(f"Using {face_recognizer} recognizer on {image_path}")
if len(detected_faces) is 0:
return 0
(startX, startY, endX, endY) = detected_faces[0]
predict_image = self.imageConverter.convert_to_np_array(
image[startY:endY, startX:endX])
nbr_predicted, confidence = face_recognizer.predict(predict_image)
return nbr_predicted
def recognize_with_single_recognizer_with_dnn(self, face_recognizer,
image_path):
detection_start = time.time()
image = cv2.imread(image_path)
detected_faces = self.faceDetectorManager.get_face_by_dnn(image)
detection_end = time.time()
detection_time = detection_end - detection_start
start_time = time.time()
self.logger.info(f"Using {face_recognizer} recognizer on {image_path}")
if len(detected_faces) is 0:
return 0
(startX, startY, endX, endY) = detected_faces[0]
predict_image = self.imageConverter.convert_to_np_array(
image[startY:endY, startX:endX])
nbr_predicted, confidence = face_recognizer.predict(predict_image)
return nbr_predicted
class DetectionRequestsManager():
def __init__(self):
self.logger = LoggerFactory()
self.faceDetectorsManager = FaceDetectorsManager()
self.resultsOperator = ResultsOperator()
self.inputFileProvider = InputFileProvider()
def process_request(self, request: Detection):
self.logger.info(
f"Working on Face Detection Request id: {request.id} started")
input_file_path = self.inputFileProvider.get_detection_input_file_path(
request.id)
self.logger.info(f"Running all face detectors")
results = self.faceDetectorsManager.get_faces_on_image_from_file_path(
input_file_path)
self.resultsOperator.prepare_and_upload_results(
request.id, results, input_file_path)
self.logger.info(f"Finished Face Detection Request id: {request.id} ")
class Tester:
def __init__(self):
self.logger = LoggerFactory()
self.imagesProvider = PeopleImagesProvider()
self.recognizersProvider = FaceRecognizerProvider()
self.openCvRecognizer = OpenCvFaceRecognizerForTesting()
self.azureRecognizer = AzureFaceRecognizer()
self.faceDetector = FaceDetectorsManager()
self.neuralNetworkToUse = 40
def test_all_photos(self):
faces = 0
all_photos_with_people = self.imagesProvider.get_image_paths_for_people(range(2, 102), 20)
self.logger.info("test")
for person_id, person_image in all_photos_with_people:
found_faces= self.faceDetector.get_face_by_azure(person_image)
if found_faces == 0 or len(found_faces) == 0:
continue
else:
faces = faces + 1
self.logger.info(f"FACES: {faces} ")
def __init__(self):
self.logger = LoggerFactory()
self.faceDetectorsManager = FaceDetectorsManager()
self.resultsOperator = ResultsOperator()
self.inputFileProvider = InputFileProvider()
def __init__(self):
self.logger = LoggerFactory()
self.faceDetectorManager = FaceDetectorsManager()
self.imageConverter = ImageConverter()
self.recognitionResultRepo = RecognitionResultRepository()
class OpenCvFaceRecognizer():
def __init__(self):
self.logger = LoggerFactory()
self.faceDetectorManager = FaceDetectorsManager()
self.imageConverter = ImageConverter()
self.recognitionResultRepo = RecognitionResultRepository()
def recognize_face_from_image(self, request_id, recognizers, image_path):
detection_start = time.time()
image = cv2.imread(image_path)
detected_faces = self.faceDetectorManager.get_face_by_dnn(image)
detection_end = time.time()
detection_time = detection_end - detection_start
for face_recognizer, file_id in recognizers:
start_time = time.time()
self.logger.info(
f"Using {face_recognizer} recognizer created from {file_id} file id"
)
if len(detected_faces) is 0:
self.__add_empty_result__(file_id, request_id, start_time,
detection_time)
for (startX, startY, endX, endY) in detected_faces:
predict_image = self.imageConverter.convert_to_np_array(
image[startY:endY, startX:endX])
nbr_predicted, confidence = face_recognizer.predict(
predict_image)
self.__add_result__(confidence, file_id, nbr_predicted,
request_id, start_time, detection_time)
def recognize_with_single_recognizer(self, face_recognizer, image_path):
detection_start = time.time()
image = cv2.imread(image_path)
detected_faces = self.faceDetectorManager.get_face_by_haar(image)
detection_end = time.time()
detection_time = detection_end - detection_start
start_time = time.time()
self.logger.info(f"Using {face_recognizer} recognizer on {image_path}")
if len(detected_faces) is 0:
return 0
(startX, startY, endX, endY) = detected_faces[0]
predict_image = self.imageConverter.convert_to_np_array(
image[startY:endY, startX:endX])
nbr_predicted, confidence = face_recognizer.predict(predict_image)
return nbr_predicted
def __add_result__(self, confidence, file_id, nbr_predicted, request_id,
start_time, detection_time):
self.logger.info(
f"Recognized identity: {nbr_predicted} confidence:{confidence}")
end_time = time.time()
process_time = end_time - start_time + detection_time
result = RecognitionResult(nbr_predicted, request_id, confidence,
file_id, str(process_time))
self.recognitionResultRepo.add_recognition_result(result)
def __add_empty_result__(self, azure_file, request_id, start_time,
detection_time):
end_time = time.time()
process_time = end_time - start_time + detection_time
result = RecognitionResult(0, request_id, 0, azure_file.id,
str(process_time), "No faces detected")
self.recognitionResultRepo.add_recognition_result(result)
def __init__(self):
self.logger = LoggerFactory()
self.faceDetectorManager = FaceDetectorsManager()
self.imageConverter = ImageConverter()