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initialize input moved to functions
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@ -89,7 +89,7 @@ def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric =
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tic = time.time()
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img_list, bulkProcess = initialize_input(img1_path, img2_path)
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img_list, bulkProcess = functions.initialize_input(img1_path, img2_path)
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functions.initialize_detector(detector_backend = detector_backend)
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resp_objects = []
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@ -304,7 +304,7 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True
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"""
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img_paths, bulkProcess = initialize_input(img_path)
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img_paths, bulkProcess = functions.initialize_input(img_path)
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functions.initialize_detector(detector_backend = detector_backend)
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#---------------------------------
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@ -470,7 +470,7 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
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tic = time.time()
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img_paths, bulkProcess = initialize_input(img_path)
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img_paths, bulkProcess = functions.initialize_input(img_path)
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functions.initialize_detector(detector_backend = detector_backend)
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#-------------------------------
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@ -756,29 +756,6 @@ def detectFace(img_path, detector_backend = 'mtcnn'):
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img = functions.preprocess_face(img = img_path, detector_backend = detector_backend)[0] #preprocess_face returns (1, 224, 224, 3)
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return img[:, :, ::-1] #bgr to rgb
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def initialize_input(img1_path, img2_path = None):
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"""
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verify, analyze and find functions build complex machine learning models in every call.
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To avoid memory problems, you can pass image pairs as array. This function manages this usage is enabled or not
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E.g.
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result = DeepFace.verify("img1.jpg", "img2.jpg")
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results = DeepFace.verify([['img1.jpg', 'img2.jpg'], ['img1.jpg', 'img3.jpg']])
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"""
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if type(img1_path) == list:
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bulkProcess = True
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img_list = img1_path.copy()
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else:
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bulkProcess = False
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if img2_path != None:
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img_list = [[img1_path, img2_path]]
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else:
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img_list = [img1_path]
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return img_list, bulkProcess
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#---------------------------
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#main
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@ -20,6 +20,20 @@ import bz2
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from deepface.commons import distance
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from mtcnn import MTCNN #0.1.0
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def initialize_input(img1_path, img2_path = None):
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if type(img1_path) == list:
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bulkProcess = True
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img_list = img1_path.copy()
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else:
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bulkProcess = False
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if img2_path != None:
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img_list = [[img1_path, img2_path]]
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else:
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img_list = [img1_path]
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return img_list, bulkProcess
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def initialize_detector(detector_backend):
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global face_detector
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