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model_name and distance_metric check
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@ -27,12 +27,6 @@ def verify(img1_path, img2_path
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#-------------------------
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#print("Face verification will be applied on ",model_name," model and ",distance_metric," metric")
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functions.validateInputs(model_name, distance_metric)
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#-------------------------
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#tuned thresholds for model and metric pair
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threshold = functions.findThreshold(model_name, distance_metric)
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@ -53,6 +47,9 @@ def verify(img1_path, img2_path
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model = Facenet.loadModel()
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input_shape = (160, 160)
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else:
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raise ValueError("Invalid model_name passed - ", model_name)
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#-------------------------
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#crop face
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@ -80,6 +77,8 @@ def verify(img1_path, img2_path
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elif distance_metric == 'euclidean_l2':
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print("Using euclidean distance l2 form")
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distance = dst.findEuclideanDistance(dst.l2_normalize(img1_representation), dst.l2_normalize(img2_representation))
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else:
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raise ValueError("Invalid distance_metric passed - ", distance_metric)
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#-------------------------
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#decision
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