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@@ -0,0 +1,67 @@
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+import face_recognition
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+from sklearn import svm
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+import joblib
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+import os
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+
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+# Training the SVC classifier
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+
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+# The training data would be all the face encodings from all the known images and the labels are their names
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+encodings = []
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+names = []
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+train_list = []
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+
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+is_train = False
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+
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+if not is_train:
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+ try:
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+ clf = joblib.load('saved_model.pkl')
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+ except:
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+ clf = None
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+ if clf is None:
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+ is_train = True
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+
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+if is_train:
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+ # Training directory
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+ train_str = os.path.join('data', 'peeps', 'anchor')
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+ train_dir = os.listdir(os.path.join('data', 'peeps', 'anchor'))
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+
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+ # Loop through each person in the training directory
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+ for person in train_dir:
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+ pix_str = os.path.join(train_str, person)
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+ pix = os.listdir(os.path.join(train_str, person))
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+
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+ # Loop through each training image for the current person
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+ for person_img in pix:
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+ # Get the face encodings for the face in each image file
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+ face = face_recognition.load_image_file(pix_str + "/" + person_img)
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+ face_bounding_boxes = face_recognition.face_locations(face)
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+
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+ # If training image contains exactly one face
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+ if len(face_bounding_boxes) == 1:
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+ face_enc = face_recognition.face_encodings(face)[0]
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+ # Add face encoding for current image with corresponding label (name) to the training data
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+ encodings.append(face_enc)
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+ names.append(person)
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+ else:
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+ print(person + "/" + person_img + " was skipped and can't be used for training")
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+
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+ # Create and train the SVC classifier
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+
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+ clf = svm.SVC(gamma='scale', probability=True)
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+ clf.fit(encodings, names)
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+ joblib.dump(clf, 'saved_model.pkl')
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+
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+# Load the test image with unknown faces into a numpy array
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+test_image = face_recognition.load_image_file(os.path.join('application_data', 'input_image', 'input_image.jpg'))
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+
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+# Find all the faces in the test image using the default HOG-based model
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+face_locations = face_recognition.face_locations(test_image)
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+no = len(face_locations)
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+print("Number of faces detected: ", no)
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+
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+# Predict all the faces in the test image using the trained classifier
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+print("Found:")
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+for i in range(no):
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+ test_image_enc = face_recognition.face_encodings(test_image)[i]
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+ name = clf.predict([test_image_enc])
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+ print(*name)
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