svm classifier

face_recognition_svm.py

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