Create face_recognition_svm.py

Find all the faces in an image then recognize them using a SVM with scikit-learn
This allows you to train multiple images per person

examples/face_recognition_svm.py

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+# Find all the faces in an image then recognize them using a SVM with scikit-learn
+# This allows you to train multiple images per person
+
+"""
+Structure:
+        <test_image>.jpg
+        <train_dir>/
+            <person_1>/
+                <person_1_face-1>.jpg
+                <person_1_face-2>.jpg
+                .
+                .
+                <person_1_face-n>.jpg
+           <person_2>/
+                <person_2_face-1>.jpg
+                <person_2_face-2>.jpg
+                .
+                .
+                <person_2_face-n>.jpg
+            .
+            .
+            <person_n>/
+                <person_n_face-1>.jpg
+                <person_n_face-2>.jpg
+                .
+                .
+                <person_n_face-n>.jpg
+"""
+
+# Install scikit-learn if you haven't already with pip
+# $ pip3 install scikit-learn
+
+import face_recognition
+from sklearn import svm
+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 = []
+
+# Training directory
+train_dir = os.listdir('/train_dir/')
+
+# Loop through each person in the training directory
+for person in train_dir:
+    pix = os.listdir("/train_dir/" + 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("/train_dir/" + person + "/" + person_img)
+        face_enc = face_recognition.face_encodings(pic)[0]
+
+        # Add face encoding for current image with corresponding label (name) to the training data
+        encodings.append(face_enc)
+        names.append(person)
+
+# Create and train the SVC classifier
+clf = svm.SVC(gamma='scale')
+clf.fit(encodings,names)
+
+# Load the test image with unknown faces into a numpy array
+test_image = face_recognition.load_image_file('test_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: \n")
+for i in range(no):
+    test_image_enc = face_recognition.face_encodings(test_image)[i]
+    name = clf.predict([test_image_enc])
+    print(*name)