First Commit

FaceSimilarity.py

@@ -0,0 +1,64 @@
+# face verification with the VGGFace2 model
+import matplotlib.pyplot as plt
+from PIL import Image
+import numpy as np
+from scipy.spatial.distance import cosine
+from mtcnn.mtcnn import MTCNN
+from utils import preprocess_input
+from vggface import VGGFace
+import sys
+
+
+# extract a single face from a given photograph
+def extract_face(filename, required_size=(224, 224)):
+    img = plt.imread(filename)
+    detector = MTCNN()
+    results = detector.detect_faces(img)
+    x1, y1, width, height = results[0]['box']
+    x2, y2 = x1 + width, y1 + height
+    # extract the face
+    face = img[y1:y2, x1:x2]
+    # resize pixels to the model size
+    image = Image.fromarray(face)
+    image = image.resize(required_size)
+    face_array = np.asarray(image)
+    return face_array
+
+
+# extract faces and calculate face embeddings for a list of photo files
+def get_embeddings(filenames):
+    # extract faces
+    faces = [extract_face(f) for f in filenames]
+    # convert into an array of samples
+    samples = np.asarray(faces, 'float32')
+    # prepare the face for the model, e.g. center pixels
+    samples = preprocess_input(samples, version=2)
+
+    model = VGGFace(model='resnet50', include_top=False, input_shape=(224, 224, 3), pooling='avg')
+    pred = model.predict(samples)
+    return pred
+
+
+# determine if a candidate face is a match for a known face
+def is_match(known_embedding, candidate_embedding, thresh=0.5):
+    # calculate distance between embeddings
+    score = cosine(known_embedding, candidate_embedding)
+
+    print('*****************************************************************')
+    print('Threshold for the face similarity score is 0.5')
+
+    if score <= thresh:
+        print('Face is a Match with score of %.3f' % score)
+    else:
+        print('Face is not a Match with score of %.3f' % score)
+
+    print('********************************************************************')
+
+
+def main():
+    embeddings = get_embeddings([sys.argv[1], sys.argv[2]])
+    is_match(embeddings[0], embeddings[1])
+
+
+if __name__ == '__main__':
+    main()