Initial commit

easy_facial_recognition.py

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+# Code Anis - Defend Intelligence
+import cv2
+import dlib
+import PIL.Image
+import numpy as np
+from imutils import face_utils
+import argparse
+from pathlib import Path
+import os
+import ntpath
+
+parser = argparse.ArgumentParser(description='Easy Facial Recognition App')
+parser.add_argument('-i', '--input', type=str, required=True, help='directory of input known faces')
+
+print('[INFO] Starting System...')
+print('[INFO] Importing pretrained model..')
+pose_predictor_68_point = dlib.shape_predictor("pretrained_model/shape_predictor_68_face_landmarks.dat")
+pose_predictor_5_point = dlib.shape_predictor("pretrained_model/shape_predictor_5_face_landmarks.dat")
+face_encoder = dlib.face_recognition_model_v1("pretrained_model/dlib_face_recognition_resnet_model_v1.dat")
+face_detector = dlib.get_frontal_face_detector()
+print('[INFO] Importing pretrained model..')
+
+
+def transform(image, face_locations):
+    coord_faces = []
+    for face in face_locations:
+        rect = face.top(), face.right(), face.bottom(), face.left()
+        coord_face = max(rect[0], 0), min(rect[1], image.shape[1]), min(rect[2], image.shape[0]), max(rect[3], 0)
+        coord_faces.append(coord_face)
+    return coord_faces
+
+
+def encode_face(image):
+    face_locations = face_detector(image, 1)
+    face_encodings_list = []
+    landmarks_list = []
+    for face_location in face_locations:
+        # DETECT FACES
+        shape = pose_predictor_68_point(image, face_location)
+        face_encodings_list.append(np.array(face_encoder.compute_face_descriptor(image, shape, num_jitters=1)))
+        # GET LANDMARKS
+        shape = face_utils.shape_to_np(shape)
+        landmarks_list.append(shape)
+    face_locations = transform(image, face_locations)
+    return face_encodings_list, face_locations, landmarks_list
+
+
+def easy_face_reco(frame, known_face_encodings, known_face_names):
+    rgb_small_frame = frame[:, :, ::-1]
+    # ENCODING FACE
+    face_encodings_list, face_locations_list, landmarks_list = encode_face(rgb_small_frame)
+    face_names = []
+    for face_encoding in face_encodings_list:
+        if len(face_encoding) == 0:
+            return np.empty((0))
+        # CHECK DISTANCE BETWEEN KNOWN FACES AND FACES DETECTED
+        vectors = np.linalg.norm(known_face_encodings - face_encoding, axis=1)
+        tolerance = 0.6
+        result = []
+        for vector in vectors:
+            if vector <= tolerance:
+                result.append(True)
+            else:
+                result.append(False)
+        if True in result:
+            first_match_index = result.index(True)
+            name = known_face_names[first_match_index]
+        else:
+            name = "Unknown"
+        face_names.append(name)
+
+    for (top, right, bottom, left), name in zip(face_locations_list, face_names):
+        cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2)
+        cv2.rectangle(frame, (left, bottom - 30), (right, bottom), (0, 255, 0), cv2.FILLED)
+        cv2.putText(frame, name, (left + 2, bottom - 2), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 1)
+
+    for shape in landmarks_list:
+        for (x, y) in shape:
+            cv2.circle(frame, (x, y), 1, (255, 0, 255), -1)
+
+
+if __name__ == '__main__':
+    args = parser.parse_args()
+
+    print('[INFO] Importing faces...')
+    face_to_encode_path = Path(args.input)
+    files = [file_ for file_ in face_to_encode_path.rglob('*.jpg')]
+
+    for file_ in face_to_encode_path.rglob('*.png'):
+        files.append(file_)
+    if len(files)==0:
+        raise ValueError('No faces detect in the directory: {}'.format(face_to_encode_path))
+    known_face_names = [os.path.splitext(ntpath.basename(file_))[0] for file_ in files]
+
+    known_face_encodings = []
+    for file_ in files:
+        image = PIL.Image.open(file_)
+        image = np.array(image)
+        face_encoded = encode_face(image)[0][0]
+        known_face_encodings.append(face_encoded)
+
+    print('[INFO] Faces well imported')
+    print('[INFO] Starting Webcam...')
+    video_capture = cv2.VideoCapture(0)
+    print('[INFO] Webcam well started')
+    print('[INFO] Detecting...')
+    while True:
+        ret, frame = video_capture.read()
+        easy_face_reco(frame, known_face_encodings, known_face_names)
+        cv2.imshow('Easy Facial Recognition App', frame)
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+    print('[INFO] Stopping System')
+    video_capture.release()
+    cv2.destroyAllWindows()