(face) small refactor/cleanup

image_recognition_face_recognition/scripts/face_recognition_node

@@ -29,6 +29,7 @@ class OpenfaceROS:
     ):
         """
         OpenfaceROS class that wraps the FaceRecognizer in a ROS node
+
         :param save_images_folder: path where to store the images
         :param topic_save_images: whether to save images originated from image topic callback
         :param service_save_images: whether to save images originated from a service call

image_recognition_face_recognition/scripts/get_face_recognition

@@ -9,35 +9,48 @@ from image_recognition_msgs.msg import Recognition
 from image_recognition_util import image_writer
 from sensor_msgs.msg import RegionOfInterest
 
-parser = argparse.ArgumentParser(description='Get face recognitions')
-
-# Add arguments
-parser.add_argument('image', type=str, help='Image')
-parser.add_argument(
-    '-v', '--verbose', help="Increase output verbosity", action="store_true")
-args = parser.parse_args()
-
-# Read the image
-img = cv2.imread(args.image)
-
-# Create openface interface
-face_recognizer = FacenetRecognition()
-
-recognized_faces = face_recognizer.face_detection(img)
-print(recognized_faces)
-
-recognitions = []
-for fr in recognized_faces:
-    face_recognition = [math.floor(xi) for xi in fr]
-    recognitions.append(Recognition(
-        roi=RegionOfInterest(
-            x_offset=face_recognition[0],
-            y_offset=face_recognition[1],
-            width=face_recognition[2] - face_recognition[0],
-            height=face_recognition[3] - face_recognition[1],
+
+def main(image, db: Optional[str] = None):
+    # Read the image
+    img = cv2.imread(image)
+
+    # Create openface interface
+    face_recognizer = FaceRecognizer()
+
+    if db:
+        face_recognizer.restore_trained_faces(db)
+
+    recognized_faces = face_recognizer.face_detection(img)
+    print(recognized_faces)
+
+    recognitions = []
+    for fr in recognized_faces:
+        face_recognition = [math.floor(xi) for xi in fr]
+        recognitions.append(
+            Recognition(
+                roi=RegionOfInterest(
+                    x_offset=face_recognition[0],
+                    y_offset=face_recognition[1],
+                    width=face_recognition[2] - face_recognition[0],
+                    height=face_recognition[3] - face_recognition[1],
+                )
+            )
         )
-    )
-)
-    annotated_original_image = image_writer.get_annotated_cv_image(img, recognitions) 
-cv2.imshow("result", annotated_original_image)
-cv2.waitKey(1000)
+
+    annotated_original_image = image_writer.get_annotated_cv_image(img, recognitions)
+    cv2.imshow("result", annotated_original_image)
+    cv2.waitKey(1000)
+
+
+if __name__ == "__main__":
+    import argparse
+    import sys
+
+    parser = argparse.ArgumentParser(description="Get face recognitions")
+
+    # Add arguments
+    parser.add_argument("image", type=str, help="Image")
+    parser.add_argument("-d", "--db", type=argparse.FileType("r"), help="Load already trained faces db from file")
+    args = parser.parse_args()
+
+    sys.exit(main(**vars(args)))

image_recognition_face_recognition/scripts/train_from_images

@@ -24,11 +24,11 @@ def main(modeldir, outfile, verbose: bool = False):
         path = os.path.join(modeldir, cat)
 
         logger.debug('loading images from %s', path)
-        for fname in os.listdir(path):
-            f = os.path.join(path, fname)
+        for filename in os.listdir(path):
+            f = os.path.join(path, filename)
             logger.debug('processing %s', f)
 
-            img = cv2.imread(f, 1) # load as color
+            img = cv2.imread(f, 1)  # load as color
 
             try:
                 logger.debug('training...')
@@ -43,12 +43,12 @@ def main(modeldir, outfile, verbose: bool = False):
 
 
 class ReadableDir(Action):
-    def __call__(self,parser, namespace, values, option_string=None):
-        prospective_dir=values
+    def __call__(self, parser, namespace, values, option_string=None):
+        prospective_dir = values
         if not os.path.isdir(prospective_dir):
             raise ArgumentTypeError("readable_dir:{0} is not a valid path".format(prospective_dir))
         if os.access(prospective_dir, os.R_OK):
-            setattr(namespace,self.dest,prospective_dir)
+            setattr(namespace, self.dest, prospective_dir)
         else:
             raise ArgumentTypeError("readable_dir:{0} is not a readable dir".format(prospective_dir))
 
@@ -59,11 +59,6 @@ if __name__ == '__main__':
     parser.add_argument('modeldir', action=ReadableDir, help='Directory with folders for each category')
     parser.add_argument('outfile', type=FileType('w'), help='Where to output the trained faces database')
 
-    parser.add_argument('-k', '--align_path', type=str, help='DLib Align path', required=False,
-                        default="~/openface/models/dlib/shape_predictor_68_face_landmarks.dat")
-    parser.add_argument('-s', '--net_path', type=str, help='Openface neural network path', required=False,
-                        default='~/openface/models/openface/nn4.small2.v1.t7')
-
     parser.add_argument('-v', '--verbose', action='store_true')
 
     args = parser.parse_args()