target size arg added into extract faces

retinaface/RetinaFace.py

@@ -1,7 +1,7 @@
 import os
 import warnings
 import logging
-from typing import Union, Any, Optional, Dict
+from typing import Union, Any, Optional, Dict, Tuple, List
 
 # this has to be set before importing tf
 os.environ["TF_USE_LEGACY_KERAS"] = "1"
@@ -220,7 +220,9 @@ def extract_faces(
     align: bool = True,
     allow_upscaling: bool = True,
     expand_face_area: int = 0,
-) -> list:
+    target_size: Optional[Tuple[int, int]] = None,
+    min_max_norm: bool = True,
+) -> List[np.ndarray]:
     """
     Extract detected and aligned faces
     Args:
@@ -230,6 +232,13 @@ def extract_faces(
         align (bool): enable or disable alignment
         allow_upscaling (bool): allowing up-scaling
         expand_face_area (int): expand detected facial area with a percentage
+        target_size (optional tuple): resize the image by padding it with black pixels
+            to fit the specified dimensions. default is None
+        min_max_norm (bool): set this to True if you want to normalize image in [0, 1].
+            this is only running when target_size is not none.
+            for instance, matplotlib expects inputs in this scale. (default is True)
+    Returns:
+        result (List[np.ndarray]): list of extracted faces
     """
     resp = []
 
@@ -289,6 +298,14 @@ def extract_faces(
                 int(rotated_y1) : int(rotated_y2), int(rotated_x1) : int(rotated_x2)
             ]
 
-        resp.append(facial_img[:, :, ::-1])
+        if target_size is not None:
+            facial_img = postprocess.resize_image(
+                img=facial_img, target_size=target_size, min_max_norm=min_max_norm
+            )
+
+        # to rgb
+        facial_img = facial_img[:, :, ::-1]
+
+        resp.append(facial_img)
 
     return resp

retinaface/commons/postprocess.py

@@ -1,7 +1,18 @@
+# built-in dependencies
 import math
 from typing import Union, Tuple
+
+# 3rd party dependencies
 import numpy as np
 from PIL import Image
+import cv2
+import tensorflow as tf
+
+tf_major_version = int(tf.__version__.split(".", maxsplit=1)[0])
+if tf_major_version == 1:
+    from keras.preprocessing import image
+else:
+    from tensorflow.keras.preprocessing import image
 
 
 # pylint: disable=unused-argument
@@ -143,6 +154,58 @@ def rotate_facial_area(
     return (x1, y1, x2, y2)
 
 
+def resize_image(
+    img: np.ndarray, target_size: Tuple[int, int], min_max_norm: bool = True
+) -> np.ndarray:
+    """
+    Resize an image to expected size of a ml model with adding black pixels.
+        Ref: github.com/serengil/deepface/blob/master/deepface/modules/preprocessing.py
+    Args:
+        img (np.ndarray): pre-loaded image as numpy array
+        target_size (tuple): input shape of ml model
+        min_max_norm (bool): set this to True if you want to normalize image in [0, 1].
+            this is only running when target_size is not none.
+            for instance, matplotlib expects inputs in this scale. (default is True)
+    Returns:
+        img (np.ndarray): resized input image
+    """
+    factor_0 = target_size[0] / img.shape[0]
+    factor_1 = target_size[1] / img.shape[1]
+    factor = min(factor_0, factor_1)
+
+    dsize = (
+        int(img.shape[1] * factor),
+        int(img.shape[0] * factor),
+    )
+    img = cv2.resize(img, dsize)
+
+    diff_0 = target_size[0] - img.shape[0]
+    diff_1 = target_size[1] - img.shape[1]
+
+    # Put the base image in the middle of the padded image
+    img = np.pad(
+        img,
+        (
+            (diff_0 // 2, diff_0 - diff_0 // 2),
+            (diff_1 // 2, diff_1 - diff_1 // 2),
+            (0, 0),
+        ),
+        "constant",
+    )
+
+    # double check: if target image is not still the same size with target.
+    if img.shape[0:2] != target_size:
+        img = cv2.resize(img, target_size)
+
+    # make it 4-dimensional how ML models expect
+    img = image.img_to_array(img)
+
+    if min_max_norm is True and img.max() > 1:
+        img = (img.astype(np.float32) / 255.0).astype(np.float32)
+
+    return img
+
+
 def bbox_pred(boxes, box_deltas):
     """
     This function is copied from the following code snippet:

tests/test_actions.py

@@ -107,3 +107,13 @@ def test_different_expanding_ratios():
                 plt.imshow(face)
                 plt.axis("off")
                 plt.show()
+
+
+def test_resize():
+    faces = RetinaFace.extract_faces(img_path="tests/dataset/img11.jpg", target_size=(224, 224))
+    for face in faces:
+        assert face.shape == (224, 224, 3)
+        if do_plotting is True:
+            plt.imshow(face)
+            plt.show()
+    logger.info("✅ resize test done")