Sped up face alignment by using cv2 for rotation rather than PIL

deepface/modules/detection.py

@@ -12,7 +12,6 @@ from deepface.models.Detector import Detector, DetectedFace, FacialAreaRegion
 from deepface.commons import image_utils
 
 from deepface.commons.logger import Logger
-import time
 
 logger = Logger()
 
@@ -247,9 +246,14 @@ def detect_faces(
         # discard rest of the items
         facial_areas = facial_areas[0:max_faces]
 
-    start_time = time.time()
     results = []
+
     for facial_area in facial_areas:
+        results.append(expand_and_align_face(facial_area=facial_area, img=img, align=align, expand_percentage=expand_percentage, width_border=width_border, height_border=height_border))
+
+    return results
+
+def expand_and_align_face(facial_area: FacialAreaRegion, img: np.ndarray, align: bool, expand_percentage: int, width_border: int, height_border: int) -> DetectedFace:
     x = facial_area.x
     y = facial_area.y
     w = facial_area.w
@@ -271,7 +275,6 @@ def detect_faces(
 
     # extract detected face unaligned
     detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
-
     # align original image, then find projection of detected face area after alignment
     if align is True:  # and left_eye is not None and right_eye is not None:
         aligned_img, angle = align_img_wrt_eyes(img=img, left_eye=left_eye, right_eye=right_eye)
@@ -292,17 +295,13 @@ def detect_faces(
         if right_eye is not None:
             right_eye = (right_eye[0] - width_border, right_eye[1] - height_border)
             
-        result = DetectedFace(
+    return DetectedFace(
         img=detected_face,
         facial_area=FacialAreaRegion(
             x=x, y=y, h=h, w=w, confidence=confidence, left_eye=left_eye, right_eye=right_eye
         ),
         confidence=confidence,
     )
-        results.append(result)
-
-    return results
-
 
 def align_img_wrt_eyes(
     img: np.ndarray,
@@ -327,7 +326,12 @@ def align_img_wrt_eyes(
         return img, 0
 
     angle = float(np.degrees(np.arctan2(left_eye[1] - right_eye[1], left_eye[0] - right_eye[0])))
-    img = np.array(Image.fromarray(img).rotate(angle, resample=Image.BICUBIC))
+
+    (h, w) = img.shape[:2]
+    center = (w // 2, h // 2)
+    M = cv2.getRotationMatrix2D(center, -angle, 1.0)
+    img = cv2.warpAffine(img, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_CONSTANT, borderValue=(0,0,0))
+
     return img, angle