Merge pull request #1025 from serengil/feat-task-1602-new-detector-interface

Feat task 1602 new detector interface

deepface/DeepFace.py

@@ -423,7 +423,7 @@ def stream(
 
 def extract_faces(
     img_path: Union[str, np.ndarray],
-    target_size: Tuple[int, int] = (224, 224),
+    target_size: Optional[Tuple[int, int]] = (224, 224),
     detector_backend: str = "opencv",
     enforce_detection: bool = True,
     align: bool = True,

deepface/detectors/DetectorWrapper.py

@@ -1,6 +1,7 @@
-from typing import Any, List
+from typing import Any, List, Tuple
 import numpy as np
-from deepface.models.Detector import Detector, DetectedFace
+from deepface.modules import detection
+from deepface.models.Detector import Detector, DetectedFace, FacialAreaRegion
 from deepface.detectors import (
     FastMtCnn,
     MediaPipe,
@@ -80,10 +81,101 @@ def detect_faces(
         - confidence (float): The confidence score associated with the detected face.
     """
     face_detector: Detector = build_model(detector_backend)
+
+    # validate expand percentage score
     if expand_percentage < 0:
         logger.warn(
             f"Expand percentage cannot be negative but you set it to {expand_percentage}."
             "Overwritten it to 0."
         )
         expand_percentage = 0
-    return face_detector.detect_faces(img=img, align=align, expand_percentage=expand_percentage)
+
+    # find facial areas of given image
+    facial_areas = face_detector.detect_faces(img=img)
+
+    results = []
+    for facial_area in facial_areas:
+        x = facial_area.x
+        y = facial_area.y
+        w = facial_area.w
+        h = facial_area.h
+        left_eye = facial_area.left_eye
+        right_eye = facial_area.right_eye
+        confidence = facial_area.confidence
+
+        # expand the facial area to be extracted and stay within img.shape limits
+        x2 = max(0, x - int((w * expand_percentage) / 100))  # expand left
+        y2 = max(0, y - int((h * expand_percentage) / 100))  # expand top
+        w2 = min(img.shape[1], w + int((w * 2 * expand_percentage) / 100))  # expand right
+        h2 = min(img.shape[0], h + int((h * 2 * expand_percentage) / 100))  # expand bottom
+
+        # extract detected face unaligned
+        detected_face = img[int(y2) : int(y2 + h2), int(x2) : int(x2 + w2)]
+
+        # aligning detected face causes a lot of black pixels
+        # if align is True:
+        #     detected_face, _ = detection.align_face(
+        #         img=detected_face, left_eye=left_eye, right_eye=right_eye
+        #     )
+
+        # 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 = detection.align_face(
+                img=img, left_eye=left_eye, right_eye=right_eye
+            )
+            x1_new, y1_new, x2_new, y2_new = rotate_facial_area(
+                facial_area=(x2, y2, x2 + w2, y2 + h2), angle=angle, direction=1, size=img.shape
+            )
+            detected_face = aligned_img[int(y1_new) : int(y2_new), int(x1_new) : int(x2_new)]
+
+        result = 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 rotate_facial_area(
+    facial_area: Tuple[int, int, int, int], angle: float, direction: int, size: Tuple[int, int]
+) -> Tuple[int, int, int, int]:
+    """
+    Rotate the facial area around its center.
+    Inspried from the work of @UmutDeniz26 - github.com/serengil/retinaface/pull/80
+
+    Args:
+        facial_area (tuple of int): Representing the (x1, y1, x2, y2) of the facial area.
+            x2 is equal to x1 + w1, and y2 is equal to y1 + h1
+        angle (float): Angle of rotation in degrees.
+        direction (int): Direction of rotation (-1 for clockwise, 1 for counterclockwise).
+        size (tuple of int): Tuple representing the size of the image (width, height).
+
+    Returns:
+        rotated_coordinates (tuple of int): Representing the new coordinates
+            (x1, y1, x2, y2) or (x1, y1, x1+w1, y1+h1) of the rotated facial area.
+    """
+    # Angle in radians
+    angle = angle * np.pi / 180
+
+    # Translate the facial area to the center of the image
+    x = (facial_area[0] + facial_area[2]) / 2 - size[1] / 2
+    y = (facial_area[1] + facial_area[3]) / 2 - size[0] / 2
+
+    # Rotate the facial area
+    x_new = x * np.cos(angle) + y * direction * np.sin(angle)
+    y_new = -x * direction * np.sin(angle) + y * np.cos(angle)
+
+    # Translate the facial area back to the original position
+    x_new = x_new + size[1] / 2
+    y_new = y_new + size[0] / 2
+
+    # Calculate the new facial area
+    x1 = x_new - (facial_area[2] - facial_area[0]) / 2
+    y1 = y_new - (facial_area[3] - facial_area[1]) / 2
+    x2 = x_new + (facial_area[2] - facial_area[0]) / 2
+    y2 = y_new + (facial_area[3] - facial_area[1]) / 2
+
+    return (int(x1), int(y1), int(x2), int(y2))

deepface/detectors/Dlib.py

@@ -4,7 +4,7 @@ import bz2
 import gdown
 import numpy as np
 from deepface.commons import folder_utils
-from deepface.models.Detector import Detector, DetectedFace, FacialAreaRegion
+from deepface.models.Detector import Detector, FacialAreaRegion
 from deepface.commons.logger import Logger
 
 logger = Logger(module="detectors.DlibWrapper")
@@ -56,50 +56,18 @@ class DlibClient(Detector):
         detector["sp"] = sp
         return detector
 
-    def detect_faces(
+    def detect_faces(self, img: np.ndarray) -> List[FacialAreaRegion]:
-        self, img: np.ndarray, align: bool = True, expand_percentage: int = 0
-    ) -> List[DetectedFace]:
         """
         Detect and align face with dlib
 
         Args:
             img (np.ndarray): pre-loaded image as numpy array
 
-            align (bool): flag to enable or disable alignment after detection (default is True)
-
-            expand_percentage (int): expand detected facial area with a percentage
-
         Returns:
-            results (List[Tuple[DetectedFace]): A list of DetectedFace objects
+            results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
-                where each object contains:
-
-            - img (np.ndarray): The detected face as a NumPy array.
-
-            - facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h
-
-            - confidence (float): The confidence score associated with the detected face.
         """
-        # this is not a must dependency. do not import it in the global level.
-        try:
-            import dlib
-        except ModuleNotFoundError as e:
-            raise ImportError(
-                "Dlib is an optional detector, ensure the library is installed."
-                "Please install using 'pip install dlib' "
-            ) from e
-
-        if expand_percentage != 0:
-            logger.warn(
-                f"You set expand_percentage argument to {expand_percentage},"
-                "but dlib hog handles detection by itself"
-            )
-
         resp = []
 
-        sp = self.model["sp"]
-
-        detected_face = None
-
         face_detector = self.model["face_detector"]
 
         # note that, by design, dlib's fhog face detector scores are >0 but not capped at 1
@@ -107,30 +75,32 @@ class DlibClient(Detector):
 
         if len(detections) > 0:
 
-            for idx, d in enumerate(detections):
+            for idx, detection in enumerate(detections):
-                left = d.left()
+                left = detection.left()
-                right = d.right()
+                right = detection.right()
-                top = d.top()
+                top = detection.top()
-                bottom = d.bottom()
+                bottom = detection.bottom()
 
                 y = int(max(0, top))
                 h = int(min(bottom, img.shape[0]) - y)
                 x = int(max(0, left))
                 w = int(min(right, img.shape[1]) - x)
 
-                detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
+                shape = self.model["sp"](img, detection)
+                left_eye = (shape.part(2).x, shape.part(2).y)
+                right_eye = (shape.part(0).x, shape.part(0).y)
 
-                img_region = FacialAreaRegion(x=x, y=y, w=w, h=h)
                 confidence = scores[idx]
 
-                if align:
+                facial_area = FacialAreaRegion(
-                    img_shape = sp(img, detections[idx])
+                    x=x,
-                    detected_face = dlib.get_face_chip(img, img_shape, size=detected_face.shape[0])
+                    y=y,
-
+                    w=w,
-                detected_face_obj = DetectedFace(
+                    h=h,
-                    img=detected_face, facial_area=img_region, confidence=confidence
+                    left_eye=left_eye,
+                    right_eye=right_eye,
+                    confidence=confidence,
                 )
-
+                resp.append(facial_area)
-                resp.append(detected_face_obj)
 
         return resp

deepface/detectors/FastMtCnn.py

@@ -1,8 +1,7 @@
 from typing import Any, Union, List
 import cv2
 import numpy as np
-from deepface.models.Detector import Detector, DetectedFace, FacialAreaRegion
+from deepface.models.Detector import Detector, FacialAreaRegion
-from deepface.modules import detection
 
 # Link -> https://github.com/timesler/facenet-pytorch
 # Examples https://www.kaggle.com/timesler/guide-to-mtcnn-in-facenet-pytorch
@@ -12,33 +11,18 @@ class FastMtCnnClient(Detector):
     def __init__(self):
         self.model = self.build_model()
 
-    def detect_faces(
+    def detect_faces(self, img: np.ndarray) -> List[FacialAreaRegion]:
-        self, img: np.ndarray, align: bool = True, expand_percentage: int = 0
-    ) -> List[DetectedFace]:
         """
         Detect and align face with mtcnn
 
         Args:
             img (np.ndarray): pre-loaded image as numpy array
 
-            align (bool): flag to enable or disable alignment after detection (default is True)
-
-            expand_percentage (int): expand detected facial area with a percentage
-
         Returns:
-            results (List[Tuple[DetectedFace]): A list of DetectedFace objects
+            results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
-                where each object contains:
-
-            - img (np.ndarray): The detected face as a NumPy array.
-
-            - facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h
-
-            - confidence (float): The confidence score associated with the detected face.
         """
         resp = []
 
-        detected_face = None
-
         img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # mtcnn expects RGB but OpenCV read BGR
         detections = self.model.detect(
             img_rgb, landmarks=True
@@ -47,31 +31,20 @@ class FastMtCnnClient(Detector):
 
             for current_detection in zip(*detections):
                 x, y, w, h = xyxy_to_xywh(current_detection[0])
-
-                # expand the facial area to be extracted and stay within img.shape limits
-                x2 = max(0, x - int((w * expand_percentage) / 100))  # expand left
-                y2 = max(0, y - int((h * expand_percentage) / 100))  # expand top
-                w2 = min(img.shape[1], w + int((w * expand_percentage) / 100))  # expand right
-                h2 = min(img.shape[0], h + int((h * expand_percentage) / 100))  # expand bottom
-
-                # detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
-                detected_face = img[int(y2) : int(y2 + h2), int(x2) : int(x2 + w2)]
-
-                img_region = FacialAreaRegion(x=x, y=y, w=w, h=h)
                 confidence = current_detection[1]
-
-                if align:
                 left_eye = current_detection[2][0]
                 right_eye = current_detection[2][1]
-                    detected_face = detection.align_face(
-                        img=detected_face, left_eye=left_eye, right_eye=right_eye
-                    )
 
-                detected_face_obj = DetectedFace(
+                facial_area = FacialAreaRegion(
-                    img=detected_face, facial_area=img_region, confidence=confidence
+                    x=x,
+                    y=y,
+                    w=w,
+                    h=h,
+                    left_eye=left_eye,
+                    right_eye=right_eye,
+                    confidence=confidence,
                 )
-
+                resp.append(facial_area)
-                resp.append(detected_face_obj)
 
         return resp
 

deepface/detectors/MediaPipe.py

@@ -1,7 +1,6 @@
 from typing import Any, List
 import numpy as np
-from deepface.models.Detector import Detector, DetectedFace, FacialAreaRegion
+from deepface.models.Detector import Detector, FacialAreaRegion
-from deepface.modules import detection
 
 # Link - https://google.github.io/mediapipe/solutions/face_detection
 
@@ -29,28 +28,15 @@ class MediaPipeClient(Detector):
         face_detection = mp_face_detection.FaceDetection(min_detection_confidence=0.7)
         return face_detection
 
-    def detect_faces(
+    def detect_faces(self, img: np.ndarray) -> List[FacialAreaRegion]:
-        self, img: np.ndarray, align: bool = True, expand_percentage: int = 0
-    ) -> List[DetectedFace]:
         """
         Detect and align face with mediapipe
 
         Args:
             img (np.ndarray): pre-loaded image as numpy array
 
-            align (bool): flag to enable or disable alignment after detection (default is True)
-
-            expand_percentage (int): expand detected facial area with a percentage
-
         Returns:
-            results (List[Tuple[DetectedFace]): A list of DetectedFace objects
+            results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
-                where each object contains:
-
-            - img (np.ndarray): The detected face as a NumPy array.
-
-            - facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h
-
-            - confidence (float): The confidence score associated with the detected face.
         """
         resp = []
 
@@ -75,7 +61,6 @@ class MediaPipeClient(Detector):
             y = int(bounding_box.ymin * img_height)
             h = int(bounding_box.height * img_height)
 
-            # Extract landmarks
             left_eye = (int(landmarks[0].x * img_width), int(landmarks[0].y * img_height))
             right_eye = (int(landmarks[1].x * img_width), int(landmarks[1].y * img_height))
             # nose = (int(landmarks[2].x * img_width), int(landmarks[2].y * img_height))
@@ -83,30 +68,9 @@ class MediaPipeClient(Detector):
             # right_ear = (int(landmarks[4].x * img_width), int(landmarks[4].y * img_height))
             # left_ear = (int(landmarks[5].x * img_width), int(landmarks[5].y * img_height))
 
-            if x > 0 and y > 0:
+            facial_area = FacialAreaRegion(
-
+                x=x, y=y, w=w, h=h, left_eye=left_eye, right_eye=right_eye, confidence=confidence
-                # expand the facial area to be extracted and stay within img.shape limits
-                x2 = max(0, x - int((w * expand_percentage) / 100))  # expand left
-                y2 = max(0, y - int((h * expand_percentage) / 100))  # expand top
-                w2 = min(img.shape[1], w + int((w * expand_percentage) / 100))  # expand right
-                h2 = min(img.shape[0], h + int((h * expand_percentage) / 100))  # expand bottom
-
-                # detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
-                detected_face = img[int(y2) : int(y2 + h2), int(x2) : int(x2 + w2)]
-
-                img_region = FacialAreaRegion(x=x, y=y, w=w, h=h)
-
-                if align:
-                    detected_face = detection.align_face(
-                        img=detected_face, left_eye=left_eye, right_eye=right_eye
             )
-
+            resp.append(facial_area)
-                detected_face_obj = DetectedFace(
-                    img=detected_face,
-                    facial_area=img_region,
-                    confidence=confidence,
-                )
-
-                resp.append(detected_face_obj)
 
         return resp

deepface/detectors/MtCnn.py

@@ -1,8 +1,7 @@
 from typing import List
 import numpy as np
 from mtcnn import MTCNN
-from deepface.models.Detector import Detector, DetectedFace, FacialAreaRegion
+from deepface.models.Detector import Detector, FacialAreaRegion
-from deepface.modules import detection
 
 # pylint: disable=too-few-public-methods
 class MtCnnClient(Detector):
@@ -13,34 +12,19 @@ class MtCnnClient(Detector):
     def __init__(self):
         self.model = MTCNN()
 
-    def detect_faces(
+    def detect_faces(self, img: np.ndarray) -> List[FacialAreaRegion]:
-        self, img: np.ndarray, align: bool = True, expand_percentage: int = 0
-    ) -> List[DetectedFace]:
         """
         Detect and align face with mtcnn
 
         Args:
             img (np.ndarray): pre-loaded image as numpy array
 
-            align (bool): flag to enable or disable alignment after detection (default is True)
-
-            expand_percentage (int): expand detected facial area with a percentage
-
         Returns:
-            results (List[Tuple[DetectedFace]): A list of DetectedFace objects
+            results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
-                where each object contains:
-
-            - img (np.ndarray): The detected face as a NumPy array.
-
-            - facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h
-
-            - confidence (float): The confidence score associated with the detected face.
         """
 
         resp = []
 
-        detected_face = None
-
         # mtcnn expects RGB but OpenCV read BGR
         # img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
         img_rgb = img[:, :, ::-1]
@@ -50,31 +34,20 @@ class MtCnnClient(Detector):
 
             for current_detection in detections:
                 x, y, w, h = current_detection["box"]
-
-                # expand the facial area to be extracted and stay within img.shape limits
-                x2 = max(0, x - int((w * expand_percentage) / 100))  # expand left
-                y2 = max(0, y - int((h * expand_percentage) / 100))  # expand top
-                w2 = min(img.shape[1], w + int((w * expand_percentage) / 100))  # expand right
-                h2 = min(img.shape[0], h + int((h * expand_percentage) / 100))  # expand bottom
-
-                # detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
-                detected_face = img[int(y2) : int(y2 + h2), int(x2) : int(x2 + w2)]
-
-                img_region = FacialAreaRegion(x=x, y=y, w=w, h=h)
                 confidence = current_detection["confidence"]
+                left_eye = current_detection["keypoints"]["left_eye"]
+                right_eye = current_detection["keypoints"]["right_eye"]
 
-                if align:
+                facial_area = FacialAreaRegion(
-                    keypoints = current_detection["keypoints"]
+                    x=x,
-                    left_eye = keypoints["left_eye"]
+                    y=y,
-                    right_eye = keypoints["right_eye"]
+                    w=w,
-                    detected_face = detection.align_face(
+                    h=h,
-                        img=detected_face, left_eye=left_eye, right_eye=right_eye
+                    left_eye=left_eye,
+                    right_eye=right_eye,
+                    confidence=confidence,
                 )
 
-                detected_face_obj = DetectedFace(
+                resp.append(facial_area)
-                    img=detected_face, facial_area=img_region, confidence=confidence
-                )
-
-                resp.append(detected_face_obj)
 
         return resp

deepface/detectors/OpenCv.py

@@ -2,8 +2,7 @@ import os
 from typing import Any, List
 import cv2
 import numpy as np
-from deepface.models.Detector import Detector, DetectedFace, FacialAreaRegion
+from deepface.models.Detector import Detector, FacialAreaRegion
-from deepface.modules import detection
 
 
 class OpenCvClient(Detector):
@@ -25,28 +24,15 @@ class OpenCvClient(Detector):
         detector["eye_detector"] = self.__build_cascade("haarcascade_eye")
         return detector
 
-    def detect_faces(
+    def detect_faces(self, img: np.ndarray) -> List[FacialAreaRegion]:
-        self, img: np.ndarray, align: bool = True, expand_percentage: int = 0
-    ) -> List[DetectedFace]:
         """
         Detect and align face with opencv
 
         Args:
             img (np.ndarray): pre-loaded image as numpy array
 
-            align (bool): flag to enable or disable alignment after detection (default is True)
-
-            expand_percentage (int): expand detected facial area with a percentage
-
         Returns:
-            results (List[Tuple[DetectedFace]): A list of DetectedFace objects
+            results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
-                where each object contains:
-
-            - img (np.ndarray): The detected face as a NumPy array.
-
-            - facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h
-
-            - confidence (float): The confidence score associated with the detected face.
         """
         resp = []
 
@@ -65,27 +51,18 @@ class OpenCvClient(Detector):
 
         if len(faces) > 0:
             for (x, y, w, h), confidence in zip(faces, scores):
-
+                detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
-                # expand the facial area to be extracted and stay within img.shape limits
-                x2 = max(0, x - int((w * expand_percentage) / 100))  # expand left
-                y2 = max(0, y - int((h * expand_percentage) / 100))  # expand top
-                w2 = min(img.shape[1], w + int((w * expand_percentage) / 100))  # expand right
-                h2 = min(img.shape[0], h + int((h * expand_percentage) / 100))  # expand bottom
-
-                # detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
-                detected_face = img[int(y2) : int(y2 + h2), int(x2) : int(x2 + w2)]
-
-                if align:
                 left_eye, right_eye = self.find_eyes(img=detected_face)
-                    detected_face = detection.align_face(detected_face, left_eye, right_eye)
+                facial_area = FacialAreaRegion(
-
+                    x=x,
-                detected_face_obj = DetectedFace(
+                    y=y,
-                    img=detected_face,
+                    w=w,
-                    facial_area=FacialAreaRegion(x, y, w, h),
+                    h=h,
+                    left_eye=left_eye,
+                    right_eye=right_eye,
                     confidence=confidence,
                 )
-
+                resp.append(facial_area)
-                resp.append(detected_face_obj)
 
         return resp
 

deepface/detectors/RetinaFace.py

@@ -1,36 +1,22 @@
 from typing import List
 import numpy as np
 from retinaface import RetinaFace as rf
-from retinaface.commons import postprocess
+from deepface.models.Detector import Detector, FacialAreaRegion
-from deepface.models.Detector import Detector, DetectedFace, FacialAreaRegion
 
 # pylint: disable=too-few-public-methods
 class RetinaFaceClient(Detector):
     def __init__(self):
         self.model = rf.build_model()
 
-    def detect_faces(
+    def detect_faces(self, img: np.ndarray) -> List[FacialAreaRegion]:
-        self, img: np.ndarray, align: bool = True, expand_percentage: int = 0
-    ) -> List[DetectedFace]:
         """
         Detect and align face with retinaface
 
         Args:
             img (np.ndarray): pre-loaded image as numpy array
 
-            align (bool): flag to enable or disable alignment after detection (default is True)
-
-            expand_percentage (int): expand detected facial area with a percentage
-
         Returns:
-            results (List[Tuple[DetectedFace]): A list of DetectedFace objects
+            results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
-                where each object contains:
-
-            - img (np.ndarray): The detected face as a NumPy array.
-
-            - facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h
-
-            - confidence (float): The confidence score associated with the detected face.
         """
         resp = []
 
@@ -41,42 +27,33 @@ class RetinaFaceClient(Detector):
 
         for face_idx in obj.keys():
             identity = obj[face_idx]
-            facial_area = identity["facial_area"]
+            detection = identity["facial_area"]
+
+            y = detection[1]
+            h = detection[3] - y
+            x = detection[0]
+            w = detection[2] - x
+
+            # notice that these must be inverse for retinaface
+            left_eye = identity["landmarks"]["right_eye"]
+            right_eye = identity["landmarks"]["left_eye"]
+
+            # eyes are list of float, need to cast them tuple of int
+            left_eye = tuple(int(i) for i in left_eye)
+            right_eye = tuple(int(i) for i in right_eye)
 
-            y = facial_area[1]
-            h = facial_area[3] - y
-            x = facial_area[0]
-            w = facial_area[2] - x
-            img_region = FacialAreaRegion(x=x, y=y, w=w, h=h)
             confidence = identity["score"]
 
-            # expand the facial area to be extracted and stay within img.shape limits
+            facial_area = FacialAreaRegion(
-            x2 = max(0, x - int((w * expand_percentage) / 100))  # expand left
+                x=x,
-            y2 = max(0, y - int((h * expand_percentage) / 100))  # expand top
+                y=y,
-            w2 = min(img.shape[1], w + int((w * expand_percentage) / 100))  # expand right
+                w=w,
-            h2 = min(img.shape[0], h + int((h * expand_percentage) / 100))  # expand bottom
+                h=h,
-
+                left_eye=left_eye,
-            # detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
+                right_eye=right_eye,
-            detected_face = img[int(y2) : int(y2 + h2), int(x2) : int(x2 + w2)]
-
-            if align:
-                landmarks = identity["landmarks"]
-                left_eye = landmarks["left_eye"]
-                right_eye = landmarks["right_eye"]
-                nose = landmarks["nose"]
-                # mouth_right = landmarks["mouth_right"]
-                # mouth_left = landmarks["mouth_left"]
-
-                detected_face = postprocess.alignment_procedure(
-                    detected_face, right_eye, left_eye, nose
-                )
-
-            detected_face_obj = DetectedFace(
-                img=detected_face,
-                facial_area=img_region,
                 confidence=confidence,
             )
 
-            resp.append(detected_face_obj)
+            resp.append(facial_area)
 
         return resp

deepface/detectors/Ssd.py

@@ -6,8 +6,7 @@ import pandas as pd
 import numpy as np
 from deepface.detectors import OpenCv
 from deepface.commons import folder_utils
-from deepface.models.Detector import Detector, DetectedFace, FacialAreaRegion
+from deepface.models.Detector import Detector, FacialAreaRegion
-from deepface.modules import detection
 from deepface.commons.logger import Logger
 
 logger = Logger(module="detectors.SsdWrapper")
@@ -71,29 +70,18 @@ class SsdClient(Detector):
 
         return detector
 
-    def detect_faces(
+    def detect_faces(self, img: np.ndarray) -> List[FacialAreaRegion]:
-        self, img: np.ndarray, align: bool = True, expand_percentage: int = 0
-    ) -> List[DetectedFace]:
         """
         Detect and align face with ssd
 
         Args:
             img (np.ndarray): pre-loaded image as numpy array
 
-            align (bool): flag to enable or disable alignment after detection (default is True)
-
-            expand_percentage (int): expand detected facial area with a percentage
-
         Returns:
-            results (List[Tuple[DetectedFace]): A list of DetectedFace objects
+            results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
-                where each object contains:
-
-            - img (np.ndarray): The detected face as a NumPy array.
-
-            - facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h
-
-            - confidence (float): The confidence score associated with the detected face.
         """
+        opencv_module: OpenCv.OpenCvClient = self.model["opencv_module"]
+
         resp = []
 
         detected_face = None
@@ -133,37 +121,26 @@ class SsdClient(Detector):
                 right = instance["right"]
                 bottom = instance["bottom"]
                 top = instance["top"]
+                confidence = instance["confidence"]
 
                 x = int(left * aspect_ratio_x)
                 y = int(top * aspect_ratio_y)
                 w = int(right * aspect_ratio_x) - int(left * aspect_ratio_x)
                 h = int(bottom * aspect_ratio_y) - int(top * aspect_ratio_y)
 
-                # expand the facial area to be extracted and stay within img.shape limits
-                x2 = max(0, x - int((w * expand_percentage) / 100))  # expand left
-                y2 = max(0, y - int((h * expand_percentage) / 100))  # expand top
-                w2 = min(img.shape[1], w + int((w * expand_percentage) / 100))  # expand right
-                h2 = min(img.shape[0], h + int((h * expand_percentage) / 100))  # expand bottom
-
                 detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
-                detected_face = img[int(y2) : int(y2 + h2), int(x2) : int(x2 + w2)]
 
-                face_region = FacialAreaRegion(x=x, y=y, w=w, h=h)
-
-                confidence = instance["confidence"]
-
-                if align:
-                    opencv_module: OpenCv.OpenCvClient = self.model["opencv_module"]
                 left_eye, right_eye = opencv_module.find_eyes(detected_face)
-                    detected_face = detection.align_face(
-                        img=detected_face, left_eye=left_eye, right_eye=right_eye
-                    )
 
-                detected_face_obj = DetectedFace(
+                facial_area = FacialAreaRegion(
-                    img=detected_face,
+                    x=x,
-                    facial_area=face_region,
+                    y=y,
+                    w=w,
+                    h=h,
+                    left_eye=left_eye,
+                    right_eye=right_eye,
                     confidence=confidence,
                 )
+                resp.append(facial_area)
 
-                resp.append(detected_face_obj)
         return resp

deepface/detectors/Yolo.py

@@ -2,8 +2,7 @@ import os
 from typing import Any, List
 import numpy as np
 import gdown
-from deepface.models.Detector import Detector, DetectedFace, FacialAreaRegion
+from deepface.models.Detector import Detector, FacialAreaRegion
-from deepface.modules import detection
 from deepface.commons import folder_utils
 from deepface.commons.logger import Logger
 
@@ -50,28 +49,15 @@ class YoloClient(Detector):
         # Return face_detector
         return YOLO(weight_path)
 
-    def detect_faces(
+    def detect_faces(self, img: np.ndarray) -> List[FacialAreaRegion]:
-        self, img: np.ndarray, align: bool = False, expand_percentage: int = 0
-    ) -> List[DetectedFace]:
         """
         Detect and align face with yolo
 
         Args:
             img (np.ndarray): pre-loaded image as numpy array
 
-            align (bool): flag to enable or disable alignment after detection (default is True)
-
-            expand_percentage (int): expand detected facial area with a percentage
-
         Returns:
-            results (List[Tuple[DetectedFace]): A list of DetectedFace objects
+            results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
-                where each object contains:
-
-            - img (np.ndarray): The detected face as a NumPy array.
-
-            - facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h
-
-            - confidence (float): The confidence score associated with the detected face.
         """
         resp = []
 
@@ -84,36 +70,25 @@ class YoloClient(Detector):
             x, y, w, h = result.boxes.xywh.tolist()[0]
             confidence = result.boxes.conf.tolist()[0]
 
+            # left_eye_conf = result.keypoints.conf[0][0]
+            # right_eye_conf = result.keypoints.conf[0][1]
+            left_eye = result.keypoints.xy[0][0].tolist()
+            right_eye = result.keypoints.xy[0][1].tolist()
+
+            # eyes are list of float, need to cast them tuple of int
+            left_eye = tuple(int(i) for i in left_eye)
+            right_eye = tuple(int(i) for i in right_eye)
+
             x, y, w, h = int(x - w / 2), int(y - h / 2), int(w), int(h)
-            region = FacialAreaRegion(x=x, y=y, w=w, h=h)
+            facial_area = FacialAreaRegion(
-
+                x=x,
-            # expand the facial area to be extracted and stay within img.shape limits
+                y=y,
-            x2 = max(0, x - int((w * expand_percentage) / 100))  # expand left
+                w=w,
-            y2 = max(0, y - int((h * expand_percentage) / 100))  # expand top
+                h=h,
-            w2 = min(img.shape[1], w + int((w * expand_percentage) / 100))  # expand right
+                left_eye=left_eye,
-            h2 = min(img.shape[0], h + int((h * expand_percentage) / 100))  # expand bottom
+                right_eye=right_eye,
-
+                confidence=confidence,
-            # detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
-            detected_face = img[int(y2) : int(y2 + h2), int(x2) : int(x2 + w2)]
-
-            if align:
-                # Tuple of x,y and confidence for left eye
-                left_eye = result.keypoints.xy[0][0], result.keypoints.conf[0][0]
-                # Tuple of x,y and confidence for right eye
-                right_eye = result.keypoints.xy[0][1], result.keypoints.conf[0][1]
-
-                # Check the landmarks confidence before alignment
-                if (
-                    left_eye[1] > LANDMARKS_CONFIDENCE_THRESHOLD
-                    and right_eye[1] > LANDMARKS_CONFIDENCE_THRESHOLD
-                ):
-                    detected_face = detection.align_face(
-                        img=detected_face, left_eye=left_eye[0].cpu(), right_eye=right_eye[0].cpu()
             )
-
+            resp.append(facial_area)
-            detected_face_obj = DetectedFace(
-                img=detected_face, facial_area=region, confidence=confidence
-            )
-            resp.append(detected_face_obj)
 
         return resp

deepface/detectors/YuNet.py

@@ -4,8 +4,7 @@ import cv2
 import numpy as np
 import gdown
 from deepface.commons import folder_utils
-from deepface.models.Detector import Detector, DetectedFace, FacialAreaRegion
+from deepface.models.Detector import Detector, FacialAreaRegion
-from deepface.modules import detection
 from deepface.commons.logger import Logger
 
 logger = Logger(module="detectors.YunetWrapper")
@@ -49,34 +48,20 @@ class YuNetClient(Detector):
             ) from err
         return face_detector
 
-    def detect_faces(
+    def detect_faces(self, img: np.ndarray) -> List[FacialAreaRegion]:
-        self, img: np.ndarray, align: bool = True, expand_percentage: int = 0
-    ) -> List[DetectedFace]:
         """
         Detect and align face with yunet
 
         Args:
             img (np.ndarray): pre-loaded image as numpy array
 
-            align (bool): flag to enable or disable alignment after detection (default is True)
-
-            expand_percentage (int): expand detected facial area with a percentage
-
         Returns:
-            results (List[Tuple[DetectedFace]): A list of DetectedFace objects
+            results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
-                where each object contains:
-
-            - img (np.ndarray): The detected face as a NumPy array.
-
-            - facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h
-
-            - confidence (float): The confidence score associated with the detected face.
         """
         # FaceDetector.detect_faces does not support score_threshold parameter.
         # We can set it via environment variable.
         score_threshold = float(os.environ.get("yunet_score_threshold", "0.9"))
         resp = []
-        detected_face = None
         faces = []
         height, width = img.shape[0], img.shape[1]
         # resize image if it is too large (Yunet fails to detect faces on large input sometimes)
@@ -108,6 +93,8 @@ class YuNetClient(Detector):
             left eye, nose tip, the right corner and left corner of the mouth respectively.
             """
             (x, y, w, h, x_re, y_re, x_le, y_le) = list(map(int, face[:8]))
+            left_eye = (x_re, y_re)
+            right_eye = (x_le, y_le)
 
             # Yunet returns negative coordinates if it thinks part of
             # the detected face is outside the frame.
@@ -123,24 +110,16 @@ class YuNetClient(Detector):
                     int(x_le / r),
                     int(y_le / r),
                 )
-            confidence = face[-1]
+            confidence = float(face[-1])
-            confidence = f"{confidence:.2f}"
 
-            # expand the facial area to be extracted and stay within img.shape limits
+            facial_area = FacialAreaRegion(
-            x2 = max(0, x - int((w * expand_percentage) / 100))  # expand left
+                x=x,
-            y2 = max(0, y - int((h * expand_percentage) / 100))  # expand top
+                y=y,
-            w2 = min(img.shape[1], w + int((w * expand_percentage) / 100))  # expand right
+                w=w,
-            h2 = min(img.shape[0], h + int((h * expand_percentage) / 100))  # expand bottom
+                h=h,
-
+                confidence=confidence,
-            # detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
+                left_eye=left_eye,
-            detected_face = img[int(y2) : int(y2 + h2), int(x2) : int(x2 + w2)]
+                right_eye=right_eye,
-
-            img_region = FacialAreaRegion(x=x, y=y, w=w, h=h)
-            if align:
-                detected_face = detection.align_face(detected_face, (x_re, y_re), (x_le, y_le))
-
-            detected_face_obj = DetectedFace(
-                img=detected_face, facial_area=img_region, confidence=confidence
             )
-            resp.append(detected_face_obj)
+            resp.append(facial_area)
         return resp

deepface/models/Detector.py

@@ -1,4 +1,4 @@
-from typing import List
+from typing import List, Tuple, Optional
 from abc import ABC, abstractmethod
 import numpy as np
 
@@ -8,28 +8,19 @@ import numpy as np
 # pylint: disable=unnecessary-pass, too-few-public-methods
 class Detector(ABC):
     @abstractmethod
-    def detect_faces(
+    def detect_faces(self, img: np.ndarray) -> List["FacialAreaRegion"]:
-        self, img: np.ndarray, align: bool = True, expand_percentage: int = 0
-    ) -> List["DetectedFace"]:
         """
         Interface for detect and align face
 
         Args:
             img (np.ndarray): pre-loaded image as numpy array
 
-            align (bool): flag to enable or disable alignment after detection (default is True)
-
-            expand_percentage (int): expand detected facial area with a percentage
-
         Returns:
-            results (List[Tuple[DetectedFace]): A list of DetectedFace objects
+            results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
                 where each object contains:
 
-            - img (np.ndarray): The detected face as a NumPy array.
+            - facial_area (FacialAreaRegion): The facial area region represented
-
+                as x, y, w, h, left_eye and right_eye
-            - facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h
-
-            - confidence (float): The confidence score associated with the detected face.
         """
         pass
 
@@ -39,12 +30,27 @@ class FacialAreaRegion:
     y: int
     w: int
     h: int
+    left_eye: Tuple[int, int]
+    right_eye: Tuple[int, int]
+    confidence: float
 
-    def __init__(self, x: int, y: int, w: int, h: int):
+    def __init__(
+        self,
+        x: int,
+        y: int,
+        w: int,
+        h: int,
+        left_eye: Optional[Tuple[int, int]] = None,
+        right_eye: Optional[Tuple[int, int]] = None,
+        confidence: Optional[float] = None,
+    ):
         self.x = x
         self.y = y
         self.w = w
         self.h = h
+        self.left_eye = left_eye
+        self.right_eye = right_eye
+        self.confidence = confidence
 
 
 class DetectedFace:

deepface/modules/detection.py

@@ -1,5 +1,5 @@
 # built-in dependencies
-from typing import Any, Dict, List, Tuple, Union
+from typing import Any, Dict, List, Tuple, Union, Optional
 
 # 3rd part dependencies
 import numpy as np
@@ -27,7 +27,7 @@ elif tf_major_version == 2:
 
 def extract_faces(
     img_path: Union[str, np.ndarray],
-    target_size: Tuple[int, int] = (224, 224),
+    target_size: Optional[Tuple[int, int]] = (224, 224),
     detector_backend: str = "opencv",
     enforce_detection: bool = True,
     align: bool = True,
@@ -76,7 +76,7 @@ def extract_faces(
     # img might be path, base64 or numpy array. Convert it to numpy whatever it is.
     img, img_name = preprocessing.load_image(img_path)
 
-    base_region = FacialAreaRegion(x=0, y=0, w=img.shape[1], h=img.shape[0])
+    base_region = FacialAreaRegion(x=0, y=0, w=img.shape[1], h=img.shape[0], confidence=0)
 
     if detector_backend == "skip":
         face_objs = [DetectedFace(img=img, facial_area=base_region, confidence=0)]
@@ -108,7 +108,6 @@ def extract_faces(
     for face_obj in face_objs:
         current_img = face_obj.img
         current_region = face_obj.facial_area
-        confidence = face_obj.confidence
 
         if current_img.shape[0] == 0 or current_img.shape[1] == 0:
             continue
@@ -117,6 +116,7 @@ def extract_faces(
             current_img = cv2.cvtColor(current_img, cv2.COLOR_BGR2GRAY)
 
         # resize and padding
+        if target_size is not None:
             factor_0 = target_size[0] / current_img.shape[0]
             factor_1 = target_size[1] / current_img.shape[1]
             factor = min(factor_0, factor_1)
@@ -171,14 +171,17 @@ def extract_faces(
                     "y": int(current_region.y),
                     "w": int(current_region.w),
                     "h": int(current_region.h),
+                    "left_eye": current_region.left_eye,
+                    "right_eye": current_region.right_eye,
                 },
-                "confidence": confidence,
+                "confidence": round(current_region.confidence, 2),
             }
         )
 
     if len(resp_objs) == 0 and enforce_detection == True:
         raise ValueError(
-            f"Detected face shape is {img.shape}. Consider to set enforce_detection arg to False."
+            f"Exception while extracting faces from {img_name}."
+            "Consider to set enforce_detection arg to False."
         )
 
     return resp_objs
@@ -188,7 +191,7 @@ def align_face(
     img: np.ndarray,
     left_eye: Union[list, tuple],
     right_eye: Union[list, tuple],
-) -> np.ndarray:
+) -> Tuple[np.ndarray, float]:
     """
     Align a given image horizantally with respect to their left and right eye locations
     Args:
@@ -200,13 +203,13 @@ def align_face(
     """
     # if eye could not be detected for the given image, return image itself
     if left_eye is None or right_eye is None:
-        return img
+        return img, 0
 
     # sometimes unexpectedly detected images come with nil dimensions
     if img.shape[0] == 0 or img.shape[1] == 0:
-        return img
+        return img, 0
 
     angle = float(np.degrees(np.arctan2(right_eye[1] - left_eye[1], right_eye[0] - left_eye[0])))
     img = Image.fromarray(img)
     img = np.array(img.rotate(angle))
-    return img
+    return img, angle

deepface/modules/recognition.py

@@ -354,6 +354,7 @@ def __find_bulk_embeddings(
         desc="Finding representations",
         disable=silent,
     ):
+        try:
             img_objs = detection.extract_faces(
                 img_path=employee,
                 target_size=target_size,
@@ -363,6 +364,11 @@ def __find_bulk_embeddings(
                 align=align,
                 expand_percentage=expand_percentage,
             )
+        except ValueError as err:
+            logger.warn(
+                f"Exception while extracting faces from {employee}: {str(err)}. Skipping it."
+            )
+            img_objs = []
 
         for img_obj in img_objs:
             img_content = img_obj["face"]

tests/visual-test.py

@@ -53,14 +53,37 @@ dfs = DeepFace.find(
 for df in dfs:
     logger.info(df)
 
+
+# img_paths = ["dataset/img11.jpg", "dataset/img11_reflection.jpg", "dataset/couple.jpg"]
+img_paths = ["dataset/img11.jpg"]
+for img_path in img_paths:
     # extract faces
     for detector_backend in detector_backends:
         face_objs = DeepFace.extract_faces(
-        img_path="dataset/img11.jpg", detector_backend=detector_backend
+            img_path=img_path,
+            detector_backend=detector_backend,
+            align=True,
+            # expand_percentage=10,
+            # target_size=None,
         )
         for face_obj in face_objs:
             face = face_obj["face"]
             logger.info(detector_backend)
+            logger.info(face_obj["facial_area"])
+            logger.info(face_obj["confidence"])
+
+            # we know opencv sometimes cannot find eyes
+            if face_obj["facial_area"]["left_eye"] is not None:
+                assert isinstance(face_obj["facial_area"]["left_eye"], tuple)
+                assert isinstance(face_obj["facial_area"]["left_eye"][0], int)
+                assert isinstance(face_obj["facial_area"]["left_eye"][1], int)
+
+            if face_obj["facial_area"]["right_eye"] is not None:
+                assert isinstance(face_obj["facial_area"]["right_eye"], tuple)
+                assert isinstance(face_obj["facial_area"]["right_eye"][0], int)
+                assert isinstance(face_obj["facial_area"]["right_eye"][1], int)
+
+            assert isinstance(face_obj["confidence"], float)
             plt.imshow(face)
             plt.axis("off")
             plt.show()