From b544a2d866a7a439d5c7087427afab248870094e Mon Sep 17 00:00:00 2001
From: galthran-wq <levmorozov900@gmail.com>
Date: Sun, 16 Feb 2025 13:54:28 +0000
Subject: [PATCH] pseudo batched retinaface

---
 deepface/models/face_detection/RetinaFace.py | 95 ++++++++++----------
 1 file changed, 49 insertions(+), 46 deletions(-)

diff --git a/deepface/models/face_detection/RetinaFace.py b/deepface/models/face_detection/RetinaFace.py
index c687322..ca98a54 100644
--- a/deepface/models/face_detection/RetinaFace.py
+++ b/deepface/models/face_detection/RetinaFace.py
@@ -1,5 +1,5 @@
 # built-in dependencies
-from typing import List
+from typing import List, Union
 
 # 3rd party dependencies
 import numpy as np
@@ -13,64 +13,67 @@ class RetinaFaceClient(Detector):
     def __init__(self):
         self.model = rf.build_model()
 
-    def detect_faces(self, img: np.ndarray) -> List[FacialAreaRegion]:
+    def detect_faces(self, img: Union[np.ndarray, List[np.ndarray]]) -> Union[List[FacialAreaRegion], List[List[FacialAreaRegion]]]:
         """
-        Detect and align face with retinaface
+        Detect and align faces with retinaface in an image or a list of images
 
         Args:
-            img (np.ndarray): pre-loaded image as numpy array
+            img (Union[np.ndarray, List[np.ndarray]]): pre-loaded image as numpy array or a list of those
 
         Returns:
-            results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
+            results (Union[List[FacialAreaRegion], List[List[FacialAreaRegion]]]): A list or a list of lists of FacialAreaRegion objects
         """
-        resp = []
+        if isinstance(img, np.ndarray):
+            imgs = [img]
+        else:
+            imgs = img
 
-        obj = rf.detect_faces(img, model=self.model, threshold=0.9)
+        batch_results = []
 
-        if not isinstance(obj, dict):
-            return resp
+        for img in imgs:
+            resp = []
+            obj = rf.detect_faces(img, model=self.model, threshold=0.9)
 
-        for face_idx in obj.keys():
-            identity = obj[face_idx]
-            detection = identity["facial_area"]
+            if isinstance(obj, dict):
+                for face_idx in obj.keys():
+                    identity = obj[face_idx]
+                    detection = identity["facial_area"]
 
-            y = detection[1]
-            h = detection[3] - y
-            x = detection[0]
-            w = detection[2] - x
+                    y = detection[1]
+                    h = detection[3] - y
+                    x = detection[0]
+                    w = detection[2] - x
 
-            # retinaface sets left and right eyes with respect to the person
-            left_eye = identity["landmarks"]["left_eye"]
-            right_eye = identity["landmarks"]["right_eye"]
-            nose = identity["landmarks"].get("nose")
-            mouth_right = identity["landmarks"].get("mouth_right")
-            mouth_left = identity["landmarks"].get("mouth_left")
+                    left_eye = tuple(int(i) for i in identity["landmarks"]["left_eye"])
+                    right_eye = tuple(int(i) for i in identity["landmarks"]["right_eye"])
+                    nose = identity["landmarks"].get("nose")
+                    mouth_right = identity["landmarks"].get("mouth_right")
+                    mouth_left = identity["landmarks"].get("mouth_left")
 
-            # 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)
-            if nose is not None:
-                nose = tuple(int(i) for i in nose)
-            if mouth_right is not None:
-                mouth_right = tuple(int(i) for i in mouth_right)
-            if mouth_left is not None:
-                mouth_left = tuple(int(i) for i in mouth_left)
+                    if nose is not None:
+                        nose = tuple(int(i) for i in nose)
+                    if mouth_right is not None:
+                        mouth_right = tuple(int(i) for i in mouth_right)
+                    if mouth_left is not None:
+                        mouth_left = tuple(int(i) for i in mouth_left)
 
-            confidence = identity["score"]
+                    confidence = identity["score"]
 
-            facial_area = FacialAreaRegion(
-                x=x,
-                y=y,
-                w=w,
-                h=h,
-                left_eye=left_eye,
-                right_eye=right_eye,
-                confidence=confidence,
-                nose=nose,
-                mouth_left=mouth_left,
-                mouth_right=mouth_right,
-            )
+                    facial_area = FacialAreaRegion(
+                        x=x,
+                        y=y,
+                        w=w,
+                        h=h,
+                        left_eye=left_eye,
+                        right_eye=right_eye,
+                        confidence=confidence,
+                        nose=nose,
+                        mouth_left=mouth_left,
+                        mouth_right=mouth_right,
+                    )
 
-            resp.append(facial_area)
+                    resp.append(facial_area)
 
-        return resp
+            batch_results.append(resp)
+
+        return batch_results if len(batch_results) > 1 else batch_results[0]