Merge pull request #1115 from serengil/feat-task-1703-fast-mtcnn-refactoring

Feat task 1703 fast mtcnn refactoring

README.md

@@ -140,16 +140,16 @@ FaceNet, VGG-Face, ArcFace and Dlib are [overperforming](https://youtu.be/i_MOwv
 
 | Model | Declared LFW Score |
 | ---   | --- |
-| VGG-Face | 98.78% |
-| Facenet | 99.20% |
-| Facenet512 | 99.65% |
-| OpenFace | 93.80% |
+| VGG-Face | 98.9% |
+| Facenet | 99.2% |
+| Facenet512 | 99.6% |
+| OpenFace | 92.9% |
 | DeepID | - |
-| Dlib | 99.38 % |
-| SFace | 99.60% |
-| ArcFace | 99.41% |
-| GhostFaceNet | 99.76 |
-| *Human-beings* | *97.53%* |
+| Dlib | 97.4 % |
+| SFace | 99.5% |
+| ArcFace | 99.5% |
+| GhostFaceNet | 99.7% |
+| *Human-beings* | *97.5%* |
 
 Conducting experiments with those models within DeepFace may reveal disparities compared to the original studies, owing to the adoption of distinct detection or normalization techniques. Furthermore, some models have been released solely with their backbones, lacking pre-trained weights. Thus, we are utilizing their re-implementations instead of the original pre-trained weights.
 

deepface/detectors/FastMtCnn.py

@@ -32,12 +32,10 @@ class FastMtCnnClient(Detector):
             and len(detections) > 0
             and not any(detection is None for detection in detections)  # issue 1043
         ):
-            for current_detection in zip(*detections):
-                x, y, w, h = xyxy_to_xywh(current_detection[0])
-                confidence = current_detection[1]
-
-                left_eye = current_detection[2][0]
-                right_eye = current_detection[2][1]
+            for regions, confidence, eyes in zip(*detections):
+                x, y, w, h = xyxy_to_xywh(regions)
+                left_eye = eyes[0]
+                right_eye = eyes[1]
 
                 left_eye = tuple(int(i) for i in left_eye)
                 right_eye = tuple(int(i) for i in right_eye)
@@ -70,21 +68,19 @@ class FastMtCnnClient(Detector):
                 "Please install using 'pip install facenet-pytorch' "
             ) from e
 
-        face_detector = fast_mtcnn(
-            image_size=160,
-            thresholds=[0.6, 0.7, 0.7],  # MTCNN thresholds
-            post_process=True,
-            device="cpu",
-            select_largest=False,  # return result in descending order
-        )
+        face_detector = fast_mtcnn(device="cpu")
         return face_detector
 
 
-def xyxy_to_xywh(xyxy: Union[list, tuple]) -> list:
+def xyxy_to_xywh(regions: Union[list, tuple]) -> tuple:
     """
-    Convert xyxy format to xywh format.
+    Convert (x1, y1, x2, y2) format to (x, y, w, h) format.
+    Args:
+        regions (list or tuple): facial area coordinates as x, y, x+w, y+h
+    Returns:
+        regions (tuple): facial area coordinates as x, y, w, h
     """
-    x, y = xyxy[0], xyxy[1]
-    w = xyxy[2] - x + 1
-    h = xyxy[3] - y + 1
-    return [x, y, w, h]
+    x, y, x_plus_w, y_plus_h = regions[0], regions[1], regions[2], regions[3]
+    w = x_plus_w - x
+    h = y_plus_h - y
+    return (x, y, w, h)