Merge branch 'master' into fixyunet

README.md

@@ -196,7 +196,7 @@ Age model got ± 4.65 MAE; gender model got 97.44% accuracy, 96.29% precision an
 
 Face detection and alignment are important early stages of a modern face recognition pipeline. Experiments show that just alignment increases the face recognition accuracy almost 1%. [`OpenCV`](https://sefiks.com/2020/02/23/face-alignment-for-face-recognition-in-python-within-opencv/), [`SSD`](https://sefiks.com/2020/08/25/deep-face-detection-with-opencv-in-python/), [`Dlib`](https://sefiks.com/2020/07/11/face-recognition-with-dlib-in-python/),  [`MTCNN`](https://sefiks.com/2020/09/09/deep-face-detection-with-mtcnn-in-python/), [`RetinaFace`](https://sefiks.com/2021/04/27/deep-face-detection-with-retinaface-in-python/), [`MediaPipe`](https://sefiks.com/2022/01/14/deep-face-detection-with-mediapipe/), [`YOLOv8 Face`](https://github.com/derronqi/yolov8-face) and [`YuNet`](https://github.com/ShiqiYu/libfacedetection) detectors are wrapped in deepface.
 
-<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/detector-portfolio-v3.jpg" width="95%" height="95%"></p>
+<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/detector-portfolio-v5.jpg" width="95%" height="95%"></p>
 
 All deepface functions accept an optional detector backend input argument. You can switch among those detectors with this argument. OpenCV is the default detector.
 

deepface/detectors/YunetWrapper.py

@@ -1,21 +1,22 @@
-import cv2
 import os
+import cv2
 import gdown
 from deepface.detectors import FaceDetector
 from deepface.commons import functions
 
 
 def build_model():
-    url = "https://github.com/opencv/opencv_zoo/raw/main/models/face_detection_yunet/face_detection_yunet_2023mar.onnx"
+    url = (
+        "https://github.com/opencv/opencv_zoo/raw/main/models/"
+        + "face_detection_yunet/face_detection_yunet_2023mar.onnx"
+    )
     file_name = "face_detection_yunet_2023mar.onnx"
     home = functions.get_deepface_home()
     if os.path.isfile(home + f"/.deepface/weights/{file_name}") is False:
         print(f"{file_name} will be downloaded...")
         output = home + f"/.deepface/weights/{file_name}"
         gdown.download(url, output, quiet=False)
-    face_detector = cv2.FaceDetectorYN_create(
+    face_detector = cv2.FaceDetectorYN_create(home + f"/.deepface/weights/{file_name}", "", (0, 0))
-        home + f"/.deepface/weights/{file_name}", "", (0, 0)
-    )
     return face_detector
 
 
@@ -72,7 +73,7 @@ def detect_face(detector, image, align=True, score_threshold=0.9):
                 int(y_le / r),
             )
         confidence = face[-1]
-        confidence = "{:.2f}".format(confidence)
+        confidence = f"{confidence:.2f}"
         detected_face = image[int(y) : int(y + h), int(x) : int(x + w)]
         img_region = [x, y, w, h]
         if align: