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Merge pull request #758 from Vincent-Stragier/YOLOv8
Integration of YOLOv8-face close #732
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Sefik Ilkin Serengil
GitHub
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0b8e5ca472
@ -194,7 +194,7 @@ Age model got ± 4.65 MAE; gender model got 97.44% accuracy, 96.29% precision an
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**Face Detectors** - [`Demo`](https://youtu.be/GZ2p2hj2H5k)
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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/) and [`MediaPipe`](https://sefiks.com/2022/01/14/deep-face-detection-with-mediapipe/) detectors are wrapped in deepface.
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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/) and [`YOLOv8 Face`](https://github.com/derronqi/yolov8-face) detectors are wrapped in deepface.
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<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/detector-portfolio-v3.jpg" width="95%" height="95%"></p>
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@ -207,7 +207,8 @@ backends = [
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'dlib',
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'mtcnn',
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'retinaface',
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'mediapipe'
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'mediapipe',
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'yolov8',
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]
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#face verification
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@ -116,7 +116,7 @@ def verify(
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This might be convenient for low resolution images.
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detector_backend (string): set face detector backend to opencv, retinaface, mtcnn, ssd,
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dlib or mediapipe
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dlib, mediapipe or yolov8.
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align (boolean): alignment according to the eye positions.
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@ -251,7 +251,7 @@ def analyze(
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resolution images.
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detector_backend (string): set face detector backend to opencv, retinaface, mtcnn, ssd,
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dlib or mediapipe.
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dlib, mediapipe or yolov8.
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align (boolean): alignment according to the eye positions.
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@ -429,7 +429,7 @@ def find(
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resolution images.
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detector_backend (string): set face detector backend to opencv, retinaface, mtcnn, ssd,
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dlib or mediapipe
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dlib, mediapipe or yolov8.
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align (boolean): alignment according to the eye positions.
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@ -456,6 +456,7 @@ def find(
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file_name = file_name.replace("-", "_").lower()
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if path.exists(db_path + "/" + file_name):
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if not silent:
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print(
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f"WARNING: Representations for images in {db_path} folder were previously stored"
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@ -640,7 +641,7 @@ def represent(
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This might be convenient for low resolution images.
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detector_backend (string): set face detector backend to opencv, retinaface, mtcnn, ssd,
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dlib or mediapipe
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dlib, mediapipe or yolov8.
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align (boolean): alignment according to the eye positions.
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@ -725,7 +726,7 @@ def stream(
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model_name (string): VGG-Face, Facenet, Facenet512, OpenFace, DeepFace, DeepID, Dlib,
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ArcFace, SFace
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detector_backend (string): opencv, retinaface, mtcnn, ssd, dlib or mediapipe
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detector_backend (string): opencv, retinaface, mtcnn, ssd, dlib, mediapipe or yolov8.
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distance_metric (string): cosine, euclidean, euclidean_l2
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@ -9,11 +9,11 @@ from deepface.detectors import (
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MtcnnWrapper,
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RetinaFaceWrapper,
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MediapipeWrapper,
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YoloWrapper,
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)
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def build_model(detector_backend):
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global face_detector_obj # singleton design pattern
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backends = {
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@ -23,6 +23,7 @@ def build_model(detector_backend):
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"mtcnn": MtcnnWrapper.build_model,
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"retinaface": RetinaFaceWrapper.build_model,
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"mediapipe": MediapipeWrapper.build_model,
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"yolov8": YoloWrapper.build_model,
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}
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if not "face_detector_obj" in globals():
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@ -42,20 +43,22 @@ def build_model(detector_backend):
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def detect_face(face_detector, detector_backend, img, align=True):
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obj = detect_faces(face_detector, detector_backend, img, align)
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if len(obj) > 0:
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face, region, confidence = obj[0] # discard multiple faces
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# If no face is detected, set face to None,
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# image region to full image, and confidence to 0.
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else: # len(obj) == 0
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face = None
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region = [0, 0, img.shape[1], img.shape[0]]
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confidence = 0
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return face, region, confidence
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def detect_faces(face_detector, detector_backend, img, align=True):
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backends = {
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"opencv": OpenCvWrapper.detect_face,
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"ssd": SsdWrapper.detect_face,
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@ -63,6 +66,7 @@ def detect_faces(face_detector, detector_backend, img, align=True):
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"mtcnn": MtcnnWrapper.detect_face,
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"retinaface": RetinaFaceWrapper.detect_face,
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"mediapipe": MediapipeWrapper.detect_face,
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"yolov8": YoloWrapper.detect_face,
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}
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detect_face_fn = backends.get(detector_backend)
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@ -76,7 +80,6 @@ def detect_faces(face_detector, detector_backend, img, align=True):
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def alignment_procedure(img, left_eye, right_eye):
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# this function aligns given face in img based on left and right eye coordinates
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left_eye_x, left_eye_y = left_eye
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@ -104,7 +107,6 @@ def alignment_procedure(img, left_eye, right_eye):
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# apply cosine rule
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if b != 0 and c != 0: # this multiplication causes division by zero in cos_a calculation
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cos_a = (b * b + c * c - a * a) / (2 * b * c)
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angle = np.arccos(cos_a) # angle in radian
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angle = (angle * 180) / math.pi # radian to degree
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62
deepface/detectors/YoloWrapper.py
Normal file
62
deepface/detectors/YoloWrapper.py
Normal file
@ -0,0 +1,62 @@
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from deepface.detectors import FaceDetector
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# Model's weights paths
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PATH = "/.deepface/weights/yolov8n-face.pt"
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# Google Drive URL
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WEIGHT_URL = "https://drive.google.com/uc?id=1qcr9DbgsX3ryrz2uU8w4Xm3cOrRywXqb"
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# Confidence thresholds for landmarks detection
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# used in alignment_procedure function
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LANDMARKS_CONFIDENCE_THRESHOLD = 0.5
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def build_model():
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"""Build YOLO (yolov8n-face) model"""
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import gdown
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import os
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# Import the Ultralytics YOLO model
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from ultralytics import YOLO
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from deepface.commons.functions import get_deepface_home
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weight_path = f"{get_deepface_home()}{PATH}"
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# Download the model's weights if they don't exist
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if not os.path.isfile(weight_path):
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gdown.download(WEIGHT_URL, weight_path, quiet=False)
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print(f"Downloaded YOLO model {os.path.basename(weight_path)}")
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# Return face_detector
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return YOLO(weight_path)
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def detect_face(face_detector, img, align=False):
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resp = []
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# Detect faces
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results = face_detector.predict(
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img, verbose=False, show=False, conf=0.25)[0]
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# For each face, extract the bounding box, the landmarks and confidence
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for result in results:
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# Extract the bounding box and the confidence
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x, y, w, h = result.boxes.xywh.tolist()[0]
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confidence = result.boxes.conf.tolist()[0]
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x, y, w, h = int(x - w / 2), int(y - h / 2), int(w), int(h)
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detected_face = img[y: y + h, x: x + w].copy()
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if align:
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# Extract landmarks
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left_eye, right_eye, _, _, _ = result.keypoints.tolist()
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# Check the landmarks confidence before alignment
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if (left_eye[2] > LANDMARKS_CONFIDENCE_THRESHOLD and
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right_eye[2] > LANDMARKS_CONFIDENCE_THRESHOLD):
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detected_face = FaceDetector.alignment_procedure(
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detected_face, left_eye[:2], right_eye[:2]
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)
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resp.append((detected_face, [x, y, w, h], confidence))
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return resp
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@ -1,3 +1,4 @@
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opencv-contrib-python>=4.3.0.36
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mediapipe>=0.8.7.3
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dlib>=19.20.0
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dlib>=19.20.0
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ultralytics @ git+https://github.com/derronqi/yolov8-face.git@b623989575bdb78601b5ca717851e3d63ca9e01c
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