Wider-Yolo Kütüphanesi ile Yüz Tespit Uygulamanı Yap
Project description
WIDER-YOLO : Yüz Tespit Uygulaması Yap
Wider-Yolo Kütüphanesinin Kullanımı
1. Wider Face Veri Setini İndir
Not: İndirilen veri setini ismini değiştirmeden wider_data klasörün içine atın.
2. Dosyaları Düzeni:
datasets/
wider_face_split/
- wider_face_train_bbx_gt.txt
- wider_face_val_bbx_gt.txt
WIDER_train/
- images
WIDER_train_annotations
WIDER_val
- images
WIDER_val_annotations
Not: WIDER_train_annotations ve WIDER_val_annotations klasörleri oluşturmanıza gerek yoktur.
3. Wider Veri Setini Voc Xml Formatına Çevir
python ./wider_to_xml.py -ap ./wider_data/wider_face_split/wider_face_train_bbx_gt.txt -tp ./wider_data/WIDER_train_annotations/ -ip ./wider_data/WIDER_train/images/
python ./wider_to_xml.py -ap ./wider_data/wider_face_split/wider_face_val_bbx_gt.txt -tp ./wider_data/WIDER_val_annotations/ -ip ./wider_data/WIDER_val/images/
4. Voc Xml Veri Setini Yolo Formatına Çevir
python ./xml_to_yolo --path ./wider_data/WIDER_train_annotations/
python ./xml_to_yolo --path ./wider_data/WIDER_val_annotations/
5. Yolo Modelini Eğit
!yolov5 train --data data.yaml --weights 'yolov5n.pt' --batch-size 16 --epochs 100 --imgs 512
6. Yolo Modelini Test Et
Tek resim test etmek için:
!yolov5 detect --weights wider-yolo.pth --source file.jpg
Tüm resim dosyasını test etmek için
!yolov5 detect --weights wider-yolo.pth --source path/*.jpg
Not: Yeterli Gpu kaynağına sahip olamadığım için wider seti için düşük parametre değerleri verdim. Parametre Değerleri:
batch-size: 256, epochs: 5, imgs 320
6. Yolov5 + Sahi Algoritmasını Test Et
from sahi.model import Yolov5DetectionModel
from sahi.utils.cv import read_image
from sahi.predict import get_prediction, get_sliced_prediction, predict
from IPython.display import Image
detection_model = Yolov5DetectionModel(
model_path="last.pt",
confidence_threshold=0.3,
device="cpu",
)
result = get_sliced_prediction(
"test_data/2.jpg",
detection_model,
slice_height = 256,
slice_width = 256,
overlap_height_ratio = 0.8,
overlap_width_ratio = 0.8
)
result.export_visuals(export_dir="demo_data/")
Image("demo_data/prediction_visual.png")
Sahi Algoritması ile ilgili Örnek Proje:
Referanslar:
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