Wider-Yolo Kütüphanesi ile Yüz Tespit Uygulamanı Yap

Last update: Aug 22, 2022

Overview

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:

yolov5-pytorch-sahi

Referanslar:

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Comments

dataset github release uzerinden indirebilir

@kadirnar oncelikle proje cok basarili, eline saglik 💯

github repolarinda yeni release olustururken, dosya basina max 2gb limit ile dosya yuklemene izin veriyor. senin widerface train/val/test splitleri bu limitin altinda kaliyor. github release uzerinden host ederek google drive'in indirme limitinden kurtulabilirsin 👍
enhancement good first issue

opened by fcakyon 9

reponun secretslarina PYPI_API_TOKEN eklemek gerekiyor

Merhaba @kadirnar, tag sorunu cozulmus, simdi su hatayi veriyor action:

Warning:  It looks like you are trying to use an API token to authenticate in the package index and your token value does not start with "pypi-" as it typically should. This may cause an authentication error. Please verify that you have copied your token properly if such an error occurs.

Bu warning yanlis tokeni kopyalamis olabilecegini gosteriyor.

Error during upload. Retry with the --verbose option for more details.
HTTPError: 403 Forbidden from https://upload.pypi.org/legacy/
Invalid or non-existent authentication information. See https://pypi.org/help/#invalid-auth for more information.

Bu hata gecerli bir api token verilmedigini gosteriyor.

https://pypi.org/ uzerinden API_TOKEN uretip bu reponun secretlarina PYPI_API_TOKEN adiyla dogru sekilde ekledin mi?

bug

opened by fcakyon 3

yeni bir tag ile release almak gerekiyor

@kadirnar tag 0.0.1 hatali oldugu oldugu icin bu tag ile pypi publish hata veriyor: https://github.com/kadirnar/wideryolo/actions/runs/1604116696

yeni bir tag ile (0.0.5) release alarak pypi'den hatasiz pypi publish alabilirsin.
enhancement

opened by fcakyon 0

Releases(0.0.9)

0.0.9(May 1, 2022)

added zip file
Source code(tar.gz)
Source code(zip)
WIDER_test.zip(1759.48 MB)
WIDER_train.zip(1398.32 MB)
WIDER_val.zip(346.09 MB)
0.0.8(Dec 22, 2021)

Source code(tar.gz)
Source code(zip)
0.0.7(Dec 22, 2021)

Source code(tar.gz)
Source code(zip)
widerface(Dec 22, 2021)

Source code(tar.gz)
Source code(zip)
wider_face_split.zip(3.42 MB)
WIDER_train.zip(1397.70 MB)
WIDER_val.zip(345.94 MB)
0.0.6(Dec 21, 2021)

Source code(tar.gz)
Source code(zip)
0.0.1(Dec 20, 2021)

Source code(tar.gz)
Source code(zip)

Owner

Kadir Nar

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Wider-Yolo Kütüphanesi ile Yüz Tespit Uygulamanı Yap

Related tags

Overview

WIDER-YOLO : Yüz Tespit Uygulaması Yap

Wider-Yolo Kütüphanesinin Kullanımı

1. Wider Face Veri Setini İndir

2. Dosyaları Düzeni:

3. Wider Veri Setini Voc Xml Formatına Çevir

4. Voc Xml Veri Setini Yolo Formatına Çevir

5. Yolo Modelini Eğit

6. Yolo Modelini Test Et

6. Yolov5 + Sahi Algoritmasını Test Et

You might also like...

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load .txt to train YOLOX, same as Yolo others

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Comments

dataset github release uzerinden indirebilir

reponun secretslarina PYPI_API_TOKEN eklemek gerekiyor

yeni bir tag ile release almak gerekiyor

Releases(0.0.9)

0.0.9(May 1, 2022)

0.0.8(Dec 22, 2021)

0.0.7(Dec 22, 2021)

widerface(Dec 22, 2021)

0.0.6(Dec 21, 2021)

0.0.1(Dec 20, 2021)

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Kadir Nar

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